Staffing & Scheduling

Multi-Robot Preemptive Task Scheduling with Fault Recovery: A Novel Approach to Automatic Logistics of Smart Factories.

This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as Multi-Robot Preemptive Task Scheduling with Fault Recovery (MRPF). It considers the interaction between running processes and their tasks for management at each new ...

Application of Reinforcement Learning Algorithm Model in Gas Path Fault Intelligent Diagnosis of Gas Turbine.

Gas turbine is widely used because of its advantages of fast start and stop, no pollution, and high thermal efficiency. However, the working environment of high temperature, high pressure, and high speed makes the gas turbine prone to failure. The traditional gas path fault intelligent diagnosis scheme of the gas turbine has the problems of poor control effect and low scheduling accuracy. Experime...

Evaluating a new verbal working memory-balance program: a double-blind, randomized controlled trial study on Iranian children with dyslexia.

BACKGROUND: It is important to improve verbal Working Memory (WM) in reading disability, as it is a key factor in learning. There are commercial verba...

The impact of training sample size on deep learning-based organ auto-segmentation for head-and-neck patients.

To investigate the impact of training sample size on the performance of deep learning-based organ auto-segmentation for head-and-neck cancer patients,...

Preoperative prediction of postoperative urinary retention in lumbar surgery: a comparison of regression to multilayer neural network.

OBJECTIVE: Postoperative urinary retention (POUR) is a common complication after spine surgery and is associated with prolongation of hospital stay, i...

Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning.

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. Howeve...

Development of a single retention time prediction model integrating multiple liquid chromatography systems: Application to new psychoactive substances.

Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; ...

Adaptive Quadruped Balance Control for Dynamic Environments Using Maximum-Entropy Reinforcement Learning.

External disturbance poses the primary threat to robot balance in dynamic environments. This paper provides a learning-based control architecture for ...

Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine.

OBJECTIVE: The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-deriv...

Prediction of N chemical shifts by machine learning.

We demonstrate the potential for machine learning systems to predict three-dimensional (3D)-relevant NMR properties beyond traditional H- and C-base...

The Increasing Centrality of Robotic Technology in the Context of Nursing Care: Bioethical Implications Analyzed through a Scoping Review Approach.

At the dawn of the fourth industrial revolution, the healthcare industry is experiencing a momentous shift in the direction of increasingly pervasive ...

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short-Term Power Prediction in Photovoltaic Power Generation System.

When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent r...

Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases.

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention...

Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results.

This study aims to determine how randomly splitting a dataset into training and test sets affects the estimated performance of a machine learning mode...

Prediction of gait trajectories based on the Long Short Term Memory neural networks.

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim ...

Artificial Intelligence in PET: An Industry Perspective.

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) ...

Skilled reach training enhances robotic gait training to restore overground locomotion following spinal cord injury in rats.

Rehabilitative training has been shown to improve motor function following spinal cord injury (SCI). Unfortunately, these gains are primarily task spe...

Deep-learning based monitoring of FOG layer dynamics in wastewater pumping stations.

Accumulation of fat, oil and grease (FOG) in the sumps of wastewater pumping stations is a common failure cause for these facilities. Floating solids ...

Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning.

BACKGROUND: Eligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of clini...

The path to precision medicine for MS, from AI to patient recruitment: an interview with Mauricio Farez and Helen Onuorah.

This year’s World Brain Day is focused on stopping Multiple Sclerosis (MS). Although amazing progress has resulted in the development of relatively su...