Integrating bulk and single-cell RNA sequencing reveals intratumor heterogeneity phenotypes and immune infiltration in pancreatic cancer by Li Zhao, Yao Huang, Jiang Li, Rui Xie, Kaiming Li

“…Methods Pancreatic cancer transcriptomic data were acquired from the TCGA and GEO databases. The DEPTH2 algorithm, in combination with differential expression analysis, was used to identify genes associated with intratumoral heterogeneity (ITH). …”
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CMSS1: A RNA binding protein with pivotal roles in non-small cell lung cancer progression and prognosis by Zhe Fan, Wanyu Liu, Zhiwei Gao, Youfa Liu, Hongyang Hai, Zhenyang Lv

“…This study may provide potential guidance for precision therapy and accurate prognosis prediction for patients with NSCLC.…”
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Heart Rate Variability in Pulmonary Arterial Hypertension by Bishal Gyawali, Dominick Roto, Michael Lachant, R. James White, Daniel Lachant

“…HRV was calculated using MC10 proprietary algorithm. Baseline, follow up, and changes in heart rate and HRV (rest and sleep) were compared between the groups and correlated to clinical outcomes at 2 years. …”
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Using a graph to schematize the test results of mining dump trucks by Dianov V.A., Bokarev A.I., Kartashov A.B., Dubinkin D.M., Pashkov D.A.

“…At the moment, there is no single algorithm for processing and using test results, so the authors of the article decided to offer their own method for schematizing test results, which can later be used to solve both general and specific problems. …”
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Building Thermal Comfort Research Based on Energy-Saving Concept by Feiran Xue, Jingyuan Zhao

“…In the experimental part, a model based on the concept of energy saving was designed to predict and analyze the energy consumption and thermal comfort effects of the building. …”
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Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method by Jinxi Su, Rong Tang, Huilong Lin

“…Given these challenges, the objectives of this study were to develop a suitable assessment and prediction model for soil erosion tailored to the SRYR’s needs. …”
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Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography by Arpitha Ravi, Philipp Bernhardt, Mathis Hoffmann, Florian Kordon, Siming Bayer, Stephan Achenbach, Andreas Maier

“…The proposed deep learning model accurately predicts cardiac anatomy for cine acquisitions, enabling the potential for quick and automatic selection of imaging parameters to optimize image quality and reduce radiation exposure. …”
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Mapping forest-agroforest frontiers in the Peruvian Amazon with deep learning and PlanetScope satellite data by Wanting Yang, Daniel Ortiz-Gonzalo, Xiaoye Tong, Dimitri Gominski, Rasmus Fensholt

“…Reducing the target classes from seven to four was found to considerably improve the accuracy of the predictions. Our findings contribute to distinguishing agroforests from forests at a large scale, providing insights into previously undetected tree-covered land uses and thus informing on sustainable ecosystem management. …”
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Federated learning for digital twin applications: a privacy-preserving and low-latency approach by Jie Li, Dong Wang

“…The digital twin (DT) concept has recently gained widespread application for mapping the state of physical entities, enabling real-time analysis, prediction, and optimization, thereby enhancing the management and control of physical systems. …”
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FORMATION OF RUSSIAN NATIONAL IDENTITY OF HIGH SCHOOL STUDENTS: HERMENEUTICAL APPROACH by Alfia F. Zakirova, Larissa A. Semenova

“…The methods of the techniques of a script, scenario requirements (a text as a source of a script for reflection and dialogue) and the role of expectations (presentation to a learner of algorithm of expected behavior from him in order to achieve mutual understanding in interaction) are used in the course of experimental investigations.Results. …”
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Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic acc... by Hanneke F M Rhodius-Meester, Ingrid S van Maurik, Juha Koikkalainen, Antti Tolonen, Kristian S Frederiksen, Steen G Hasselbalch, Hilkka Soininen, Sanna-Kaisa Herukka, Anne M Remes, Charlotte E Teunissen, Frederik Barkhof, Yolande A L Pijnenburg, Philip Scheltens, Jyrki Lötjönen, Wiesje M van der Flier

“…Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.…”
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Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier by Ali Saeed Almuflih, Ilyos Abdullayev, Sergey Bakhvalov, Rustem Shichiyakh, Bibhuti Bhusan Dash, K. B. V. Brahma Rao, Kritika Bansal

“…DL-based models, particularly convolutional neural networks (CNN) with regularization techniques, direct this problem, offer a superior prediction value with unidentified data, and prevent over-fitting. …”
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Evaluating Radio Occultation (RO) Constellation Designs Using Observing System Simulation Experiments (OSSEs) for Ionospheric Specification by Nicholas Dietrich, Tomoko Matsuo, Chi‐Yen Lin, Brandon diLorenzo, Charles Chien‐Hung Lin, Tzu‐Wei Fang

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Optimization of ATIG Weld Based on a Swarm Intelligence Approach: Application to the Design of Welding in Selected Manufacturing Processes by Kamel Touileb, Sahbi Boubaker

“…Based on the numerical observations, linear and nonlinear models for describing the effect of the thermophysical parameters on the weld characteristics were tuned using a particle swarm optimization algorithm. While the linear model provided good prediction accuracy, the nonlinear exponential model outperformed the linear one for the depth yielding a mean absolute percentage error of 17%, a coefficient of determination of 0.8266, and a root mean square error of 0.9665 mm. …”
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Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) by Leonardo Volpato, Evan M. Wright, Francisco E. Gomez

“…The CNN-LSTM model showed high accuracy in RM prediction across various conditions, outperforming traditional image preprocessing approaches. …”
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Grey-Box Modelling of District Heating Networks Using Modified LPV Models by Olamilekan E. Tijani, Sylvain Serra, Patrick Lanusse, Rachid Malti, Hugo Viot, Jean-Michel Reneaume

“…Given the dynamic nature of DHNs and the challenges in predicting disturbances, a dynamic real-time optimisation (DRTO) approach is proposed. …”
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AI-powered simulation-based inference of a genuinely spatial-stochastic gene regulation model of early mouse embryogenesis. by Michael Alexander Ramirez Sierra, Thomas R Sokolowski

“…Leveraging the power of Simulation-Based Inference (SBI) through the AI-driven Sequential Neural Posterior Estimation (SNPE) algorithm, we conduct a large-scale Bayesian inferential analysis to identify parameter sets that faithfully reproduce experimentally observed features of ICM specification. …”
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FCB-YOLOv8s-Seg: A Malignant Weed Instance Segmentation Model for Targeted Spraying in Soybean Fields by Zishang Yang, Lele Wang, Chenxu Li, He Li

“…The results of ablation experiments revealed that the FCB-YOLOv8s-Seg model achieved a mean average precision of 95.18% for bounding box prediction and 96.63% for segmentation, marking an increase of 5.08% and 7.43% over the original YOLOv8s-Seg model. …”
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Geo-Information Modeling for Determining the Movement of Human Capital by R. V. Fattakhov, P. V. Stroev, M. M. Nizamutdinov, O. V. Pivovarova, M. I. Akhmetzyanova

“…The authors developed the scheme of the algorithm for the functioning of GIS monitoring the attractiveness of municipalities of the Republic of Bashkortostan, as well as they introduced the interface and user tools. …”
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Grapes leaf disease dataset for precision agricultureMendeley Data by Madhuri Dharrao, Nilima Zade, R. Kamatchi, Rakesh Sonawane, Rabinder Henry, Deepak Dharrao

“…This High-quality annotated image dataset can help to design standard advanced AI models for automated disease detection, classification, and prediction. The dataset was validated through a transfer learning approach using the ResNet-18 algorithm and demonstrated the remarkable classification accuracy of 96 %. …”
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