Unsupervised and Semisupervised Machine Learning Frameworks for Multiclass Tool Wear Recognition by Maryam Assafo, Peter Langendoerfer

“…To address these issues, we propose different unsupervised and semisupervised five-class tool wear recognition frameworks to handle fully unlabeled and partially labeled data, respectively. The underlying methods include Laplacian score, sparse autoencoder (SAE), stacked SAE (SSAE), self-organizing map, Softmax, support vector machine, and random forest. …”
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How to utilize far-red photons effectively: substitution or supplementation with photosynthetically active radiation? A case study of greenhouse lettuce by Zhengnan Yan, Xin Li, Zhixin Li, Jinxiu Song, Haijie Dou, Yanjie Yang, Geng Zhang

“…Compared with that of NL, the stacking of thylakoids increased most intensely in response to the WR130 + FR50 and WR100 + FR30 treatments. …”
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Highly efficient, free-breathing whole-chest equilibrium phase bT1RESS MR angiography: Initial clinical experience by Robert R. Edelman, Amit Pursnani, Kevin Lee, Hang Chen, Reza Nezafat, Tess Wallace, Nondas Leloudas, Derek Clarke, Ioannis Koktzoglou

“…There was also excellent correlation (0.92/0.93, p<0.001) between left/right end-diastolic ventricular volumes obtained from short axis cine stacks vs. bT1RESS, and good-to-excellent correlation (0.84/0.64), p<0.001) for left/right end-diastolic atrial volumes. …”
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