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This study aims to propose an efficient method for solving reliability-based design optimization (RBDO) problems.

In the proposed algorithm, genetic algorithm (GA) is employed to search the global optimal solution of design parameters satisfying the reliability and deterministic constraints. The Kriging model based on U learning function is used as a classification tool to accurately and efficiently judge whether an individual solution in GA belongs to feasible region.

Compared with existing methods, the proposed method has two major advantages. The first one is that the GA is employed to construct the optimization framework, which is helpful to search the global optimum solutions of the RBDO problems. The other one is that the use of Kriging model is helpful to improve the computational efficiency in solving the RBDO problems.

Since the boundaries are concerned in two Kriging models, the size of the training set for constructing the convergent Kriging model is small, and the corresponding efficiency is high.

The earth is facing challenges to work for the survival of human life during domino effect disasters. The emergency resource storage locations should be selected considering the probability of domino effect disasters. The first purpose of this study is to select the storage locations where domino effect probability is less. And second, facility development cost and transportation costs and costs for unutilized capacity have been optimized.

The work is a multiobjective optimization problem and solved with weighted sum approach. At first, the probabilities of domino effect due to natural disasters are calculated based on the earthquake zones. Then with that result along with other necessary data, the location to set up storage facilities and the quantities of resources that need to be transported has been determined.

The work targeted a country, Bangladesh for example. The authors have noticed that Bangladesh is currently storing relief items at warehouse which is under the domino effect prone region. The authors are proposing to avoid this location and identified the optimized cost for setting up the facilities. In this work, the authors pointed out which location has high probability of domino effect and after avoiding this location whether cost can be optimized, and the result demonstrated that this decision can be economical.

Disaster response authorities should try to take necessary proactive steps during cascading disasters. The novelty of this work is determining the locations to select storage facilities if the authors consider the probability of the domino effect. Then a facility location optimization model has been developed to minimize the costs. This paper can support policymakers to assess the strategies for selecting the location of emergency resource facilities.

The purpose of this study, is to deals with capacity design (strong column – weak beam) in reinforced concrete frames, slightly slender, which depends on the determination of a capacity ratio necessary to reach a structural plastic mechanism. To find the capacity ratio allowing to achieve a fairly ductile behavior in reinforced concrete frames, it is necessary to validate this concept by a non-linear static analysis (push-over). However, this analysis is carried out by the use of the ETABS software, and by the introduction into the beams and columns of plastic hinges according to FEMA-356 code.

This approach makes it possible to assess seismic performance, which facilitates the establishment of a system for detecting the plasticization mechanisms of structures. It is also necessary to use a probabilistic method allowing to treat the dimensioning by the identification of the most probable mechanisms and to take only those that contribute the most to the probability of global failure of the structural system.

In this study, three reinforced concrete frame buildings with different numbers of floors were analyzed by varying the capacity ratio of the elements. The results obtained indicate that it is strongly recommended to increase the ratio of the resistant moments of the columns on those of the beams for the Algerian seismic regulation (RPA code), knowing that the frameworks in reinforced concrete are widespread in the country.

The main interest of this paper is to criticize the resistance condition required by RPA code, which must be the subject of particular attention to reach a mechanism of favorable collapse. This study recommends, on the basis of a reliability analysis, the use of a capacity dimensioning ratio greater than or equal to two, making it possible to have a sufficiently low probability of failure to ensure a level of security for users.

Among the proposed radio access strategies for improving system execution in 5G networks, the non-orthogonal multiple access (NOMA) scheme is the prominent one.

Among the most fundamental NOMA methods, power-domain NOMA is the one where at the transmitter, superposition coding is used, and at the receiver, successive interference cancellation (SIC) is used. The importance of power allocation (PA) in achieving appreciable SIC and high system throughput cannot be overstated.

This research focuses on an outage probability analysis of NOMA downlink system under various channel conditions like Rayleigh, Rician and Nakagami-m fading channel. The system design's objectives, techniques and constraints for NOMA-based 5G networks' PA strategies are comprehensively studied.

From the results of this study, it is found that the outage probability performance of downlink ordered NOMA under Rayleigh, Rician and Nakagami-m fading channel was good.

Outage probability analysis of downlink ordered NOMA under various channel conditions like Rayleigh, Rician and Nakagami-m fading channels were employed. Though the performance of Nakagami-m fading channel is lesser compared to Rayleigh channel, the performance for user 1 and user 2 are good.

This study aims to explore the smallholder farmers’ perceptions of climate change and its adaptation options (changing crop variety; improved crop and livestock; soil and water conservation [SWC]; and irrigation practices) and drought indices in the Dire Dawa Administration Zone, Eastern Ethiopia.

A cross-sectional household survey was used. A structured interview schedule for respondent households for key informants and focus group discussions were used. This study used both descriptive statistics and an econometric model. The model was used to compute the determinants of climate adaptation options in the study area. Drought characterization was carried out by DrinC software.

The results revealed households adapted to selected adaptation options. The model results confirmed that education level, farm size, tropical livestock units (TLUs) and access to agricultural extension services have positive and significant impacts on changing crop variety by 0.0014%, 0.045%, 0.032% and 0.035%, respectively. The likelihood of farmers’ decisions to use adaptation strategies (family size, TLU, agricultural extension service and distance from the market) has positive and significant impacts on SWC. The reconnaissance drought index (RDI6) of ONDJFM and AMJJAS showed extreme and severe drought index values of −2.88 and −1.96, respectively.

This study used a locally adopted climate change adaptation intervention for smallholder farmers, revealing the importance of drought characterization indices both seasonally and annually.

This paper presents a case study on the problem of loading air containers in air express carriers motivated from DHL and Air Hong Kong. The problem is to determine the containers to be loaded and the locations of the loaded containers in an aircraft while maintaining stability of the aircraft. The objective of the problem is to maximize the revenue obtained from delivering containers. We present an integer programming model to represent and optimally solve the problem. Computational experiments done on a number of randomly generated test instances show that the integer program can be a viable tool for generating loading plans in the companies since optimal or near-optimal solutions for the test instances are obtained within a reasonable amount of computation time.

The purpose of this study is to extend the classical noncentral F-distribution under normal settings to noncentral closed skew F-distribution for dealing with independent samples from multivariate skew normal (SN) distributions.

Based on generalized Hotelling's T² statistics, confidence regions are constructed for the difference between location parameters in two independent multivariate SN distributions. Simulation studies show that the confidence regions based on the closed SN model outperform the classical multivariate normal model if the vectors of skewness parameters are not zero. A real data analysis is given for illustrating the effectiveness of our proposed methods.

This study’s approach is the first one in literature for the inferences in difference of location parameters under multivariate SN settings. Real data analysis shows the preference of this new approach than the classical method.

For the real data applications, the authors need to remove outliers first before applying this approach.

This study’s approach may apply many multivariate skewed data using SN fittings instead of classical normal fittings.

This paper is the research paper and the authors’ new approach has many applications for analyzing the multivariate skewed data.

This paper aims to offer a tutorial/introduction to new statistics arising from the theory of optimal transport to empirical researchers in econometrics and machine learning.

Presenting in a tutorial/survey lecture style to help practitioners with the theoretical material.

The tutorial survey of some main statistical tools (arising from optimal transport theory) should help practitioners to understand the theoretical background in order to conduct empirical research meaningfully.

This study is an original presentation useful for new comers to the field.

This paper aims mainly at introducing applied statisticians and econometricians to the current research methodology with non-Euclidean data sets. Specifically, it provides the basis and rationale for statistics in Wasserstein space, where the metric on probability measures is taken as a Wasserstein metric arising from optimal transport theory.

The authors spell out the basis and rationale for using Wasserstein metrics on the data space of (random) probability measures.

In elaborating the new statistical analysis of non-Euclidean data sets, the paper illustrates the generalization of traditional aspects of statistical inference following Frechet's program.

Besides the elaboration of research methodology for a new data analysis, the paper discusses the applications of Wasserstein metrics to the robustness of financial risk measures.