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This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.

The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform.

Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better.

The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.

To improve the deficiencies of the existing journal influencing evaluation indexes, which have single influencing factors and are greatly affected by the number of papers published, this paper proposes a new evaluation of the journal influence method based on the RA index.

In the metrics create, the paper introduces the RA index for evaluating the impact of authors and analyzing its feasibility in journal evaluation. Then the RA index is divided by the number of journal's published papers to obtain the RAQ index to evaluate journal impact. In the metrics analysis, the authors analyze the statistical characteristics of the RAQ index using statistical analysis, and t-test, Roc curve and PLS were used to analyze the relationship between the RAQ index and other indicators.

Empirical results show that the RAQ index can effectively identify high-quality journals and reduce the number of published papers on journal evaluation. The empirical results show that the RAQ index has higher stability and discrimination than the h-index and g-index. Compared with other indexes, the RAQ index has better evaluation effects and statistical characteristics.

The current study proposes journals' RAQ index, which integrates the influence of high-, low- and zero-cited papers. It solves the problem of the virtual high of the h-type index. The findings will contribute to the evaluation of journal influence by offering a new research idea that facilitates the fairness and rationality of journal evaluation.

This study investigates the carbon footprint of the alternative structure types and materials used for the reconstruction of schools in Haiti. Are recycled construction materials more environmental than virgin materials? To estimate which alternative construction solution has the smallest carbon footprint, a survey was made for the school model used for the reconstruction programme in Haiti after the 2010 earthquake.

The carbon footprint was calculated using life cycle assessment methodology for five different concrete structure alternatives and five different cement mixes for the same design of a school building. In addition, the uptake of CO₂ through the carbonation of concrete during 50 years was calculated.

The carbon footprint of recycled materials can be either the best or worst option, depending on how the materials are used. The difference to using virgin materials is not big. This is mainly due to the lower structural performance of recycled materials, which needs to be compensated for by using additional reinforcements. Using cement mixes that have high amounts of substitutes for cement seems to lower the carbon footprint of structures considerably. The uptake of CO₂ in carbonation has potential but requires an optimal design and environment.

The findings give information for humanitarian project managers and designers on lowering the carbon footprint of their construction projects.

The purpose of this paper is to present research methods in processing uncertain information.

Vague set and rough set are both‐wings‐mode for expressing uncertainty systems, and based on the both‐wings‐mode of expressing uncertainty systems, the connections of vague set and rough set are discussed.

This paper presents the relationships between vague set and rough set.

Based on these connections between vague set and rough set, theoretical and means of vague set can be used for rough set; also theoretical and means of rough set can be used for vague set.

The paper contributes to the discussion on the research of vague set and rough set. The conclusions are useful in information processing.

In the daily energy dispatch process in a power system, accurate short-term electricity load forecasting is a very important tool used by spot market players. It is a critical requirement for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The purpose of this study is to present an improved grey Verhulst electricity load forecasting model.

To test the effectiveness of the proposed model for short-term load forecast, studies made use of Kenya’s load demand data for the period from January 2014 to June 2019.

The convectional grey Verhulst forecasting model yielded a mean absolute percentage error of 7.82 per cent, whereas the improved model yielded much better results with an error of 2.96 per cent.

In the daily energy dispatch process in a power system, accurate short-term load forecasting is a very important tool used by spot market players. It is a critical ingredient for optimal generator unit commitment, economic dispatch, system security and stability assessment, contingency and ancillary services management, reserve setting, demand side management, system maintenance and financial planning in power systems. The fact that the model uses actual Kenya’s utility data confirms its usefulness in the practical world for both economic planning and policy matters.

In terms of generation and transmission investments, proper load forecasting will enable utilities to make economically viable decisions. It forms a critical cog of the strategic plans for power utilities and other market players to avoid a situation of heavy stranded investment that adversely impact the final electricity prices and the other extreme scenario of expensive power shortages.

This research combined the use of natural logarithm and the exponential weighted moving average to improve the forecast accuracy of the grey Verhulst forecasting model.

The purpose of this paper is to present work which is a part of the Comprehensive Automation for Specialty Crops project (CASC). Desired trajectory tracking objective has been previously performed by using a non‐model based approach in this project. Long distance autonomous drive has been achieved; however the results haven't met the expectations of the project requirements. In order to provide these requirements, this study is conducted. In this study, long distance autonomous trajectory tracking for an orchard vehicle is studied. Besides longitudinal motion, lateral motion of the vehicle is also considered. The longitudinal and lateral errors are objected to keep into a region of less than 10 cm.

Car‐like robot kinematic modeling approach is used to create desired trajectory. In order to control longitudinal velocity and steering angle of the vehicle, a controller methodology is proposed. Stability of the controller proposed is shown by using Lyapunov stability approach.

The proposed model is adapted into a four‐wheeled autonomous orchard vehicle and tested in an experimental orchard for long distance autonomous drives. More than 15 km autonomous drive is successfully achieved and the details are presented in this paper.

In this study, long distance autonomous trajectory tracking for an orchard vehicle is focused. A model based control strategy, including the information about longitudinal and lateral motion of the vehicle, is constructed. A new approach to create steering angles for turning operations of the orchard vehicle is introduced. It is objected that the longitudinal and lateral errors should be less than 10 cm during the trajectory tracking task.