Enhancing the Harrow-Hassidim-Lloyd (HHL) algorithm in systems with large condition numbers by Peniel Bertrand Tsemo, Akshaya Jayashankar, K. Sugisaki, Nishanth Baskaran, Sayan Chakraborty, V. S. Prasannaa

“…In this work, we address the issue by introducing the postselection-improved HHL (Psi-HHL) framework that operates on a simple yet effective premise: subtracting mixed and wrong signals to extract correct signals while providing the benefit of optimal scaling in the condition number of A (denoted as κ) for large κ scenarios. …”
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Performance of machine learning algorithms to evaluate the physico-mechanical properties of nanoparticle panels by Derrick Mirindi, James Hunter, David Sinkhonde, Tajebe Bezabih, Frederic Mirindi

“…The Pearson correlation analysis reveals a strong relationship between board properties (R = 0.94 for WA–TS; R = 0.93 for MOR–MOE), confirming that nanoparticle treatments enhance performance while maintaining inherent material behavior. Hierarchical clustering grouped nanoparticles by performance: zinc oxide and chitosan+UF+epoxy formed a cluster with the lowest WA and TS, indicating optimal dimensional stability, while GO, rGO, and chitosan-based composites clustered with moderate values. …”
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Risk averse reproduction numbers improve resurgence detection. by Kris V Parag, Uri Obolski

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A Hybrid EV Charging Approach Based on MILP and a Genetic Algorithm by Syed Abdullah Al Nahid, Junjian Qi

“…This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience.…”
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RTL-Net: real-time lightweight Urban traffic object detection algorithm by Zhiqing Cui, Jiahao Yuan, Haibin Xu, Yamei Wei, Zhenglong Ding

“…Additionally, this paper introduces the Dilatation-wise Residual (DWR) module to facilitate the algorithm’s extraction of detailed features. In addition, we optimize the Bidirectional Feature Pyramid Network (Bi-FPN), enabling the fusion of multiple features to improve overall feature integration and performance. …”
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LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios by Ying Lu, Xiaodan Wang, Yang Yang, Man Ding, Shaochun Qu, Yanfang Fu

“…The results demonstrate that the LSTM-DQN-APF algorithm, combined with the digital twin platform, can significantly improve the issues of unreachable goals, local optimality, and real-time performance in UAV operations in complex environments. …”
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A novel economic load dispatch method of microgrid based on hybrid slime mould and genetic algorithm by Wei Ba, Wei Sun, Chunjiang Zhao, Qi Li

“…For performance evaluation, GSMA is compared with slime mould algorithm (SMA), grey wolf optimizer (GWO), sparrow search algorithm (SSA), Harris Hawks optimization (HHO), whale optimization algorithm (WOA) and particle swarm optimization (PSO) using standard optimization functions. …”
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Optimizing Medical IoT Disaster Management with Data Compression by Nunudzai Mrewa, Athirah Mohd Ramly, Angela Amphawan, Tse Kian Neo

“…To tackle this issue, we introduce "Beyond Orion," a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. …”
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A Constrained Solution Update Strategy for Multiobjective Evolutionary Algorithm Based on Decomposition by Yuchao Su, Qiuzhen Lin, Jia Wang, Jianqiang Li, Jianyong Chen, Zhong Ming

“…This paper proposes a constrained solution update strategy for multiobjective evolutionary algorithm based on decomposition, in which each agent aims to optimize one decomposed subproblem. …”
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Service chain deployment algorithms for deterministic end-to-end delay upper bound by Ze’nan WANG, Jiao ZHANG, Shuo WANG, Tao HUANG, F.Richard Yu

“…To solve the problem that the current service chain deployment algorithms cannot guarantee the delay of each packet passing through the service chain (SC), a SC deployment algorithm for deterministic end-to-end delay upper bound was proposed.First, the end-to-end delay bound of the SC was derived based on network calculus.Then, the deterministic end-to-end delay bound of the SC was achieved by collaboratively optimizing the routing of SC and the resource allocation of the virtual network function nodes in the SC.The experimental results show that the proposed algorithm can effectively improve the volume of accepted SC while guaranteeing that the end-to-end delay of each packet satisfies the delay requirements.…”
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Evolution of Cleft Lip and Palate Surgery and the Pursuit for Consensus on Standardized Algorithms of Care by Priyanka Naidu, MBChB, MS, Alexander T. Plonkowski, MBBS, MRes, Caroline A. Yao, MD, MS, William P. Magee, III, MD, DDS

“…Cleft lip and palate (CLP) surgery has evolved over centuries in an attempt to achieve anatomical closure while optimizing speech and limiting fistulas and midface hypoplasia. …”
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Research on an internal model control algorithm and parameter tuning for metro traction PMSMs by SI Junmin, YUE Xuelei, TIAN Di, XU Kelei

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Dynamic and adaptive multi-path routing algorithm based on software-defined network by Wu Jiawei, Qiao Xiuquan, Nan Guoshun

“…Moreover, our proposed algorithm can significantly reduce the computational overhead of the controller while completing a fine-grained flow balance. …”
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Implementation of Apriori Algorithm in Identifying Purchase Relationships at Bluder Cokro Pakuwon Mall by Angeline Ivana, Indra Maryati

“…These findings indicate that strategic product placement and bundling promotions can enhance sales performance. Optimizing the store layout by placing coklat keju near coklat can increase purchase likelihood, while targeted discounts, such as "Buy coklat keju, get 10% off keju," can drive transaction values. …”
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Control Optimization of Steam Boilers via Reinforcement Learning by Emmanuel Okafor, Maad Alowaifeer

“…Extensive experimental validation confirms the superiority of hybrid controllers, achieving significantly faster settling times, peak overshoot reductions to <inline-formula> <tex-math notation="LaTeX">$(\leq 2\%)$ </tex-math></inline-formula>, and lower error metrics than MRAC alone. While optimized PID serves as a baseline, hybrid controllers consistently achieve faster settling times and improved robustness under most nonlinear operating conditions. …”
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An efficient fire detection algorithm based on Mamba space state linear attention by Yuming Li, Yongjie Wang, Xiaorui Shao, Anbo Zheng

“…Instead, it performs dimensionality reduction on input features through adaptive average pooling and utilizes the state update mechanism of the SSM module to significantly enhance feature representation and optimize information flow. Second, to address the limitations of SSM models in local feature modeling, this study incorporates the ConvNeXtV2 module to optimize the backbone network, improving the model’s ability to capture fine-grained local details and thereby strengthening its overall representation capability. …”
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Vehicle Authentication-Based Resilient Routing Algorithm With Dynamic Task Allocation for VANETs by Nagaraju Pacharla, K. Srinivasa Reddy

“…With the goal of reducing task offloading delay and improving enhanced reaction time, a VANET-based task scheduling system is proposed after selecting an optimal route in the VANET. …”
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Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey model by HONG Jingke, DU Wei, SHAO Jin*, LAO Huimin

“…In light of these findings, It is recommended that Zhejiang Province should focus on optimizing its industrial structure, improving energy efficiency, increasing investment in low-carbon research and development, and steadily advancing the goal of "carbon peaking" .…”
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Automatic Vehicles’ Trajectories Optimization on Highway Exclusive Lanes by Lingjuan Chen, Yangbo Ruan, Yiquan Gou

“…The rapid development of V2X communication has made it possible to optimize and control the trajectories of vehicles from the whole traffic flow’s perspective and improve traffic performance. …”
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Multivariate machine learning algorithms for energy demand forecasting and load behavior analysis by Farhan Hussain, M. Hasanuzzaman, Nasrudin Abd Rahim

“…This article presents deep learning frameworks for predicting electricity demand in the Western region of Bangladesh, utilizing Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms. Critical hyperparameters, such as the number of hidden neurons in ANN and the number of input membership functions in ANFIS, are optimized to effectively capture uncertainties in load patterns. …”
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