Algorithm operation steps. by Junyan Wang (4738518)

“…Specifically, to tackle the difficulties associated with small object detection, we replace conventional convolutions in the Backbone with SPDConv modules to enhance feature capture capabilities for small targets and low-resolution images while reducing computational overhead. …”

Includes a file with the results on step parameters. by Sanne Ettema (18786312)

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This is the data set that we used to reach the conclusions drawn in the manuscript with related metadata and methods. by Farayi Kaseke (19560197)

Data Sheet 1_Bio-functional hydrogel coated membranes to decrease T-cell exhaustion in manufacturing of CAR T-cells.docx by Aida López Ruiz (21611177)

“…To address this need, in this work, we utilized a bioinspired, scalable, tunable platform to direct T-cell activation and decrease exhaustion during CAR T production.…”

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. …”

Uganda 2023 NCD risk factor STEPS survey data. by Ronald Kusolo (13200268)

“…Both surveys drew countrywide samples, and the World Health Organization’s STEPS tool was used to collect the data. We calculated weighted prevalence of the following NCD risk factors: high blood pressure, high blood glucose, overweight and obesity, current alcohol consumption, current tobacco use, inadequate consumption of fruits and vegetables, inadequate physical activity, and sedentariness. …”

Uganda 2014 NCD risk factor STEPS survey data. by Ronald Kusolo (13200268)

“…Both surveys drew countrywide samples, and the World Health Organization’s STEPS tool was used to collect the data. We calculated weighted prevalence of the following NCD risk factors: high blood pressure, high blood glucose, overweight and obesity, current alcohol consumption, current tobacco use, inadequate consumption of fruits and vegetables, inadequate physical activity, and sedentariness. …”

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

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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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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Mean muscle activation patterns across the gait cycle during walking with mediolateral (ML) forces for <i>A)</i> able-bodied individuals and <i>B)</i> individuals after stroke duri... by Sanne Ettema (18786312)

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Includes a file with results on electromyography. by Sanne Ettema (18786312)

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Characteristics of the study population. by Sanne Ettema (18786312)

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Mean muscle activation patterns across the gait cycle during walking with anteroposterior (AP) forces for <i>A)</i> able-bodied individuals and <i>B)</i> individuals after stroke d... by Sanne Ettema (18786312)

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Mean muscle activation patterns across the gait cycle during walking with Body Weight Support (BWS) for <i>A)</i> able-bodied individuals and <i>B)</i> individuals after stroke dur... by Sanne Ettema (18786312)

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The impact of different diffusion model time steps on the model. by Weijia Zhu (65481)

Stepped-Wedge Trial Diagram. by Sugy Choi (800862)

“…This report specifically focuses on Project 3, which involves a stepped-wedge trial with 35 clinics receiving a quality management intervention that includes performance coaching. …”

Time step optimization can greatly improve the efficiency of training and inference. by Weijia Zhu (65481)

Expression of lipid biosynthetic genes is decreased with polyamine depletion. by Yazmin E. Cruz-Pulido (20285238)

Step and trend changes in the percentage of unmet needs for healthcare during and after the COVID-19 lockdown, compared to the pre-lockdown period (n = 259,501). by Rodrigo Vargas-Fernández (10164344)