Staffing & Scheduling

Machine Learning-Based Resource Management in Fog Computing: A Systematic Literature Review.

This systematic literature review analyzes machine learning (ML)-based techniques for resource management in fog computing. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, this paper focuses on ML and deep learning (DL) solutions. Resource management in the fog computing domain was thoroughly analyzed by identifying the key factors and constraint...

Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study.

The Arteriovenous Access Stage (AVAS) classification simplifies information about suitability of vessels for vascular access (VA). It's been previously validated in a clinical study. Here, AVAS performance was tested against multiple ultrasound mapping measurements using machine learning. A prospective multicentre international study (NCT04796558) with patient recruitment from March 2021-July 2024...

From Sea Cucumbers to Soft Robots: A Photothermal-Responsive Hydrogel Actuator with Shape Memory.

Soft robotics has undergone considerable progress driven by materials that can effectively transduce external stimuli into mechanical actuation. Here,...

Predicting Robotic Hysterectomy Incision Time: Optimizing Surgical Scheduling with Machine Learning.

BACKGROUND AND OBJECTIVES: Operating rooms (ORs) are critical for hospital revenue and cost management, with utilization efficiency directly affecting...

ABCoRT: Retention Time Prediction for Metabolite Identification via Atom-Bond Co-Learning.

Liquid chromatography retention time (RT) prediction plays a crucial role in metabolite identification, a challenging and essential task in untargeted...

The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis.

The 5' UTRs of mRNAs are critical for translation regulation during development, but their in vivo regulatory features are poorly characterized. Here,...

Novel Machine-Learning Modeling of Facial Trauma Volume With Regional Event and Weather Data.

OBJECTIVE: Facial trauma volume is difficult to predict accurately. We aim to understand the capacity of climate and regional events to predict daily ...

Artificial neural networks to estimate the sorption and desorption of the herbicide linuron in Brazilian soils.

Generally, herbicides used in Brazil follow manufacturer's recommendations, which often do not consider soil attributes. Statistical models that inclu...

Characterizing the microbiome recruited by the endangered plant in phosphorus-deficient acidic soil.

Phosphorus (P)-deficient soils serve as crucial habitats for endangered plant species. Microbiomes play pivotal roles in soil element cycling and in d...

Prospects of cold plasma in enhancing food phenolics: analyzing nutritional potential and process optimization through RSM and AI techniques.

Consumption of plant-based food is steadily increasing and follows an augmented trend owing to their nutritive, functional, and energy potential. Diff...

Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach.

Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration p...

Recruiting Young People for Digital Mental Health Research: Lessons From an AI-Driven Adaptive Trial.

BACKGROUND: With increasing adoption of remote clinical trials in digital mental health, identifying cost-effective and time-efficient recruitment met...

Illustration image style transfer method design based on improved cyclic consistent adversarial network.

To improve the expressiveness and realism of illustration images, the experiment innovatively combines the attention mechanism with the cycle consiste...

The integration and innovative practice of intelligent AI and local opera in college teaching.

This paper explores the impacts of integrating AI into the teaching of Chinese Opera using a mixed-methods approach, examining performance, engagement...

Interpretable machine learning reveals transport of aged microplastics in porous media: Multiple factors co-effect.

Microplastics (MPs) easily migrate into deeper soil layers, posing potential risks to subterranean habitats and groundwater. However, the mechanisms g...

Artificial Intelligence-Driven Translation Tools in Intensive Care Units for Enhancing Communication and Research.

UNLABELLED: There is a need to improve communication for patients and relatives who belong to cultural minority communities in intensive care units (I...

Leveraging Large Language Models to Enhance Dermatology Clinical Trial Patient Recruitment and Retention.

The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.

Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and com...

A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.

Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by c...

ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.

PURPOSE: Enhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to ...