Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility by Zhenqiang Xiong, Zhaokun Song, Jianwei Li, Heran Wang, Xiaoxin Zhang, Bin Liang, Dong Wang

“…The model was integrated with the Non-dominated Sorting Genetic Algorithm (NSGA-III) to design alloys with superior comprehensive properties. …”
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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

“…The goal of this research is • to make sophisticated models for precise crop production forecasting and thorough evaluation of soil health, • to improve sustainability by optimize farming methods, and • to assist farmers in making well-informed decisions.Iterative Partitioning-Ensemble Filter (IP-EF) is a technique used for feature selection, enhancing model performance by iteratively partitioning data and refining feature subsets. …”
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A GRNN based frame work to test the influence of nano zinc additive biodiesel blends on CI engine performance and emissions by Chiranjeeva Rao Seela, B. Ravisankar, B.M.V.A. Raju

“…A classical differential evolution algorithm (DEA) is further used on the network model to find out optimal combination of nanoparticles, biodiesel and diesel and proven through experimental validation. …”
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注射机增力机构优化研究 by 李铁军, 朱成实, 鄢利群, 王学平, 宁建荣

“…The analysis of motion and mechanics property is carried out on the five hinged incline arranged and double elbowed force increasing mechanism of injection machine.A complete optimal design procedure is carried out by using improved ant colony algorithms,so as to increase the stroke ratio and the amplification of the force,and to decrease the total length of mechanism.Its optimization mathematics model is established.The procedure of optimal design belongs to multi-object optimization problem.The optimal solution of the force increasing mechanism is found by improved ant colony algorithms.Compared with the traditional methods,the result shows that the total length of mechanism is decreased,the stroke ratio is increased,and the amplification of the force is increased.…”
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Shoulder–Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration by Siyu Zong, Wei Li, Dawen Sun, Zhuoda Jia, Zhengwei Yue

“…To address the precision challenges in upper-limb joint motion prediction, this study proposes a novel artificial neural network (COANN) enhanced by the Cheetah Optimization Algorithm (COA). The model integrates surface electromyography (sEMG) signals with joint angle data through multi-source information fusion, effectively resolving the local optima issue in neural network training and improving the accuracy limitations of single sEMG predictions. …”
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DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions by Chao Cao, Mengli Li, Chunyu Wang, Lei Xu, Quan Zou, Yansu Wang, Wu Han

“…Next, we present a joint model that combines an improved neural graph collaborative filtering method with a feature extraction network for optimization. …”
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Photovoltaic Power Forecasting with Weather Conditioned Attention Mechanism by Xuetao Jiang, Yuchun Gou, Meiyu Jiang, Lihui Luo, Qingguo Zhou

“…The proposed Conditional Decomposition (CD) algorithm searches for the decomposition algorithms and corresponding hyperparameters of the prediction model, aiming to achieve the optimal prediction performance. …”
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Classification of Complex Power Quality Disturbances Based on Lissajous Trajectory and Lightweight DenseNet by Xi Zhang, Jianyong Zheng, Fei Mei, Huiyu Miao

“…The experimental results demonstrate that, compared with current mainstream PQD classification methods, the proposed algorithm not only achieves superior disturbance classification accuracy and noise robustness but also significantly improves response speed in PQD classification tasks through its concise visualization conversion process and lightweight model design.…”
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Enhanced Pedestrian Navigation with Wearable IMU: Forward–Backward Navigation and RTS Smoothing Techniques by Yilei Shen, Yiqing Yao, Chenxi Yang, Xiang Xu

“…First, to efficiently re-estimate past system state and reduce accumulated navigation error once zero-velocity measurement is available, both the forward and backward integration method and the corresponding error equations are constructed. Second, to further improve navigation accuracy and reliability by exploiting historical observation information, both backward and forward RTS algorithms are established, where the system model and observation model are built under the output correction mode. …”
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Driving Pattern Analysis, Gear Shift Classification, and Fuel Efficiency in Light-Duty Vehicles: A Machine Learning Approach Using GPS and OBD II PID Signals by Juan José Molina-Campoverde, Juan Zurita-Jara, Paúl Molina-Campoverde

“…A multiple linear regression model was developed to estimate instantaneous fuel consumption (in L/100 km) using the gear predicted by the KNN algorithm and other relevant variables. …”
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The significance of the leaf area index for evapotranspiration estimation in SWAT-T for characteristic land cover types of West Africa by F. Merk, T. Schaffhauser, F. Anwar, Y. Tuo, J.-M. Cohard, M. Disse

“…The comprehensive parameter set is then optimized using the shuffled complex evolution algorithm. …”
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Design of a dynamic trust management and defense decision system for shared vehicle data based on blockchain and deep reinforcement learning by Jinxiang Chen, Yan Li, Jiaxing Deng, Beicheng Qin, Chengcai He, Qiangsheng Huang, Jingchun Wu

“…Using the Deep Q-Network (DQN) algorithm, the system identifies optimal defensive strategies through multidimensional data interactions. …”
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Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networks by Jian-Dong Yao, Wen-Bin Hao, Zhi-Gao Meng, Bo Xie, Jian-Hua Chen, Jia-Qi Wei

“…Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods, including Stackelberg game models and model predictive control, achieving an 18.73% reduction in costs and a 22.46% increase in VPP profits. …”
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A Cross-Stage Focused Small Object Detection Network for Unmanned Aerial Vehicle Assisted Maritime Applications by Gege Ding, Jiayue Liu, Dongsheng Li, Xiaming Fu, Yucheng Zhou, Mingrui Zhang, Wantong Li, Yanjuan Wang, Chunxu Li, Xiongfei Geng

“…The CFSD-UAVNet model was evaluated on the publicly available SeaDronesSee maritime dataset and compared with other cutting-edge algorithms. …”
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A METHOD FOR SOLVING THE CANONICAL PROBLEM OF TRANSPORT LOGISTICS IN CONDITIONS OF UNCERTAINTY by Lev Raskin, Yurii Parfeniuk, Larysa Sukhomlyn, Mykhailo Kravtsov, Leonid Surkov

“…Development of an accurate algorithm for solving this problem according to the probabilistic criterion in the assumption of the random nature of transportation costs has been done. …”
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Comprehensive benefits evaluation of low impact development using scenario analysis and fuzzy decision approach by Ting Ni, Xiaohong Zhang, Peng Leng, Mark Pelling, Jiuping Xu

“…The framework’s novelty lies in the integration of the hesitant fuzzy weighted average algorithm to handle subjective uncertainties in expert judgment and the incorporation of multi-return period scenarios to enhance the robustness of the evaluation. …”
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Adaptive Covariance Matrix for UAV-Based Visual–Inertial Navigation Systems Using Gaussian Formulas by Yangzi Cong, Wenbin Su, Nan Jiang, Wenpeng Zong, Long Li, Yan Xu, Tianhe Xu, Paipai Wu

“…Our algorithm has shown significantly higher accuracy compared to the famous VINS-Mono framework, outperforming it by 18.18% on average, as well as the optimization rate of RMS, which reaches 65.66% for the F1 dataset and 41.74% for F2 in the field tests outdoors.…”
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Research on power system transformation based on edge node technology under the background of carbon neutrality by LI Jin, GAO Hongliang, LIU Kemeng, XIE Hu

“…In each island, the improved particle swarm optimization algorithm is used to reasonably distribute the power tasks, forming a novel power system with the coordinated planning of source-network-load-storage as the core. …”
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CT-TDMA:efficient TDMA protocol for underwater sensor networks by Lu HONG, Feng HONG, Zheng-bao LI, Zhong-wen GUO

“…Aimed at underwater acoustic sensor networks(UWSN),a novel sender based conflict model with the schemes of allocating continuous time was presented,including local conflict graph(LCG)and a distribute algorithm to generate LCG.Moreover,CT-TDMA,an efficient TDMA protocol based on the conflict model was also proposed,which used heuristic priority rules to allocate transmitting moments for all nodes.CT-TDMA exploits the diversity of propagation delay of different links in UWSN to decrease the idle time between packets at the same receiving node,which helps in improving the throughput.And a heuristic schedule algorithm is applied to shorten the process of allocating continuous time for each node.Simulation results show that,compared with traditional TDMA protocols such as ST-MAC,network throughput of CT-TDMA has increased 20% and end to end delay has decreased 18%;compared to the theoretically optimal scheme with global knowledge,CT-TDMA has achieved 80% network throughput and the end to end delay is only 12% longer.…”
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CT-TDMA:efficient TDMA protocol for underwater sensor networks by Lu HONG, Feng HONG, Zheng-bao LI, Zhong-wen GUO

“…Aimed at underwater acoustic sensor networks(UWSN),a novel sender based conflict model with the schemes of allocating continuous time was presented,including local conflict graph(LCG)and a distribute algorithm to generate LCG.Moreover,CT-TDMA,an efficient TDMA protocol based on the conflict model was also proposed,which used heuristic priority rules to allocate transmitting moments for all nodes.CT-TDMA exploits the diversity of propagation delay of different links in UWSN to decrease the idle time between packets at the same receiving node,which helps in improving the throughput.And a heuristic schedule algorithm is applied to shorten the process of allocating continuous time for each node.Simulation results show that,compared with traditional TDMA protocols such as ST-MAC,network throughput of CT-TDMA has increased 20% and end to end delay has decreased 18%;compared to the theoretically optimal scheme with global knowledge,CT-TDMA has achieved 80% network throughput and the end to end delay is only 12% longer.…”
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