Training-induced changes in bipedal stance. by Cristina Dolciotti (16317330)

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Content analysis of groups with decreased confidence. by Yasutaka Yanagita (11105508)

Mean ± SD of the different parameters relative to bipedal stance on soft support with the eyes open recorded in the Chewing and Hand Grip groups. by Cristina Dolciotti (16317330)

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Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

Relation between training-related changes in unipedal and bipedal stance. by Cristina Dolciotti (16317330)

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Time course of Path Length, LFS and Velocity during postural training. by Cristina Dolciotti (16317330)

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Time course of 95% Area, X and Y Amplitude Sway during postural training. by Cristina Dolciotti (16317330)

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Correlations between unipedal and bipedal stance parameters recorded at the beginning of the training and of the control session. by Cristina Dolciotti (16317330)

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Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

Relation of the changes in Y Sway Amplitude and LFS induced by postural training with those in 95% Area. by Cristina Dolciotti (16317330)

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Between-group differences in 95% Area, Y Sway Amplitude and LFS during postural training. by Cristina Dolciotti (16317330)

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Consolidation of training-induced changes in unipedal stance: 95% Area, Y Sway Amplitude and LFS. by Cristina Dolciotti (16317330)

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Consolidation of training-induced changes in unipedal stance: Path Length, X Sway Amplitude and Velocity. by Cristina Dolciotti (16317330)

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Mean ± SD values of unipedal stance parameters evaluated at the different time points of the postural training session. by Cristina Dolciotti (16317330)

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Distribution of scaling factor γ after 100 rounds of sparsity training with different sparsity rates λ. by Zhongjian Xie (4633099)

Flow diagram. by Cristina Dolciotti (16317330)

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Step parameters for Unsupported Walking (UW), Body Weight Support (BWS, upper panel), Mediolateral Forces (ML, middle panel) and Anteroposterior Forces (AP, lower panel). by Sanne Ettema (18786312)

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Experimental protocol consisting of four blocks (Unsupported Walking (UW), Body Weight Support (BWS), BWS + Mediolateral (ML) Forces, BWS + Anteroposterior (AP) Forces), separated... by Sanne Ettema (18786312)

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AdaBoost training flowchart. by Yang Guo (399435)

“…HINGO enhances the uniformity of the initial population distribution by means of refraction opposition-based learning strategy in initialization, and improves the local and global search ability of the algorithm by means of sine and cosine strategy, Lévy flight and nonlinear decreasing factor in the search stage. The HINGO-LSSVM-IAdaBoost model is trained and validated on the typical air conditioning fault samples of ASHRAE RP-1043. …”

Information of dataset with number of images in each class for training, validation, and testing. by Afira Aslam (21433666)