Design and evaluation of blended teaching in the smart classroom combined with virtual simulation training in basic nursing courses by Ya Meng, Jian Song, Xiaojing Yu, Xiaoxia Xu, Hao Zhang

“…Abstract Objective This study explored the application effect of smart classrooms combined with virtual simulation training in basic nursing courses for nursing undergraduates. …”
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Effects of Biological Age on Athletic Adaptations to Combined Plyometric and Sprint with Change of Direction with Ball Training in Youth Soccer Players by Hamza Marzouki, Samar Sbai, Ibrahim Ouergui, Okba Selmi, Marilia S. Andrade, Ezdine Bouhlel, Mabliny Thuany, Katja Weiss, Pantelis T. Nikolaidis, Beat Knechtle

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Short- and Long-Term Effectiveness of Low-Level Laser Therapy Combined with Strength Training in Knee Osteoarthritis: A Randomized Placebo-Controlled Trial by Martin Bjørn Stausholm, Ingvill Fjell Naterstad, Patricia Pereira Alfredo, Christian Couppé, Kjartan Vibe Fersum, Ernesto Cesar Pinto Leal-Junior, Rodrigo Álvaro Brandão Lopes-Martins, Jon Joensen, Jan Magnus Bjordal

“…We conducted a randomized clinical trial to investigate the short- and long-term effectiveness of LLLT combined with strength training in persons with KOA. …”
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Combined Mass Spectrometry and Histopathology Imaging for Perioperative Tissue Assessment in Cancer Surgery by Laura Connolly, Amoon Jamzad, Martin Kaufmann, Catriona E. Farquharson, Kevin Ren, John F. Rudan, Gabor Fichtinger, Parvin Mousavi

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Coefficient of variation method combined with XGboost ensemble model for wheat growth monitoring by Xinyan Li, Changchun Li, Fuchen Guo, Xiaopeng Meng, Yanghua Liu, Fang Ren

“…The three models of Random Forest, Ridge Regression and XGBoost were used to construct the wheat growth inversion model with the best effect at the flowering stage, and the XGBoost model had the highest inversion accuracy when comparing in the same period, with the training and test sets reaching 0.904 and 0.870, and the RMSEs were 0.050 and 0.079, so that the XGBoost model can be used as an effective method of monitoring the growth of wheat. …”
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Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16 by Xuli Zan, Xinlu Zhang, Ziyao Xing, Wei Liu, Xiaodong Zhang, Wei Su, Zhe Liu, Yuanyuan Zhao, Shaoming Li

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A DEEP LEARNING APPROACH FOR CROP TYPE MAPPING BASED ON COMBINED TIME SERIES OF SATELLITE AND WEATHER DATA by N. Addimando, M. Engel, F. Schwarz, M. Batič

“…We exploit the potential of FlexMod to test different feature extractors, temporal encoding frameworks and decoders and we present a comparison between results obtained training a long-short term memory (LSTM) implementation (Breizhcrops, Rußwurm et al. 2020) and a Self-attention transformer model (Vaswani et al. 2017), the latter showing the best performances with accuracy 0.904 and Cohen’s kappa 0.824. …”
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Efficacy of Repetitive Transcranial Magnetic Stimulation (rTMS) Combined with Psychological Interventions: A Systematic Review and Meta-Analysis of Randomized Controlled Trials by Xiaomin Xu, Mei Xu, Yon Su, Thanh Vinh Cao, Stevan Nikolin, Adriano Moffa, Colleen Loo, Donel Martin

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Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Ho... by Mi He, Yubao Lu, Lei Zhang, Hehua Zhang, Yushun Gong, Yongqin Li

“…RESULTS:A total of61 (61.0%) patients required subsequent shocks (N = 173) in the validation dataset. Combining AMSA with PSI and ΔAMSA obtained highest AUC (0.904 vs. 0.819, p<0.001) among different combination approaches for subsequent shocks. …”
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The Added Value of MRI-Based Targeted Biopsy in Biopsy-Naïve Patients: A Propensity-Score Matched Comparison by Gernot Ortner, Charalampos Mavridis, Veronika Fritz, Jörg Schachtner, Charalampos Mamoulakis, Udo Nagele, Theodoros Tokas

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Ultrasound identification of hepatic echinococcosis using a deep convolutional neural network model in China: a retrospective, large-scale, multicentre, diagnostic accuracy study by Yongfeng Yang, MS, Yangdan Cairang, ProfBS, Tian'an Jiang, ProfMD, Jianhua Zhou, ProfPhD, Li Zhang, ProfMS, Baowen Qi, MS, Shumei Ma, ProfMS, Lina Tang, ProfBS, Dong Xu, ProfMD, Lingdai Bu, ProfBS, Rui Bu, ProfMD, Xiang Jing, ProfMD, Hui Wang, ProfMD, Zubang Zhou, ProfMS, Cheng Zhao, ProfMS, Baoming Luo, ProfMD, Liwen Liu, ProfMD, Jianqin Guo, ProfMS, Yuzhen Nima, ProfBS, Guoyong Hua, ProfMS, Zengcheng Wa, ProfBS, Yuying Zhang, ProfBS, Guoyi Zhou, BS, Wen Jiang, MS, Changcheng Wang, MS, Yang De, BS, Xiaoling Yu, ProfMD, Zhigang Cheng, ProfMD, Zhiyu Han, ProfMD, Fangyi Liu, ProfMD, Jianping Dou, ProfMD, Hui Feng, ProfMS, Chong Wu, MS, Ruifang Wang, MD, Jie Hu, BS, Qi Yang, MD, Yanchun Luo, BS, Jiapeng Wu, MD, Haining Fan, Ping Liang, MD, Jie Yu, MD

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Proposal of an Algorithm to Early Detect Attenuated Type I Mucopolysaccharidosis (MPS Ia) among Children with Growth Abnormalities by Federico Baronio, Stefano Zucchini, Francesco Zulian, Mariacarolina Salerno, Rossella Parini, Alessandro Cattoni, Federica Deodato, Alberto Gaeta, Carla Bizzarri, Serena Gasperini, Andrea Pession

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