Research on the Method of Stabilizing the Power Output Fluctuation of Wind Farm by Electric Vehicles by Yu CHEN, Xiao PENG, Jing DING, Yazhou WANG, Jingping SHAO

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The ATLAS/NOA-29 study protocol: a phase III randomized controlled trial of anterior temporal lobectomy versus gross-total resection in newly-diagnosed temporal lobe glioblastoma by Matthias Schneider, Anna-Laura Potthoff, Yahya Ahmadipour, Valeri Borger, Hans Clusmann, Stephanie E. Combs, Marcus Czabanka, Lasse Dührsen, Nima Etminan, Thomas M. Freiman, Ruediger Gerlach, Florian Gessler, Frank A. Giordano, Eleni Gkika, Roland Goldbrunner, Erdem Güresir, Hussam Hamou, Peter Hau, Sebastian Ille, Max Jägersberg, Naureen Keric, Maryam Khaleghi-Ghadiri, Ralph König, Jürgen Konczalla, Harald Krenzlin, Sandro Krieg, Aaron Lawson McLean, Julian P. Layer, Jens Lehmberg, Vesna Malinova, Bernhard Meyer, Hanno S. Meyer, Dorothea Miller, Oliver Müller, Christian Musahl, Barbara E. F. Pregler, Ali Rashidi, Florian Ringel, Constantin Roder, Karl Rössler, Veit Rohde, I. Erol Sandalcioglu, Niklas Schäfer, Christina Schaub, Nils Ole Schmidt, Gerrit A. Schubert, Clemens Seidel, Corinna Seliger, Christian Senft, Julia Shawarba, Joachim Steinbach, Veit Stöcklein, Walter Stummer, Ulrich Sure, Ghazaleh Tabatabai, Marcos Tatagiba, Niklas Thon, Marco Timmer, Johannes Wach, Arthur Wagner, Christian Rainer Wirtz, Katharina Zeiler, Thomas Zeyen, Patrick Schuss, Rainer Surges, Christine Fuhrmann, Daniel Paech, Matthias Schmid, Yvonne Borck, Torsten Pietsch, Rafael Struck, Alexander Radbruch, Christoph Helmstaedter, Robert Németh, Ulrich Herrlinger, Hartmut Vatter

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Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates by Manzar Masud, Aamir Mubashar, Shahid Iqbal, Hassan Ejaz, Saad Abdul Raheem

“…Symmetric configurations with a uniform distribution of flax layers across the composite laminate exhibited better impact performance. …”
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Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach by Samrat Kumar Dey, Khandaker Mohammad Mohi Uddin, Arpita Howlader, Md. Mahbubur Rahman, Hafiz Md. Hasan Babu, Nitish Biswas, Umme Raihan Siddiqi, Badhan Mazumder

“…To address class imbalance, the Random Oversampling (ROS) technique is employed. Hyperparameter optimization is performed using GridSearchCV for various machine-learning models. …”
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To investigate the impact of different post-space disinfectants Nd: YAP laser, Radachlorin, and calcium oxide nanoparticles on smear layer removal efficacy and bond durability of P... by Saad Alresayes

“…Aims: Effect of post-space disinfectants Nd: YAP laser, Radachlorin® photosensitizer, and calcium oxide nanoparticles (CaONPs) on the smear layer (SL) removal efficacy and push out bond strength (PBS) of polyetheretherketone (PEEK) post to root dentin. …”
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Phase Masking and Time-Frequency Chaotic Encryption for DFMA-PON by Chongfu Zhang, Yangyang Yan, Tingwei Wu, Xiaoling Zhang, Guangjun Wen, Kun Qiu

“…In the proposed secure DFMA-PON, all digital orthogonal filters have different random phase and the random phase is controlled by a hyperchaotic Chen system. …”
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Predicting the high-strain-rate deformation behavior and constructing processing maps of 304L stainless steel through machine learning and deep learning by M. Ghaffari Farid, H.R. Abedi, R. Ghasempour, A. Taylor, S. Khoddam, P.D. Hodgson

“…The Artificial Neural Network (ANN) was tested with various hidden layer and neuron configurations. The optimal model was a three-layer architecture with an input layer, a hidden layer of 20 neurons, and an output layer. …”
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Apply Ridge Regression Model to Predict the Lateral Velocity Difference of Tight Reservoirs by HAN Longfei, ZHANG Yongfei, WANG Miaomiao, LI Yu

“…Accurately predicting the lateral wave time lag is the basis for calculating rock mechanics parameters in drilling and hydraulic fracturing construction design, but there is a large error in predicting the lateral wave time lag using empirical formula method and multiple regression method in water layer, oil layer, and porous dry layer. This error cannot meet the subsequent construction requirements. …”
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Noise Level Modulation for Secure Optical Communications by Stefano Caputo, Silvia Viciani, Stefano Gherardini, Giacomo Borghini, Francesco Cataliotti, Lorenzo Mucchi

“…Noise Level Modulation (NLM) is a robust physical-layer security technique which uses the injection of random phase noise into the transmitted signal to provide confidentiality in optical networks. …”
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Chinese Medical Named Entity Recognition Based on Context-Dependent Perception and Novel Memory Units by Yufeng Kang, Yang Yan, Wenbo Huang

“…The model also incorporates fully connected layers (FC) and conditional random fields (CRF) to further optimize the performance of entity classification and sequence labeling. …”
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CSI-based symmetric encryption end-to-end communication system by Yongli AN, Zongrui LI, Haifei BAI, Xueyu MI

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Spatial transcriptomics unravels the interactive network of aflatoxin B1-driven gastric cancer: Multi-omics integration of transcriptome and mendelian randomization by Yong-Le Li, Cong Liu, Meng Tang, Wan-Shou Wei, Xiao-Mei Xie, Yuan Gan, Xuan-Hua Chen, Shan-Shan Dong, Li-He Jiang

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Optical coherence tomography findings in beta-thalassemia major: a systematic review and Meta-analysis by Maryam Firdous, Muhammad Farooq Umer, Suriyakala Perumal Chandran

“…AIM: To describe the optical coherence tomography (OCT) findings of the retinal nerve fiber layer thickness (RNFLT) and choroidal thickness (CT) in beta-thalassemia major. …”
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Systematic review and network meta-analysis of retinal imaging biomarkers in neurodegenerative diseases: Correlation with brain changes by Farzaneh Nikparast, Zohreh Ganji, Hoda Zare, Nooshin Akbari-Sharak

“…Methods: We conducted an NMA using random-effects models to assess retinal layer thickness changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI). …”
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Vertical stratification-enabled early monitoring of cotton Verticillium wilt using in-situ leaf spectroscopy via machine learning models by Yi Gao, Yi Gao, Changping Huang, Changping Huang, Xia Zhang, Ze Zhang, Bing Chen

“…Results showed that spectral reflectance varied significantly by severity and layer, with the most pronounced variations in the bottom layer’s visible spectrum. …”
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An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

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LULC change detection and future LULC modelling using RF and MLPNN-Markov algorithms in the uMngeni catchment, KwaZulu-Natal, South Africa by Orlando Bhungeni, Michael Gebreslasie, Ashadevi Ramjatan

“…Hence, this work harnessed Landsat imageries and the Random Forests (RF) classification as well as a hybrid model from the Multi-Layer Perceptron and Markov chain (MLPNN-Markov) to detect changes in LULC and forecast future changes. …”
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Impact of Reset Pulse Width on Gradual Conductance Programming in Al₂O₃/TiO_x-Based RRAM by Hyeonseong Lim, Wonbo Shim, Tae-Hyeon Kim

“…This work investigates the impact of reset pulse width on multilevel conductance programming in Al₂O₃/TiO_x-based resistive random access memory. A 32 × 32 cross-point array of Ti (12 nm)/Pt (62 nm)/Al₂O₃ (3 nm)/TiO_x (32 nm)/Ti (14 nm)/Pt (60 nm) devices (2.5 µm × 2.5 µm active area) was fabricated via e-beam evaporation, atomic layer deposition, and reactive sputtering. …”
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The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning by Fatemeh Bahrambanan, Meysam Alizamir, Kayhan Moradveisi, Salim Heddam, Sungwon Kim, Seunghyun Kim, Meysam Soleimani, Saeid Afshar, Amir Taherkhani

“…Then, in this study, seven artificial intelligence models including decision tree, K-nearest neighbors, Adaboost, random forest, Gradient Boosting, multi-layer perceptron, and convolutional neural network were implemented to detect patients responder and non-responder to radiochemotherapy. …”
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Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery by Tarek Berghout, Eric Bechhoefer, Faycal Djeffal, Wei Hong Lim

“…The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. …”
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