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SSRN 2025年

作者： Li, Jingfang Ma, Huiting Hu, Xiaoming Chen, Liang Fallgren, Paul H. State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation Tianjin University Tianjin300350 China School of Civil Engineering Tianjin University Tianjin300350 China Shandong Province Traffic Planning and Design Institute Group Co. Ltd. Jinan Shandong250101 China Advanced Environmental Technologies LLC Fort CollinsCO80525 United States

Antibiotics are an emerging environmental concern that may impact water sources via transport through soil. Woody plants with well-developed root systems and longer root lengths change soil properties that may influence the migration of antibiotics. This study investigated the migration of ciprofloxacin (CIP) and tylosin (TYL) through soil with woody plant roots. Experiments were conducted with soil columns with three different woody plants with different root types, which included Sophora japonica (taproot), Malus baccata (fibrous root), and Malus pumila Mill. (characteristically both taproot and fibrous root). The woody plant roots increased the infiltration capacity by 39.53–56.29%, which was a factor in increasing antibiotic mass in the soil column discharge. The CIP discharge mass increased by 0.05–0.52 times and the TYL discharge mass increased by 1.36–2.59 times, where higher discharge mass was observed from the taproot system of S. japonica compared to the other root system types. The woody plant roots promoted CIP adsorption (increase of 30.41–101.22%) and inhibited TYL adsorption (decrease of 62.41–86.49%). The woody plant roots promoted antibiotic migration to the middle and lower root layers in the soil column. The correlation between CIP adsorption to soil and soil organic matter (SOM) was insignificant (p > 0.05, r = 0.41), while there was a negative correlation between TYL adsorption and SOM content (p © 2025, The Authors. All rights reserved.

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Test piece design, mechanical properties and fracture strain in two-step tensile testing of lightweight materials at high strain rates

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International Journal of Impact Engineering 2025年 206卷

作者： Da Cai Hao Jiang Peng Wang Qing Wang Weifeng Gan Junjia Cui Guangyao Li State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle Hunan University Changsha 410082 China Battery System Development Department Contemporary Amperex Technology Co. Ltd Ningde 352100 China Shenzhen Huashun Intelligent Equipment Co. Ltd Shenzhen 518119 China Shenzhen Automotive Research Institute Beijing Institute of Technology Shenzhen 518119 China

Continuous forming at high strain rates is an important research direction for future precision forming of light alloys. In this study, dynamic behavior of AA5052 aluminum alloy in two-step tensile testing at high strain rates were investigated. Special two-step tensile test piece at high strain rate was designed. The test piece of the special design contained a weak structural notch area and a dog-bone shaped area. The test piece was pre-stretched to a pre-determined strain so that the weak region broke. Then a secondary stretching was performed. The response surface equations of pre-strain and pre-strain rate were established separately to evaluate the functional relationship between the notch interval, tensile velocity and strain rate, and equivalent plastic strain. The effects of the parameters on the fracture strain were compared. The results showed that the test pieces could meet the requirements of the two-step tensile test at high strain rates. The pre-strain and pre-strain rate were affected by the size of the notch interval and the tensile velocity. The increase of tensile strength was related to pre-strain and secondary strain rate. The linear cumulative damage criterion can be used to interpret the two-step stretching at high strain rates. When the strain rate in single-step stretching is the same as the strain rate in each step of two-step stretching, the fracture strain in single-step stretching can be substituted for the fracture strain in two-step stretching. Research can help solve the problems of poor formability and early failure of lightweight alloys.

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Reliability allocation method based on Intuitionistic Trapezoidal Fuzzy Number and Grey Correlation

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IOP Conference Series: Materials Science and Engineering 2021年第2期1043卷

作者： Qiang Cheng Yongbo Kang Hongyan Chu Zhifeng Liu Chang Wang Institute of Advanced Manufacturing and Intelligent Technology Beijing CN Beijing Key Laboratory of Advanced Manufacturing Technology Beijing CN Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing Beijing CN

The reliability allocation method of machine tools has the feature of high complexity and uncertainty. To reduce the fuzziness in the process of reliability allocation, this paper proposes a novel reliability allocation method of CNC machine tools based on Intuitionistic Trapezoidal Fuzzy Number and Grey Correlation. First, the subsystems of CNC machine tools are divided based on function, and the reliability of subsystems are also allocated. Then, expert decision matrixes are obtained based on intuitionistic trapezoidal fuzzy numbers. The result is obtained by the calculation of the grey relation degree between decision matrixes and ideal matrixes. Finally, an example is taken to verify the effectiveness of the proposed method. Compared to the traditional method, the proposed method can reduce the calculation and more convenient to use.

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Step-length optimization based on genetic algorithm for a parallel mobile robot

Step-length optimization based on genetic algorithm for a pa...

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International Conference on Information Science and Control Engineering (ICISCE)

作者： Shuang Liu Wei Chen Hang Wu Wenchang Zhang Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Tianjin University of Technology Tianjin China Academy of Military Sciences of Chinese PLA Institute of Medical Support Technology Tianjin China

With the development of technology, parallel mobile robots have been paid more attention in various industries due to the strong load capacity, high running stability and rapid response speed. However, step-length optimization is an important but challenging task for this kind of mobile robot. Step length has dramatic effect on the stability of robot and the performance of electric cylinder. Balancing the speed characteristics and stability margin, we propose a genetic algorithm to solve this problem and design a novel fitness function that can measure the motion characteristics of parallel mobile robot. Effectiveness of our method is verified by experiments.

关键词： Industries Information science Control engineering Service robots Mobile robots Motion measurement Task analysis

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GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection

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Plant Communications 2025年

作者： Hongshan Wu Shichao Jin Chao Xiang Jianling Tang Junhong Xian Jiaoping Zhang Jinming Zhao Xianzhong Feng Dong Jiang Yufeng Wu Yanfeng Ding State Key Laboratory of Crop Genetics and Germplasm Enhancement Zhongshan Biological Breeding Laboratory Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry Jiangsu Key Laboratory of Soybean Biotechnology and Intelligent Breeding Engineering Research Center of Plant Phenotyping Ministry of Education Academy for Advanced Interdisciplinary Studies Nanjing Agricultural University Nanjing 210095 China Crop Research Institute of Sichuan Academy of Agricultural Sciences/Environment-Friendly Crop Germplasm Innovation and Genetic Improvement Key Laboratory of Sichuan Province Chengdu 610066 Sichuan China State Key Laboratory of Crop Genetics and Germplasm Enhancement National Innovation Platform for Soybean Breeding and Industry-Education Integration Key Laboratory for Biology and Genetic Improvement of Soybean (General Ministry of Agriculture) Nanjing Agricultural University Nanjing 211800 China Key Laboratory of Soybean Molecular Design Breeding State Key Laboratory of Black Soils Conservation and Utilization Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun 130102 China

Genomic selection (GS) and phenotypic selection (PS) are widely used for accelerating plant breeding. However, the accuracy, robustness, and transferability of these two selection methods are underexplored, especially when addressing complex traits. In this study, we introduce a novel data fusion framework, GPS (Genomic and Phenotypic Selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion. The effectiveness and generalizability of GPS framework were rigorously tested using an extensive suite of models, including statistical approaches (GBLUP and BayesB), machine learning models (Lasso, RF, SVM, XGBoost, and LightGBM), a deep learning method (DNNGP) and a latest phenotype-assisted prediction model (MAK). These models were applied to large-scale datasets from four crop species: maize, soybean, rice, and wheat, demonstrating the versatility and robustness of the framework. The results indicated that: (1) Data fusion achieved the best accuracy than the feature fusion and result fusion strategies. The top-performing data fusion model (Lasso_D) improved the selection accuracy by 53.4% compared to the best GS models (LightGBM) and by 18.7% compared to the best PS models (Lasso). (2) Lasso_D exhibited exceptional robustness, achieving high predictive accuracy even in sample size as small as 200. Additionally, the model demonstrated resilience to variations in SNP (single nucleotide polymorphism) density, underscoring its adaptability to diverse data conditions. Moreover, the model’s accuracy improved with the number of auxiliary traits and their correlation strength with target traits, further highlighting its adaptability to complex trait prediction. (3) The best-performing data fusion model demonstrated broad transferability, with substantial improvements in predictive accuracy when incorporating multi-environmental data. Notably, this enhanc

关键词： crop data fusion framework genomic selection phenotypic selection cross-environment prediction

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A meta-inspired termite queen algorithm for global optimization and engineering design problems

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Engineering Applications of Artificial Intelligence 2022年 111卷

作者： Peng Chen Shihua Zhou Qiang Zhang Nikola Kasabov Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1010 New Zealand Intelligent Systems Research Center Ulster University Londonderry BT52 1SA UK

This paper proposes a novel bio-inspired termite queen algorithm (TQA) to solve optimization problems by simulating the division of labor in termite populations under a queen’s rule. TQA is benchmarked on a set of 23 functions to test its performance at solving global optimization problems, and applied to six real-world engineering design problems to verify its reliability and effectiveness. Comparative simulation studies with other algorithms are conducted, from whose results it is observed that TQA satisfactorily solves global optimization problems and engineering design problems.

关键词： Termite queen algorithm Benchmarked functions Loss minimization Metaheuristic technique Engineering design problems

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Logic Operation Model of the Complementer Based on Two-domain DNA Strand Displacement

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Fundamenta Informaticae 2019年第2-3期164卷 277-288页

作者： Xie, Wendan Zhou, Changjun Lv, Hui Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University of Technology ChinaMinistry of Education Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China.

DNA strand replacement technology has the advantages of simple operation which makes it becomes a common method of DNA computing. A four bit binary number Complementer based on two-domain DNA strand displacement is proposed in this paper. It implements the function of converting binary code into complement code. Simulation experiment based on Visual DSD software is carried out. The simulation results show the correctness and feasibility of the logic model of the Complementer, and it makes useful exploration for further expanding the application of molecular logic circuit.

关键词： Two-Domain DNA Strand Displacement Complementer Logic Circuit

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Compound nonlinear energy sink with multiple motion types for absorbing energy from wide excitation ranges

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Journal of Sound and Vibration 2025年 617卷

作者： Jianen Chen Jiaqi Zhao Wei Zhang Min Sun Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin 300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin University of Technology Tianjin 300384 China School of Civil Engineering and Architecture Guangxi University Nanning 530004 China School of Science Tianjin Chengjian University Tianjin 300384 China

Nonlinear energy sink (NES) has garnered significant attention since its inception due to its notable broadband vibration reduction capability. However, the introduction of strong nonlinear characteristics by NESs complicates system responses, leading to a loss of effectiveness when the excitation force exceeds a specific threshold. Consequently, NESs have limited applicability in engineering because they are effective only within a constrained range of excitation amplitude. In this study, a compound NES (CNES) with multiple motion types is designed to extend the effective force range while maintaining broadband vibration reduction capability. The CNES is distinguished by its piecewise stiffness, collision mechanisms, and unique design of two-degree-of-freedom oscillators. The wide effective range of the CNES is demonstrated through slow linear sweep and fixed-frequency tests. The results indicate that the CNES effectively reduces vibrations in the primary oscillator (PO) with various resonant frequencies. Notably, the CNES achieves significant vibration reduction over a particularly wide range of excitation amplitude when controlling the PO with relatively high resonance frequencies (approximately 10.5 Hz and 19 Hz). However, a nonnegligible limitation of the CNES is its restricted effective range of excitation amplitude when the PO resonates at lower frequency (approximately 7 Hz). Moreover, five motion types of the CNES are identified and analyzed, with one motion type autonomously emerging in response to changes in excitation parameters, ensuring excellent vibration reduction performance across a wide excitation range. Finally, the performance of the CNES and a degraded single-degree-of-freedom CNES are compared to further demonstrate the extension effect of the multiple motion types on the effective range. The CNES can be used in scenarios where the parameters of protected systems and excitations vary widely, providing a thought to improve the NESs.

关键词： Compound nonlinearity Compound features Vibration reduction Vibration test Multiple motion types Effective range

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Model Predictive Control-Assisted Energy Management Strategy for Hybrid Mining Dump Trucks Based on Speed and Slope Prediction

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Electronics 2025年第10期14卷 1999-1999页

作者： Guojin Xie Rongjun Ding Heping Xie Hongmao Qin Yougang Bian School of Mechanical and Automotive Engineering Guangxi University of Science and Technology Liuzhou 545616 China State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle College of Mechanical and Vehicle Engineering Hunan University Changsha 410082 China Wuxi Intelligent Control Research Institute of Hunan University Wuxi 214115 China Xuzhou XCMG Mining Machinery Co. Ltd. Xuzhou 210009 China

This article proposes an innovative energy management strategy for hybrid multi-source dump trucks operating under real slope conditions in mining areas. Although previous studies have addressed the energy management issues of hybrid vehicles, few studies have taken into account complex environmental factors such as slopes under actual working conditions. The article overcomes this limitation by integrating a radial basis function (RBF) neural network to directly and accurately predict future vehicle demand power, thereby optimizing the DP-MPC strategy and improving energy efficiency. The results indicate that, compared with the traditional MPC strategy, the proposed strategy reduces fuel consumption by 3.34% and engine start-stop events by 76.2%. Additionally, when compared with another strategy that uses historical data to predict future speed and slope, calculates the vehicle’s future power demand, and incorporates it into the DP-MPC algorithm, the proposed strategy achieves comparable fuel consumption while also reducing engine start-stop events by 69.7%. Notably, the average calculation time for each step is 43.85 ms, which is substantially less than the sampling time of 1 s. To further confirm the real-time performance of the strategy, a hardware-in-the-loop (HIL) test is conducted.

关键词： hybrid mining dump truck model predictive control (MPC) artificial neural network model dynamic programming (DP) energy management strategy

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Urban Trip Generation Forecasting Based on Gradient Boosting Algorithm

Urban Trip Generation Forecasting Based on Gradient Boosting...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Zhishuai Li Gang Xiong Yu Zhang Meng Zheng Xisong Dong Yisheng Lv State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Municipal Institute of City Planning and Design Beijing China

The four-step transportation model plays an important role in urban planning. The quality of the first phase, i.e. trip generation, determines the performance of the global course. The majority of trip generation forecasting models highly rely on mathematical derivation and have many predictor variables during the prediction, which leads to low accuracy of results and requires laboriously hand-crafted design of input vectors. This paper is the first to introduce the gradient boosting decision tree (GBDT) algorithm for trip generation prediction, and harmonizes such a powerful machine learning method with traditional urban planning requirements to achieve better prediction performance. Unlike the commonly used linear regression method, GBDT can automatically perform feature selection and model the non-linear relationships between input and output variables. Experimental results on real-world residential travel census in Beijing prove that the GBDT model significantly outperforms the baseline and can forecast the trip generation more accurately.

关键词： Machine learning algorithms Urban planning Transportation Predictive models Prediction algorithms Feature extraction Boosting

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