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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.

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.

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.

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.

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.

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.

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.

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.

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

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.

Accurate measurement of residual stress in metallic components using the contour method relies on the achievement of a good quality cut, on the appropriate measurement of the deformed cut surface and on the robust analysis of the measured data. There is currently no published standard or code of practice for the contour method. As a first step towards such a standard, this study draws on research investigations addressing the three main steps in the method: how best to cut the specimens; how to measure the deformation contour of the cut surface; and how to analyse the data. Good practice guidance is provided throughout the text accompanied by more detailed observations and advice tabulated in Appendix.