Staffing & Scheduling

A deep learning-driven discovery of berberine derivatives as novel antibacterial against multidrug-resistant Helicobacter pylori.

Helicobacter pylori (H. pylori) is currently recognized as the primary carcinogenic pathogen associa...

Say Real-time Linguistic Guidance Supports Novices in Writing Utterances for Conversational Agent Training.

Writing utterances to train conversational agents can be a challenging and time-consuming task, and ...

Artificial intelligence: Reducing inconsistency in the surgical residency application review process.

The incorporation of artificial intelligence (AI) into the general surgery residency recruitment pro...

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network.

Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis fo...

Developing an Effective Off-the-job Training Model and an Automated Evaluation System for Thoracoscopic Esophageal Atresia Surgery.

BACKGROUND: Pediatric minimally invasive surgery requires advanced technical skills. Off-the-job tra...

Evidence that robot-assisted gait training modulates neuroplasticity after stroke: An fMRI pilot study based on graph theory analysis.

OBJECTIVES: To investigate alterations of whole-brain network after stroke and therapeutic mechanism...

In vitro metabolic studies and machine learning analysis of mass spectrometry data: A dual strategy for differentiating alpha-pyrrolidinohexiophenone (α-PHP) and alpha-pyrrolidinoisohexanophenone (α-PiHP) in urine analysis.

Synthetic cathinones are some of the most prevalent new psychoactive substances (NPSs) globally, wit...

Development of an artificial neural network (ANN) for the prediction of a pilot scale mobile wastewater treatment plant performance.

Productive activities such as pig farming are a fundamental part of the economy in Mexico. Unfortuna...

ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification.

State-of-the-art deep learning models often fail to generalize in the presence of distribution shift...

Au-decorated TiCT/porous carbon immunoplatform for ECM1 breast cancer biomarker detection with machine learning computation for predictive accuracy.

Electrochemical immunosensors, surpassing conventional diagnostics, exhibit significant potential fo...

The effect of robot-assisted versus standard training on motor function following subacute rehabilitation after ischemic stroke - protocol for a randomised controlled trial nested in a prospective cohort (RoboRehab).

BACKGROUND: Body weight unloaded treadmill training has shown limited efficacy in further improving ...

Identifying Key Training Load and Intensity Indicators in Ice Hockey Using Unsupervised Machine Learning.

To identify key training load (TL) and intensity indicators in ice hockey, practice, and game data w...

Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation.

OBJECTIVES: This work aims to explore the impact of multicenter data heterogeneity on deep learning ...

Letter to the Editor: "The Combined Effect of Robot-assisted Therapy and Activities of Daily Living Training on Upper Limb Recovery in Persons With Subacute Stroke: A Randomized Controlled Trial".

Directly training temporal Spiking Neural Network with sparse surrogate gradient.

Brain-inspired Spiking Neural Networks (SNNs) have attracted much attention due to their event-based...

Continual pre-training mitigates forgetting in language and vision.

Pre-trained models are commonly used in Continual Learning to initialize the model before training o...

Synthesize Personalized Training for Robot-Assisted Upper Limb Rehabilitation With Diversity Enhancement.

For upper limb rehabilitation, the robot-assisted technique in combination with serious games requir...

Overground Gait Training With a Wearable Robot in Children With Cerebral Palsy: A Randomized Clinical Trial.

IMPORTANCE: Cerebral palsy (CP) is the most common developmental motor disorder in children. Robot-a...

Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder.

BACKGROUND: Buprenorphine for opioid use disorder (B-MOUD) is essential to improving patient outcome...

Discriminative and exploitive stereotypes: Artificial intelligence generated images of aged care nurses and the impacts on recruitment and retention.

This article uses critical discourse analysis to investigate artificial intelligence (AI) generated ...

Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy.

INTRODUCTION: Deep learning (DL) models predicting biomarker expression in images of hematoxylin and...