The association of Whole and Segmental Body Composition and Anaerobic Performance in Crossfit® athletes: sex differences and performance prediction by Tomás Ponce-García, Jerónimo García-Romero, Laura Carrasco-Fernández, Alejandro Castillo-Dominguez, Javier Benítez-Porres

“… The main purpose of this study was to establish the association between total and segmental body composition (BC) variables and anaerobic performance and to create optimal models that best predict such performance in CrossFit® (CF) athletes. …”
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Modelling distillate output of a solar distiller with eco-friendly wood-based steam generation basin using machine learning model and starling murmuration optimizer by Mohammed Azmi Al-Betar, Otto L. Muskens, Karrar A. Hammoodi, Mohamed Abd Elaziz, Qusai Yousef Shambour, Salam Fraihat, Ammar H. Elsheikh

“…These models were standalone LSTM and LSTM optimized by an artificial hummingbird algorithm (AHA) or manta ray foraging optimizer (MRFO). …”
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Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers by Evan Scope Crafts, Umberto Villa

“…However, it is unclear how to optimally integrate a diffusion model trained on the prior distribution with a given likelihood function to obtain posterior samples. …”
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Influence of Virtual Fencing Technology in Cattle Management and Animal Welfare by Ishaya Usman Gadzama, Homa Asadi, Qazal Hina, Saraswati Ray

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Enhancing Overall Equipment Effectiveness (OEE) of a Selected Machine in a Light Manufacturing Factory in Bangladesh by Jonayed Abdullah, Md. Al Hossain Rifat, Avirup Deb Ray

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Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

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Leveraging boosting machine learning for drilling rate of penetration (ROP) prediction based on drilling and petrophysical parameters by Raed H. Allawi, Watheq J. Al-Mudhafar, Mohammed A. Abbas, David A. Wood

“…Drilling optimization requires accurate drill bit rate-of-penetration (ROP) predictions. …”
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Enhanced water saturation estimation in hydrocarbon reservoirs using machine learning by Ali Akbari, Ali Ranjbar, Yousef Kazemzadeh, Dmitriy A. Martyushev

“…Nine well log parameters—Depth (DEPT), High-Temperature Neutron Porosity, True Resistivity, Computed Gamma Ray, Spectral Gamma Ray, Hole Caliper, Compressional Sonic Travel Time, Bulk Density, and Temperature—were used as input features to train and test five ML algorithms: Linear Regression, Support Vector Machine (SVM), Random Forest, Least Squares Boosting, and Bayesian methods. …”
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Game Theoretic Approach to QoS Oriented Machine Learning Model Development Toward 5G Network Migration Planning by Arjun Ray, Manish Kr. Yadav, Babu R. Dawadi, Krishna R. Bhandari

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A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection by Noppadol Maneerat, Athasart Narkthewan, Kazuhiko Hamamoto

“…Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. …”
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Comparison of nanoimaging and nanoflow based detection of extracellular vesicles at a single particle resolution by Shihan Xu, Zhengrong Zhang, Bridgette C. Melvin, Nibedita Basu Ray, Seiko Ikezu, Tsuneya Ikezu

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Improving IMU based human activity recognition using simulated multimodal representations and a MoE classifier by Lala Shakti Swarup Ray, Qingxin Xia, Vitor Fortes Rey, Vitor Fortes Rey, Kaishun Wu, Paul Lukowicz, Paul Lukowicz

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Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks by Santu Mondal, Sneha Ray, Aritra Acharyya, Rudra Sankar Dhar, Arindam Biswas, Hiroaki Satoh, Gurudas Mandal, Vitaliy Maksimenko, Victor Krishtop

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Evidence Generation for a Host-Response Biosignature of Respiratory Disease by Kelly E. Dooley, Michael Morimoto, Piotr Kaszuba, Margaret Krasne, Gigi Liu, Edward Fuchs, Peter Rexelius, Jerry Swan, Krzysztof Krawiec, Kevin Hammond, Stuart C. Ray, Ryan Hafen, Andreas Schuh, Nelson L. Shasha Jumbe

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DETECTION OF TUBERCULOUS MYCOBACTERIA IN THE BLOOD OF PATIENTS WHEN SUSPECTING TUBERCULOUS SEPSIS by V. N. Zimina, O. E. Mikova, D. A. Oborin, S. Yu. Degtyareva, I. B. Viktorova

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A versatile framework for attitude tuning of beamlines at light source facilities by Peng-Cheng Li, Xiao-Xue Bi, Zhen Zhang, Xiao-Bao Deng, Chun Li, Li-Wen Wang, Gong-Fa Liu, Yi Zhang, Ai-Yu Zhou, Yu Liu

“…The tuning of a polycapillary lens and of an X-ray emission spectrometer are given as examples for the general use of this framework, featuring powerful command-line interfaces (CLIs) and friendly graphical user interfaces (GUIs) that allow comfortable human-in-the-loop control. …”
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Probabilistic phase labeling and lattice refinement for autonomous materials research by Ming-Chiang Chang, Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Lan Zhou, John M. Gregoire, Carla P. Gomes, R. Bruce van Dover, Michael O. Thompson

“…Abstract X-ray diffraction (XRD) is a powerful method for determining a material’s crystal structure in high-throughput experimentation, and is widely being incorporated in artificially intelligent agents for autonomous scientific discovery. …”
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Rugularizing generalizable neural radiance field with limited-view images by Wei Sun, Ruijia Cui, Qianzhou Wang, Xianguang Kong, Yanning Zhang

“…Moreover, our reconstructed radiance field can be effectively optimized by fine-tuning the target scene to achieve higher quality results with reduced optimization time. …”
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An improved permeability estimation model using integrated approach of hybrid machine learning technique and Shapley additive explanation by Christopher N. Mkono, Chuanbo Shen, Alvin K. Mulashani, Patrice Nyangi

“…The group method of data handling with differential evolution (GMDH-DE) algorithm was used to predict permeability due to its capability to manage complex, nonlinear relationships between variables, reduced computation time, and parameter optimization through evolutionary algorithms. Using 1953 samples from Gunya-1 and Gunya-2 wells for training and 1563 samples from Gunya-3 for testing, the GMDH-DE outperformed the group method of data handling (GMDH) and random forest (RF) in predicting permeability with higher accuracy and lower computation time. …”
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COVID-19 detection using federated machine learning. by Mustafa Abdul Salam, Sanaa Taha, Mohamed Ramadan

“…In this paper, we studied the efficacy of federated learning versus traditional learning by developing two machine learning models (a federated learning model and a traditional machine learning model)using Keras and TensorFlow federated, we used a descriptive dataset and chest x-ray (CXR) images from COVID-19 patients. During the model training stage, we tried to identify which factors affect model prediction accuracy and loss like activation function, model optimizer, learning rate, number of rounds, and data Size, we kept recording and plotting the model loss and prediction accuracy per each training round, to identify which factors affect the model performance, and we found that softmax activation function and SGD optimizer give better prediction accuracy and loss, changing the number of rounds and learning rate has slightly effect on model prediction accuracy and prediction loss but increasing the data size did not have any effect on model prediction accuracy and prediction loss. finally, we build a comparison between the proposed models' loss, accuracy, and performance speed, the results demonstrate that the federated machine learning model has a better prediction accuracy and loss but higher performance time than the traditional machine learning model.…”
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