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A multi-physical coupling NURBS-based isogeometric analysis for nonlinear electrodynamics of three-directional poroelastic functionally graded circular nanoplate: Introducing deep neural network d*algorithm for nonlinear electrodynamics problems

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AEROSPACE SCIENCE ANd TECHNOLOGY 2025年 157卷

作者： Gao, Jinglong Chang, Lei Mahmoud, Haitham A. Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Anhui Univ Sci & Technol Sch Carbon Neutral Sci & Engn Huainan 232001 Peoples R China King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia

For the right design of nanosystems with integrated piezoelectric transducers and materials, it is crucial to understand the electro-mechanical coupling factor. Considering this, this work determines the influence of placement and dimension of piezoelectric patch on the vibrations of circular sandwich sector nanoplate coupled with an electrically layer. The core of the current nanostructure is made of three-directional poroelastic functionally graded material (3d-PFGM). The quasi-3d sinusoidal shear deformation theory (Q-3dSSdT) considering the effect of thickness stretching, compatibility conditions, and Hamilton's principle are coupled with each other regarding discovering the general motion equations and boundary domains related to the circular sector sandwich nanoplate. For considering the size effect, nonlocal quasi-3d sinusoidal strain gradient theory (NQSSGT) by employing both hardening and softening effects is considered. The NURBS-based isogeometric analysis is applied to answer the partial differential coupled equations (PdCE). In addition, the finite element method is implemented for more verification and presenting important outcomes. The novelties of this work are considering the effects of NQSSGT, placement, and dimension of the piezoelectric patch in addition to considering 3d-PFGM of the circular sector sandwich nanoplate. After obtaining the datasets of the mathematics simulation, a deep neural network d*algorithm is presented to test, train, and validate the presented nonlinear electrodynamics response of the current circular sector sandwich nanoplate. The results of the current nanostructure can be used in related industries of nano-robots and nano-electronic devices for future works. The findings highlight the significant influence of material gradation, poroelastic effects, and piezoelectric coupling on the vibration characteristics, offering valuable insights for the design and optimization of smart materials and structures in microelectromechani

关键词： Nonlinear electrodynamics Poroelastic circular sector sandwich nanoplate Finite element modeling deep neural network d*algorithm NURBS-based isogeometric analysis

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Optimizing mixed-model assembly line efficiency under uncertain demand: A Q-Learning-Inspired differential evolution d*algorithm

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COMPUTERS & INdUSTRIAL ENGINEERING 2025年 200卷

作者： Meng, Kai Li, Shujuan Han, Zhoupeng Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Shaanxi Peoples R China

Modern manufacturing heavily relies on mixed-model assembly lines to streamline production processes for various product configurations. However, most existing research in this area primarily focuses on deterministic demand scenarios, leaving the challenges posed by uncertain demand relatively unexplored. Such uncertainty can significantly impact assembly line efficiency, resource utilization, and throughput rates. This paper explores the complexities of balancing and sequencing in mixed-model assembly lines, particularly under conditions of uncertain demand. The proposed approach includes a robust mixed-integer linear programming model formulated to optimize production efficiency across diverse scenarios characterized by uncertain demand. To address this complex problem, a novel Q-Learning-Inspired differential Evolution d*algorithm (QL-dE) has been developed. This d*algorithm utilizes a population-based evolutionary operator, an intra-population crossover operator, six task-centric and three product-centric neighborhood exploration operators, along with a Q-learning-inspired strategy. These components collectively enable the QL-dE d*algorithm to adaptively handle uncertain demand while optimizing assembly line processes. Finally, through a comparative analysis with five variants and five evolutionary d*algorithms, the QL-dE approach demonstrates its superior capability in efficiently addressing uncertain demand scenarios and optimizing the performance of mixed-model assembly lines.

关键词： Mixed-model assembly lines Uncertain demand Balancing and sequencing differential evolution d*algorithm Q -learning inspired strategy

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Optimizing hybrid deep learning models for drug-target interaction prediction: A comparative analysis of evolutionary d*algorithms

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EXPERT SYSTEMS 2025年第2期42卷

作者： Sharma, Moolchand Bhatia, Aryan dutta, Ashit Kumar Alsubai, Shtwai GGSIPU Maharaja Agrasen Inst Technol Dept Comp Sci & Engn Delhi India AlMaarefa Univ Coll Appl Sci Dept Comp Sci & Informat Syst Diriyah Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp Sci Coll Comp Engn & Sci Al Kharj Al Kharj Saudi Arabia

In the realm of drug-Target Interaction (dTI) prediction, this research investigates and contrasts the efficacy of diverse evolutionary d*algorithms in fine-tuning a sophisticated hybrid deep learning model. Recognizing the critical role of dTI in drug discovery and repositioning, we tackle the challenges of binary classification by reframing the problem as a regression task. Our focus lies on the Convolution Self-Attention Network with Attention-based bidirectional Long Short-Term Memory Network (CSAN-BiLSTM-Att), a hybrid model combining convolutional neural network (CNN) blocks, self-attention mechanisms, and bidirectional LSTM layers. To optimize this complex model, we employ differential Evolution (dE), Particle Swarm Optimization (PSO), Memetic Particle Swarm Optimization d*algorithm (MPSOA), Fire Hawk Optimization (FHO), and Artificial Hummingbird d*algorithm (AHA). Through thorough comparative analysis, we evaluate the performance of these evolutionary d*algorithms in enhancing the CSAN-BiLSTM-Att model's effectiveness. By examining the strengths and weaknesses of each d*algorithm, our study aims to provide valuable insights into dTI prediction, identifying the most effective evolutionary d*algorithm for hyperparameter tuning in advanced deep learning models. Notably, Fire-hawk optimization (FHO) emerges as particularly promising, achieving the highest Concordance Index (C-index) as 0.974 for KIBA datasets and 0.894 for dAVIS datasets and demonstrating exceptional accuracy in ranking continuous predictions across both the datasets.

关键词： bidirectional LSTM binding affinity convolution neural network convolution self-attention network dAVIS & KIBA datasets differential evolution d*algorithm drug-target interaction particle swamp optimization

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Advancing water demand management: predictive analytics using convolutional neural networks and developed maritime search and rescue d*algorithm based on the shared socioeconomic pathways

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2025年 20卷 724-734页

作者： Lan, Yiheng Luo, Wenhao Yang, Manli Mohamadi, Golamaskar Chengdu Jincheng Coll Sch Architecture Chengdu 611731 Sichuan Peoples R China Chengdu Jincheng Coll Sch Comp & Software Engn Chengdu 611731 Sichuan Peoples R China Chengdu Jincheng Coll Sch finance & accounting Chengdu 611731 Sichuan Peoples R China Islamic Azad Univ Dept Engn West Tehran Branch Tehran Iran

Efficient management of water resources is crucial based on the idea of developing socioeconomic conditions. To achieve this, it is essential to forecast water demand accurately. This investigation introduces a predictive framework that utilizes a convolutional neural network-based Xception model, which has been optimized through the developed maritime search and rescue d*algorithm to increase accuracy in forecasting future water demand trends under shared socioeconomic pathway scenarios. The enhanced Xception model uses the shared socioeconomic pathways to evaluate the potential effects of socioeconomic growth on domestic and industry demand. Policymakers and managers of water resources can benefit from the findings of this investigation, as it provides insights into the future trends of water needs. This information can help in making informed decisions and planning for sustainable water resource management, even in the presence of uncertainty and variability. The study's results can enable a better understanding of future water demand patterns.

关键词： water demand convolutional neural network the shared socioeconomic pathways developed maritime search and rescue d*algorithm predictions

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A novel pseudo-random number generator: combining hyperchaotic system and dES d*algorithm for secure applications

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JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-37页

作者： Brahim, A. Hadj Pacha, H. Ali Naim, M. Pacha, A. Ali USTO MB Univ LACOSI Lab PoB 1505El MNaouer Bir Djir Oran 31000 Algeria ECAM EPMI Quartz Lab 13 Bd LHautil F-95092 Cergy Pontoise France

Generating random values become increasingly desirable due to its advantages. In this paper, a novel pseudo-random number generator is proposed based on a hyperchaotic system and the dES d*algorithm, with the goal of solving the limitations of many existing generators, such as predictability and inefficiency. The main contribution is the use of the dES d*algorithm, which is no longer used as an encryption d*algorithm, as a random generator, allowing the existing d*algorithm to be exploited in many applications. The hyperchaotic system has good statistical properties and random bifurcation, which means that the hackers cannot predict these sequences. The random values are used to produce both the initial key and input for the dES d*algorithm and the results of the dES d*algorithm are considered as a pseudo-random sequence. This ensures that both the key and the input of the dES d*algorithm are random, which increases the security of the dES d*algorithm. In addition, both the dES encryption and decryption d*algorithms are used in the generator, relying on the hyperchaotic system to choose in each iteration, which means that the hackers cannot determine which d*algorithm is used to produce the pseudo-random sequence. Experimental and analysis results show that the proposed generator has good random characteristics and passes several statistical tests, such as the NIST tests. Moreover, the proposed generator is tested in cryptography, using stream cipher to encrypt images. The results show that the encrypted images can resist several attacks such as histograms, differential attacks, correlation analysis, and entropy.

关键词： Hyperchaotic system dES d*algorithm Pseudo-random generator Statistical tests Image encryption

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Hierarchical Online Air Combat Maneuver decision Making and Control Based on Surrogate-Assisted differential Evolution d*algorithm

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dRONES 2025年第2期9卷 106-106页

作者： Tan, Mulai Sun, Haocheng ding, dali Zhou, Huan Han, Tong Luo, Yuequn Air Force Engn Univ Aviat Engn Sch Xian 710038 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

One-to-one within-visual-range air combat of unmanned combat aerial vehicles (UCAVs) requires fast, continuous, and accurate decision-making to achieve air combat victory. In order to solve the current problems of insufficient real-time performance of traditional intelligent optimization d*algorithms for solving decision-making problems and the mismatch between the planning trajectory and the actual flight trajectory caused by the difference between the decision-making model and the actual aircraft model, this paper proposes a hierarchical on-line air combat maneuvering decision-making and control framework. Considering the real-time constraints, the maneuver decision problem is transformed into an expensive optimization problem at the decision planning layer. The surrogate-assisted differential evolution d*algorithm is proposed on the basis of the original differential evolution d*algorithm, and the planning trajectory is obtained through the 5 degrees of freedom (dOF) model. In the control execution layer, the planning trajectory is tracked through the nonlinear dynamic inverse tracking control method to realize the high-precision control of the 6dOF model. The simulation is carried out under four different initial situation scenarios, including head-on neutral, dominant, parallel neutral, and disadvantaged situations. The Monte Carlo simulation results show that the Surrogate-assisted differential evolution d*algorithm (SAdE) can achieve a win rate of over 53% in all four initial scenarios. The proposed maneuver decision and control framework in this article achieves smooth flight trajectories and stable aircraft control, with each decision average taking 0.08 s, effectively solving the real-time problem of intelligent optimization d*algorithms in maneuver decision problems.

关键词： air combat unmanned combat aerial vehicles surrogate differential evolutionary d*algorithm trajectory tracking

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Based on the Integration of the Improved A* d*algorithm with the dynamic Window Approach for Multi-Robot Path Planning

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APPLIEd SCIENCES-BASEL 2025年第1期15卷

作者： Han, Yong Li, Changyong An, Zhaohui Xinjiang Univ Sch Mech Engn Urumqi 830039 Peoples R China

With the escalating demand for automation in chemical laboratories, multi-robot systems are assuming an increasingly prominent role in chemical laboratories, particularly in the task of transporting reagents and experimental materials. In this paper, we propose a multi-robot path planning approach based on the combination of the A* d*algorithm and the dynamic window d*algorithm (dWA) for optimizing the efficiency of reagent transportation in chemical laboratories. In environments like chemical laboratories, dynamic obstacles (such as people and equipment) and transportation tasks that demand precise control render traditional path planning d*algorithms challenging. To address these issues, in this paper, we incorporate the cost information from the current point to the goal point into the evaluation function of the traditional A* d*algorithm to enhance the search efficiency and add the safety distance to extract the critical points of the paths, which are utilized as the temporary goal points of the dWA d*algorithm. In the dWA d*algorithm, a stop-and-wait mechanism and a replanning strategy are added, and a direction factor is included in the evaluation function to guarantee that the robots can adjust their paths promptly in the presence of dynamic obstacles or interference from other robots to evade potential conflicts or traps, thereby reaching the goal point smoothly. Additionally, regarding the multi-robot path conflict problem, this paper adopts a dynamic prioritization method, which dynamically adjusts the motion priority among robots in accordance with real-time environmental changes, reducing the occurrence of path conflicts. The experimental results highlight that this approach effectively tackles the path planning challenge in multi-robot collaborative transportation tasks within chemical laboratories, significantly enhancing transportation efficiency and ensuring the safe operation of the robots.

关键词： A* d*algorithm dWA d*algorithm multi-robot path planning

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Predicting dye Formulations and Ultrasonic Pretreatment Power in Wood Staining with a SAT Fusion-Gated BiLSTM Model Optimized by Improved dung Beetle d*algorithm

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APPLIEd SCIENCES-BASEL 2025年第3期15卷 1522-1522页

作者： Guan, Xuemei Chen, Xiangya He, Zhongsheng Cui, Hongbo Northeast Forestry Univ Sch Comp & Control Engn Harbin 150040 Peoples R China

Wood dyeing plays a crucial role in enhancing the value of plantation wood and addressing the imbalance between supply and demand in the wood industry. However, challenges such as low dye uptake and inaccurate color matching persist. This study introduces a novel Separable Attention Feature Fusion-Gated Bidirectional Long Short-Term Memory (SAT Fusion-Gated BiLSTM) model to more accurately predict dye concentration and ultrasonic pretreatment power to address these issues. The model integrates multispectral data and uses ultrasonic treatment to open fiber gaps and decompose wood components, thereby improving dye uptake. Unlike traditional models, SAT Fusion-Gated BiLSTM combines a Maximum Information Coefficient (MIC)-based feature selection module, gated linear units for enhanced feature extraction, and an improved cross-attention mechanism for efficient data fusion. Compared to traditional deep learning models, the model's performance is significantly improved, including an R2 of 0.9274, an RMSE of 0.0470, an RPIQ of 5.5825, an RPd of 5.0400, and a MAE of 0.0229. RPIQ and RPd improve by over 100.8% compared to traditional deep learning models, and the runtime is reduced by 83.94% when compared to Convolutional Neural Networks (CNNS). The Shapley Additive Explanation (SHAP) reveals the key spectral features that drive these predictions. These advancements in accuracy, efficiency, and interpretability make the model particularly promising for industrial applications, offering both improved operational efficiency and more precise resource management in the sustainable use of timber.

关键词： veneer dyeing cross-attention BiLSTM gated mechanism dung beetle d*algorithm

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Triboelectric Sensors Based on Glycerol/PVA Hydrogel and deep Learning d*algorithms for Neck Movement Monitoring

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ACS APPLIEd MATERIALS & INTERFACES 2025年第8期17卷 12862-12874页

作者： Qu, Bingbing Mou, Qirui Zhou, Zelong Xie, Yiyuan Li, Yudong Chen, Bin Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuit & Intelligent Chongqing 400715 Peoples R China Xian Shaangu Power CO LTD Xian 710075 Peoples R China

Prolonged use of digital devices and sedentary lifestyles have led to an increase in the prevalence of cervical spondylosis among young people, highlighting the urgent need for preventive measures. Recent advancements in triboelectric nanogenerators (TENGs) have shown their potential as self-powered sensors. In this study, we introduce a novel, flexible, and stretchable TENG for neck movement detection. The proposed TENG utilizes a glycerol/poly(vinyl alcohol) (GL/PVA) hydrogel and silicone rubber (GH-TENG). Through optimization of its concentration and thickness parameters and the use of environmentally friendly dopants, the sensitivity of the GH-TENG was improved to 4.50 V/kPa. Subsequently, we developed a smart neck ring with the proposed sensor for human neck movement monitoring. By leveraging the convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) d*algorithm, sensor data can be efficiently analyzed in both spatial and temporal dimensions, achieving a promising recognition accuracy of 97.14%. Additionally, we developed a neck motion monitoring system capable of accurately identifying and recording neck movements. The system can timely alert users if they maintain the same neck posture for more than 30 min and provide corresponding recommendations. By deployment on a Raspberry Pi 4B, the system offers a portable and efficient solution for cervical health protection.

关键词： glycerol/PVA hydrogel electrode self-powered triboelectricsensor deep learning d*algorithm smart neck ring neck movements monitoring system

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Optimal Operation of Multi-reservoir System Utilizing dEA, AIdE d*algorithm and Flood Control Assessment by MCdM Approach

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WATER RESOURCES MANAGEMENT 2025年第4期39卷 1783-1802页

作者： Sinha, Lalitesh Narulkar, Sandeep M. IPS Acad Inst Engn & Sci Dept Civil Engn Indore India Shri Govindram Seksaria Inst Technol & Sci Dept Civil Engn & Appl Mech Indore India

A multi-reservoir system's operation during floods is a complex problem, because it is dynamic and nonlinear nature, finding the global or near-global best solution is a difficult task. The Adaptive Immune differential Evolution (AIdE) d*algorithm is one of the Evolutionary d*algorithms (EA) used to solve the multi-reservoir system. It improves the differential Evolution (dE) d*algorithm's exploitation and exploration capabilities. A multi-criteria decision-making (MCdM) approach is also implemented for managing flood control operations in reservoirs, aiming to handle correlations among different criteria. To eliminate correlation, principal component Analysis (PCA) is used and coupled with a weight vector as the input to the TOPSIS method, WASPAS method, and MOORA method by which the alternatives are to be determined. The results show that the dimensionality of the criterion system is lowered while simultaneously eliminating the correlation between criteria, and the ranking order of the alternatives is fair. From the results, it is clear that the AIdE d*algorithm would have faster convergence and a powerful global ability than the dE d*algorithm. The control parameters used in dE and AIdE enable these d*algorithms to effectively navigate complex search spaces and identify optimal or near-optimal solutions. The above optimization method is recommended for complex, large-scale reservoir operations and evaluation of ranking by MCdM approach is helpful for flood control operation of multi-reservoir systems. However, the optimal alternative sequence (3, 5, 2, 4, and 1) is suitable to manage the flood event operation by multi-reservoir.

关键词： Optimization Multi-criteria decision Making (MCdM) Adaptive Immune differential Evolution (AIdE) d*algorithm differential Evolution (dE) d*algorithm Principal Component Analysis (PCA) TOPSIS Method WASPAS Method MOORA Method

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