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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.

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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Guo, Zhang and Yue.)

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]

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]

Ignition design of air injection wells is the crucial factor to a successful in-situ combustion. In this paper, experimental studies were carried out on post steam flooding wells at JIN-91 of Liaohe oilfield for improving ignition success rate. The experiments were conducted by a high temperature-pressure ignition device specifically designed for in-situ combustion, and the effects of various parameters were investigated by factorial experiments. The results showed that the ignition temperature was reduced and the ignition time was shortened due to three factors: heating temperature, reservoir pressure, and clay minerals. When heating temperature exceeded 360 °C, experimental pressure was higher than 4.0 MPa, and 10% of montmorillonite clay was added, the crude oil samples could be ignited rapidly. According to the factorial results, the most effective method to shorten ignition time is by increasing heating temperature, the safest method to reduce crude oil spontaneous combustion temperature and to shorten ignition time is by applying clay minerals, and reservoir pressure is the key factor for safety operation during ignition operation. These results will provide experimental theoretical guidance for high efficiency ignition, process optimization, and safe implementation of crude oil reservoir, they also will deliver tech support for the high efficiency operation of in-situ combustion. • The influence on pressure, heating temperature and clay minerals were analyzed. • The influence on the ignition process was conducted by factorial experiment. • One experimental device and test methods on ignition were developed for the first time. [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., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Many real-world complex systems, such as power grids, the Internet, transportation networks, and complex information systems networks, which can be modeled abstractly with complex networks, may exhibit coupling or interdependence when subjected to random failure or deliberate attacks, i.e., cascading failures. Therefore, we want to understand the robustness of complex networks against cascading failures. The cascading failure has a failure propagation process, and it is necessary to establish an appropriate propagation model as the research basis. Hence, we adopted a new and more universal cascading failure propagation model based on discrete dynamic system. Based on this propagation model and combined with risk assessment, we proposed several cascading robustness index for complex networks. Then we conducted numerical experiments on several types of existing complex networks and real-world network. These experiments confirm the feasibility and rationality of our proposed indicators to reflect the cascading robustness, which can provide a reference and value for simulation and protection of real-world interdependent networks. [ABSTRACT FROM AUTHOR]