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The potential distribution of crops will be impacted by climate change, but there is limited research on potential wheat distributions under specific global warming targets. This study employed the Maxent model to predict the potential distribution of wheat under the 1.5°C and 2°C warming targets based on data from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) multimodel ensemble, and the effect of global warming on wheat planting suitability was analyzed. Our results indicated global warming would significantly change wheat planting suitability. Over half of the areas experienced changes in wheat planting suitability under two warming targets, and the effect became more pronounced with increasing temperatures. Additionally, global warming might promote wheat planting in more regions. The area with an increase in wheat planting suitability was observed to be 9% higher than those experiencing a decrease on average. Moreover, global warming could exacerbate the disparity between global wheat supply and demand in countries/regions. Traditional wheat-producing countries/regions are poised to benefit from the warming effects of climate change, while less developed and wheat import-dependent countries/regions may face greater challenges in achieving wheat self-sufficiency. To address this potential challenge, the promotion and inter-regional exchange of agronomic technologies, and the development of more rational trade standards are urgently needed. Since socioeconomic factors have a significant impact on wheat cultivation, further investigation is required to determine how the wheat planting distribution may change in the future under the combined impact of climate change, supply-demand relationship, and policy. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance drivers of Foreign Direct Investment (FDI) at the country level, exploring the socio-demographic specifics of donor and receiver countries. To this end, a novel Robust Compromise (RoCo) Multi-Criteria Decision-Making (MCDM) model is proposed using non-linear programming solved by genetic algorithms. The model builds upon established traditional models for alternative ranking and criteria weighting. Subsequently, a stochastic robust regression is performed, building upon previously computed bootstrapped Tobit, Simplex, and Beta regressions to handle performance scores ranging between 0 and 1. The goal is to test FDI performance against a set of contextual variables. The findings suggest that the performance of FDI is relatively low, and relevant improvements should be made. Our second stage analysis reports that higher GDP per capita and good social welfare, including lower infant mortality and higher life expectancy, contribute to the improvement in FDI performance. Furthermore, it is found that a large percentage of women in the total population, wealth concentration in the destination country, as well as the degree of urbanization, are helpful to improve FDI performance. Finally, we find that FDI performance is mainly concentrated on industries that are high-tech and high value-added. [ABSTRACT FROM AUTHOR]

The robust design and application of a novel type-2 fuzzy tilt controller (Type2-FTC) has been well demonstrated in this manuscript. This controller is anticipated to build automatic generation control (AGC) of a multi-area hybrid power system in concern to the optimal power generation scheduling. The combined action of AGC and optimal scheduling of a power system is referred as economic AGC or commonly ELD-AGC. At the initial stage of the analysis, the concept of only AGC has been discussed through various robust approaches. Then the extensive study on ELD-AGC of the proposed power system has been progressed under various controller actions. Further, the proposed Type2-FTC controller is designed ideally with employing an innovative quassi-opposition path finder algorithm (QO-PFA) in various operating conditions. The effectiveness and the viability of the proposed Type2-FTC controller has been verified over type-I fuzzy PID and conventional PID controllers for AGC as well as ELD-AGC of the multi area power system. In controller comparison study, it has been revealed that proposed Type2-FTC is a potential candidate to improve frequency stability of the system. Further, the analysis over technique study justifies supremacy of the suggested QO-FPA algorithm over standard PFA and PSO algorithms for optimal designing all implemented controllers.

This paper focuses on the performance drivers of Foreign Direct Investment (FDI) at the country level, exploring the socio-demographic specifics of donor and receiver countries. To this end, a novel Robust Compromise (RoCo) Multi-Criteria Decision-Making (MCDM) model is proposed using non-linear programming solved by genetic algorithms. The model builds upon established traditional models for alternative ranking and criteria weighting. Subsequently, a stochastic robust regression is performed, building upon previously computed bootstrapped Tobit, Simplex, and Beta regressions to handle performance scores ranging between 0 and 1. The goal is to test FDI performance against a set of contextual variables. The findings suggest that the performance of FDI is relatively low, and relevant improvements should be made. Our second stage analysis reports that higher GDP per capita and good social welfare, including lower infant mortality and higher life expectancy, contribute to the improvement in FDI performance. Furthermore, it is found that a large percentage of women in the total population, wealth concentration in the destination country, as well as the degree of urbanization, are helpful to improve FDI performance. Finally, we find that FDI performance is mainly concentrated on industries that are high-tech and high value-added.

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Gene–environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy‐tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike‐and‐slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single‐nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives. [ABSTRACT FROM AUTHOR]

The swarm system is a specific system composed of multiple agents interacting with information locally. Part of agents’ state is difficult to available or even undetectable in practical applications, and the relationship between agents can be cooperative or competitive. In this study, a bipartite formation control protocol for distributed swarm systems based on output feedback is designed under the signed network topology. This strategy takes the output state between neighboring agents as the feedback information without relying on the global information of the communication topology. Considering that the real systems are often affected by external disturbances, some sufficient conditions that the control protocol can guarantee the system error is finally uniformly bounded are established based on Lyapunov stability theory. After analyzing the robustness of the protocol, the residual set of the final uniformly convergent errors is given. According to the designed control protocol and formation conditions, simulation is carried out to verify the effectiveness of the protocol, and the time for the system to reach stability under different initial ranges is given. [ABSTRACT FROM AUTHOR]

The homogeneous swarm system is limited in function because of its single component. The individual composition of the heterogeneous cluster system is diverse， so the functions of the homogeneous cluster system can be extended. In view of the problem of formation tracking control in heterogeneous swarm system， based on leader-follower model， a distributed PI formation tracking control protocol is designed under the directed communication topology. The expected time-varying formation is designed， and the followers keep the expected formation while tracking the leader， so that all the agents in the system can reach the expected position. Considering that the real system is often affected by the external unknown disturbance and some uncertain factors， based on the Lyapunov stability theory， some sufficient conditions are given to ensure the tracking error of the system to be uniformly bounded， and the robustness analysis is carried out. The simulation verification is carried out according to the designed control protocol and control conditions. The results show that the designed PI formation tracking control strategy can effectively solve the formation tracking control problem of heterogeneous cluster systems. [ABSTRACT FROM AUTHOR]

Grinding, which determines the final dimension of parts, is an important process in manufacturing companies. In praxis, in order to avoid quality problems on the customer's side, an online dimension check is normally used after the grinding process to ensure the product dimensions; however, it is always hysteretic and needs extra space and machine investment. To deal with the issue, dimensional error prediction of the grinding process is highly needed, and does not require extra space or machinery, as well as having better real-time performance. In this paper, a dimensional error prediction algorithm using principal component analysis (PCA), extreme learning machine (ELM), genetic algorithm (GA), and ensemble strategy (bagging algorithm) is designed. Specifically, PCA is used as a pre-treatment method to extract the main relevant components, then a bagging–GA–ELM model is constructed to predict the final product dimensional error after the grinding process, in which extreme learning machine (ELM) is utilized as a basic framework because of its fast calculation speed. GA, with its excellent global optimization capability, is implemented to search optimal input weights and thresholds of ELM, enabling ELM to obtain a better prediction performance. In addition, considering the complex environment of the industrial field, the bagging algorithm is employed to enhance the anti-noise ability of the proposed algorithm. Finally, the proposed algorithm is verified by a case from a bearing company. [ABSTRACT FROM AUTHOR]

Taking Tangjiahui Mining Area in Inner Mongolia as the research object, the UAV photographic image data of August 2020 and March 2021 in this area were obtained, and DEM was produced. The subsidence basin in this area was obtained by subtracting the DEM data, and the denoising effects of different denoising methods were compared with MATLAB software. Based on the subsidence data of the whole basin, the subsidence coefficient and the main influence tangent of the subsidence basin are obtained by using the probability integral parameter inversion with method of simulated annealing(SA). Using this parameter to simulate the subsidence basin, it is calculated that the measurement error is 589 mm, accounting for 8.1% of the maximum subsidence value. Finally, the robust analysis of the parameters is made, and when the error in the measurement accounts for (1% to 10%) the maximum subsidence value, the result of parameter calculation is reliable. The results show that the BP neural network algorithm can effectively remove the noise in the basin and improve the accuracy of the subsidence basin. Based on SA and the data of the whole basin in the mining area, the probability integral parameters can be obtained effectively, which can compensate for the influence of the low accuracy of UAV photogrammetry technology.

• A hybrid broadband and narrowband active sound profiling algorithm is proposed for active hybrid noise control. • Simulations of stationary and non-stationary hybrid noise sources prove the stability and robustness of the algorithm. • The proposed algorithm can selectively control the desired narrowband noise components with specific magnitudes in hybrid noise. • Real vehicle experiments, conducted using headrests, validate the theoretical analysis. The vehicle interior noise consists of both broadband road noise and narrowband engine order noise. Currently, the road noise control (RNC) system and engine noise control (EOC) system operate independently in engineering applications. This paper introduces a novel multi-channel hybrid broadband and narrowband active noise control (HBNANC) algorithm, designed to simultaneously attenuate road noise and engine order noise inside vehicles. On this basis, by integrating an error signal design (ESD) subsystem and a sinusoidal error separation (SES) subsystem, a multi-channel hybrid broadband and narrowband active sound profiling (HBNASP) algorithm is further proposed, enabling specific attenuation or amplification of individual narrowband components to meet subjective acoustic comfort requirements. The paper validates the algorithms' effectiveness and robustness through simulations, and real vehicle experimental measurements under various conditions. The research offers a comprehensive approach to enhancing the auditory experience inside vehicles, contributing to the advancement of active noise control technology. [ABSTRACT FROM AUTHOR]

This paper presents an interlaboratory comparison of radiated emission measurements in the frequency range of 30-1000 MHz. A tubular dipole was specifically designed and employed as a reference emitting source. The most important for a tubular dipole is stability in the testing process. The stability is not the performance of the sample, but the emission signal of the sample is stable. In addition, two ways of determining the reference value of the electromagnetic field strength are considered. The first reference value is obtained by using robust analysis. It is a robust average value that is calculated by averaging the measurement results provided by the participating testing laboratories. The other reference value is obtained through the simulation-experimental results of the tubular dipole in the semi-anechoic chamber or full anechoic chamber, for horizontal and vertical polarizations and 3 m distance measurement, respectively. In addition, this value is assigned by the coordinator. Measurement results are compared using the robust z-scores and -scores, respectively.

Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for between-trial heterogeneity in order to derive an informative prior from historical data. In this paper, we introduce the package RBesT (R Bayesian evidence synthesis tools) which implements the MAP approach with normal (known sampling standard deviation), binomial and Poisson endpoints. The hierarchical MAP model is evaluated by Markov chain Monte Carlo (MCMC). The MCMC samples representing the MAP prior are approximated with parametric mixture densities which are obtained with the expectation maximization algorithm. The parametric mixture density representation facilitates easy communication of the MAP prior and enables fast and accurate analytical procedures to evaluate properties of trial designs with informative MAP priors. The paper first introduces the framework of robust Bayesian evidence synthesis in this setting and then explains how RBesT facilitates the derivation and evaluation of an informative MAP prior from historical control data. In addition we describe how the meta-analytic framework relates to further applications including probability of success calculations.

Minimizing the divergence between two probability distributions offers an alternative parameter estimation method. The current literature mainly focuses on minimizing the Kullback-Leibler (K-L) divergence between the true and the proposed models in which the true model is assumed to be known or fixed. In this paper, we propose a parameter estimation method that minimizes the $f$ -divergence between two probability distributions. The method is suitable for different situations, no matter the true distribution is known or not. The statistical properties of the estimator, including consistency and asymptotic normality, are established. As an illustration, our method is employed to estimate the degradation model, which is a model frequently used to assess the lifetime of highly reliable products. A simulation study and a real degradation data analysis are presented to illustrate the effectiveness of the proposed estimation method. [ABSTRACT FROM AUTHOR]

The operational and safety performance of intersections is the key to ensuring the efficient operation of urban traffic. With the development of automated driving technologies, the ability of adaptive traffic signal control has been improved according to data detected by connected and automated vehicles (CAVs). In this paper, an adaptive traffic signal control was proposed to optimize the operational and safety performance of the intersection. The proposed algorithm based on Q-learning considers the data detected by loop detectors and CAVs. Furthermore, a comprehensive analysis was conducted to verify the performance of the proposed algorithm. The results show that the average delay and conflict rate have been significantly optimized compared with fixed timing and traffic actuated control. In addition, the performance of the proposed algorithm is good in the test of the irregular intersection. The algorithm provides a new idea for the intelligent management of isolated intersections under the condition of mixed traffic flow. It provides a research basis for the collaborative control of multiple intersections. [ABSTRACT FROM AUTHOR]

This study aims to examine the causes of boiler accidents in the context of the ready-made garment (RMG) industry of Bangladesh as an emerging economy. On the basis of a comprehensive review of the existing literature, previous accident reports, and technical discussion with relevant personnel in the industries and regulating authorities, a total of 14 causes of boiler accidents were identified. This study merged neutrosophic (N) theory with the analytic hierarchy process (AHP) for prioritizing the causes of boiler accidents. Finally, to examine the reliability of the results, a robustness analysis was performed. The findings reveal that the lack of standard legislation, non-standard boiler operation, use of expired, non-registered, and non-certified boilers, faulty design of boilers, and the shortage of skilled boiler operators are the top five notable causes of boiler accidents in the RMG industry. The findings provide valuable insights for industrial managers and policymakers to formulate strategies to reduce boiler accidents.

Here the idea of data driven is introduced in model predictive control to establish our proposed data-driven model predictive control. Considering one first-order discrete time nonlinear dynamical system, the main essence of data driven means the actual output value in cost function for model predictive control is identified through input–output observed data in case of unknown but bounded noise and martingale difference sequence. After substituting the identified actual output in cost function, the total cost function in model predictive control is reformulated as its standard form, i.e. one quadratic program problem with input and output constraints. Then semidefinite relaxation scheme is used to derive a lower bound for its optimal value, and the robust counterpart of an uncertain quadratic program is reduced to one conic quadratic problem. The above semidefinite relaxation scheme and conic quadratic problem correspond to the similar robust analysis based on convex optimization theory. Finally, one simulation example is used to prove the efficiency of our proposed theory.

In this paper, the notion of sign-consensus for the Lipschitz nonlinear multi-agents over a signed graph is introduced and controller design conditions are provided for realizing consensus among signs of states of agents. The previous works on sign-consensus are limited to the linear systems only. The attainment of sign-consensus for the nonlinear multi-agent systems is a non-trivial control problem, for which Lipschitz nonlinear dynamics are reformulated using the linear parameter varying (LPV) transformation. To guarantee the sign-consensus in a network of multiple agents, fully-distributed adaptive protocol is applied using reformulated Lipschitz nonlinear dynamics, and two design conditions are provided based on the Lipschitz constant oriented and LPV-based approaches. The first approach is comparatively less computationally complex while LPV approach holds significance for developing less conservative sign-consensus control scheme. To the best of our knowledge, distributed sign consensus methods for Lipschitz systems, based on LPV formulation, have been analyzed for the first time. The proposed approach is adaptive and can be regarded as fully-distributed, as it does not require the central knowledge of graph properties. Further, results are extended for nonlinear systems undergoing disturbances and ultimate boundedness of sign-consensus error is guaranteed. Numerical simulations on groups of Chua's circuits and mechanical oscillators are presented to validate the results. [ABSTRACT FROM AUTHOR]

Here the idea of data driven is introduced in model predictive control to establish our proposed data-driven model predictive control. Considering one first-order discrete time nonlinear dynamical system, the main essence of data driven means the actual output value in cost function for model predictive control is identified through input–output observed data in case of unknown but bounded noise and martingale difference sequence. After substituting the identified actual output in cost function, the total cost function in model predictive control is reformulated as its standard form, i.e. one quadratic program problem with input and output constraints. Then semidefinite relaxation scheme is used to derive a lower bound for its optimal value, and the robust counterpart of an uncertain quadratic program is reduced to one conic quadratic problem. The above semidefinite relaxation scheme and conic quadratic problem correspond to the similar robust analysis based on convex optimization theory. Finally, one simulation example is used to prove the efficiency of our proposed theory. [ABSTRACT FROM AUTHOR]

Grinding, which determines the final dimension of parts, is an important process in manufacturing companies. In praxis, in order to avoid quality problems on the customer’s side, an online dimension check is normally used after the grinding process to ensure the product dimensions; however, it is always hysteretic and needs extra space and machine investment. To deal with the issue, dimensional error prediction of the grinding process is highly needed, and does not require extra space or machinery, as well as having better real-time performance. In this paper, a dimensional error prediction algorithm using principal component analysis (PCA), extreme learning machine (ELM), genetic algorithm (GA), and ensemble strategy (bagging algorithm) is designed. Specifically, PCA is used as a pre-treatment method to extract the main relevant components, then a bagging–GA–ELM model is constructed to predict the final product dimensional error after the grinding process, in which extreme learning machine (ELM) is utilized as a basic framework because of its fast calculation speed. GA, with its excellent global optimization capability, is implemented to search optimal input weights and thresholds of ELM, enabling ELM to obtain a better prediction performance. In addition, considering the complex environment of the industrial field, the bagging algorithm is employed to enhance the anti-noise ability of the proposed algorithm. Finally, the proposed algorithm is verified by a case from a bearing company.

This article proposes a novel asymmetric-excited flux reversal permanent magnet linear machine (AE-FRPMLM). Different from the conventional FRPMLM with uniform “NS-NS” permanent magnet (PM) sequence, the proposed AE-FRPMLM adopts an asymmetric “NSN-S” PM sequence, which can greatly reduce half of the flux leakage and generate more effective harmonics for thrust force production. Utilizing multiobjective genetic algorithm coupled with 2-D finite-element analysis (FEA), the proposed machine is globally optimized to obtain the optimal deterministic values. Furthermore, the sensitivity and robust analysis is employed to check its robustness. The electromagnetic performances are investigated and compared with conventional FRPMLM, which shows that the thrust force capability can be significantly improved more than 20% at full range of electrical loadings. The proposed AE-FRPMLM is suitable for low-cost and long-stroke direct drive propulsion. [ABSTRACT FROM AUTHOR]

Nowadays under COVID 2019, e-learning has become a potential prop approach of technology in education that provides contemporary learners with authentic knowledge acquisitions. As a practical contribution, electronic examination (e-exam) is a novel approach in e-learning designed to solve traditional examination issues. It is a combination of assorted questions designed by specialized software to detect an individual’s performance. Despite intensive research in this area, the performance of e-exams faces challenges such as authentication of the examinee’s identity and answered papers. This paper aims to present the experiences of educational organizations in e-exam and e-evaluation as an essential tool of e-learning in various countries. The paper recommends that under the global pandemic COVID 2019 evaluating students using intensive continuous evaluation, including e-exam supported by authentication methods, which may help detect and reduce or even prevent student violations. The results show that the most used LMS tools were the Moodle and proprietary solutions which were 75% both among many other LMS tools i.e., Blackboard and eFront. The least develop countries are prefer to use open source and proprietary due to the zero cost of these solutions. The internet speed, cost and authenticity were the most challenges faced e-exams centers, which were 99%, 82%, and 68%, respectively.

Feature fatigue (FF) analysis is of critical importance for product development. To deal with the FF problem, it is essential to decide what features should be added to make the product attractive enough and not too hard to use at the same time. This paper introduces a novel methodology to search for robust feature combinations under uncertain customer preferences in FF analysis. Non-dominated sorting genetic algorithm II (NSGA-II) is firstly adopted to establish a multi-objective optimisation model. Then a grey forecasting model and a worst-case strategy are used to create different coefficient scenarios based on customer evaluation data. After running NSGA-II under each scenario, several candidate solutions for final selection can be obtained according to robustness analysis. Compared with existing studies in FF, the proposed method can obtain multiple optimal solutions rather than just a single one for designers to choose from, and the robustness degree of each solution is computed considering both present and future customer preferences. The usefulness of the proposed methodology is illustrated using a smart phone case study. [ABSTRACT FROM PUBLISHER]

The concepts of technology fusion describe the phenomenon of technology overlap. Despite its importance, the evolutionary trajectories of technology fusion are not explored well. The main contributions of this article have two aspects: One is the mathematical modeling of a two-dimensional technology fusion system with technical efficiency and convergence parameters that can determine the evolution trajectories of technology fusion; the other is to reveal the dynamics of the evolution process through robust analysis and improving the stability of technology fusion under external disturbances. A novel technology fusion analysis method is proposed for technology fusion by applying dynamic development scenarios of manufacturing technology and information technology to be robust under external disturbances. In this article, firstly, we introduce technical efficiency and convergence parameters to establish a two-dimensional technology fusion system. Secondly, according to the characteristics of different fusion stages, we select the order parameters at each stage and establish order parameters function to determine the evolution trajectories of technology fusion. Finally, the fixed-point theory and the bifurcation theory are used to analyze the robustness of each stage, and the robust theorem at different technology fusion stages is proposed. The research results show: (1) We find that the characteristics analysis of technology fusion can divide the technology fusion process into three different stages, and the main characteristics of each stage as the order parameter can better reveal the evolution trajectory of technology fusion. (2) We find that the dynamics of technology fusion are caused by competition between the driving force and the dissipating force in the process of technology fusion. (3) We find that the dynamics of the technology fusion process may cause technology fusion to fall into chaos. (4) We find that by reasonably changing the relationship between the driving force and the dissipating force, the technology fusion process can be kept stable. [ABSTRACT FROM AUTHOR]

As a marketing strategy, discount promotion is adopted by plenty of companies, which can be regarded as a variant of the previous Profit Maximization (PM) problem. Based on a discount-based marketing scenario, we propose a Profit Maximization with Discount Advertisement (PMDA) problem. Then, we show that the objective function of PMDA is submodular but not monotone, which can be categorized as an instance of Unconstrained Submodular Maximization problem. Even that similar problem has been studied before, the approximation performance is not satisfactory. Learned from the latest results, we combine the idea of greedy algorithm and randomized double greedy algorithm to solve our problem, which overcomes the shortcomings of both and obtains a more acceptable approximation ratio. It can be used as a general algorithmic framework. Moreover, the existing researches about PM only considered to maximize total profit based on certain diffusion probabilities. Because of the uncertainty of diffusion probabilities, we study the robustness of PMDA and propose Robust-PMDA problem further. It aims to acquire the maximum worst ratio between the profit of selected seed set and the optimal seed set. To solve the Robust-PMDA, we design LU-PMDASolver algorithm first, and then, we propose P-UniSampling algorithm to improve the robustness by reducing the uncertainly of diffusion probabilities, which is implemented by the technique of uniform sampling. Finally, the correctness and performance of our proposed algorithms are verified by conducting experiments on real-world social networks. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a systematic method to decompose uncertain linear quantum input‐output networks into uncertain and nominal subnetworks, when uncertainties are defined in SLH representation. To this aim, two decomposition theorems are stated, which show how an uncertain quantum network can be decomposed into nominal and uncertain subnetworks in cascaded connection and how uncertainties can be translated from SLH parameters into state‐space parameters. As a potential application of the proposed decomposition scheme, robust stability analysis of uncertain quantum networks is briefly introduced. The proposed uncertainty decomposition theorems take account of uncertainties in all three parameters of a quantum network and bridge the gap between SLH modeling and state‐space robust analysis theory for linear quantum networks. [ABSTRACT FROM AUTHOR]

The explicit consideration of a communication channel model in a feedback control loop is known to be constrained by a fundamental limitation for stabilizability on the channel signal-to-noise ratio (SNR) when the linear time invariant (LTI) plant model is unstable. The LTI modelling approach for real, usually nonlinear, processes compromises accuracy versus complexity of the resulting model. This in turn introduces a gap between the proposed model and the real process, which is known as the model uncertainty. In this paper we then study SNR limitations by considering the continuous-time scenario and the case of an additive coloured Gaussian noise (ACGN) channel with bandwidth limitation, for which we then quantify the infimal SNR subject to the simultaneous presence of plant, channel and noise model uncertainties. We observe, for the special case of memoryless additive white Gaussian noise (AWGN) channels, that the obtained SNR limitation subject to plant model uncertainty can be redefined as a channel capacity limitation for stabilization. • Signal transmission through communication channels is increasingly relevant in Control Theory. • Many relevant communication features can be considered: power, channel bandwidth, time delay, etc. • Control Theory require analysis tools for studying model uncertainty and communication features simultaneously. • The SNR approach can provide such analysis tool, and in turn better inform the controller design. [ABSTRACT FROM AUTHOR]

We propose novel quadratic performance tests for linear discrete-time impulsive systems based on viewing these systems as feedback interconnections of some non-impulsive linear system with an impulsive operator. In order to systematically analyze such interconnections, we employ the framework of integral quadratic constraints and propose novel constraints of this kind for capturing the behavior of the involved impulsive operator. As a major benefit, the modularity of this framework permits seamless extensions to interconnections affected by heterogeneous uncertainties in a straightforward fashion. This contrasts with alternative approaches which are based on capturing the system's impulsive behavior by means of a clock. Building upon the developed analysis criteria, we characterize the existence of non-impulsive estimators for such impulsive interconnections in a lossless fashion and in terms of linear matrix inequalities. Finally, our approach is illustrated by means of several numerical examples. [ABSTRACT FROM AUTHOR]

Summary: In this paper, a robust fractional‐order PID (FOPID) controller design method for fractional‐order delay systems is proposed based on positive stability region (PSR) analysis. Firstly, the PSR is presented to improve the existing stability region (SR) in D‐decomposition method. Then, the optimal fractional orders λ and μ of FOPID controller are achieved at the biggest three‐dimensional PSR, which means the best robustness. Given the optimal λ and μ, the other FOPID controller parameters kp, ki, kd can be solved under the control specifications, including gain crossover frequency, phase margin, and an extended flat phase constraint. In addition, the steps of the proposed robust FOPID controller design process are listed at length, and an example is given to illustrate the corresponding steps. At last, the control performances of the obtained robust FOPID controller are compared with some other controllers (PID and FOPI). The simulation results illustrate the superior robustness as well as the transient performance of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

The Proportional Integral Derivative Controller is a typical controller implemented frequently in many services and integrating the Smith predictor is an extremely useful control system structure for processes with dead time. This paper has evaluated two control schemes with the modified structures of the Smith predictor incorporating dead time compensators and conventional controllers for first order process with dead time. The disturbance response and the set point response for both the control schemes were decoupled from each other. Therefore two degrees of freedom control design was formulated, and hence the responses could be designed separately. The two control schemes have mainly two variables to be adjusted that decide the robustness and closed-loop behaviour. This paper also contains the calculation of various parameters that were used in each scheme. A comparison of the two control schemes along with the general Smith predictor control scheme was made using Simulink/Matlab. The conclusion is the second control scheme gave better response overall for the processes with dead time having dead time uncertainty and for the processes with dead time without dead time uncertainty. [ABSTRACT FROM AUTHOR]

Robust semiparametric models for recurrent events have received increasing attention in the analysis of clinical trials in a variety of diseases including chronic heart failure. In comparison to parametric recurrent event models, robust semiparametric models are more flexible in that neither the baseline event rate nor the process inducing between-patient heterogeneity needs to be specified in terms of a specific parametric statistical model. However, implementing group sequential designs in the robust semiparametric model is complicated by the fact that the sequence of Wald statistics does not follow asymptotically the canonical joint distribution. In this manuscript, we propose two types of group sequential procedures for a robust semiparametric analysis of recurrent events. The first group sequential procedure is based on the asymptotic covariance of the sequence of Wald statistics and it guarantees asymptotic control of the type I error rate. The second procedure is based on the canonical joint distribution and does not guarantee asymptotic type I error rate control but is easy to implement and corresponds to the well-known standard approach for group sequential designs. Moreover, we describe how to determine the maximum information when planning a clinical trial with a group sequential design and a robust semiparametric analysis of recurrent events. We contrast the operating characteristics of the proposed group sequential procedures in a simulation study motivated by the ongoing phase 3 PARAGON-HF trial (ClinicalTrials.gov identifier: NCT01920711) in more than 4600 patients with chronic heart failure and a preserved ejection fraction. We found that both group sequential procedures have similar operating characteristics and that for some practically relevant scenarios, the group sequential procedure based on the canonical joint distribution has advantages with respect to the control of the type I error rate. The proposed method for calculating the maximum information results in appropriately powered trials for both procedures. [ABSTRACT FROM AUTHOR]

In this study, the effects of open innovation practices on enterprise value were discussed based on the supply chain perspective. A nonlinear programming mode was constructed considering the uncertainty of open innovation effect. On this basis, the prototype was analyzed using a robust optimization approach with comprehensive considerations to the infeasible probability of constraint and goal accuracy. Findings show that parameter Γ could convert the nonlinear programming model with uncertainty into a robust model with a strong stability. Furthermore, Γ could be used to regulate the preference of the manufacturer to profit and the uncertainties in innovative practices. A small Γ indicates manufacturers can refer to more information, and the manufacturers are more inclined to a mass production, so the profit is larger. Meanwhile, a high Γ means that manufacturers can refer to less information, so that they pay more attention to making the product output adapt to the uncertainty of innovative effect. Manufacturers could select the appropriate Γ value and reasonably arrange the production outputs of different goods according to the technological level of open innovation subject and their preference to profit and uncertainty. These conclusions were verified by a case study. In this work, the uncertainty of the open innovation effect was investigated from the supply chain perspective, which is of important significance to the decision making on optimal supply chain output and innovation risk control under an uncertain environment. [ABSTRACT FROM AUTHOR]

The presence of outliers in the data has implications for stochastic frontier analysis, and indeed any performance analysis methodology, because they may lead to imprecise parameter estimates and, crucially, lead to an exaggerated spread of efficiency predictions. In this paper we replace the normal distribution for the noise term in the standard stochastic frontier model with a Student's t distribution, which generalises the normal distribution by adding a shape parameter governing the degree of kurtosis. This has the advantages of introducing flexibility in the heaviness of the tails, which can be determined by the data, as well as containing the normal distribution as a limiting case, and we outline how to test against the standard model. Monte Carlo simulation results for the maximum simulated likelihood estimator confirm that the model recovers appropriate frontier and distributional parameter estimates under various values of the true shape parameter. The simulation results also indicate the influence of a phenomenon we term 'wrong kurtosis' in the case of small samples, which is analogous to the issue of 'wrong skewness' previously identified in the literature. We apply a Student's t-half normal cost frontier to data for highways authorities in England, and this formulation is found to be preferred by statistical testing to the comparator normal-half normal cost frontier model. The model yields a significantly narrower range of efficiency predictions, which are non-monotonic at the tails of the residual distribution. [ABSTRACT FROM AUTHOR]

The present paper extends the -gain analysis towards variable structure systems (VSS) with sliding-mode (SM) dynamics. The developed analysis is applied to feedback linearisable VSS, driven by standard SM algorithms of the first order. Constructive parameter-tuning of the resulting closed-loop system to possess the -gain less than a desired attenuation level is additionally deduced. The proposed tuning is shown to be capable not only of rejecting matched uniformly bounded disturbances but also of attenuating unbounded ones, including mismatched disturbances. Numerical simulations of an inverted pendulum, driven by a first-order SM controller, are involved to support the theory in the presence of the position measurement inaccuracy, being a source of a mismatched disturbance. [ABSTRACT FROM AUTHOR]

This paper analyzes 12 years of tropospheric ozone (O3) concentration measurements using robust techniques. The measurements were taken at an air quality monitoring station called Belisario, which is in Quito, Ecuador; the data collection time period was 1 January 2008 to 31 December 2019, and the measurements were carried out using photometric O3 analyzers. Here, the measurement results were used to build variables that represented hours, days, months, and years, and were then classified and categorized. The index of air quality (IAQ) of the city was used to make the classifications, and robust and nonrobust confidence intervals were used to make the categorizations. Furthermore, robust analysis methods were compared with classical methods, nonparametric methods, and bootstrap-based methods. The results showed that the analysis using robust methods is better than the analysis using nonrobust methods, which are not immune to the influence of extreme observations. Using all of the aforementioned methods, confidence intervals were used to both establish and quantify differences between categories of the groups of variables under study. In addition, the central tendency and variability of the O3 concentration at Belisario station were exhaustively analyzed, concluding that said concentration was stable for years, highly variable for months and hours, and slightly changing between the days of the week. Additionally, according to the criteria established by the IAQ, it was shown that in Quito, the O3 concentration levels during the study period were not harmful to human health.

In this paper, a robust analysis of nitrogen dioxide (NO2) concentration measurements taken at Belisario station (Quito, Ecuador) was performed. The data used for the analysis constitute a set of measurements taken from 1 January 2008 to 31 December 2019. Furthermore, the analysis was carried out in a robust way, defining variables that represent years, months, days and hours, and classifying these variables based on estimates of the central tendency and dispersion of the data. The estimators used here were classic, nonparametric, based on a bootstrap method, and robust. Additionally, confidence intervals based on these estimators were built, and these intervals were used to categorize the variables under study. The results of this research showed that the NO2 concentration at Belisario station is not harmful to humans. Moreover, it was shown that this concentration tends to be stable across the years, changes slightly during the days of the week, and varies greatly when analyzed by months and hours of the day. Here, the precision provided by both nonparametric and robust statistical methods served to comprehensively proof the aforementioned. Finally, it can be concluded that the city of Quito is progressing on the right path in terms of improving air quality, because it has been shown that there is a decreasing tendency in the NO2 concentration across the years. In addition, according to the Quito Air Quality Index, most of the observations are in either the desirable level or acceptable level of air pollution, and the number of observations that are in the desirable level of air pollution increases across the years.

This paper addresses the stochastic stability problem of positive Markov jump linear systems (PMJLSs). A necessary and sufficient condition of stochastic stability for PMJLSs is given which can be checked by solving linear programming feasibility problems due to the positivity property. A common co-positive Lyapunov function is constructed to solve the problem, and the equivalence among stochastic stability, 1-moment stability and exponential mean stability is proved afterwards. Considering the uncertain transition rates situation, PMJLSs with partially known transition rate matrix are investigated, and a necessary and sufficient condition for robust stochastic stability is proposed in linear programming form. Numerical examples are presented to show the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Set-to-set face recognition has drawn much attention thanks to its rich set information. We propose a robust and efficient Set-to-Set Nearest Neighbor Classification (S2S-NNC) approach for face recognition by using the maximum weighted correlation between sets in low-dimensional projection subspaces. A pair of face sets is represented as two sets of Mutual Typical Samples (MTS) based on their maximum weighted correlation, and the S2S distance is equivalent to that between two sets of MTS. For the variation of objects within a set, the faces are partitioned into patches and projected onto a correlation subspace to find the MTS between two sets. Furthermore, we develop a S2S-NNC approach for image set-based face recognition. Compared with existing approaches, the S2S-NNC unifies the image-to-image, image-to-set and set-to-set recognition problems into one model. Experimental results show the S2S-NNC approach significantly outperforms the state-of-art approaches on large video samples and small occluded samples. [ABSTRACT FROM AUTHOR]

In this paper, a general approach to address modeling of aeroelastic systems, with the final goal to apply μ analysis, is discussed. The chosen test bed is the typical section with unsteady aerodynamic loads, which enables basic modeling features to be captured and so extend the gained knowledge to practical problems treated with modern techniques. The aerodynamic operator has a nonrational dependence on the Laplace variable s, and hence, 2 formulations for the problem are available: frequency domain or state-space (adopting rational approximations). The study attempts to draw a parallel between the 2 consequent linear fractional transformation modeling processes, emphasizing critical differences and their effect on the predictions obtained with μ analysis. A peculiarity of this twofold formulation is that aerodynamic uncertainties are inherently treated differently and therefore the families of plants originated by the possible linear fractional transformation definitions are investigated. One of the main results of the paper is to propose a unified framework to address the robust modeling task, which enables the advantages of both the approaches to be retained. On the analysis side, the application of μ analysis to the different models is shown, emphasizing its capability to gain insight into the problem. [ABSTRACT FROM AUTHOR]

A common bottleneck in evaluating extremal performance measures is that, because of their very nature, tail data are often very limited. The conventional approach selects the best probability distribution from tail data using parametric fitting, but the validity of the parametric choice can be difficult to verify. This paper describes an alternative based on the computation of worst-case bounds under the geometric premise of tail convexity, a feature shared by all common parametric tail distributions. We characterize the optimality structure of the resulting optimization problem, and demonstrate that the worst-case convex tail behavior is in a sense either extremely light tailed or extremely heavy tailed. We develop low-dimensional nonlinear programs that distinguish between the two cases and compute the worst-case bound. We numerically illustrate how the proposed approach can give more reliable performances than conventional parametric methods. The online appendix is available at . [ABSTRACT FROM AUTHOR]

Societies face an increasing number of challenges due to climate change – including enhanced hydro-meteorological risk (HMR). Typically, HMRs are exacerbated by rapid urban development, ecosystem degradation, and water use changes. All though data is scarce and scattered, evidence-based case studies have shown that implementing Nature-based Solutions (NBS) can reduce HMR. With further influence from anthropogenic climate change and continuing ecosystem degradation, HMH are projected to increase in frequency and severity. Consequently, NBS implemented for HMR reduction will have to be robust enough to deal with HMHs that exceed their design criteria and/or expected performance. However, currently available literature does not adequately outline processes that aid in designing and evaluating of robust NBS for HMR reduction. In response to this gap in literature, this study developed a process for designing and evaluating robust NBS for water. In the present study, robust design of a NBS for water is illustrated using an existing flood risk management system and NBS (100-year-old traditional irrigation furrows) in an agricultural area situated within the floodplain of the Chao Phraya River basin in the Pathum Thani province, Thailand. The robustness of the existing NBS for water is then evaluated through robust design and quantified through the construction of a responsive curve. Once the robustness of the existing NBS for water is quantified, an iterative process is used to enhance individual characteristics of the flood risk management system – resulting in efficient use of the flood water storage capacity offered by the existing NBS. [ABSTRACT FROM AUTHOR]

Predložený príspevok sumarizuje priebeh a výsledky geodetických prác počas budovania polohovej geodetickej siete založenej v podzemných priestoroch Dobšinskej ľadovej jaskyne za účelom monitorovania časových a priestorových zmien jej ľadovej výplne. V snahe objektívneho vyhodnotenia týchto zmien boli zo súborov terénnych geodetických meraní stanovené odhady I. a II. rádu geodetickej siete a z hľadiska posúdenia vplyvu potenciálnych odľahlých meraní na geometriu siete bola aplikovaná robustná analýza tejto siete

This paper deals with the output tracking control of gear transmission servo (GTS) systems in the presence of deadzone nonlinearity with nonsymmetric beak points and unknown parameters. A novel differentiable deadzone model with nonsymmetric break points is put forward, which greatly facilitates the control design for a class of mechanical systems in the presence of deadzone nonlinearity. A new smooth backstepping controller, based on the newly-developed model, is proposed for the nominal system. Then, guaranteed robust steady-state performance of the closed-loop system with parametric uncertainties is derived by using Lyapunov analysis for the perturbed nonlinear systems. Simulations are carried out to validate the proposed algorithm and analysis in this paper. [ABSTRACT FROM AUTHOR]

The consideration of approximate reasoning merging rough approximation and formal deduction in pre-rough logic leads to three different types of metric spaces, which we call pre-rough logic metric space, pre-rough upper logic metric space and pre-rough lower logic metric space, respectively. In this paper, we investigate the structure of these three metric spaces mainly from the perspective of topology. It is shown that the three metric spaces have no isolated points. In addition, the continuity of logic connectives w.r.t. rough upper pseudo-metric and rough lower pseudo-metric is examined and the robust analysis of rough logic is also studied. Lastly, the notion of rough divergency degree of any logic theory is proposed and its topological characterization is presented. [ABSTRACT FROM AUTHOR]

Demand-side management presents significant benefits in reducing the energy load in smart grids by balancing consumption demands or including energy generation and/or storage devices in the user’s side. These techniques coordinate the energy load so that users minimize their monetary expenditure. However, these methods require accurate predictions in the energy consumption profiles, which make them inflexible to real demand variations. In this paper, we propose a realistic model that accounts for uncertainty in these variations and calculates a robust price for all users in the smart grid. We analyze the existence of solutions for this novel scenario, propose convergent distributed algorithms to find them, and perform simulations considering energy expenditure. We show that this model can effectively reduce the monetary expenses for all users in a real-time market, while at the same time it provides a reliable production cost estimate to the energy supplier. [ABSTRACT FROM PUBLISHER]

In this article, we consider a linear model in which the covariates are measured with errors. We propose a t-type corrected-loss estimation of the covariate effect, when the measurement error follows the Laplace distribution. The proposed estimator is asymptotically normal. In practical studies, some outliers that diminish the robustness of the estimation occur. Simulation studies show that the estimators are resistant to vertical outliers and an application of 6-minute walk test is presented to show that the proposed method performs well. [ABSTRACT FROM PUBLISHER]

Vehicle anti-collision warning system is a key research area of vehicle safety. It is also a necessary means to enhance driving safety and reduce traffic accidents. The visual location method is presented for this system based on license plate image. This method realizes visual positioning with the help of the mounting points of license plate. At the same time, the feasible robust analysis method is proposed for the visual positioning method with license plate frame image. Finally, the robust analysis determines good angles for commutating rotation parameter in the method of license plate image visual positioning. At the same time, it determines good angles for translation parameter and for rotation and translation transform. Therefore, robust analysis determines positions where the visual positioning method has high positioning accuracy. Robust analysis for license plate vision positioning method is useful in analysis of high positioning accuracy positions for the proposed license plate image visual location method combined with plate mounting points. The research content of this paper is beneficial to driver decision-making and improves the safety of collision warning system and intelligent vehicle. [ABSTRACT FROM AUTHOR]

Dados extremos influenciados por fatores não considerados no modelo estatístico, podem enviesar as estimativas dos parâmetros e valores genéticos. Além disso, diversas características de importância econômica não seguem uma distribuição normal ou apresentam dados censurados. O objetivo deste trabalho é descrever e ilustrar a aplicação de modelos hierárquicos bayesianos para a detecção e mitigação de dados extremos e para análise de dados censurados. Primeiro, é apresentada a especificação tradicional do modelo animal em estágios hierárquicos sob o enfoque bayesiano, para dados não censurados com distribuição Normal. A seguir, esse modelo é generalizado pela introdução de uma variável de ponderação independente, que permite a especificação de densidades residuais de caudas longas da família de distribuições Normal/independente. Finalmente, para contemplar a análise de dados censurados, o modelo básico é ampliado pela inclusão de uma variável com distribuição normal truncada no limite inferior do valor observado da característica no momento da avaliação, para aqueles animais que ainda não completaram sua vida reprodutiva no momento da avaliação.Data strongly influenced by factors not accounted for by the statistical model can bias estimates of genetic parameters and values. Moreover, several traits of economic importance do not follow a normal distribution or have censored data. The objective of this study is to describe and illustrate the application of hierarchical Bayesian models for the detection and muting of outliers and for the analysis of censored data. First, the traditional specification of the animal model in hierarchical stages is presented under the Bayesian approach for normally distributed uncensored data. Then, this model is extended by introducing an independent weighting variable, which allows for the specification of thick tail residual densities from the Normal/independent distribution family. Finally, to cover censored data analysis, the basic model is extended by the inclusion of a variable with truncated normal distribution based on the lower limit in the observed value of the trait at the evaluation time, for those animals that have not yet completed their reproductive life at the evaluation time.

Based on the nonlinear measure method and the matrix inequality techniques, this paper addresses the global asymptotic stability for the complex-valued neural networks with delay. Furthermore, robust stability of the addressed neural network with norm-bounded parameter uncertainties is also tackled. By constructing an appropriate Lyapunov functional candidate, several sufficient criteria are obtained to ascertain the existence, uniqueness and global stability of the equilibrium point of the addressed complex-valued neural networks, which are easy to be verified and implemented in practice. Finally, one example is given to illustrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This study presents a mixed‐integer second‐order conic programming (MISOCP) model for the robust reconfiguration of electrical distribution systems, considering the minimisation of active power losses and reliability constraints. The uncertainty at the reliability data is considered by using a linear and adjustable robust approach. The indices used to evaluate the reliability of the system are the system average interruption frequency index (SAIFI) and the system average interruption duration index (SAIDI), both of which are calculated as functions of the switch status (open or closed). Under radiality constraints, the solution generated by the proposed model is robust in terms of the reliability, i.e. the SADI and SAIFI limits imposed by regulators are not violated even if the uncertain data changes stochastically. The use of an MISOCP model guarantees convergence to optimality by using existing convex optimisation solvers. To evaluate the robustness of each solution generated by the proposed model, a set of Monte Carlo simulations were deployed. Finally, all tests were executed in a 136‐node real distribution system. [ABSTRACT FROM AUTHOR]