An Optimized Method for Single Cell Cloning of Human CAR-T Cells Based on FBS-Coated Plates by Mahdie Jafari, Shahriyar Abdoli, Masoud Moghaddam Pour, Mohammad Ali Shokrgozar, Zahra Sharifzadeh

“…Despite significant improvements in CAR-T cell production processes, the isolation and expansion of CAR-engineered T cells continue to pose significant challenges. …”
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Development and validation of machine learning classifiers for predicting treatment-needed retinopathy of prematurity by Nasser Shoeibi, Majid Abrishami, Seyedeh Maryam Hosseini, Mohammad-Reza Ansari-Astaneh, Razieh Farrahi, Bahareh Gharib, Fatemeh Neghabi, Mojtaba Abrishami, Mehdi Sakhaee, Mehrdad Motamed Shariati

“…Conclusion While AI can enhance decision-making processes by providing real-time risk assessments, these tools must be used to augment—not replace—clinical judgment. …”
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Bioinformatic identification of signature miRNAs associated with fetoplacental vascular dysfunction in gestational diabetes mellitus by Yulan Lu, Chunhong Liu, Xiaoxia Pang, Xinghong Chen, Chunfang Wang, Huatuo Huang

“…This study aims to identify signature miRNA genes involved in this process using bioinformatics analysis via multiple algorithms. …”
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EMOTIONS, FEELINGS AND AFFECTS IN EARLY FRENCH ROMANTICISM: PROBLEMS OF STUDY (the first article) by Ninel A. Litvinenko

“…The study of the heritage of French Romanticism, which went through several stages of evolution in the 19th century, the interaction with other literary movements, such as Sentimentalism, Realism, Symbolism, Naturalism, is in touch with these processes. Feelings, sensations, impressions, real and fictitious, all vibrations and threads connecting the individual with society and the world, the states experienced by him acquire in Romanticism an invaluable and valuable dimension, penetrating deeply into the sphere of moral issues, morality, personal and social, forming new vectors of evolution of psychology, philosophy and ethics. …”
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Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF by Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI

“…Also, the proposed model's training process is more stable, and the entire curve converges faster, which can achieve higher accuracy with fewer training batches. …”
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Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF by Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI

“…Also, the proposed model's training process is more stable, and the entire curve converges faster, which can achieve higher accuracy with fewer training batches. …”
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Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography by Xiaodong Sheng, Tao Fan, Xiaoqi Jin, Jing Jin, Zhixian Chen, Guanqun Zheng, Min Lu, Zongcheng Zhu

“…The experimental results show that the method can accurately extract the blood vessel center line, direction vector and other information in the coronary angiography image without manual intervention, and can be used in the computer-assisted diagnosis and treatment process of clinical cardiovascular disease.…”
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Stochastic differential equation modeling approach for grading astrocytomas on brain MRI images by Mahsa Raisi-Nafchi, Mahnoosh Tajmirriahi, Hossein Rabbani, Zahra Amini

“…The method is implemented in both 1D and 2D processing modes, with customized preprocessing for each. …”
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Machine learning-based assessment of flood susceptibility in the Eastern Mediterranean: a case study of Baniyas River basin by Hazem Ghassan Abdo, Sahar Mohammed Richi, Taorui Zeng, Saeed Alqadhi, Pankaj Prasad, Thong Nguyen-Huy, Maged Muteb Alharbi, Javed Mallick

“…The multicollinearity test results showed that all selected factors can be incorporated into the modelling process. Additionally, all the applied algorithms showed reliable and accurate performance in modeling flood susceptibility; however, the XGBoost algorithm achieved the strongest performance compared to other models with an AUC value of 0.98. …”
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Robot-Based Diver Safety Support Scenario and Concept Design for NADIA by Hyungjoo Kang, Gun Rae Cho, Hansol Jin, Min-Gyu Kim, Ji-Hong Li, Seongho Jin, Jungsoo Lee, Jeongtack Min

“…To implement these functions, design elements such as modularization and vector arrangement of propulsion units were derived. …”
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Roller Bearing Fault Diagnosis Based on Adaptive Sparsest Narrow-Band Decomposition and MMC-FCH by Yanfeng Peng, Junhang Chen, Yanfei Liu, Junsheng Cheng, Yu Yang, He Kuanfang, Guangbin Wang, Yi Liu

“…ASNBD obtains the local narrow-band (LNB) components during the optimization process. Firstly, an optimal filter is designed. The parameter vector in the filter is obtained during optimization. …”
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Construction of Analogy Indicator System and Machine-Learning-Based Optimization of Analogy Methods for Oilfield Development Projects by Muzhen Zhang, Zhanxiang Lei, Chengyun Yan, Baoquan Zeng, Fei Huang, Tailai Qu, Bin Wang, Li Fu

“…Using an initial set of basic indicators and a database of 1436 oilfield samples, a combined subjective–objective weighting strategy that integrates statistical methods with expert judgment is used to select, classify, and assign weights to the indicators. This process results in 26 key indicators for practical analogy analysis. …”
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Dynamic Classifier Auditing by Unsupervised Anomaly Detection Methods: An Application in Packaging Industry Predictive Maintenance by Fernando Mateo, Joan Vila-Francés, Emilio Soria-Olivas, Marcelino Martínez-Sober, Juan Gómez-Sanchis, Antonio José Serrano-López

“…The central innovation lies in a two-stage process: a classifier generates a binary decision on whether a machine requires maintenance, and an unsupervised anomaly detection module subsequently audits the classifier’s probabilistic output to refine and interpret its predictions. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…Including NMR T2 data as an input feature enhanced the model's ability to capture multimodal peaks in unconventional rocks, making the prediction problem overdetermined. By predicting vector functions from vector input features, we effectively reduced prediction errors. …”
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Reconstruction and prediction of tunnel surrounding rock deformation data based on PSO optimized LSSVR and GPR models by Zhenqian Huang, Zhen Huang, Pengtao An, Jun Liu, Chen Gao, Juncai Huang

“…Predicting the deformation of surrounding rock is an important task to ensure the safety of mountain tunnel construction.This study, set against the backdrop of an actual under-construction tunnel, reconstructed the missing surrounding rock monitoring data using a Particle Swarm Optimization-based Least Squares Support Vector Regression model (PSO-LSSVR), and subsequently predicted the tunnel surrounding rock deformation using the constructed Gaussian Process Regression model (PSO-GPR).The research results indicate that the average relative error of the PSO-LSSVR reconstruction model is 1.21 %, lower than the 4.82 % of the LSSVR reconstruction model and the 4.69 % of the BP reconstruction model. …”
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Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-Based BCIs by Yue Zhang, Sheng Quan Xie, Chaoyang Shi, Jun Li, Zhi-Qiang Zhang

“…The relationships between the training trial, the individual template, and the artificially constructed reference are involved in the training process. The spatial filters are applied to the above templates to form two new transferred templates, and the transferred spatial filters are obtained accordingly via the least-square regression. …”
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Tensor Calculus in Digital Colorimetry by Y. N. Saukova, M. A. Hundzina

“…From the point of view of measurement, a digital image can be represented by a combinatorial model of an information and measurement channel with the appearance of the phenomenon of a color covariance hypercube requiring a significant amount of memory for data storage and processing. The transition from the covariance hypercube to high-dimensional matrices and tensors of the first, second and higher ranks provides the prospect of optimizing the color parameters of a digital image by the criterion of information entropy.Tensor calculus provides opportunities for expanding the dynamic range in color measurements describing multidimensional vector fields and quantized spaces with indexing tensors and decomposing them into matrices of low orders.The proposed complex approach based on tensor calculus. …”
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Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting by Bambang Kun Cahyono, Muhammad Hidayatul Ummah, Ruli Andaru, Neil Andika, Adjie Pamungkas, Hepi Hapsari Handayani, Paramita Atmodiwirjo, Rory Nathan

“…This study demonstrated that utilizing openly accessible atmospheric datasets processed through the GEE could yield reliable rainfall predictions, facilitating informed decisions on a wide scale. …”
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Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach by David Carrascal, Paula Bartolomé, Elisa Rojas, Diego Lopez-Pajares, Nicolas Manso, Javier Diaz-Fuentes

“…A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the grid. …”
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Measurement-Based Neural Network Technique for Modeling the Low-Frequency Electric Field Radiated Behavior of Satellite Units by Anna N. Lampou, Anargyros T. Baklezos, Konstantinos K. Spyridakis, Dimitrios A. Rigas-Papakonstantinou, Ioannis O. Vardiambasis, Christos D. Nikolopoulos

“…Contrary to the previous time-consuming post-process methodologies, the proposed electric dipole neural network (<i>EDMnet</i>) can offer a real-time characterization of the measured unit (Device Under Test) after a proper training stage, especially as a fast pre-compliance method. …”
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