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Forecasts from dynamic factor models potentially benefit from refining the data set by eliminating uninformative series. This paper proposes to use prediction weights as provided by the factor model itself for this purpose. Monte Carlo simulations and an empirical application to short-term forecasts of euro area, German, and French GDP growth from unbalanced monthly data suggest that both prediction weights and least angle regressions result in improved nowcasts. Overall, prediction weights provide yet more robust results.

The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.

This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.

The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.

Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

The purpose of this paper is to establish a combined forecasting model to predict regional logistics demand, which is an important procedure on decision making of regional logistics planning.

There are several kinds of mathematical models often used in forecasting regional logistics demand. Trend extrapolation method extrapolates the future development trends bases on the hypothesis that the regional logistics will develop steadily. Grey system method predicts the change of logistics demand by the generation and development of original data sequence and excavation of inherent rules of the original data. Regression method obtains the change rules through the analysis between explained variable and explanatory variables. Each method has unique characteristics. In order to improve the accuracy of the prediction, combined methods are established. Genetic algorithm is used to determine the weights of different single models.

The results show that the combined forecasting model optimised by genetic algorithm can improve the accuracy.

Combined forecasting model can integrate the advantages of different single forecasting models. The key of improving the accuracy is to determine the weights of single forecasting models. Genetic algorithm can do well in finding suitable weights of each single forecasting model.

The paper succeeds in providing a combined forecasting model using genetic algorithm to determine the weights of each single prediction model, which helps to the decision making of regional logistics demand.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

THE importance of achieving a low structural weight is illustrated by simple estimates of the large decreases in aircraft gross weight and size made possible by conscientious weight saving in structural design. A brief review is then made of the many variables in aircraft design which affect the weight of the structure. The review is made chiefly to emphasize the close interplay in project work between the structural and aerodynamic effects of changes of layout. Finally some remarks are made about comparative structural design efficiency. It is concluded that good weight prediction formulae are at present the best means by which the structural design efficiencies of different aircraft may be readily compared.

The purpose of this paper is to predict the success possibility of supply chain practices (SCPs) implementation based on ten selected SCPs using the analytic hierarchy process (AHP).

A case study was conducted, and for the same, data were collected from two organizations. The data collected from both the organizations were analyzed using AHP. The pair-wise comparisons of SCPs (usually, alternatives and attributes) are established using a scale indicating the strength with which one SCP dominates another with respect to higher-level SCPs. This scaling process then translated into priority weights. Once the priority weights of the elements/determinants of the module have been calculated, the presence of the corresponding element in the organization was evaluated. An evaluation rating of these elements multiplied by the established priority weights have determined the prediction weight for each SCP.

The application of this procedure is described for the institutionalization module and can be similarly extended to the other SCPs/SCM implementation modules. By establishing the prediction weight for each module, the organizations will be able to evaluate the strength of the corresponding factors present before embarking on the SCPs. The organization can identify and create awareness of the essential elements in the SCPs implementation process and identify the actions necessary before implementing SCPs. The AHP can be a useful guide in the decision-making process of SCPs implementation, especially for medium- and large-scale organizations.

The priority weights are subjective and assigned as per the judgment of SCM managers from both the organizations. Further, the priority weights can be obtained from more industry experts through a questionnaire. Second, in this model, only ten practices are taken into consideration for successful SCPs implementation; more practices may be included in future research.

The methodological approach presented can be a useful guide in the decision-making process of SCPs implementation in an organization. The outcome will aid practitioners to know the SCPs and benchmark the organizations on the basis of the methodological research conducted. Again, this model can simply act as a possible research model and the data can act as an example that can be utilized for other studies.

This is the first kind of study which identified ten SCPs and further deployed AHP approach to see the success possibility of combined SCPs that influence the SCM implementation in an organization.

The purpose of this study is to propose a novel plane-based mapping method for legged-robot navigation in a stairway environment.

The approach implemented in this study estimates a plane for each step of a stairway using a weighted average of sensor measurements and predictions. It segments planes from point cloud data via random sample consensus (RANSAC). The prediction uses the regular structure of a stairway. When estimating a plane, the algorithm considers the errors introduced by the distance sensor and RANSAC, in addition to stairstep irregularities, by using covariance matrices. The plane coefficients are managed separately with the data structure suggested in this study. In addition, this data structure allows the algorithm to store the information of each stairstep as a single entity.

In the case of a stairway environment, the accuracy delivered by the proposed algorithm was higher than those delivered by traditional mapping methods. The hardware experiment verified the accuracy and applicability of the algorithm.

The proposed algorithm provides accurate stairway-environment mapping and detailed specifications of each stairstep. Using this information, a legged robot can navigate and plan its motion in a stairway environment more efficiently.

A method for predicting the material consumption of a sweater is presented before it is knitted. It can be achieved with the five basic models combined with the parameters related to the dimensions of the knitting machine and needles. The paper aims to discuss these issues.

Based on the parameters of the needle bar flat knitting machine, the sweater is modeled with five basic structures. The mathematical expression of each basic structure can be derived with corresponding parameters under some consumptions. In following, the predictive weight of the sweater can be formulated with the expression of the length of the basic structures and the linear density of the yarn.

To evaluate the performance of the proposed scheme, experiments of three types of sweaters on four different knitting machines are carried out. The results show that the proposed method can achieve the performance with the bias values by percentage ranging from −1.54 to −2.84 percent.

Due to the present limited research, more experiments could not be carried out. To improve the performance and robustness of the proposed method, statistical performance measures such as the statistical mean and variance in massive experiments will be studied in the further research.

The evaluation of the material consumption can be obtained before it is knitted with the known basic parameters related to the machine and yarn.

This paper derives the general expressions of five basic structures based on the corresponding parameters of knitting machine. The predictive weight of the sweater is expressed according to the above basic structures before the sweater is knitted.

The purpose of this paper is to find out: strengths/weaknesses, environmental opportunities/threats encountered by traditional market; traditional market business position; and a proper business strategy to be applied by traditional market and its implication on the marketing strategy.

The population in this research was policy makers in Bali Province, in relation to the development and revitalization planning of traditional market, and traders doing their activity in a traditional market. The data analysis used internal and external strategic factor matrix (IE).

Key external strategic variables that pose both current and future threats are information technology, bargaining power with buyers and competitors. The key internal strategic variables which are included both present and future strengths are product quality, product variety offered, uniqueness of products offered, affordable product prices, bargaining process, strategic market location, service speed and vast parking lot. Based on the business position of the present Denpasar traditional market which is in quadrant V, while in the future it will be in quadrant II, the strategy properly applied is the proper competition strategy to be grown and built.

The research was only conducted to traditional markets in Bali, thus it cannot be generalized to wider areas, and this research used the survey method where the data collection process was done in a certain point of time or cross-section, whilst the environment would experience extra quick changes. Therefore, it is important to do this research in the future.

The originality for this paper shows the comprehensively development strategies, revitalization strategies in traditional market, by using strength weakness opportunity threat and IE matrix analysis, and research location which is conducted in Bali that has different tourist condition and potentials from other regions.

Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.

In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.

Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.

The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.

CV-LCB approach can balance the exploration and exploitation objectively.