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The purpose of this paper is to provide a modeling approach using grey power model with first‐order one‐variable (abbreviated as GPM(1,1)) for forecasting small sample oscillating series.

An optimization method is used to determine the initial value in GPM(1,1) model, and furthermore, the power value in the model is optimized by utilizing a non‐linear programming model. An operations research software LINGO is employed to solve the non‐linear optimization model.

The results show that the optimized GPM(1,1) model can flexibly adjust the parameters to make the forecasting results more in line with the actual data; therefore, for a given small sample oscillating series, if an appropriate way to find the optimal parameters is taken, accurate predictions should be obtained.

The modeling approach proposed in the paper can be used to forecast new product sales, new industry development trend, equipment remaining life, disaster emergency material demand, etc.

The paper extends the application range of the grey model for forecasting small sample oscillating series by using grey power model GPM(1,1).

To develop the theory and application of the grey prediction model, this investigation constructs a novel discrete grey Riccati model termed DGRM(1,1).

By examining a special kind of Riccati difference equation and the structure of the conventional discrete grey model (DGM), we advance a novel DGRM, and the model's prediction effect is evaluated by two numerical examples and an application case and compared with that of other conventional grey models.

The average relative simulation error of DGRM(1,1) does not change if the model is built after the original sequence has been transformed by a multiplier, and the new model is suitable to predict monotonically increasing, monotonically decreasing and unimodal sequences.

DGRM(1,1) is utilized to forecast the development cost of a small plane owned by the Aviation Industry Corporation of China (AVIC) with an original data sequence from 2006 to 2013. The outcomes indicate that DGRM(1,1) exhibits high precision and potential in development cost prediction.

Combining the Riccati difference equation with the conventional DGM, the author advances a new grey model that is suitable to predict three kinds of data series with different changing trends.

This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.

The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.

The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

Early in 1980 the Flight Instrumentation Laboratories received an order to design and manufacture a Trim Tab Excitation System for the BAe 146.

A computer environment to support the strategic decision‐making process in construction firms is presented. The system implements modelling concepts originally developed to evaluate project execution strategies, extending and generalizing the modelling methodology to a broader range of strategic decisions. An application to a construction firm's strategic planning is used in this paper to illustrate the modelling process. The computer system is designed to help the users in building a conceptual model for the decision problem, this model is a simplified structure of the variables and interactions that influence the decisions being analysed, including internal as well as external factors. An analytical model is then designed to predict the impact of these strategies, integrating expert knowledge and assessments of the strategic planning team into a mathematical model. The mathematical component uses concepts of cross‐impact analysis and probabilistic inference to capture uncertainties and interactions among project variables. The system provides multiple analysis capabilities, including sensitivity analysis, selected outcome prediction, isolated or combined effect of strategies and changes in performance due to changes in the external environment. The system allows management to test different combinations of long‐term strategies and predict expected sales, market share or other measures of performance.

Electricity plays an important role in the economic condition of any country. Nowadays, Pakistan is badly affected by shortage of electricity, which directly affected the economic growth of state. The purpose of this study is to propose an improved grey model DGPM(1,1,N) to forecast Pakistan's production of electricity, installed capacity and consumption.

To significantly simulate and predict accuracy, the discrete grey polynomial model DGPM(1,1,N) is improved with new information priority accumulation. The particle swarm optimization (PSO) algorithm is used for parameter optimization. The value of parameter is adjusted into improved grey model. By adjusting the parameter value in the model, the accuracy of prediction is enhanced.

The installed capacity of electricity needs more attention to improvement through implementation of effective polices, resolving major issues and funding scheme to fulfill the electricity demand of country. And improved DGPM(1,1,N) has better accuracy than original DGPM(1,1,N), DGM(1,1), nongrey models, linear regression and Holt–Winters methods.

This paper provides a practical and efficient improved grey method to predict the electricity production, consumption and installed capacity in Pakistan. This research and suggestion will help Pakistani government to formulate better policies to decrease the consumption of electricity and increase the installed capacity of electricity.

This paper not only improves the grey model with accumulation generation operator but also forecasts Pakistan's electricity production, installed capacity and consumption. It is a new idea to predict the installed capacity of electricity and the findings provide suggestions for the government to make policies.

This study aims to solve the problem that the traditional hierarchically performed hazard origin and propagation studies (HiP-HOPS) cannot make dynamic model for the complex system such as integrated modular avionics (IMA) system.

A new combination method that combines HiP-HOPS with architecture analysis and design language (AADL) is proposed.

The combination method potentially reduces the amount of rework required for safety analysis and modelling of a modified design.

Modelling the IMA system with the combination method can just make qualitative analysis but cannot make quantitative analysis.

The static model depicts the fault propagation among the components while the dynamic model describes the composite fault with AADL for IMA system.

The results of the case study show that the proposed method not only keeps model consistency but also makes safety analysis and modelling for IMA system efficiently.

The paper aims to analyze some properties of GM(1,1,β) model based on the principle that the grey GM(1,1) model parameters are grey and adjustable.

At first, according to the principle that grey GM(1,1) model parameters are grey and adjustable, and the GM(1,1,β) model with parameter packet is put forward. Second, some properties of the GM(1,1,β) model are discussed, and the applicable region of the GM(1,1,β) model is given based on the grey differential equation of the GM(1,1,β) model. At last, the background value coefficient's calculation formula and optimization algorithm of the GM(1,1,β) model are also given. A numeric example is also computed in the last part of the paper.

The result of the study shows that the application scope of the GM(1,1,β) model is (−8,+8).

The GM(1,1,β) model provides the theoretical basis for the GM(1,1) model's optimization and can hence forecast its precision.

The paper succeeds in realizing the GM(1,1) model's application scope (−2,+2) is broadened to (−8,+8).

The green aspects in current management practices are strongly emphasized for sustainable and environment friendly business operations. Thus, building on ability-motivation-opportunity (AMO) theory, this study aims to test the mediating role of green absorptive capacity in the relationship between green human resources management (GHRM) practices (i.e., green training and development, and green performance management) and organizational citizenship behavior towards environment (OCBE).

Using a paper-pencil survey, the authors collected data from 170 middle-tier officers working in cement industry of Pakistan. Structural equation modeling technique was applied for data analysis through Smart-PLS.

Results indicated that GHRM practices significantly influence OCBE and green absorptive capacity partially mediates the relationship between GHRM practices and OCBE.

This study offers new theoretical and practical insights by empirically investigating the mediating role of green absorptive capacity between GHRM practices and OCBE through the lens of AMO theory. Furthermore, this study contributed in disclosing the predictors of OCBE through intervening mechanism in manufacturing sector of developing country for sustainable outcomes.