Staffing & Scheduling

AI driven fault diagnosis approach for stator turn to turn faults in induction motors.

Induction motors (IMs) are vital in industrial applications. Although all motor faults can disrupt its operation significantly, stator turn to turn faults (ITFs) are the most challenging one due to their detection difficulties. This paper introduces an AI-based approach to detect ITFs and assess their severity. A simulation based on an accurate mathematical model of the IM under ITFs is employed t...

Comparative assessment of standalone and hybrid deep neural networks for modeling daily pan evaporation in a semi-arid environment.

Evaporation represents a fundamental hydrological cycle process that demands dependable methods to quantify its fluctuation to ascertain sustainable agriculture, irrigation systems, and overall water resource management. Meteorological variables such as relative humidity, temperature, wind speed, and sunshine hours affect evaporation non-linearly, resulting in challenges while developing predictio...

Self-assembling peptide hydrogels: design, mechanisms, characterization, and biomedical applications.

Self-assembled peptide hydrogels have emerged as a research frontier in biomedical engineering due to their exceptional water-retention capacity and s...

Physicochemical characterization of a new porous 2D semiconductor carbon allotrope, C: an investigation density functional theory and machine learning-based molecular dynamics.

This study comprehensively characterizes, with suggested applications, a novel two-dimensional carbon allotrope, C, using density functional theory an...

Integrating AI and genomics: predictive CNN models for schizophrenia phenotypes.

This study explores the use of deep learning to analyze genetic data and predict phenotypic traits associated with schizophrenia, a complex psychiatri...

Recent advances and future perspectives of long-acting ophthalmic preparations (LAOPs) in clinical applications.

The intricate structure of ocular barriers significantly impedes drug penetration, leading to suboptimal efficacy of conventional ophthalmic formulati...

Artificial intelligence in dentistry: a narrative review of applications, challenges, and future directions.

Artificial intelligence (AI) is increasingly influencing dental practice, offering advancements in diagnostics, treatment planning, and patient manage...

Multitarget Generate Electrolyte Additive for Lithium Metal Batteries.

Electrolyte additives are crucial for accelerating the commercialization of lithium metal batteries (LMBs), yet designing effective additives is chall...

Coordinated Residue Motions at the Enzyme-Substrate Interface Promote DNA Translocation in Polymerases.

The translocation of DNA in polymerase (Pol) enzymes is a critical step for Pol-mediated nucleic acid polymerization, essential for storing and transm...

Sensitivity-Enhanced Pure Shift Spectroscopy Empowered by Deep Learning and PSYCHE.

Proton nuclear magnetic resonance (NMR) is the most used NMR technique. However, the limited range of chemical shifts and the complicated multiplet sp...

Analysis of the neural mechanisms of social anxiety based on EEG features and machine learning and construction of a diagnostic model.

Social anxiety is a common psychological problem, and its accurate diagnosis and investigation of underlying neurophysiological mechanisms are of sign...

The role of tumor microenvironment and immune cell crosstalk in triple-negative breast cancer (TNBC): Emerging therapeutic opportunities.

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by its lack of estrogen, progesterone, and HER2 receptors, leading to limi...

Stem loop binding protein promotes SARS-CoV-2 replication via -1 programmed ribosomal frameshifting.

The -1 programmed ribosomal frameshifting (-1 PRF) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for keeping the balance ...

MoS Channel-Enhanced High-Density Charge Trap Flash Memory and Machine Learning-Assisted Sensing Methodologies for Memory-Centric Computing Systems.

Driven by the shift of artificial intelligence (AI) workloads to edge devices, there is a growing demand for nonvolatile memory solutions that offer h...

Aggregating soft labels from crowd annotations improves uncertainty estimation under distribution shift.

Selecting an effective training signal for machine learning tasks is difficult: expert annotations are expensive, and crowd-sourced annotations may no...

A 3D lightweight network with Roberts edge enhancement model (LR-Net) for brain tumor segmentation.

In clinical medicine, a reliable and resource-friendly computer-aided diagnosis (CAD) method for brain tumor segmentation is essential to enhance diag...

Integrating Nurse Preferences Into AI-Based Scheduling Systems: Qualitative Study.

BACKGROUND: Nurse scheduling is a complex challenge in health care, impacting both patient care quality and nurse well-being. Traditional scheduling m...

The potentials and challenges of integrating generative artificial intelligence (AI) in dental and orthodontic education: a systematic review.

BACKGROUND: Generative AI technologies offer significant opportunities to enhance orthodontic education by improving knowledge retention, clinical dec...

Out-of-distribution reject option method for dataset shift problem in early disease onset prediction.

Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, its predictive accuracy for u...

Early detection of occupational stress: Enhancing workplace safety with machine learning and large language models.

Occupational stress is a major concern for employers and organizations as it compromises decision-making and overall safety of workers. Studies indica...