ScRNA-seq reveals novel immune-suppressive T cells and investigates CMV-TCR-T cells cytotoxicity against GBM by Wei Gao, Qing Liu, Xiang Lin, Yiming Zhao, Minghua Wu, Shuai Chen, Peng Du, Meng Huang, Cheng Huang, Xiangyu Wang, Xinmiao Long, Zuping Zhang, Yuzhe Li, Kun Deng, Xiaoling She, Minfu Zhang, Shiyi Wang, Yinfei Du

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Effect of Social Sustainability on Supply Chain Resilience Before, During, and After the COVID-19 Pandemic in Mexico: A Partial Least Squares Structural Equation Modeling and Evolu... by Miguel Reyna-Castillo, Alejandro Santiago, Ana Xóchitl Barrios-del-Ángel, Francisco Manuel García-Reyes, Fausto Balderas, José Ignacio Anchondo-Pérez

“…This work starts from an epistemological premise, in which non-parametric statistical methodologies and mathematical analytics are complementary perspectives to comprehensively understand the same social phenomenon. Second-generation predictive statistics, such as the PLS-SEM algorithm, have demonstrated robustness in treating multivariate social information, making it feasible to prepare data for knowledge transfer with mathematical techniques specialized for fuzzy data. …”
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Tensor-Based Efficient Federated Reinforcement Learning for Cyber-Physical-Social Intelligence by Xin Nie, Laurence T. Yang, Fulan Fan, Zecan Yang

“…By leveraging tensor to model state-action values and employing tensor decomposition techniques for dimensionality reduction, this method effectively reduces model parameters and communication overhead while maintaining strong interpretability, accelerates algorithm convergence speed. …”
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Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models by Nikos Themelis, George Nikolaidis, Vasilios Zagkas

“…For the degradation assessment, an appropriate key performance indicator is defined comparing the expected shaft power required with the measured power under the same operational conditions. The power prediction models are data-driven based on machine learning algorithms. …”
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Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark by Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, Ayman Nabil

“…This paper presented a real-time system to predict breast cancer based on streaming patient’s health data from Twitter. …”
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Supervised Learning of Depth Estimation From Imperfect Rectified Stereo Laparoscopic Images of Liver Surface by Chongan Zhang, Ziyi Jin, Xiaoyue Liu, Yuelong Liang, Xiao Liang, Peng Wang, Xiujun Cai, Xuesong Ye

“…A dataset is created for laparoscopic liver depth images, and the ACVNet++ algorithm is tested on this dataset. The experiments demonstrate that, compared to non-learning algorithms based on white light image features, the ACVNet++ algorithm reduces the average absolute error (MAE) and endpoint error (EPE) of depth prediction under white light conditions by 51% and 44%, reaching 2.46 mm and 2.56 pixels, respectively. …”
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Halogenated 2,4-diphenyl indeno[1,2-B]pyridinol derivatives as potential inhibitors of the androgen receptor (PDB ID: 58TE): A study of QSAR modeling, molecular docking, and pharma... by Auwal Salisu Isa, Adamu Uzairu, Umar Mele Umar, Muhammad Tukur Ibrahim, David Ebuka Arthur, Samuel Ndaghiya Adawara

“…The present research was centered on the development of a predictive model aimed at assessing the responses of prostate tumors to a range of compounds within the scope of the pIC50 project. …”
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Analysing and Forecasting the Energy Consumption of Healthcare Facilities in the Short and Medium Term. A Case Study by Ali Koç, Serap Ulusam Seçkiner

“…Furthermore, all regression algorithms have undergone hyper-parameter optimisation using random search, grid search and Bayesian optimisation to achieve the minimum prediction errors represented by different metrics. …”
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Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model. by Gregory L Watson, Di Xiong, Lu Zhang, Joseph A Zoller, John Shamshoian, Phillip Sundin, Teresa Bufford, Anne W Rimoin, Marc A Suchard, Christina M Ramirez

“…We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. …”
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Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling. by Biche Osong, Eric Sribnick, Jonathan Groner, Rachel Stanley, Lauren Schulz, Bo Lu, Lawrence Cook, Henry Xiang

“…Data were split into two sets, where 80% developed a decision tree, and 20% tested predictive performance. We employed a conditional inference tree algorithm with bootstrap (B = 100) and grid search options to grow the decision tree and measure discrimination ability using the area under the curve (AUC) and calibration plots.…”
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Preoperative assessment of tertiary lymphoid structures in stage I lung adenocarcinoma using CT radiomics: a multicenter retrospective cohort study by Xiaojiang Zhao, Yuhang Wang, Mengli Xue, Yun Ding, Han Zhang, Kai Wang, Jie Ren, Xin Li, Meilin Xu, Jun Lv, Zixiao Wang, Daqiang Sun

“…Tumor segmentation was achieved using an automatic virtual adversarial training segmentation algorithm based on a three-dimensional U-shape convolutional neural network (3D U-Net). …”
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The association between inflammatory, metabolic, and hepatic fibrosis-related biomarkers and atrial fibrillation in older patients with metabolic dysfunction-associated steatotic l... by Shuai Zhang, Hao Liang, Jun Liu, Zhipeng Huang, Xijing Shi, Ye Zhu

“…The variable importance ranking results based on the Boruta algorithm, as well as the Receiver Operating Characteristic (ROC) curve and Decision Curve Analysis (DCA), all indicate that NFS and ALBI have high predictive performance and clinical benefits. …”
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Aspects of Developing A Methodology for Managing Digital Financial Assets by A. Yu. Proskuryakov

“…The subject of the study is methods, models and algorithms for automated monitoring and management of digital financial assets and cryptocurrencies. …”
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Human-Annotated Label Noise and Their Impact on ConvNets for Remote Sensing Image Scene Classification by Longkang Peng, Tao Wei, Xuehong Chen, Xiaobei Chen, Rui Sun, Luoma Wan, Jin Chen, Xiaolin Zhu

“…We found that 1) human-annotated label noise exhibits significant class and instance dependence; 2) an additional 1% of human-annotated label noise in training data leads to a 0.5% reduction in the overall accuracy of ConvNets classification; and 3) the error pattern of ConvNet predictions was strongly correlated with that of participant's labels. …”
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Comparative Analysis of a Quantum SVM With an Optimized Kernel Versus Classical SVMs by Matheus Cammarosano Hidalgo

“…Support Vector Machine (SVM) is a widely used algorithm for classification, valued for its flexibility with kernels that effectively handle non-linear problems and high-dimensional data. …”
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Planificación y optimización asistida por computadora de secuencias de ensamble mecánico // Computer aided Planning and optimization for mechanical assembly. by L. L. Tomás-García

“…The<br />generated assembly sequences are preprocessed and optimized for the assembly Process Planning<br />using Genetic Algorithms. This approach integrates the geometric and technological information of<br />the assembly process, which allows reducing the number of elements and sequences to be<br />processed with the consequent processing time and cost reduction.…”
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Modern fixation techniques versus traditional tension band wiring for olecranon fractures: a systematic review and meta-analysis of functional outcomes, healing time, and complicat... by Chengjing Wang, Changqing Li

“…Larger standardized trials are needed to confirm these preliminary conclusions and refine evidence-based treatment algorithms.…”
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Surface Clay and Mineral Exploration Using Hyperspectral Imaging: Advanced Techniques for Geospatial Analysis by S. Raut, N. Andure, A. Kadam, A. Dongare, A. Fulari, G. M. Sayyad, S. B. Sayyad, S. K. Shaikh, S. Sawant

“…The study employs advanced HSI processing techniques, including Minimum Noise Fraction (MNF) transformation for noise reduction and feature space optimization, and Pixel Purity Index (PPI) for endmember extraction, followed by mineral classification using Spectral Angle Mapper (SAM). …”
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Unveiling the power of high-dimensional cytometry data with cyCONDOR by Charlotte Kröger, Sophie Müller, Jacqueline Leidner, Theresa Kröber, Stefanie Warnat-Herresthal, Jannis Bastian Spintge, Timo Zajac, Anna Neubauer, Aleksej Frolov, Caterina Carraro, DELCODE Study Group, Frank Jessen, Simone Puccio, Anna C. Aschenbrenner, Joachim L. Schultze, Tal Pecht, Marc D. Beyer, Lorenzo Bonaguro

“…The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. …”
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Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma by Hehe Wang, Junge Zhang, Peng Cheng, Lujie Yu, Chunlin Li, Yaowen Wang

“…Differential expression analysis identified 293 differentially expressed genes (DEGs). Twelve ML algorithms (including Stepglm, glmBoost, and RF) were systematically combined into 113 distinct models to classify NPC versus normal tissues. …”
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