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Improving the eco-efficiency of cultivated land use (ECLU) is important for ensuring food security, promoting social and economic development, and reducing carbon emissions. However, dynamic inter-period comparisons of the ECLU and clarifications of its influencing factors are limited. We calculated the ECLU at the county level in the Beijing–Tianjin–Hebei region, China, based on the super-efficiency slacks-based measure and global Malmquist–Luenberger index and analyzed its influencing factors utilizing a geographically and temporally weighted regression model. From 2000 to 2020, the number of higher counties decreased and that of medium counties increased. Geographically, the ECLU values in the north are higher than those in other districts and counties; counties in Beijing and Tianjin maintained moderate ECLU values, whereas Zhangjiakou and Chengde maintained high ECLU values. The ECLU value in the study area showed a trend of rapid decline–slow rise–continuous rise, with the upward trend of the ECLU value in Beijing–Tianjin–Hebei region being significantly less pronounced than those in most counties of Hebei Province. Resource allocation and scale expansion where initially dominant; however, technological progress and investment eventually prevailed. The ECLU is mainly affected by the multiple cropping index, industrial structure, irrigation index, mechanized farming level, and per capita cultivated land. This study assesses the ECLU in the Beijing–Tianjin–Hebei region, providing a scientific basis for the formulation and implementation of relevant policies for its improvement. Furthermore, this study enriches the theory and methods of research on the ECLU and has practical value and theoretical significance. Overall, the results have important social value as they contribute to ensuring national food security, reducing carbon emissions, promoting regional coordinated and sustainable development.

Abstract Sand cat swarm optimization algorithm is a meta-heuristic algorithm created to replicate the hunting behavior observed by sand cats. The presented sand cat swarm optimization method (CWXSCSO) addresses the issues of low convergence precision and local optimality in the standard sand cat swarm optimization algorithm. It accomplished this through the utilization of elite decentralization and a crossbar approach. To begin with, a novel dynamic exponential factor is introduced. Furthermore, throughout the developmental phase, the approach of elite decentralization is incorporated to augment the capacity to transcend the confines of the local optimal. Ultimately, the crossover technique is employed to produce novel solutions and augment the algorithm's capacity to emerge from local space. The techniques were evaluated by performing a comparison with 15 benchmark functions. The CWXSCSO algorithm was compared with six advanced upgraded algorithms using CEC2019 and CEC2021. Statistical analysis, convergence analysis, and complexity analysis use statistics for assessing it. The CWXSCSO is employed to verify its efficacy in solving engineering difficulties by handling six traditional engineering optimization problems. The results demonstrate that the upgraded sand cat swarm optimization algorithm exhibits higher global optimization capability and demonstrates proficiency in dealing with real-world optimization applications.

To solve the problem of reservoir sediment accumulation, this paper intends to study the feasibility of preparing reservoir sediment-cement composite cementitious material by partially replacing cement with reservoir sediment. Using the single factor test method, the replacement rate of reservoir sediment and mechanical activation time were selected as variables to explore the effects of water requirement of normal consistency, setting time, mortar fluidity, and mechanical properties of mortar specimens. The results show that with the increase of sediment replacement rate, the water requirement of normal consistency of composite cementitious material paste increases, the setting time increases, the fluidity of mortar decreases, and the mechanical properties of mortar specimens decrease significantly. With the increase of mechanical activation time, the water requirement of normal consistency decreases, the setting time decreases, the fluidity of mortar increases, and the mechanical properties of the mortar specimen increase. The microcosmic results show that mechanical activation can promote the formation of C-S-H gel in reservoir sediment, and fill each other with unreacted reservoir sediment particles, resulting in a denser microstructure. The purpose of this paper is to provide theoretical support for the preparation of reservoir mud-cement composite cementitious material.

Abstract Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L–C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm’s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population’s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm’s advantages in solving complex problems with unknown search spaces.

In an effort to overcome the problems with typical optimization algorithms’ slow convergence and tendency to settle on a local optimal solution, an improved golden jackal optimization technique is proposed. Initially, the development mechanism is enhanced to update the prey’s location, addressing the limitation of just relying on local search in the later stages of the algorithm. This ensures a more balanced approach to both algorithmic development and exploration. Furthermore, incorporating the instinct of evading natural predators enhances both the effectiveness and precision of the optimization process. Then, cross-mutation enhances population variety and facilitates escaping from local optima. Finally, the crossbar strategy is implemented to change both the individual and global optimal solutions of the population. This technique aims to decrease blind spots, enhance population variety, improve solution accuracy, and accelerate convergence speed. A total of 20 benchmark functions are employed for the purpose of comparing different techniques. The enhanced algorithm’s performance is evaluated using the CEC2017 test function, and the results are assessed using the rank-sum test. Ultimately, three conventional practical engineering simulation experiments are conducted to evaluate the suitability of IWKGJO for engineering issues. The results obtained demonstrate the beneficial effects of the altered methodology and illustrate that the expanded golden jackal optimization algorithm has superior convergence accuracy and a faster convergence rate.

Wild Horse Optimizer (WHO), a new metaheuristic algorithm proposed in recent years, has some weaknesses in solving practical problems, such as low searching accuracy and slow convergence speed. Herein, a Hybrid Multi‐Strategy improved Wild Horse Optimizer (HMSWHO) is proposed, which includes four strategies to improve the optimization capability. The Halton sequence is used to initialize the foal population to make the population more diverse. The adaptive parameter TDR is improved to balance the global exploration and local exploitation. The simplex method is used to improve the worst position of the population. Wild horse escaping behavior is added to improve search efficiency and optimization accuracy. The main innovation strategies are the improvement of TDR and the addition of escaping behavior. To verify the effectiveness of the improved strategies, 12 benchmark test functions, CEC2017, and CEC2021 test functions are selected for simulation experiments. Mechanical design examples are added for optimization, and the optimization results are 16.61%, 1.65%, and 0.21% less than that of WHO. The results show that the improved algorithm has obvious advantages in convergence speed, accuracy, and stability. HMSWHO can be applied to more practical engineering optimization problems and provide new ideas for structural optimization methods.

Abstract To improve the efficiency of the structural optimization design in truss calculation, an improved fruit fly optimization algorithm was proposed for truss structure optimization. The fruit fly optimization algorithm was a novel swarm intelligence algorithm. In the standard fruit fly optimization algorithm, it is difficult to solve the high-dimensional nonlinear optimization problem and easy to fall into the local optimum. To overcome the shortcomings of the basic fruit fly optimization algorithm, the immune algorithm self–non-self antigen recognition mechanism and the immune system learn–memory–forgetting knowledge processing mechanism were employed. The improved algorithm was introduced to the structural optimization. Optimization results and comparison with other algorithms show that the stability of improved fruit fly optimization algorithm is apparently improved and the efficiency is obviously remarkable. This study provides a more effective solution to structural optimization problems.

BackgroundPediatric burns of all the ages are prevalent worldwide, posing a severe health risk to children. This study aims to examine pediatric burns' clinical characteristics and epidemiology in central China.MethodsThe pediatric patients of the Burn Research Center, Department of the First People's Hospital of Zhengzhou City from 2013 to 2019 were retrospectively studied and the relevant data were collected from the hospitalized medical records [e.g., demographic, etiology, length of stay (LOS), age, gender, burn area and depth, number of surgeries, cost, and outcome].ResultsA total of 5,569 pediatric burn patients were included, accounting for 43.9% of the total burn population. Electric burns represented a relatively small proportion (1.17%) but were more likely to lead to disabilities or death than scalds (90.63%) and flames (5.12%). The median age was 2 years [interquartile range (IQR): 1–4] and the boys/girls ratio ranged from 1.3:1 to 1.6:1. The most commonly burnt anatomic sites were the limbs (38.3%), with a median %TBSA (total body surface area) of 6 (IQR: 4–10). The complications of shock and pneumonia accounted for 7.6 and 19.2%, respectively. The peak months of pediatric burns included January, May, and August and the rural/urban ratio reached 1.61:1. The percentage of burn wounds treated surgically increased considerably from 2013 to 2019 (3.8 vs. 37.8%). The median hospital LOS was 15 days (IQR: 8–28 days), with the three high-risk factors (e.g., more surgeries, more %TBSA, full-thickness skin burns). The median cost of hospitalization was 1,511 USD (IQR: 848–2,648 USD) and the main risk factors consisted of full-thickness burns, more %TBSA, longer LOS, and more surgical procedures. Among all the patients, LA50 was 78.63% (95% CI = 75.12–83.45) and the overall mortality reached 0.1% since seven deaths were recorded.ConclusionScalds, flames, contact, and chemicals are the main causes of burns among children aged 1–5 years in central China. Accordingly, various prevention strategies should be employed depending upon the cause of the burn.

Asphalt aging has a direct influence on the durability of asphalt mixture. In order to improve thermal oxidizing aging resistance of asphalt, the commercial antioxidants including Irganox 1010 and Irgafos 168 were selected to modify base asphalt and SBR asphalt. The basic properties of modified asphalt before and after the rolling thin film oven test (RTFOT) are evaluated by penetration, softening point, ductility, and viscosity. The Fourier transform infrared spectroscopy (FTIR) test was applied to determine the antiaging mechanism of modified and unmodified asphalt. According to the test result, Irganox 1010 not only reduces the aging degree of asphalt but also has little adverse effect on other properties. Irgafos 168 with a content of 1% improves the low temperature ductility of asphalt and improves the antiaging performance of asphalt significantly. After RTFOT, the content of oxygen-containing functional groups (carbonyl group and sulfoxide group) declines significantly in antioxidation modified asphalt, and Irganox 1010 and Irgafos 168 inhibit oxidizing reaction in the aging procedure. This study can provide a useful reference for improving the aging property of asphalt in highway construction.

It is the key and difficult problem for the current project management to consider the multi-objective optimization of the four elements, such as quality, duration, cost and safety. To improve the accuracy and efficiency of project management during the engineering construction, considering the advantages and disadvantages of the traditional quality-cost-time model, the four elements were regarded as a system, and a multi-objective optimization model was established. The improved ant colony algorithm was used to carry out multi-objectives of construction projects to overcome the premature defect of the traditional method. The optimal plan of the project was found and the overall efficiency of the construction project management was improved. Results show the optimized ant colony algorithm can avoid the low efficiency of the optimal solution search and the shortcoming of the initial pheromone. The engineering practice proves that the enhanced algorithm has solved the problem of the multi-objective optimization of quality, duration, cost and safety. The obtained conclusions are of significant reference value to direct the similar engineering practice.

The main objective of this paper is to investigate the dynamic performance and behaviour of glass-fiber reinforced plastic (GRP) mortar pipes under heavy truck loads. According to the field conditions, a finite element dynamic analysis (FEDA) model of GRP pipe culverts was established. Modal analysis of the vibration system was carried out, and the main natural frequencies with 26.6Hz,32Hz,35.7Hz and corresponding mode shapes were obtained. On the basis of modal analysis, dynamic response of GRP pipe culverts under vehicle loads was simulated through random vibration. Based on the measured vibration velocity time histories, the modal and random vibration responses of the vibration system were analyzed. The results show that the spectral response value of pipeline to vehicle load decreases with the increase of buried depth. When the depth of GRP pipe culverts buried is greater than 0.8m, the influence of ground vehicle dynamic loads on the vibration of GRP pipe culverts cannot be taken into consideration. It can provide the basis for the design of GRP pipe culverts, especially under heavy loads.

Abstract With the increase in system complexity, the intelligent heuristic optimization methods have received more and more attention on system reliability analysis. However, the objective functions and constraint conditions of system reliability are nonlinear. Thereby, a hybrid optimization method was proposed, based on the shuffled frog leaping algorithm and bacterial foraging algorithm, to solve the problem of system reliability and redundancy allocation. First, random grouping strategy was added to maintain the diversity of the population. Then, the Levy flight update strategy was used to increase the global search ability. Finally, the method of migration operation was introduced to escape from local optimums. The proposed methodology, a new version of the SFLA algorithm, was then applied to the mathematical test and the operation of the system reliability model, respectively. Results show that compared to the common methods, it can obtain the best solution, with the maximum value of the system reliability.

The fuzzy logic reasoning based on the “If... then...” rule is not the inaccurate reasoning of AI against ambiguity because fuzzy reasoning is antilogical. In order to solve this problem, a redundancy theory for discriminative weight filtering containing six theorems and one M(1,2,3) model was proposed and the approximate reasoning process was shown, the system logic of AI handling ambiguity as an extension of the classical logic system was proposed. The system is a generalized dynamic logic system characterized by machine learning, which is the practical-application logic system of AI, and can effectively deal with practical problems including conflict, noise, emergencies and various unknown uncertainties. It is characterized by combining approximate reasoning and computing for specific data conversion through machine learning. Its core is data and calculations and the condition is “sufficient” high-quality training data. The innovation is that we proposed a discriminative weight filtering redundancy theory and designed a computable approximate reasoning logic system that combines approximate reasoning and calculation through machine learning to convert specific data. It is a general logic system for AI to deal with uncertainty. The study has significance in theory and practice for AI and logical reasoning research.

To improve the efficiency of the structural optimization design in truss calculation, an improved chicken swarm optimization algorithm was proposed for truss structure optimization. The chicken swarm optimization is a novel swarm intelligence algorithm. In the basic chicken swarm optimization algorithm, the concept of combining chaos strategy and reverse learning strategy was introduced in the initialization to ensure the global search ability. And the inertia weighting factor and the learning factor were introduced into the chick position update process, so as to better combine the global and local search. Finally, the overall individual position of the algorithm was optimized by the differential evolution algorithm. The improved algorithm was tested by multipeak function and applied to the truss simulation experiment. The study provided a new method for the truss structure optimization.

To find a more effective method for the assessment of sustainable urban transport development, the comprehensive assessment model of sustainable urban transport development was established based on the unascertained measure. On the basis of considering the factors influencing urban transport development, the comprehensive assessment indexes were selected, including urban economical development, transport demand, environment quality and energy consumption, and the assessment system of sustainable urban transport development was proposed. In view of different influencing factors of urban transport development, the index weight was calculated through the entropy weight coefficient method. Qualitative and quantitative analyses were conducted according to the actual condition. Then, the grade was obtained by using the credible degree recognition criterion from which the urban transport development level can be determined. Finally, a comprehensive assessment method for urban transport development was introduced. The application practice showed that the method can be used reasonably and effectively for the comprehensive assessment of urban transport development.

In order to overcome the premature convergence defect of the basic particle swarm optimization (PSO) algorithm and provide an effective method for shape and sizing optimization of truss structure, an improved PSO was proposed. The random direction method was employed to produce high-quality initial population, the fuzzy system was applied in the dynamic adaptive adjustment of parameters of the PSO, and the Metropolis criteria were used to improve the performance of PSO. Then, the improved PSO was introduced to the truss structure shape and sizing optimization design. Engineering practice and comparison with the other optimization algorithms show that the algorithm has good convergence and global searching capability. The study provides a promising algorithm for the structural optimization.

The experimental investigation on the mechanical parameters compositions of glass-fiber reinforced plastic mortar (GRPM) pipe is the basis of simulating and analyzing the mechanical characteristics of the complete structure of GRPM pipe. Due to the anisotropic material properties of GRPM pipe, the compressive property test is carried out, and the results show that the circumferential compressive strength(CCS) is 49.93 MPa, the axial compressive strength(ACS) is 40.79 MPa, the circumferential elastic modulus(CEM) is 4.84 GPa, and the axial elastic modulus(AEM) is 4.04 GPa. The difference between the CCS and ACS and EM is about 20%, as well as the CCS and EM are greater than the ACS and modulus of elasticity. Conclusions drawn from the tests provide an important basis for checking whether the materials meet the design requirements. [ABSTRACT FROM AUTHOR]