Search

This paper introduces a numerical approach for the fractional-order Rössler chaotic systems and gives error analysis. The effectiveness of the present method is determined by comparing the numerical results of the high-precision difference scheme and predictor-correctors scheme. Some complex dynamical behaviors for the fractional-order Rössler chaotic systems are shown. [ABSTRACT FROM AUTHOR]

Stratospheric airships require a lightweight envelope to contain lighter-than-air buoyancy gas, making the lightweight design and pressure-bearing performance of the envelope structure a key research issue. The stress state at different cross-sectional positions of the airship envelope structure is different, resulting in a low utilization rate of the overall material performance of the envelope structure. This paper proposes a design scheme for reinforcing envelope structures with sliding reinforcing cable to improve the bearing capacity of the composite fabric structure while reducing its weight, ultimately achieving the optimal strength-to-weight ratio. Two types of composite fabric structures (A-airship and B-airship) were subjected to inflatable burst tests, and the strain changes in the envelope gores were analyzed by digital image correlation. Through re-assembly of the broken composite fabric pieces and analysis of their tear textures, crack origination positions, failure causes, and the stress behavior and state at the failure position were identified. An envelope structural model with consideration of the cutting pattern effect was established, allowing the stress distribution of the envelope to be analyzed and the damage positions to be more accurately predicted. Based on the analysis of the ultimate pressure-bearing performance of an airship envelope structure, a novel idea of incorporating coupled tensile-shear stress into the strength criterion was proposed. Through the data in the study and existing references, it is verified that the strength criterion can accurately predict the ultimate pressure-bearing performance of the envelope structure. [ABSTRACT FROM AUTHOR]

This paper proposes an assembled X-shaped flexural steel damper. Welding is waived for the novel damper. As the kernel element of the damper, the X-shaped flexural steel plates have the advantages of ease of installation and replacement if necessary. In the analytical part, the effective height of the flexural steel plate was suggested, and then, the yield displacement, yield load and initial stiffness of the damper were derived. The proof-of-concept cyclic loading test was conducted using one reduced-scale specimen within a four-bar linkage experimental setup. The hysteretic performance indexes, including strength, stiffness and energy dissipation, were quantified. The high-fidelity finite element (FE) models were established by ABAQUS. The parametric study was further conducted based on the verified FE model. The considered parameters were the height and thickness of the steel plates. The testing results indicate that the proposed damper has plump hysteretic curve with good energy dissipation capacity and excellent ductility, although slight slip behavior can be found due to the gap caused by the assembling process. The results obtained from analytical method and numerical model are in good agreement with the experimental data. The results of parametric analysis show that the numerical results well agree with analytical predictions, indicating the numerical model can reflect the mechanical behavior of AXSFD. A benchmark frame building was selected to demonstrate the seismic control efficacy of the damper. The nonlinear time history analysis results indicate that the damper reduced both peak and residual interstory drift ratios for the protected structure. [ABSTRACT FROM AUTHOR]

High altitude relight is a critical aspect of the aeronautical engine certification and may be addressed with the numerical simulation of two-phase ignition. However, such configurations are stiff and combined with local evaporation may lead to numerical issues. This paper provides several methods to perform two-phase ignition simulations using analytically reduced chemistry in the context of unstructured large Eddy simulation and Euler–Lagrange formalism. Firstly, an exponential formulation combined with a local and dynamic sub-cycling of the stiff chemistry is demonstrated to allow stable integration at the flow time step. Secondly, a particle-bursting method is applied to limit the impact of stiffness induced by the Lagrangian point-source approach in fine meshes. These methods are then applied in the simulation of ignition of a mono-disperse, multi-component kerosene spray in air. The use of the analytically reduced chemistry model enables us to describe in detail the chemical structure of the flame kernel during its formation. Moreover, local increase of fuel concentration is observed as the ignition proceeds which has a large influence on the combustion processes and the flame kernel development. [ABSTRACT FROM AUTHOR]

This paper explores the application of affect control theory (ACT) to the study of morality. A concise statement of ACT sets the stage for presenting examples of applying the theory to morality. This includes exploring the moral implications or overtones of social concepts (social identities, behaviors, traits, and settings); computer simulations of impressions created by moral and immoral events; and a discussion of several studies directly applying ACT to morality. The paper concludes with a detailed discussion of what ACT can contribute to moral psychology and the sociology of morality. [ABSTRACT FROM AUTHOR]

The current paper presents case study of the Budenovskoe sandstone-hosted uranium deposit, the host sequence of which has been modelled geostatistically, using Plurigaussian conditional simulation (PGS) and Sequential Gaussian conditional simulation (SGS). The PGS method minimises subjectivism of the geological interpretations, by constraining the lithological models to geostatistical and lithostratigraphic characteristics of the host sequence, and accurately reproduces litho-stratigraphy of the sedimentary sequence, realistically conveying its complexity. This was integrated with the rock permeability generated using SGS. Integrating two models has significantly enhanced definition of the fluid infiltration passages in the host sequence. The model was built using stochastic algorithms of PGS and SGS, and therefore can be used for estimation the project risk. In practice, the errors, initially estimated to the blocks used for the conditional simulation, are grouped to the volumes of a practical interest, including the resource and reserve domains, production blocks and the leach cells. [ABSTRACT FROM AUTHOR]

Objective: We review the sampling models described in John Senders's doctoral thesis on "visual sampling processes" via a ready and accessible exposition. Background: John Senders left a significant imprint on human factors/ergonomics (HF/E). Here, we focus on one preeminent aspect of his career, namely visual attention. Methods: We present, clarify, and expand the models in his thesis through computer simulation and associated visual illustrations. Results: One of the key findings of Senders's work on visual sampling concerns the linear relationship between signal bandwidth and visual sampling rate. The models that are used to describe this relationship are the periodic sampling model (PSM), the random constrained sampling model (RCM), and the conditional sampling model (CSM). A recent replication study that used results from modern eye-tracking equipment showed that Senders's original findings are manifestly replicable. Conclusions: Senders's insights and findings withstand the test of time and his models continue to be both relevant and useful to the present and promise continued impact in the future. Application: The present paper is directed to stimulate a broad spectrum of researchers and practitioners in HF/E and beyond to use these important and insightful models. [ABSTRACT FROM AUTHOR]

Due to many social problems caused by Internet consumer finance, Internet consumer finance regulation has always become one of the main concerns of governments and society. Based on a novel three-dimensional mixed strategy game matrix, this paper constructs a tripartite evolutionary game model for regulators, Internet consumer financial institutions, and consumers. In this model, we establish the dynamic replication equation of the three parties above, analyze the influence of factors on the tripartite strategy selection and discuss the stability of model equilibrium points through numerical simulation. The results show that the model will get six evolutionary stable points in Internet consumer financial regulation under different conditions. Moreover, strict regulation, verification, and contract keeping are the most ideal evolutionarily stable strategies. In addition, three critical factors affect the evolution results of the three parties, namely losses to institutions caused by loose regulation and verification, punishment for loose verification under strict regulation, and losses from breaking the contract. [ABSTRACT FROM AUTHOR]

The joint green R&D of suppliers and manufacturers and the green marketing of manufacturers directly affect the degree of green products. Based on this, this paper establishes a coordination decision-making model of green innovation on the basis of cost-sharing contracts to stimulate supply chain partnerships, and studies the green R&D efforts and green marketing investment decisions of the secondary supply chain composed of a single supplier and a single manufacturer under different leadership scenarios, as well as the relationship between the green R&D decisions of the supply chain and the green marketing decisions of the manufacturer. The influence of green innovation capability on enterprise profit, product greenness and marketing investment is analyzed by numerical simulation. The results show that if an enterprise increases the unit cost coefficient of green innovation, it will reduce the green input of all members of the supply chain; When consumers' green preference is low, manufacturers will tend to increase investment in green marketing; Cost-sharing contracts can promote the overall green degree of products. The research conclusion can provide reference value for green supply chain management decision-making. [ABSTRACT FROM AUTHOR]

The air supported membrane structure is a typical nonlinear flexible long-span space structure, the wind-induced drift and the resulting accumulation and distribution of snow particles on the structure may be the primary design concern among all loads in heavy-snowfall region. Thus, an accurate prediction of snow distribution on membrane surface is vital to structural design. A numerical simulation method is used to estimate snowdrift in this paper. Based on Euler-Euler method in multi-phase flow theory, this numerical model adopted Mixture model and combined with the snow deposition and erosion model, the snowdrift on an air-supported membrane coal shed is simulated, the distribution factor for roof snow load is given under different wind speed and different directions to estimate the worst load case, snow load on the air-supported membrane structure is significantly affected by snowdrift which causes significant non-uniform snow load. Furthermore, the response analysis of the air-supported membrane structure under snow load is studied, for comparison, uniform snow load case, non-uniform snow load case, and simulated snow load case under 0° wind direction are all considered. The results show that non-uniform snow load caused by snow drifting is more dangerous and should be considered. [ABSTRACT FROM AUTHOR]

Computer-based and web-based testing have become increasingly popular in recent years. Their popularity has dramatically expanded the availability of response time data. Compared to the conventional item response data that are often dichotomous or polytomous, response time has the advantage of being continuous and can be collected in an unobstrusive manner. It therefore has great potential to improve many measurement activities. In this paper, we propose a change point analysis (CPA) procedure to detect test speededness using response time data. Specifically, two test statistics based on CPA, the likelihood ratio test and Wald test, are proposed to detect test speededness. A simulation study has been conducted to evaluate the performance of the proposed CPA procedure, as well as the use of asymptotic and empirical critical values. Results indicate that the proposed procedure leads to high power in detecting test speededness, while keeping the false positive rate under control, even when simplistic and liberal critical values are used. Accuracy of the estimation of the actual change point, however, is highly dependent on the true change point. A real data example is also provided to illustrate the utility of the proposed procedure and its contrast to the response-only procedure. Implications of the findings are discussed at the end. [ABSTRACT FROM AUTHOR]

Servo dynamic stiffness is a pivotal index in characterizing the rejecting performance for the exogenous periodic disturbances of the repetitive learning control system. In this paper, the mathematical modeling of the servo dynamic stiffness for the well-designed repetitive learning control system with multiple-periodic signals is studied by the frequency analysis method. To analysis the complex form of the servo dynamic stiffness, the magnitudes of the direct (real part) and quadrature (imaginary part) servo dynamic stiffness at harmonics of the disturbance are discussed based on the parameters of the repetitive learning controller. An illustrated example of the two linear servomotors with the mechanical coupling is given in this study. Computer simulation are presented to illustrate the effect of the proposed measurement method of the servo dynamic stiffness for the multiple-periodic repetitive learning control system. The proposed servo dynamic stiffness modeling can be used to accurate design the parameters of the repetitive learning control system, such that the batter control performance of the repetitive learning control system at most of the harmonics of the multi-periodic input signal can be achieved. [ABSTRACT FROM AUTHOR]

A lot of studies have shown that the hydraulic characteristics of orifice plate are mainly controlled by its contraction ratio, but the thickness of square-edged orifice plate also has many impacts on energy loss characteristics. The primary objective of this study was to investigated the effects of square-edged orifice plate thickness on energy loss characteristics. In this paper, the effects of square-edged orifice plate thickness on energy loss characteristics are investigated by numerical simulation using CFD. Orifice plate discharge tunnel is axial symmetric, two dimensional numerical simulations of orifice plate discharge tunnel flow was used. The equation (9) for calculating energy loss coefficient of square-edged orifice plate energy dissipater considering the influence of thickness is proposed. The results of the present research demonstrate that energy loss coefficient decreases with increase of the orifice plate thickness. The results of model experiment are consistence with the results calculated by using rectified equation in present paper. The CFD simulations and Model experiment for the flow through an orifice plate are carried out. For square-edged orifice plate energy dissipater, the relative orifice plate thickness T/D has remarkable impacts on its energy loss coefficient ξ. The Traditional equation (8) is corrected by numerical results. The equation (9) for calculating energy loss coefficient of square-edged orifice plate energy dissipater considering the influence of thickness is proposed and this equation is available in the condition of d/D = 0.4–0.8, T/D = 0.05–0.25, and Re > 105(Re is Reynolds number). Comparing with the physical model experimental data, the relative errors of equation (9) is smaller than 15%. [ABSTRACT FROM AUTHOR]

The sparse regularization (SReg) model is widely used for structural damage identification by employing the sparsity peculiarity of the structural damage. The conventional SReg model often separately conducts damage identification on each measurement, while ignores the utilization of the similarity information among different measurements. In this paper, we propose a novel joint sparse regularization model for structural damage identification. In detail, combined with the sparsity of the structural damage by the conventional SReg model, our model employs the similarity of different measurements to further improve the damage identification performance, which is realized by the generalized fused lasso penalty. Numerical simulations on the six-bay planar truss structure and experimental studies on the cantilever beam structure illustrate that the damage identification accuracy of our model is promoted 3.59% and 20.75% than the conventional SReg one, respectively. [ABSTRACT FROM AUTHOR]

Iteration methods are widely used in numerical simulation. This paper suggests the Elzaki transform in the variational iteration method for simple identification of the Lagrange multiplier. The Elzaki transform is a modification of the Laplace transform, and it is extremely useful for treating with nonlinear oscillators as illustrated in this paper, a single iteration leads to a high accuracy of the solution. [ABSTRACT FROM AUTHOR]

Propensity score methods are commonly used in statistical analyses of observational data to reduce the impact of confounding bias in estimations of average treatment effect. While the propensity score is defined as the conditional probability of a subject being in the treatment group given that subject's covariates, the most precise estimation of average treatment effect results from specifying the propensity score as a function of true confounders and predictors only. This property has been demonstrated via simulation in multiple prior research articles. However, we have seen no theoretical explanation as to why this should be so. This paper provides that theoretical proof. Furthermore, this paper presents a method for performing the necessary variable selection by means of elastic net regression, and then estimating the propensity scores so as to obtain optimal estimates of average treatment effect. The proposed method is compared against two other recently introduced methods, outcome-adaptive lasso and covariate balancing propensity score. Extensive simulation analyses are employed to determine the circumstances under which each method appears most effective. We applied the proposed methods to examine the effect of pre-cardiac surgery coagulation indicator on mortality based on a linked dataset from a retrospective review of 1390 patient medical records at Jewish Hospital (Louisville, KY) with the Society of Thoracic Surgeons database. [ABSTRACT FROM AUTHOR]

The numerical simulation of casting process can calculate casting filling process, solidification process, and obtain change and coupling information of temperature field, velocity field, pressure field, stress field and microstructure. At present, numerical simulation of casting process has been quite mature at the macro level, and is developing towards the direction of micro level. The coupling and integration of different fields between temperature, flow, stress, and microstructure, is the shape of things for numerical simulation research of casting process. This paper reviews and summarizes the research history and current situation of numerical simulation of casting process. The progress in numerical simulation from five aspects of casting solidification, casting filling, stress field, microstructure, commercial software, is presented. [ABSTRACT FROM AUTHOR]

This paper presents a self-learning control algorithm for model uncertain suspension systems using single network adaptive critic (SNAC) approach. First, a differential neural network (DNN) observer in conjunction with the weight updating law is established to observe the uncertain dynamic. Then, the nominal optimal value function is approximated by a critic NN whose weight is updated by a novel design learning law driven by the filtered parameter error. The online self-learning control policy is thus derived by approximately solving the Hamilton–Jacobi–Bellman (HJB) equation based on SNAC technique. The Lyapunov approach is synthesized to ensure the convergent characteristics of the entire closed-loop system composed of the DNN observer and the self-learning control policy. Computer simulation of a quarter car suspension system is established to verify the effectiveness of the proposed approach. Simulation results illustrated that the designed method can ensure the good performance in terms with the road hold and ride quality. In addition, independent of model and online self-learning characteristics make it possible to design a high-performance vehicle active suspension controller. [ABSTRACT FROM AUTHOR]

Estimating the medical costs from disease diagnosis to a terminal event is of immense interest to researchers. However, most of existing literature on such research focused on the estimation of cumulative mean function (CMF) for history process. In this paper, the combined scheme of both inverse probability of censoring weighting (IPCW) technique and longitudinal quantile regression model is used to develop a novel procedure to the estimation of cumulative quantile function (CQF) based on history process with time-dependent covariates and right censored time-to-event variable. The consistency of proposed estimator is derived. The extensive simulation study is conducted to investigate the performance of the estimator given in this paper. A medical cost data from a multicenter automatic defibrillator implantation trial (MADIT) is analyzed to illustrate the application of developed method. [ABSTRACT FROM AUTHOR]

Late-onset toxicities often occur in phase I trials investigating novel immunotherapy and molecular targeted therapies. For trials with cohort based designs (such as modified toxicity probability interval, Bayesian optimal interval, and i3+3), patients are often turned away since the current cohort are still being followed without definite dose-limiting toxicities, which results in prolonged trial duration and waste of patient resources. In this paper, we incorporate a probability-of-decision framework into the i3+3 design and allow real-time dosing inference when the next patient becomes available. Both follow-up time for the pending patients and time to dose-limiting toxicities for the observed patients are used in calculating the posterior probability of each possible dosing decision. An intensive simulation study is conducted to evaluate the operating characteristics of the newly proposed probability-of-decision-i3+3 design under various dosing scenarios and patient accrual settings. Results show that the probability-of-decision-i3+3 design achieves comparable safety and reliability performances but much shorter trial duration compared to the complete designs. [ABSTRACT FROM AUTHOR]

This paper presents a semi-theoretical empirical formula to predict the shear-flexural cracking strength of an RC beam enhanced with the external vertical prestressing rebar (EVPR) technique. Besides, nonlinear finite element models (FEM) created by software ABAQUS were used to analyze the effect of crucial parameters on the shear-flexural cracking strength. The parameters involve shear span-to-depth ratio, concrete strength, longitudinal tension reinforcement ratio, initial pulling force and spacing of EVPRs, and the vertical stiffness of the EVPR supports. Results show that the cracking strength increased linearly with the tensile strength of the concrete and the initial pulling force. The small shear span-to-depth ratio was predominantly conducive to the cracking strength. Adequate longitudinal tension rebars contributed to the cracking strength improvement. A reasonable EVPR spacing was recommended to ensure the cracking strength. Greater vertical stiffness of the EVPR supports can ensure higher compressive stress for the RC beam to improve the cracking strength. [ABSTRACT FROM AUTHOR]

Autonomous landing is a fundamental aspect of drone operations which is being focused upon by the industry, with ever-increasing demands on safety. As the drones are likely to become indispensable vehicles in near future, they are expected to succeed in automatically recognizing a landing spot from the nearby points, maneuvering toward it, and ultimately, performing a safe landing. Accordingly, this paper investigates the idea of vision-based location detection on the ground for an automated emergency response system which can continuously monitor the environment and spot safe places when needed. A convolutional neural network which learns from image-based feature representation at multiple scales is introduced. The model takes the ground images, assign significance to various aspects in them and recognize the landing spots. The results provided support for the model, with accurate classification of ground image according to their visual content. They also demonstrate the feasibility of computationally inexpensive implementation of the model on a small computer that can be easily embedded on a drone. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the potential for calculation methods to determine the thermal resistance of a wall system containing vacuum insulation panels (VIPs) that has been experimentally characterised using a guarded hot box (GHB) apparatus. The VIPs used in the wall assembly have not been characterised separately to the wall assembly, and therefore exact knowledge of the thermal performance of the VIP including edge effect is not known. The calculations and simulations are completed using methods found in literature as well as manufacturer published values for the VIPs to determine the potential for calculation and simulation methods to predict the thermal resistance of the wall assembly without the exact characterisation of the VIP edge effect. The results demonstrate that disregarding the effect of VIP thermal bridges results in overestimating the thermal resistance of the wall assembly in all calculation and simulation methods, ranging from overestimates of 21% to 58%. Accounting for the VIP thermal bridges using the manufacturer advertised effective thermal conductivity of the VIPs resulted in three methods predicting the thermal resistance of the wall assembly within the uncertainty of the GHB results: the isothermal planes method, modified zone method and the 3D simulation. Of these methods only the 3D simulation can be considered a potential valid method for energy code compliance, as the isothermal planes method requires too drastic an assumption to be valid and the modified zone method requires extrapolating the zone factor beyond values which have been validated. The results of this work demonstrate that 3D simulations do show potential for use in lieu of guarded hot box testing for predicting the thermal resistance of wall assemblies containing both VIPs and steel studs. However, knowledge of the VIP effective thermal conductivity is imperative to achieve reasonable results. [ABSTRACT FROM AUTHOR]

The sealing performance will directly affect the operation of downhole robot under HTHP condition. Traditional analysis methods of sealing performance are that the temperature and pressure is loaded respectively. This can not really evaluate the sealing performance. Besides, the simulation process is: Step 1: pre-compress O-ring to produce contact force. According to the contact pressure, select the compression ratio to calculate the displacement of the slip ring. Step 2: load fluid pressure on the O-ring. This simulation method is to directly load pressure on the undeformed O-ring. However, the O-ring will deform after pre-compression. Therefore, this simulation method is not accurate. In order to make the simulation data more accurate, calculate the data of shape, stress, and strain of O-ring caused by pre-compression caused by assembly. Then, import the deformation body containing the real data of shape, stress, and strain into a new model. On the basis, establish the numerical simulation model of piston, piston guide rod, O-ring, and FTS-ring with HTHP loads is. Finally, calculate and analyze. When the compression ratio of the O-ring is about 14%, the sealing performance is good. What's more, the distribution of contact stress and Von Mises of the O-ring at 8.3 mm/s of motion speed are analyzed. The results show that the foot shaped combined sealing structure can keep a good dynamic sealing performance under HTHP condition. This paper provides a theoretical basis for the analysis of the dynamic sealing performance by using HTHP coupling method. In the analysis of sealing performance: the hydraulic pressure is loaded to the real model with the real shape, stress, and strain produced by the O-ring assembly. This can more accurately evaluate the sealing performance under HTHP condition. It also provides a reference for the dynamic sealing structure design of downhole tools. [ABSTRACT FROM AUTHOR]

The sealing performance will directly affect the operation of downhole robot under HTHP condition. Traditional analysis methods of sealing performance are that the temperature and pressure is loaded respectively. This can not really evaluate the sealing performance. Besides, the simulation process is: Step 1: pre-compress O-ring to produce contact force. According to the contact pressure, select the compression ratio to calculate the displacement of the slip ring. Step 2: load fluid pressure on the O-ring. This simulation method is to directly load pressure on the undeformed O-ring. However, the O-ring will deform after pre-compression. Therefore, this simulation method is not accurate. In order to make the simulation data more accurate, calculate the data of shape, stress, and strain of O-ring caused by pre-compression caused by assembly. Then, import the deformation body containing the real data of shape, stress, and strain into a new model. On the basis, establish the numerical simulation model of piston, piston guide rod, O-ring, and FTS-ring with HTHP loads is. Finally, calculate and analyze. When the compression ratio of the O-ring is about 14%, the sealing performance is good. What's more, the distribution of contact stress and Von Mises of the O-ring at 8.3 mm/s of motion speed are analyzed. The results show that the foot shaped combined sealing structure can keep a good dynamic sealing performance under HTHP condition. This paper provides a theoretical basis for the analysis of the dynamic sealing performance by using HTHP coupling method. In the analysis of sealing performance: the hydraulic pressure is loaded to the real model with the real shape, stress, and strain produced by the O-ring assembly. This can more accurately evaluate the sealing performance under HTHP condition. It also provides a reference for the dynamic sealing structure design of downhole tools. [ABSTRACT FROM AUTHOR]

This paper addresses a fundamentally important issue in health care, namely how to make informed decisions on product selection when two products, from different manufacturers, appear to be similar and have medical claims that sound comparable. In such cases, manufacturers of competing products often use each other's evidence. They argue that the published evidence is generally applicable even if the original bioengineering tests and clinical trials were performed on a specific product, and no equivalence was obtained for their product that has similar medical claims. In this work, we use prophylactic dressings for pressure injury prevention as a good demonstrative example on how patient safety may be compromised if study conclusions are generally projected to such unstudied products. The medical device industry is regulated differently than the pharmaceutical industry, and consequently, voids in current medical device regulation are sometimes used to promote commercial interests. This paper analyzes gaps and potential pitfalls that occur where guiding documentations (e.g. guidelines, standards) do not cope well with medical technology. We explain how that can eventually lead to potential compromises to the well-being of patients, primarily if nurses are unaware of the aforementioned pitfalls. We conclude that currently, there is no alternative to rigorousness: Clinicians and decision-makers need to scrutinize up-to-date literature, decide which products have the best portfolio of bioengineering and clinical research to support the claims made, and which products have the best cost–benefit models. This is fundamentally different from simply buying the least expensive product because of appealing sale arguments. [ABSTRACT FROM AUTHOR]

Reduced web section (RWS) connections and welded flange plate (WFP) connections can both effectively improve the seismic performance of a structure by moving plastic hinges to a predetermined location away from the column face. In this paper, two kinds of steel frames—with RWS connections and WFP connections—as well as different frames with welded unreinforced flange connections were studied through seismic fragility analysis. The numerical simulation was conducted by using multiscale FE modelling. Based on the incremental dynamic analysis and pushover analysis methods, probabilistic seismic demand analysis and seismic capability analysis were carried out, respectively. Finally, combined with the above analysis results, probabilistic seismic fragility analysis was conducted on the frame models. The results showed that the RWS connection and WFP connection (without double plates) have little influence on reducing the maximum inter-storey drift ratio under earthquake action. RWS connections slightly reduce the seismic capability in non-collapse stages and improve the seismic collapse resistance of a structure, which exhibits good structural ductility. WFP connections can comprehensively improve the seismic capability of a structure, but the seismic collapse resistance is worse than that of RWS connections when the structure has a large number of storeys. The frame with WFP connections has a lower failure probability at every seismic limit state, while the frame with RWS connections sacrifices some of its structural safety in non-collapse stages to reduce the collapse probability. [ABSTRACT FROM AUTHOR]

The quantile regression model has increasingly become a useful approach for analyzing survival data due to its easy interpretation and flexibility in exploring the dynamic relationship between a time-to-event outcome and the covariates. In this paper, we consider the quantile regression model for survival data with missing censoring indicators. Based on the augmented inverse probability weighting technique, two weighted estimating equations are developed and corresponding easily implemented algorithms are suggested to solve the estimating equations. Asymptotic properties of the resultant estimators and the resampling-based inference procedures are established. Finally, the finite sample performances of the proposed approaches are investigated in simulation studies and a real data application. [ABSTRACT FROM AUTHOR]

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a semi-parametric regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance-covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer. [ABSTRACT FROM AUTHOR]

In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem. In this paper, we propose an estimating equation method to correct for the exposure misclassification-caused bias in the estimation of exposure-outcome associations. We also discuss the asymptotic properties and derive the asymptotic variances of the proposed estimators. We conduct a simulation study to evaluate the performance of the proposed estimators in various settings. As an illustration, we apply our method to correct for the misclassification-caused bias in estimating the association of PM2.5 level with lung cancer mortality using a nationwide prospective cohort, the Nurses' Health Study. The proposed methodology can be applied using our user-friendly R program published online. [ABSTRACT FROM AUTHOR]

Bivariate responses with mixed continuous and binary variables arise commonly in applications such as clinical trials and genetic studies. Statistical methods based on jointly modeling continuous and binary variables have been available. However, such methods ignore the effects of response mismeasurement, a ubiquitous feature in applications. It has been well studied that in many settings, ignorance of mismeasurement in variables usually results in biased estimation. In this paper, we consider the setting with a bivariate outcome vector which contains a continuous component and a binary component both subject to mismeasurement. We propose estimating equation approaches to handle measurement error in the continuous response and misclassification in the binary response simultaneously. The proposed estimators are consistent and robust to certain model misspecification, provided regularity conditions. Extensive simulation studies confirm that the proposed methods successfully correct the biases resulting from the error-in-variables under various settings. The proposed methods are applied to analyze the outbred Carworth Farms White mice data arising from a genome-wide association study. [ABSTRACT FROM AUTHOR]

The long-span ice composite shell structure is a new type of ice and snow structure developed in recent years. The engineering practice of ice composite shell shows that sublimation is one of the important reasons for its damage and even collapse. In this paper, we firstly supplemented the existing H-K equation and obtained the revised ice sublimation equation through indoor evaporative plate experiment considering the influence of admixtures and wind speed. Afterwards, combining the simulations of solar radiation and CFD, the numerical simulation of sublimation distribution on the surface of were realized by programming in Grasshopper platform. During sublimation, the thickness of the ice composite shell decreases by 0.38 mm every 10 days and the sublimation rate on the sunny side was 1.7 times that on the shady side. Finally, the static performance and stability of the sublimated ice composite spherical shell were analyzed. After 70 days of sublimation, the thickness of the ice composite shell structure becomes thinner and uneven, which leads its sensitivity to external load increases. [ABSTRACT FROM AUTHOR]

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors. [ABSTRACT FROM AUTHOR]

For micro air vehicles (MAV), the precise prediction of aerodynamic force plays an important role. The aerodynamic force of a comparative low Reynold number (Re) vehicle tends to be affected by the different flow modes. In this paper, the aerodynamic performance of a three-dimensional NACA0012 airfoil is studied numerically. A range of angles of attack (α) 0°−25° and Reynolds number 1000 is considered. Mean and fluctuating coefficients of aerodynamic forces around NACA0012 airfoil are analyzed for different wake modes. The difference of aerodynamic forces between two and three-dimensional simulations are compared. The results show that the wake remains steady two-dimensional for lower angles of attack. At α = 9°, Von Karman vortex pattern is noticed. Flow transition to three-dimensional as the angle of attack increases from α = 13°. 3D wake is found to be stable with parallel shedding mode for 14°-17°. However, these modes become finer with the gradual increase in angle of incidence. While, wake loses its three-dimensional stability to chaotic with gradual increment in angle of attack afterwards. [ABSTRACT FROM AUTHOR]

Aero-servo-elastic analysis of a complex hypersonic aircraft is presented in this paper. A structure geometry was designed and built based on the X-43A vehicle. First, a three-dimensional structural finite element model was proposed with effective two-dimensional elements, which can obtain effective modal analysis results without useless local modes. Second, computational fluid dynamic (CFD) simulation was adopted to find aero-heating distribution of thermal mode via this structure. Aero-heating effect was included to study thermal-modal characteristics of the present structure. Influence due to material characteristic change and thermal stress was studied. After structural finite element analysis was completed, flutter of the present vehicle was investigated. Aero-servo-elastic analysis was then started from the definition of an aero-servo-elastic closed-loop system. In this system, the present aircraft is treated as flexible structure, in which the control sensor on the aircraft received not only rigid motion signal but also elastic vibration signal, and this signal can translate into the deflection signal to form aerodynamic control force through this aero-servo control system, and this force can continually influence aerodynamic force. One of the most important steps for this analysis was computation of unsteady aerodynamic force of the present structure, and the related process was developed based on an effective fitting method. Finally, bode diagrams of pitching, rolling and yawing were investigated, form which the law of aero-servo stability of the X-43A vehicle can be observed and analyzed. It can be found from the results of this paper that effective investigation of aero-servo-elastic characteristics of a complex hypersonic aircraft should be based on accurate structural finite element modeling, modal analysis and flutter analysis. The proposed method in this paper can provide effective analysis process for the design of controller for hypersonic aircraft. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive algorithm for active control of noise sources that are of impulsive nature. The impulsive type sources can be better modeled as a stable distribution than the Gaussian. However, for stable distributions, the variance (second order moment) is infinite. The standard adaptive filtering algorithms, which are based on minimizing variance and assuming Gaussian distribution, converge slowly or become even unstable for stable (impulsive) processes. In order to improve the performance of the standard filtered-x least mean square (FxLMS)-based impulsive active noise control (IANC) systems, we propose two enhancements in this paper. First, we propose employing modified tanh function-based nonlinear process in the reference and error paths of the standard FxLMS algorithm. The main idea is to automatically give an appropriate weight to the various samples in the process, i.e. appropriately threshold the very large values so that system remains stable, and give more weight to samples below threshold limit so that the convergence speed can be improved. A second proposal is to incorporate the fractional-gradient computation in the update vector of IANC adaptive filter. Computer simulations have been carried out using experimental data for the acoustic paths. The simulation results demonstrate that the proposed algorithm is very effective for IANC systems. [ABSTRACT FROM AUTHOR]

In survival analysis, the semiparametric accelerated failure time model is an important alternative to the widely used Cox proportional hazard model. The existing methods for accelerated failure time models include least-squares, log rank-based estimating equations and approximations to the nonparametric error distribution. In this paper, we propose another fitting method for the accelerated failure time model, formulated from the hazard function of the exponential error term. Our method can handle partly interval-censored data which contains event time, as well as left, right and interval censoring time. We adopt the maximum penalized likelihood method to estimate all the parameters in the model, including the nonparametric component. The penalty function is used to regularize the nonparametric component of the accelerated failure time model. Asymptotic properties of the penalized likelihood estimate are developed. A simulation study is conducted to investigate the performance of the proposed method and an application of this method to an AIDS study is presented as an example. [ABSTRACT FROM AUTHOR]

Propensity score weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting, assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for inverse probability weighting estimation is the positivity assumption, i.e. the propensity score must be bounded away from 0 and 1. In practice, violations of the positivity assumption often manifest by the presence of limited overlap in the propensity score distributions between treatment groups. When these practical violations occur, a small number of highly influential inverse probability weights may lead to unstable inverse probability weighting estimators, with biased estimates and large variances. To mitigate these issues, a number of alternative methods have been proposed, including inverse probability weighting trimming, overlap weights, matching weights, and entropy weights. Because overlap weights, matching weights, and entropy weights target the population for whom there is equipoise (and with adequate overlap) and their estimands depend on the true propensity score, a common criticism is that these estimators may be more sensitive to misspecifications of the propensity score model. In this paper, we conduct extensive simulation studies to compare the performances of inverse probability weighting and inverse probability weighting trimming against those of overlap weights, matching weights, and entropy weights under limited overlap and misspecified propensity score models. Across the wide range of scenarios we considered, overlap weights, matching weights, and entropy weights consistently outperform inverse probability weighting in terms of bias, root mean squared error, and coverage probability. [ABSTRACT FROM AUTHOR]

Modern network applications place higher demands on its controller, especially for those with time-varying delays and limited communication capacity. For such cases, the fuzzy system has already become an advanced and powerful tool to deal with the control problem in consideration of a guaranteed cost performance. In this paper, we introduce the event-triggered mechanism with quantization effect to the controller, which proves to be more effective in terms of the information transmission. We adopt the classical Lyapunov approach to find the sufficient conditions for the controller and we illustrate the effectiveness of the controller with a numerical simulation. [ABSTRACT FROM AUTHOR]

Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to clinically relevant and efficient decision-making about treatment superiority. Common statistical procedures to make these superiority decisions with multiple outcomes have two important shortcomings, however: (1) Outcome variables are often modeled individually, and consequently fail to consider the relation between outcomes; and (2) superiority is often defined as a relevant difference on a single, on any, or on all outcome(s); and lacks a compensatory mechanism that allows large positive effects on one or multiple outcome(s) to outweigh small negative effects on other outcomes. To address these shortcomings, this paper proposes (1) a Bayesian model for the analysis of correlated binary outcomes based on the multivariate Bernoulli distribution; and (2) a flexible decision criterion with a compensatory mechanism that captures the relative importance of the outcomes. A simulation study demonstrates that efficient and unbiased decisions can be made while Type I error rates are properly controlled. The performance of the framework is illustrated for (1) fixed, group sequential, and adaptive designs; and (2) non-informative and informative prior distributions. [ABSTRACT FROM AUTHOR]

There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, Bayesian additive regression trees, targeted maximum likelihood estimator, and inverse probability of treatment weighting using generalized boosted models provide better bias reduction and smaller root mean squared error. Bayesian additive regression trees and targeted maximum likelihood estimator provide more consistent 95% confidence interval coverage and better large-sample convergence property. Second, we supply Bayesian additive regression trees with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. Bayesian additive regression trees retain more inferential units than the generalized propensity score-based strategy, and shows lower bias, compared to targeted maximum likelihood estimator or generalized boosted model, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods. [ABSTRACT FROM AUTHOR]

This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent multivariate Gaussian process. Numerical experiments, including real data analysis and a simulation study, demonstrate the promising empirical properties of the proposed approach. [ABSTRACT FROM AUTHOR]

In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases. [ABSTRACT FROM AUTHOR]

Iterative multiple imputation is a popular technique for missing data analysis. It updates the parameter estimators iteratively using multiple imputation method. This technique is convenient and flexible. However, the parameter estimators do not converge point-wise and are not efficient for finite imputation size m. In this paper, we propose a regression multiple imputation method. It uses the parameter estimators obtained from multiple imputation method to estimate the parameter estimators based on expectation maximization algorithm. We show that the resulting estimators are asymptotically efficient and converge point-wise for small m values, when the iteration k of the iterative multiple imputation goes to infinity. We evaluate the performance of the new proposed methods through simulation studies. A real data analysis is also conducted to illustrate the new method. [ABSTRACT FROM AUTHOR]

We propose a new family of linear mixed-effects models based on the generalized Laplace distribution. Special cases include the classical normal mixed-effects model, models with Laplace random effects and errors, and models where Laplace and normal variates interchange their roles as random effects and errors. By using a scale-mixture representation of the generalized Laplace, we develop a maximum likelihood estimation approach based on Gaussian quadrature. For model selection, we propose likelihood ratio testing and we account for the situation in which the null hypothesis is at the boundary of the parameter space. In a simulation study, we investigate the finite sample properties of our proposed estimator and compare its performance to other flexible linear mixed-effects specifications. In two real data examples, we demonstrate the flexibility of our proposed model to solve applied problems commonly encountered in clustered data analysis. The newly proposed methods discussed in this paper are implemented in the R package nlmm. [ABSTRACT FROM AUTHOR]

QTL mapping is an important tool for identifying regions in chromosomes which are relevant to explain a response of interest. It is a special case of the regression model where an unknown number of missing (non-observable) covariates is involved leading to a complex variable selection procedure. Although several methods have been proposed to identify QTLs and to estimate parameters in the associated model, minimum attention has been devoted to the estimated model adequacy. In this paper, we present an overview of a few methods for residual and diagnostic analysis in the context of Bayesian regression modeling and adapt them to work with QTL mapping. The motivation of this study is to identify QTLs associated with the blood pressure of F2 rats and check the fitted model adequacy. [ABSTRACT FROM AUTHOR]

Measures of substance concentration in urine, serum or other biological matrices often have an assay limit of detection. When concentration levels fall below the limit, the exact measures cannot be obtained. Instead, the measures are censored as only partial information that the levels are under the limit is known. Assuming the concentration levels are from a single population with a normal distribution or follow a normal distribution after some transformation, Tobit regression models, or censored normal regression models, are the standard approach for analyzing such data. However, in practice, it is often the case that the data can exhibit more censored observations than what would be expected under the Tobit regression models. One common cause is the heterogeneity of the study population, caused by the existence of a latent group of subjects who lack the substance measured. For such subjects, the measurements will always be under the limit. If a censored normal regression model is appropriate for modeling the subjects with the substance, the whole population follows a mixture of a censored normal regression model and a degenerate distribution of the latent class. While there are some studies on such mixture models, a fundamental question about testing whether such mixture modeling is necessary, i.e. whether such a latent class exists, has not been studied yet. In this paper, three tests including Wald test, likelihood ratio test and score test are developed for testing the existence of such latent class. Simulation studies are conducted to evaluate the performance of the tests, and two real data examples are employed to illustrate the tests. [ABSTRACT FROM AUTHOR]

In this paper, we propose empirical likelihood methods based on influence function and Jackknife techniques to construct confidence intervals for quantile medical costs with censored data. We show that the influence function-based empirical log-likelihood ratio statistic for the quantile medical cost has a standard Chi-square distribution as its asymptotic distribution. Simulation studies are conducted to compare coverage probabilities and interval lengths of the proposed empirical likelihood confidence intervals with the existing normal approximation-based confidence intervals for quantile medical costs. The proposed methods are observed to have better finite-sample performances than existing methods. The new methods are also illustrated through a real example. [ABSTRACT FROM AUTHOR]

In longitudinal clinical trials with daily patient-reported outcomes, the analysis endpoints are often defined as the averaged daily diary outcomes in a treatment cycle (such as a month or a week). Conventional methods often deal with missing data at the cycle level by imputing the average, and the cycle average is treated as missing if the number of days with available outcomes in the treatment cycle is less than a certain number. This was the method used for a case study of a phase 3 clinical trial evaluating a treatment for insomnia with daily patient-reported outcomes. Such methods may introduce bias. Motivated by this, we propose methods to impute missing daily outcomes in this paper. Specifically, we define a two-level missing pattern for clinical trials with daily patient-reported outcomes, and propose two-level methods to impute missing data at daily base. Other than the standard methods by multiple imputations, we derive analytic formulas for the proposed two-level methods to reduce computational intensity and improve the estimates of variances. The proposed two-level methods provide more powerful approaches to estimate the treatment difference compared to the conventional cycle-level methods, which are evaluated by theoretical development and simulation studies. In addition, the methods are applied to the motivating phase 3 trial evaluating a treatment for insomnia with daily patient-reported outcomes. [ABSTRACT FROM AUTHOR]

The rigid graph needs to be decomposed to solve the multi-equilibrium problem of the multi-agent formation control based on navigation function method. In this paper, a theorem and a scalable algorithm based on the Henneberg sequence of graphs are proposed for the decomposition of minimally rigid graph. The theorem demonstrates that if graph G (V, E) is a minimally rigid graph, then it can be decomposed as G = Gt ∪ Gc, where G t is a spanning tree of G and G c contains the remaining edges and their vertices. Moreover, the scalable algorithm is given to construct G t = (V t , E t ) and G c = (V c , E c ), and assign the edges in E c to n -- 2 distinct vertices in a scalable way via communication. Furthermore, a lemma is given to show when the number of vertices is less than eight, any edge can be chosen as the initialized edge, and the scalable algorithm mentioned above is always feasible. Finally, the effectiveness of the scalable algorithm is verified by numerical simulation. [ABSTRACT FROM AUTHOR]