Staffing & Scheduling

Toward Improving Human Training by Combining Wearable Full-Body IoT Sensors and Machine Learning.

This paper proposes DigitalUpSkilling, a novel IoT- and AI-based framework for improving and personalising the training of workers who are involved in physical-labour-intensive jobs. DigitalUpSkilling uses wearable IoT sensors to observe how individuals perform work activities. Such sensor observations are continuously processed to synthesise an avatar-like kinematic model for each worker who is b...

Perceptions and experiences of Korean American older adults with companion robots through long-term use: a comparative analysis of robot retention vs. return.

To date, limited research has been conducted on technology use among socially marginalized groups, such as older immigrants who may have limited digital literacy. This pilot study aims to explore Korean American older adults' perceptions and experiences with a companion version of the social robot, Hyodol. We hypothesize that the Hyodol robot's social presence may facilitate technology use among t...

Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments.

Calcium oxalate (CaOx) nephrolithiasis constitutes approximately 75% of nephrolithiasis cases, resulting from the supersaturation and deposition of Ca...

Structural-based uncertainty in deep learning across anatomical scales: Analysis in white matter lesion segmentation.

This paper explores uncertainty quantification (UQ) as an indicator of the trustworthiness of automated deep-learning (DL) tools in the context of whi...

A novel hybrid variable cross layer-based machine learning model improves the accuracy and interpretation of energy intensity prediction of wastewater treatment plant.

Energy intensity (EI) prediction in wastewater treatment plants (WWTPs) suffers from inaccuracy and non-interpretability due to poor data quality, com...

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review.

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-bas...

Do I Write Like Artificial Intelligence?

This article discusses the limitations of artificial intelligence (AI) content detectors, citing studies by Flitcroft et al. The author shares persona...

Machine learning for improved medical device management: A focus on defibrillator performance.

BackgroundPoorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clin...

Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification.

Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing ...

Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. ...

OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler.

The material acceleration platform, empowered by robotics and artificial intelligence, is a transformative approach for expediting material discovery ...

Advanced Low-Cost Technology for Assessing Metal Accumulation in the Body of a Metropolitan Resident Based on a Neural Network Model.

This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term an...

Detection of mussels contaminated with cadmium by near-infrared reflectance spectroscopy based on RELS-TSVM.

Eating mussels contaminated with cadmium (Cd) can seriously harm health. In this study, a non-destructive and rapid detection method for Cd-contaminat...

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

OBJECTIVES: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clea...

Utilization of Machine Learning Models to More Accurately Predict Case Duration in Primary Total Joint Arthroplasty.

BACKGROUND: Accurate operative scheduling is essential for the appropriation of operating room esources. We sought to implement a machine learning mod...

Development of deep learning software to improve HPLC and GC predictions using a new crown-ether based mesogenic stationary phase and beyond.

The application of AI to analytical and separative sciences is a recent challenge that offers new perspectives in terms of data prediction. In this wo...

LTMSegnet: Lightweight multi-scale medical image segmentation combining Transformer and MLP.

Medical image segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. In recent years, neural network-b...

Sparse Coding Inspired LSTM and Self-Attention Integration for Medical Image Segmentation.

Accurate and automatic segmentation of medical images plays an essential role in clinical diagnosis and analysis. It has been established that integra...

Harnessing explainable artificial intelligence for patient-to-clinical-trial matching: A proof-of-concept pilot study using phase I oncology trials.

This study aims to develop explainable AI methods for matching patients with phase 1 oncology clinical trials using Natural Language Processing (NLP) ...

Deep-AutoMO: Deep automated multiobjective neural network for trustworthy lesion malignancy diagnosis in the early stage via digital breast tomosynthesis.

Breast cancer is the most prevalent cancer in women, and early diagnosis of malignant lesions is crucial for developing treatment plans. Digital breas...