Design of stiffened panels for stress and buckling via topology optimization: data by Sheng Chu (19655605)

“…<p>This paper "Design of stiffened panels for stress and buckling via topology optimization" investigates the weight minimization of stiffened panels simultaneously optimizing sizing, layout and topology under stress and buckling constraints. …”

Data_Sheet_3_Deciphering the Subtype Differentiation History of SARS-CoV-2 Based on a New Breadth-First Searching Optimized Alignment Method Over a Global Data Set of 24,768 Sequen... by Qianyu Lin (4687312)

Data_Sheet_4_Deciphering the Subtype Differentiation History of SARS-CoV-2 Based on a New Breadth-First Searching Optimized Alignment Method Over a Global Data Set of 24,768 Sequen... by Qianyu Lin (4687312)

Data_Sheet_2_Deciphering the Subtype Differentiation History of SARS-CoV-2 Based on a New Breadth-First Searching Optimized Alignment Method Over a Global Data Set of 24,768 Sequen... by Qianyu Lin (4687312)

Data_Sheet_1_Deciphering the Subtype Differentiation History of SARS-CoV-2 Based on a New Breadth-First Searching Optimized Alignment Method Over a Global Data Set of 24,768 Sequen... by Qianyu Lin (4687312)

Data_Sheet_6_Deciphering the Subtype Differentiation History of SARS-CoV-2 Based on a New Breadth-First Searching Optimized Alignment Method Over a Global Data Set of 24,768 Sequen... by Qianyu Lin (4687312)

Data_Sheet_5_Deciphering the Subtype Differentiation History of SARS-CoV-2 Based on a New Breadth-First Searching Optimized Alignment Method Over a Global Data Set of 24,768 Sequen... by Qianyu Lin (4687312)

Data_Sheet_1_Selecting and Scaling of Energy-Compatible Ground Motion Records.DOCX by George Papazafeiropoulos (7120757)

Flow Φ of the controlled continuous time PDMP. by Benoîte de Saporta (20454662)

“…Tools to assist doctors in treatment decisions and scheduling follow-ups based on patient preferences and medical data would be highly beneficial. These tools should incorporate realistic models of disease progression under treatment, multi-objective optimization of treatment strategies, and efficient algorithms to personalize follow-ups by considering patient history. …”

Table_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf by Jenish Maharjan (11998331)

“…</p>Methods<p>A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …”

Image_1_Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm.pdf by Jenish Maharjan (11998331)

“…</p>Methods<p>A retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. …”

Data_Sheet_1_Early Prediction of Cardiogenic Shock Using Machine Learning.PDF by Yale Chang (11701611)

“…The algorithm was trained on 8 years of de-identified data (from 2010 to 2017) collected from a large regional healthcare system. …”

Data_Sheet_1_Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU.docx by Elham Jamshidi (11013993)

“…<p>Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.</p><p>Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission.…”

Data Sheet 1_A machine learning model for predicting the risk of diabetic nephropathy in individuals with type 2 diabetes mellitus.docx by Tingting Li (10553)

“…In this study, leveraging extensive clinical datasets, we sought to develop and validate a predictive model employing machine learning techniques to assess the risk of DKD in patients with type 2 diabetes mellitus (T2DM).</p>Research design and methods<p>We conducted a retrospective collection of clinical data from 10,057 patients diagnosed with T2DM at Shijiazhuang Second Hospital. …”

Data_Sheet_1_Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based SNN.PDF by Salah Daddinounou (18565915)

Data_Sheet_1_Validation of a Triplex Quantitative Polymerase Chain Reaction Assay for Detection and Quantification of Traditional Protein Sources, Pisum sativum L. and Glycine max... by Adam C. Faller (10865430)

“…Ratios based on mass of protein powder were also tested, resulting in non-linear patterns in data that suggested the requirement of further sample preparation optimization or algorithmic correction. …”

Table 1_Machine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers.docx by Lei Hua (465535)

“…The combination of rigorous validation and user-centered design positions this model as a valuable clinical decision-support tool for optimizing personalized perioperative care.…”