From Simulation to Field Validation: A Digital Twin-Driven Sim2real Transfer Approach for Strawberry Fruit Detection and Sizing by Omeed Mirbod, Daeun Choi, John K. Schueller

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Design and Characterization of the Modified Purdue Subcritical Pile for Nuclear Research Applications by Matthew Niichel, Vasileios Theos, Riley Madden, Hannah Pike, True Miller, Brian Jowers, Stylianos Chatzidakis

“…First demonstrated in 1942, subcritical and zero-power critical assemblies, also known as piles, are a fundamental tool for research and education at universities. Traditionally, their role has been primarily instructional and for measuring the fundamental properties of neutron diffusion and transport. …”
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What are new opportunities for clinical practice the VERIFY study opens and which values for native diabetes patients? Joint conclusion on the advisory board results. November 6, 2... by Marina V. Shestakova, Mikhail B. Antsiferov, Alexander S. Ametov, Gagik R. Galstyan, Tatiana Y. Demidova, Alexey V. Zilov, Tatiana N. Markova, Nina A. Petunina, Natalya A. Chernikova, Minara S. Shamkhalova

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Capsule neural network and its applications in drug discovery by Yiwei Wang, Binyou Wang, Jun Zou, Anguo Wu, Yuan Liu, Ying Wan, Jiesi Luo, Jianming Wu

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Machine learning for medical image classification by Gazi Husain, Jonathan Mayer, Molly Bekbolatova, Prince Vathappallil, Mihir Matalia, Milan Toma

“… This review article focuses on the application of machine learning (ML) algorithms in medical image classification. It highlights the intricate process involved in selecting the most suitable ML algorithm for predicting specific medical conditions, emphasizing the critical role of real-world data in testing and validation. …”
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Leveraging mixed-effects regression trees for the analysis of high-dimensional longitudinal data to identify the low and high-risk subgroups: simulation study with application to g... by Mina Jahangiri, Anoshirvan Kazemnejad, Keith S. Goldfeld, Maryam S. Daneshpour, Mehdi Momen, Shayan Mostafaei, Davood Khalili, Mahdi Akbarzadeh

“…Previous studies have shown that this model can be sensitive to parametric assumptions and provides less predictive performance than non-parametric methods such as random effects-expectation maximization (RE-EM) and unbiased RE-EM regression tree algorithms. …”
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A comparative study of bone density in elderly people measured with AI and QCT by Min Guo, Min Guo, Yu Zhang, Yu Zhang, XinXin Gu, XinXin Gu, Xuhui Liu, Xuhui Liu, Fei Peng, Fei Peng, Zongjun Zhang, Zongjun Zhang, Mei Jing, Mei Jing, Yingxia Fu, Yingxia Fu

“…The linear regression fit between the R2 values of QCT and Bone Density AI for measuring lumbar spine BMD with different equipment ranged from 0.88 to 0.96, indicating a high degree of consistency between the two measurement methods across devices.ConclusionThis multicenter study pioneers a dual-validation framework to establish the clinical validity of deep learning-based BMD prediction algorithms using routine thoracic/abdominal CT scans. …”
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Coastal Urban Ecological Security Pattern Identification Integrating Land Subsidence Factors: A Deep Learning-Based Case Study of Zhuhai City by Yuan Shaoxiong, Gong Qinghua, Ye Yuyao, Wang Jun, Hao Yinlei, Zhang Yaze, Liu Bowen

“…Results showed that the MLP model achieved an average prediction accuracy of 84.5% with an F1-score of 0.844, demonstrating the feasibility of deep learning approaches in ESP construction. …”
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Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

“…Regression and classification models were developed using gradient boosting, random forest, and CatBoost algorithms. Regression models predicted vault height as a continuous variable, while classification models categorized vault heights into binary and multi-class tasks. …”
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PDP1 related ferroptosis risk signature indicates distinct immune microenvironment and prognosis of breast cancer patients by Yufeng Wang, Huifen Dang, Gongjian Zhu, Yingxia Tian

“…The model remained significant in multivariate Cox regression analysis, indicating that it could independently predict the survival of BC patients. ACSL1, BNIP3, and EMC2 were downregulated after knockdown of PDP1.ConclusionRiskScore model constructed by PDP1-ferroptosis-related genes ACSL1, BNIP3, and EMC2 is able to help predict the prognosis of BC patients.…”
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Thyroid nodule classification in ultrasound imaging using deep transfer learning by Yan Xu, Mingmin Xu, Zhe Geng, Jie Liu, Bin Meng

“…In this study, we investigate the predictive efficacy of distinguishing between benign and malignant thyroid nodules by employing traditional machine learning algorithms and a deep transfer learning model, aiming to advance the diagnostic paradigm in this field. …”
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Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis. by Shang-Ming Zhou, Fabiola Fernandez-Gutierrez, Jonathan Kennedy, Roxanne Cooksey, Mark Atkinson, Spiros Denaxas, Stefan Siebert, William G Dixon, Terence W O'Neill, Ernest Choy, Cathie Sudlow, UK Biobank Follow-up and Outcomes Group, Sinead Brophy

“…<h4>Objectives</h4>1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs.…”
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Gene selection based on adaptive neighborhood-preserving multi-objective particle swarm optimization by Sumet Mehta, Fei Han, Muhammad Sohail, Bhekisipho Twala, Asad Ullah, Fasee Ullah, Arfat Ahmad Khan, Qinghua Ling

“…Traditional optimization algorithms often produce inconsistent and suboptimal results, while failing to preserve local data structures limiting both predictive accuracy and biological interpretability. …”
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A prospective cluster randomized trial of an interventions bundle to reduce inappropriate antibiotic use for upper respiratory tract infections in the outpatient setting by Adeel A. Butt, Sherin Shams, Atika Jabeen, Asma Ali Al-Nuaimi, Jeyaram Illiayaraja Krishnan, Aimon B. Malik, Samah Saleem, Maryam Hassan Abdulaziz, Naheel Ismail Seyam, Kamran Aziz, Dalia Kandil, Anil G. Thomas, Hanaa Nafady-Hego, Muzna I. Lone, Jameela Al Ajmi, Zain A. Bhutta, Noora AlSulaiti, Wael E. Said Hussein, Sandy Semaan, Samya Ahmad Al-Abdulla, Mohamed Ghaith Al-Kuwari, Abdul-Badi Abou-Samra

“…Intervention Bundled 4-component intervention including extensive provider education, a decision support algorithm, option for deferred antibiotics prescription, and monthly feedback on prescription patterns, vs. a single randomly assigned intervention (decision support algorithm). …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…Aiming at the problem of drought forecasting accuracy in a short time scale, this study proposed a drought forecasting model named VMD-JAYA-Informer based on Variational Mode Decomposition (VMD) and the JAVA optimization algorithm to improve the Informer model. This study conducted a comparative analysis of VMD-JAYA-ARIMA, VMD-JAYA-LSTM, VMD-JAYA-CNN, and VMD-JAYA-Informer drought prediction models. …”
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PIC2O-Sim: A physics-inspired causality-aware dynamic convolutional neural operator for ultra-fast photonic device time-domain simulation by Pingchuan Ma, Haoyu Yang, Zhengqi Gao, Duane S. Boning, Jiaqi Gu

“…Directly applying off-the-shelf models to predict the optical field dynamics shows unsatisfying fidelity and efficiency since the model primitives are agnostic to the unique physical properties of Maxwell equations and lack algorithmic customization. …”
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Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China by Qingfeng Zhou, Chun Janice Wong, Xian Su

“…Second, five classification algorithms were compared in the accuracy of distinguishing the type of bicycle gathering areas using 25 impact factors. …”
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Design and development of advanced Al-Ti-V alloys for beampipe applications in particle accelerators by Kamaljeet Singh, Kangkan Goswami, Raghunath Sahoo, Sumanta Samal

“…The present investigation reports the design and development of an advanced material with a high figure of merit (FoM) for beampipe applications in particle accelerators by bringing synergy between computational and experimental approaches. Machine-learning algorithms have been used to predict the phase(s), low density, and high radiation length of the designed Al-Ti-V alloys. …”
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CPP: a path planning method taking into account obstacle shadow hiding by Ruixin Zhang, Qing Xu, Youneng Su, Ruoxu Chen, Kai Sun, Fengchang Li, Guo Zhang

“…We also proposed a Minimum-Jerk Trajectory Optimization method with controllable path noise points, which enhanced path smoothness and reduced predictability. Comparative analysis showed that CPP significantly outperformed five other algorithms—RRT, Improved B-RRT, RRT*, Informed RRT*, and Potential Field-by reducing running time by 46.01% to 93.3%, increasing path safety by 10.42% to 83.44%, and improving path smoothness, making it particularly effective for path planning in tactical scenarios involving unmanned vehicles.…”
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Robust Hybrid Data-Level Approach for Handling Skewed Fat-Tailed Distributed Datasets and Diverse Features in Financial Credit Risk by Musara Keith R, Ranganai Edmore, Chimedza Charles, Matarise Florence, Munyira Sheunesu

“…The results suggested that our novelty, SMOTEENN-ENC, integrated with the XGBoost algorithm demonstrated superiority and stability in the predictive performance when applied to skewed fat-tailed distributed datasets with inherent diverse features.…”
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