Meta-learning assisted robust control of universal quantum gates with uncertainties by Shihui Zhang, Zibo Miao, Yu Pan, Sibo Tao, Yu Chen

“…The inner reinforcement learning network focuses on decision making for specific optimization problems, while the outer meta-learning network adapts to varying environments and provides feedback to the inner network. …”
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Selecting the Strategic Port of “the Belt and Road” Based on the Global Network by Zihui Yang, Qingchun Meng, Chanjuan Li

“…Under “the Belt and Road” initiative, China promoted cooperation between domestic enterprises and international ports vigorously, which brought back fruitful results, while the rational selection of strategic pivots ports and the optimization of the layout of the port network are important guarantees to a further promotion to the economic development of “the Belt and Road” ports and give full play to the driving and radiation role of strategic pivots ports. …”
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Distributed blockchain assisted secure data aggregation scheme for risk-aware zone-based MANET by V R. Sugumaran, E. Dinesh, R. Ramya, Elangovan Muniyandy

“…Data aggregation is secured through a Two-Step Secure (STS) method and Elliptic Curve Cryptography (ECC), while optimal routing is achieved using the Improved Elephant Herd Optimization (IEHO) algorithm. …”
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Management of Calcified Coronary Lesions by Khaled Al-Shaibi, Aditya Bharadwaj, Atul Mathur, Ashish Jaikishen, Robert Riley

“…A variety of treatment options for these lesions exist, including specialized balloons, atherectomy, and intravascular lithotripsy. While there is currently no universally accepted algorithm for choosing between these treatment strategies, several different algorithms exist, and the optimization of these treatment regimens will continue to evolve in the coming years. …”
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An Efficient Multiparty Threshold ECDSA Protocol against Malicious Adversaries for Blockchain-Based LLMs by Jing Wang, Xue Yuan, Yingjie Xu, Yudi Zhang

“…When compared with some related schemes, this optimization speeds up both the key generation and signing phases with constant rounds while maintaining security against malicious adversaries.…”
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Computational Construction of Sequential Efficient Designs for the Second-Order Model by Norah Alshammari, Stelios Georgiou, Stella Stylianou

“…By relaxing the restriction of non-replicated points within equally spaced intervals, our approach maintains space-filling properties while allowing greater flexibility for model-specific optimization. …”
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Network security situation assessment based on dual attention mechanism and HHO-ResNeXt by Dongmei Zhao, Guoqing Ji, Shuiguang Zeng

“…To solve these problems, this paper combines ResNeXt with the Efficient Channel Attention (ECA) module and the Contextual Transformer (COT) block to construct a model to assess network conditions. The optimal hyperparameters of the model are selected by the Harris Hawks Optimization (HHO) algorithm. …”
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An efficient hybrid framework with knowledge transfer for solving capacitated vehicle routing problems by Yanlin Wu, Yanguang Cai, Chuncheng Fang

“…This study proposes a hybrid framework (HF) that addresses these limitations through four key components: (1) An improved Clarke–Wright savings algorithm for rapid initialization of high-quality solutions, (2) an incremental auxiliary task formulation that optimizes knowledge transfer while minimizing computational overhead, (3) a similarity prediction strategy enabling efficient offspring selection without additional evaluations, and (4) the framework integrates enhanced search strategy and diversity maintenance strategy to prevent premature convergence. …”
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Intelligent resource scheduling scheme for UAV swarm collaborative sensing by ZHAO Pengcheng, LI Tianyang, LENG Supeng, XIONG Kai

“…Furthermore, a multi-agent reinforcement learning (MARL) algorithm with an attention mechanism was employed to solve the optimization problem, enabling agents to extract critical environmental features more effectively. …”
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Stability of optical knots in atmospheric turbulence by D. G. Pires, D. Tsvetkov, H. Barati Sedeh, N. Chandra, N. M. Litchinitser

“…Additionally, we propose an optimization algorithm to maximize the distance between the phase singularities at each longitudinal plane, facilitating measurements of optical knots and improving their performance in the presence of turbulence.…”
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Variational Quantum Simulation: A Case Study for Understanding Warm Starts by Ricard Puig, Marc Drudis, Supanut Thanasilp, Zoë Holmes

“…While our main focus is on this case study of variational quantum simulation, we end by discussing how our results work in other iterative settings.…”
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Swarm Robot Exploration Strategy for Path Formation Tasks Inspired by Physarum polycephalum by Yandong Luo, Jianwen Guo, Zhenpeng Lao, Shaohui Zhang, Xiaohui Yan

“…For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. …”
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Minimum Error Integration Method for Quadrilateral Flat Plate Fitting in Steel Construction by Zhuoju Huang, Jiemin Ding

“…Current industry practices often approximate mildly curved surfaces with flat plates within tolerances, but optimizing this substitution to minimize fitting errors while reducing the quantity of curved plates remains a critical engineering challenge. …”
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Flexible hardware for teaching and research in renewable energy and off-grid microgrids by Cristian Escudero-Quintero, Juan Pablo Guzman-Rodriguez, Juan Pablo Villegas-Ceballos, Elkin Edilberto Henao-Bravo, Daniel Gonzalez-Montoya

“…Microgrids are an active research field due to their integration of renewable and non-renewable energy sources alongside energy storage systems to supply both DC and AC loads. While existing studies extensively explore hierarchical control structures, focusing on algorithm design and optimization, practical implementation remains challenging due to system complexity. …”
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A Gain-Computation Enhancements Resource Allocation for Heterogeneous Service Flows in IEEE 802.16 m Mobile Networks by Wafa Ben Hassen, Meriem Afif

“…This method has a crucial role in the resource management and optimization. In a single service access, we propose a dynamic resource allocation algorithm at the physical layer aiming to maximize the cell data rate while ensuring fairness among users. …”
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Hierarchical energy management of distribution network with multi-microgrids based on double Stackelberg game by XIE Yuanhao, LIN Shenghong, ZHU Jianquan

“…In the external game, the distribution network as leader sets the prices, while microgrids as followers adjust their trading strategy with the distribution network. …”
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Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning by Sangwoo Park, Osvaldo Simeone

“…Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate-scale quantum computers. …”
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Improved method for a pedestrian detection model based on YOLO by Yanfei LI, Chengyi DONG

“…The proposed method ultimately provides a computationally efficient and robust intelligent monitoring solution for agricultural mechanization, facilitating the transition from conventional agricultural practices toward sustainable, low-carbon production paradigms through algorithmic optimization.…”
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Detection of child depression using machine learning methods. by Umme Marzia Haque, Enamul Kabir, Rasheda Khanam

“…<h4>Background</h4>Mental health problems, such as depression in children have far-reaching negative effects on child, family and society as whole. It is necessary to identify the reasons that contribute to this mental illness. …”
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End-to-end deep learning pipeline for real-time Bragg peak segmentation: from training to large-scale deployment by Cong Wang, Valerio Mariani, Frédéric Poitevin, Matthew Avaylon, Jana Thayer

“…We present an end-to-end deep learning pipeline with three key components: (1) a data engine that combines traditional algorithms with our peak matching algorithm to generate high-quality training data at scale, (2) a modular architecture that scales from a few million to hundreds of million parameters, enabling us to train large expert-level models offline while deploying smaller, distilled models at the edge, and (3) a decoupled producer-consumer architecture that separates specialized data source layer from model inference, enabling flexible deployment across diverse computing environments. …”
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