Inertial-Based Gait Metrics During Turning Improve the Detection of Early-Stage Parkinson’s Disease Patients by Lin Meng, Jun Pang, Yifan Yang, Lei Chen, Rui Xu, Dong Ming

“…A total of one hundred and thirty-nine gait parameters were derived for each gait task. We explored the factor effect of group and gait tasks on gait parameters using a two-way mixed analysis of variance. …”
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Exploration of Using an Open‐Source Large Language Model for Analyzing Trial Information: A Case Study of Clinical Trials With Decentralized Elements by Ki Young Huh, Ildae Song, Yoonjin Kim, Jiyeon Park, Hyunwook Ryu, JaeEun Koh, Kyung‐Sang Yu, Kyung Hwan Kim, SeungHwan Lee

“…We utilized three Llama 3 models with a different number of parameters: 8b (model 1), fine‐tuned 8b (model 2) with curated data, and 70b (model 3). …”
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Design of an Integrated Model Combining CycleGAN, PPO, and Vision Transformer for Adaptive Scene Rendering in the Metaverse by Durga Prasad Kavadi, Kailash Chandra Mishra, Sai Babu Veesam, Mudimela Madhusudhan, Palacharla Ravi Kumar, Pannangi Naresh, Yuvaraju Chinnam

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Development of a single-center predictive model for conventional in vitro fertilization outcomes excluding total fertilization failure: implications for protocol selection by Hai Wang, Haojie Pan, Zitong Xu, Xianjue Zheng, Shuqi Xia, Jiayong Zheng

“…Methods This retrospective single-center study analyzed 691 cycles (594 c-IVF, 97 rescue ICSI) from January 2019 to August 2024. Key parameters included female age, BMI, male semen parameters (sperm concentration, total progressive motile sperm count [TPMC], DNA fragmentation index [DFI]), and infertility duration. …”
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The role of particle shape in the mechanical behavior of granular soils: A state-of-the-art review by Mohammad Hadi Hatefi, Mahyar Arabani, Meghdad Payan, Payam Zanganeh Ranjbar, Suraparb Keawsawasvong, Pitthaya Jamsawang

“…With these practical models, engineers can readily estimate the compression and strength-related parameters of granular soils by simply examining their particle shape, grain size distribution, density, and overburden pressure.…”
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Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection by Li-Chin Chen, Kuo-Hsuan Hung, Yi-Ju Tseng, Hsin-Yao Wang, Tse-Min Lu, Wei-Chieh Huang, Yu Tsao

“…Objective: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. …”
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Efeito da frequência e amplitude de vibração sobre a derriça de frutos de café Frequency and amplitude of vibration on coffee harvesting by Fábio L. Santos, Daniel M. de Queiroz, Francisco de A. de C. Pinto, Ricardo C. de Resende

“…Therefore, the study of the frequency and amplitude parameters is important for the design of a specific harvesting machine. …”
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Process Development for the Continuous Manufacturing of Carbamazepine-Nicotinamide Co-Crystals Utilizing Hot-Melt Extrusion Technology by Lianghao Huang, Wen Ni, Yaru Jia, Minqing Zhu, Tiantian Yang, Mingchao Yu, Jiaxiang Zhang

“…Risk assessment highlighted material attributes, process parameters, and equipment design as critical factors affecting CC formation. …”
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Dosimetric performance of HyperArc and VMAT techniques using full or ipsilateral arcs for unilateral temporal lobe tumor radiotherapy by Hongtao Chen, Lijun Wang, Zhuangling Li, Shihai Wu, Zihuang Li

“…Using the Eclipse treatment plan system with the Truebeam machine model, HyperArc and VMAT plans were designed with full or ipsilateral arcs, respectively Dosimetric parameters for the planning target volume (PTV) and organs at risk (OARs) were computed and analyzed. …”
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A data generator for covid-19 patients’ care requirements inside hospitals by Juan A. Marin-Garcia, Angel Ruiz, Maheut Julien, Jose P. Garcia-Sabater

“…From a theoretical point of view, it would be interesting to develop machine learning tools that, by analyzing specific data samples in real hospitals, can identify the parameters necessary for the automatic prototyping of generators adapted to each hospital. …”
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The hybrid feature extraction method for classification of adolescence idiopathic scoliosis using Evolving Spiking Neural Network by Nurbaity Sabri, Haza Nuzly Abdull Hamed, Zaidah Ibrahim, Kamalnizat Ibrahim, Mohd Adham Isa, Norizan Mat Diah

“…Therefore, a new FE method has been proposed to reduced the number of parameters. A fusion of LBP and 1DCNN (1-Dimensional Convolutional Neural Network) FE methods that can reduce the number of parameters by 50% is proposed. …”
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COD Optimization Prediction Model Based on CAWOA-ELM in Water Ecological Environment by Lili Jiang, Liu Yang, Yang Huang, Yi Wu, Huixian Li, XiYan Shen, Meng Bi, Lin Hong, Yiting Yang, Zuping Ding, Wenjie Chen

“…In order to detect high error rate and poor convergence of the water ecological chemical oxygen demand (COD) prediction model, combining the limit learning machine (ELM) model and whale optimization algorithm, CAWOA is improved by the sin chaos search strategy, while the ELM optimizes the parameters of the algorithm to improve convergence speed, thus improving the generalization performance of the ELM. …”
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Quantifying Baryonic Feedback on the Warm–Hot Circumgalactic Medium in CAMELS Simulations by Isabel Medlock, Chloe Neufeld, Daisuke Nagai, Daniel Anglés-Alcázar, Shy Genel, Benjamin D. Oppenheimer, Xavier Sims, Priyanka Singh, Francisco Villaescusa-Navarro

“…Using the SIMBA and IllustrisTNG suites from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, we explore the effect of parameters governing the subgrid implementation of stellar and AGN feedback. …”
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Optimized SVR model for predicting dissolved oxygen levels using wavelet denoising and variable reduction: Taking the Minjiang River estuary as an example by Peng Zhang, Xinyang Liu, Huiru Zhang, Chengchun Shi, Gangfu Song, Lei Tang, Ruihua Li

“…Support Vector Regression (SVR) parameters were optimized using Particle Swarm Optimization (PSO), culminating in an optimized WD-MIC-PSO-SVR model for DO prediction. …”
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Electrochemical Corrosion Resistance of Al₂O₃–YSZ Coatings on Steel Substrates by Ramona Cimpoeșu, Marian Luțcanu, Alin Marian Cazac, Ionuț Adomniței, Costică Bejinariu, Liviu Andrușcă, Marius Prelipceanu, Lucian-Ionel Cioca, Daniela Lucia Chicet, Ancuța Mirela Radu, Nicanor Cimpoeșu

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SOLEDGE3X full vessel plasma boundary simulations of ITER non-active phase plasmas by N. Rivals, P. Tamain, Y. Marandet, X. Bonnin, J.-S. Park, H. Bufferand, R.A. Pitts, G. Falchetto, H. Yang, G. Ciraolo

“…The onset of detachment in the ITER machine is analyzed in this work through the help of 2D-axisymmetric boundary plasma simulations with the SOLEDGE3X-EIRENE code, which features a numerical domain for the plasma solver extending up to the first wall. …”
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Deep learning to evaluate seismic-induced soil liquefaction and modified transfer learning between various data sources by Hongwei Guo, Chao Zhang, Hongyuan Fang, Timon Rabczuk, Xiaoying Zhuang

“…Also, the deep learning model is compared with several classical machine learning and ensemble learning models and the modified transfer learning model is formulated by comparing different feature transformation techniques integrated with various feature-based and instance-based transfer learning methods. …”
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Predicting pediatric patient rehabilitation outcomes after spinal deformity surgery with artificial intelligence by Wenqi Shi, Felipe O. Giuste, Yuanda Zhu, Ben J. Tamo, Micky C. Nnamdi, Andrew Hornback, Ashley M. Carpenter, Coleman Hilton, Henry J. Iwinski, J. Michael Wattenbarger, May D. Wang

“…In total, 171 pre-operative clinical features are used to train six machine-learning models for post-operative outcomes prediction. …”
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Quantum Circuit Architecture Search on a Superconducting Processor by Kehuan Linghu, Yang Qian, Ruixia Wang, Meng-Jun Hu, Zhiyuan Li, Xuegang Li, Huikai Xu, Jingning Zhang, Teng Ma, Peng Zhao, Dong E. Liu, Min-Hsiu Hsieh, Xingyao Wu, Yuxuan Du, Dacheng Tao, Yirong Jin, Haifeng Yu

“…Variational quantum algorithms (VQAs) have shown strong evidence to gain provable computational advantages in diverse fields such as finance, machine learning, and chemistry. However, the heuristic ansatz exploited in modern VQAs is incapable of balancing the trade-off between expressivity and trainability, which may lead to degraded performance when executed on noisy intermediate-scale quantum (NISQ) machines. …”
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Trap Assisted Dynamic Mechanoluminescence Toward Self‐Referencing and Visualized Strain Sensing by Tianli Wang, Pengfei Zhang, Jianqiang Xiao, Ziyi Guo, Xiongwu Xie, Jiahao Huang, Jiaojiao Zheng, Xuhui Xu, Lei Zhao

“…This approach offers new insights into the use of dynamic ML materials in strain sensing and human‐machine interaction.…”
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