UV-VIS laser parameter effects on the physicochemical properties of Laser-Induced Graphene by Jhonattan de la Roche, Carlos Lubo Mestanza, Laura Tenjo Patiño, Santiago Lasso, Santiago Ospina Arroyave, Sebastián Mendoza, Lucero Alvarez Mino

“…We employed a UV-VIS diode laser mounted on a CNC (Computer Numerical Control) system constructed by our team to engrave on polyimide sheets. …”
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DualHet-YOLO: A Dual-Backbone Heterogeneous YOLO Network for Inspection Robots to Recognize Yellow-Feathered Chicken Behavior in Floor-Raised House by Yaobo Zhang, Linwei Chen, Hongfei Chen, Tao Liu, Jinlin Liu, Qiuhong Zhang, Mingduo Yan, Kaiyue Zhao, Shixiu Zhang, Xiuguo Zou

“…Meanwhile, it reduces model parameters by 14.6% and computational complexity by 29.2%, achieving a synergistic optimization of accuracy and efficiency. …”
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Computational Analysis of Benzenoid Systems Using Valency-Based Entropy Metrics and Topological Indices by Muhammad Kamran, Muhammad Nadeem, Manal E. M. Abdulla, Ismail Naci Cangul, Nurullayev Mirolim Nosirovich

“…Benzenoid systems with their homogeneous structures are especially fit for computer study because of their predictable geometries. …”
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SDES-YOLO: A high-precision and lightweight model for fall detection in complex environments by Xiangqian Huang, Xiaoming Li, Limengzi Yuan, Zhao Jiang, Hongwei Jin, Wanghao Wu, Ru Cai, Meilian Zheng, Hongpeng Bai

“…With only 2.9M parameters and 7.2 GFLOPs of computation, SDES-YOLO achieves an mAP@0.5 of 85.1%, representing a 3.41% improvement over YOLOv8n, while reducing parameter count and computation by 1.33% and 11.11%, respectively. …”
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EEMtoolbox: A user‐friendly R package for flexible ensemble ecosystem modelling by Luz Valerie Pascal, Sarah A. Vollert, Malyon D. Bimler, Christopher M. Baker, Maude Vernet, Stefano Canessa, Christopher Drovandi, Matthew P. Adams

“…Ensemble ecosystem modelling (EEM) is a quantitative method used to parameterize models from theoretical ecosystem features rather than data. Two approaches have been considered to find parameter values satisfying those features: a standard accept–reject algorithm, appropriate for small ecosystem networks, and a sequential Monte Carlo (SMC) algorithm that is more computationally efficient for larger ecosystem networks. …”
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Monitoring water quality parameters using multi-source data-driven machine learning models by Yubo Zhao, Mo Chen, Jinyu He, Yanping Ma

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Enhancing Multi-Key Fully Homomorphic Encryption with Efficient Key Switching and Batched Multi-Hop Computations by Liang Zhou, Ruwei Huang, Bingbing Wang

“…Multi-Key Fully Homomorphic Encryption (MKFHE) offers a powerful solution for secure multi-party computations, where data encrypted under different keys can be jointly computed without decryption. …”
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High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks by Haoyi Tan, Yukun Teng, Guangcun Shan

“…Initially, the relationship between the structural characteristics of MOF materials (including density, surface area and pore features) and their adsorption properties was explored, with the aim of identifying the optimal structural parameters for iodine capture. …”
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LatentResNet: An Optimized Underwater Fish Classification Model with a Low Computational Cost by Muhab Hariri, Ercan Avsar, Ahmet Aydın

“…This paper presents LatentResNet, a computationally lightweight deep learning model involving two key innovations: (i) using the encoder from the proposed LiteAE, a lightweight autoencoder for image reconstruction, as input to the model to reduce the spatial dimension of the data and (ii) integrating a DeepResNet architecture with lightweight feature extraction components to refine encoder-extracted features. …”
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A Futuristic Approach to Security in Cloud Data Centers Using a Hybrid Algorithm by Dipankar Chatterjee, Mostaque Md. Morshedur Hassan, Nazrul Islam, Asmita Ray, Munsifa Firdaus Khan Barbhuyan

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Parameter Prediction for Metaheuristic Algorithms Solving Routing Problem Instances Using Machine Learning by Tomás Barros-Everett, Elizabeth Montero, Nicolás Rojas-Morales

“…Tuning the parameters of a metaheuristic is a computationally costly task. …”
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Influence of Printing Parameters on the Morphological Characteristics of Plasma Directed Energy-Deposited Stainless Steel by Luis Segovia-Guerrero, Antonio José Gil-Mena, Nuria Baladés, David L. Sales, Carlota Fonollá, María de la Mata, María de Nicolás-Morillas

“…Moreover, advanced 3D scanning and computational analysis were used to assess the key morphological features, including bead width and height. …”
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Noise Reduction with Recursive Filtering for More Accurate Parameter Identification of Electrochemical Sources and Interfaces by Mitar Simić, Milan Medić, Milan Radovanović, Vladimir Risojević, Patricio Bulić

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THE METHOD OF JITTER DETERMINING IN THE TELECOMMUNICATION NETWORK OF A COMPUTER SYSTEM ON A SPECIAL SOFTWARE PLATFORM by Mykhailo Mozhaiev, Nina Kuchuk, Maksym Usatenko

“…Relevance of the study: When choosing a platform, the quality criteria for computer system service depend significantly on the parameters of the basic telecommunications network. …”
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A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control by Zaid Raad Saber Zubair

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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…This study presents a comparative analysis of hyper-parameter optimization methods used in developing predictive models for patients at risk of heart failure readmission and mortality. …”
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GELSTATS: A Computer Program for Population Genetics Analyses Using VNTR Multilocus Probe Data by Steven H. Rogstad, Stephan Pelikan

“…A jackknife test for heterozygosity differences between groups is also computed. Examples of GELSTATS analyses illustrate some features of the program.…”
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Heterogeneous appetite patterns in depression: computational modeling of nutritional interoception, reward processing, and decision-making by Yuuki Uchida, Yuuki Uchida, Takatoshi Hikida, Manabu Honda, Yuichi Yamashita

“…Furthermore, effects of interoception manipulation were compared with traditional reinforcement learning parameters (e.g., inverse temperature β and delay discount γ), which represent cognitive-behavioral features of depression. …”
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Enhancement of rime algorithm using quadratic interpolation learning for parameters identification of photovoltaic models by Shazly A. Mohamed, Abdullah M. Shaheen, Mohammed H. Alqahtani, Badr M. Al Faiya

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Intelligent Recognition and Parameter Estimation of Radar Active Jamming Based on Oriented Object Detection by Jiawei Lu, Yiduo Guo, Weike Feng, Xiaowei Hu, Jian Gong, Yu Zhang

“…Second, for the five ISRJ types, a post-processing algorithm based on boxes fusion is designed to further extract features for secondary recognition. Finally, by integrating the detection box information and secondary recognition results, parameters of different ISRJ are estimated. …”
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