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. …”

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

A novel RNN architecture to improve the precision of ship trajectory predictions by Martha Dais Ferreira (18704596)

“…To solve these challenges, Recurrent Neural Network (RNN) models have been applied to STP to allow scalability for large data sets and to capture larger regions or anomalous vessels behavior. …”

AI KRegression by CHIN-CHENG CHU (20718575)

Presentation 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip by Yan Cong (1491478)

Interaction plot with 95% confidence intervals generated from the GAM regression model 1 in Table 2 examining initial ED SBP and management group interactions. by Randeep S. Jawa (20489181)

GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)

Analysis of errors of the AI model on accuracy and decision times for different tasks during the retrospective reader study. (a) by Kerol Djoumessi (21349992)

Table 1_Horticultural performance and QTL mapping of snap bean (Phaseolus vulgaris L.) populations with organic and conventional breeding histories.xlsx by Hayley E. P. Richardson (21383333)

“…After developing linkage maps for each of our four populations, we identified QTL associated with days to germination, early-season vigor, root morphology, disease, days to flowering, and seed weight.</p>Discussion<p>This study lays the groundwork for improving snap bean germplasm for performance in organic systems by tracking the microevolutions created through long-term selection under organic or conventional management (i.e., breeding history). …”

Image 3_Horticultural performance and QTL mapping of snap bean (Phaseolus vulgaris L.) populations with organic and conventional breeding histories.jpeg by Hayley E. P. Richardson (21383333)

“…After developing linkage maps for each of our four populations, we identified QTL associated with days to germination, early-season vigor, root morphology, disease, days to flowering, and seed weight.</p>Discussion<p>This study lays the groundwork for improving snap bean germplasm for performance in organic systems by tracking the microevolutions created through long-term selection under organic or conventional management (i.e., breeding history). …”

Image 4_Horticultural performance and QTL mapping of snap bean (Phaseolus vulgaris L.) populations with organic and conventional breeding histories.jpeg by Hayley E. P. Richardson (21383333)

“…After developing linkage maps for each of our four populations, we identified QTL associated with days to germination, early-season vigor, root morphology, disease, days to flowering, and seed weight.</p>Discussion<p>This study lays the groundwork for improving snap bean germplasm for performance in organic systems by tracking the microevolutions created through long-term selection under organic or conventional management (i.e., breeding history). …”

Image 2_Horticultural performance and QTL mapping of snap bean (Phaseolus vulgaris L.) populations with organic and conventional breeding histories.jpeg by Hayley E. P. Richardson (21383333)

“…After developing linkage maps for each of our four populations, we identified QTL associated with days to germination, early-season vigor, root morphology, disease, days to flowering, and seed weight.</p>Discussion<p>This study lays the groundwork for improving snap bean germplasm for performance in organic systems by tracking the microevolutions created through long-term selection under organic or conventional management (i.e., breeding history). …”

Image 1_Horticultural performance and QTL mapping of snap bean (Phaseolus vulgaris L.) populations with organic and conventional breeding histories.jpeg by Hayley E. P. Richardson (21383333)

“…After developing linkage maps for each of our four populations, we identified QTL associated with days to germination, early-season vigor, root morphology, disease, days to flowering, and seed weight.</p>Discussion<p>This study lays the groundwork for improving snap bean germplasm for performance in organic systems by tracking the microevolutions created through long-term selection under organic or conventional management (i.e., breeding history). …”

Loss of the C1-FDX function results in CI and CV assembly defects. by Baoyin Chen (2635399)

Over-expression of C1-FDX increased the assembly of CI and CV. by Baoyin Chen (2635399)

Feature engineering. by Youli Jiang (15249070)

Inherently interpretable deep learning framework highlights clinically relevant image regions. by Kerol Djoumessi (21349992)

Meta data of <i>In vitro</i> and <i>In vivo</i> experiments. by Mohammad Abdullah Taher (18691717)

“…It is easily recognized by its compound leaves and long seed pods. Traditionally, it has been widely used in Ayurvedic medicine and as a nutrient-rich food source, with its leaves, pods, and seeds employed for treating malnutrition, inflammation, and over 300 ailments across South Asia and Africa. …”

Data Sheet 1_Differential regulation of soil microecology in crop rotation systems of maize, seed pumpkin, and processing tomato.docx by Xingxing Liu (715957)

“…To investigate the mitigation mechanisms of different crop rotation systems, this study established maize-tomato rotation (SZa), pumpkin (for seeds)-tomato rotation (SLa), and continuous cropping control (SSa) treatments in a long-term continuously cropped tomato field. …”