Staffing & Scheduling

Conductive nanofiltration: From materials to applications.

Conductive nanofiltration membranes are emerging as a transformative class of separation materials capable of coupling molecular selectivity with dynamic electrochemical control. Unlike conventional NF systems that operate as passive diffusion barriers, conductive NF architecture enables tunable transport behavior, on-demand fouling mitigation, and electrically mediated interfacial processes, posi...

Machine learning-enabled meta-analysis reveals the effect of microplastics on nitrogen removal performance in constructed wetlands and its potential mechanisms.

Constructed wetlands (CWs) are an effective wastewater treatment system and an important sink for microplastics (MPs). However, MPs impacts on nitrogen removal performance of CWs remain unclear. Here, we collected 1903 datasets from 34 studies for a meta-analysis to explore MPs effects on nitrogen removal performance of CWs from characteristics and experimental parameters of MPs. The results showe...

The Emerging Role of Artificial Intelligence in Drug Discovery and Development: Implications for Cardiovascular Pharmacology.

Cardiovascular diseases remain the leading cause of global morbidity and mortality, highlighting the urgent need for more efficient, precise, and cost...

Prediction of recurrent ischemic stroke using machine learning from real-world data.

BACKGROUND: Recurrent ischemic stroke (RIS) is a significant challenge in Malaysia, affecting approximately 33% of patients. However, studies using ar...

Multidimensional chromatographic fingerprint fusion with machine learning: Entropy-based feature evaluation for TCM quality marker discovery.

Chromatography is a cornerstone methodology employed for quality evaluation of traditional Chinese medicine (TCM). However, the complexity of chromato...

Uncertainty in deep learning for EEG under dataset shifts.

As artificial intelligence (AI) is increasingly integrated into medical diagnostics, it is essential that predictive models provide not only accurate ...

Use of a Machine Learning Program for Urogynecology Fellowship Applicant Review.

IMPORTANCE: There is a gap in objective methods to review applications for advanced medical training. OBJECTIVE: The objective of this study was to ev...

Artificial intelligence in human-assisted reproduction: a paradigm shift still in search of clinical impact.

PURPOSE: Artificial intelligence in human-assisted reproduction has attracted intense interest and inflated expectations, with proposed applications r...

An exploratory randomized controlled trial of an AI-enabled mental health intervention for generalized anxiety.

Generalized Anxiety Disorder (GAD) is prevalent and often co-occurs with depression, contributing to significant disability and healthcare burden. Alt...

Observer: creation of a novel multimodal dataset for outpatient care research.

OBJECTIVE: To support ambulatory care innovation, we created Observer, a multimodal dataset comprising videotaped outpatient visits, electronic health...

Seeing Is Believing? Exploring Gender Bias in Artificial Intelligence Imagery of Specialty Doctors.

BACKGROUND: In medicine and medical education, women are disproportionately affected by gender bias. Artificial intelligence (AI) is increasingly bein...

A Scoping Review of the Photographic Assessment of Donor Liver Steatosis in Transplantation Using Artificial Intelligence.

INTRODUCTION: Accurate evaluation of liver steatosis and overall organ quality is critical for optimizing safe organ utilization in liver transplantat...

Multi-timescale representation with adaptive routing for deep tabular learning under temporal shift.

In real-world applications, tabular datasets often evolve over time, leading to temporal shift that degrades the long-range neural network performance...

A unified framework for pre-screening and screening tools in oncology clinical trials.

Clinical trial enrollment in oncology remains limited by increasingly complex eligibility criteria, biomarker stratification, and fragmented clinical ...

Prediction of Behavioral Health Employee Turnover With HR Data-Based Machine Learning Combined With Job Well-Being Indicators.

OBJECTIVE: Identifying employees at risk for turnover is critical to supporting staff retention in community behavioral health organizations (CBHOs). ...

Machine learning-based estimation and optimization of phoenix Dactylifera Seed Powder reinforced vinyl ester bio-composites.

This study aims to develop a robust machine learning (ML) framework for accurately predicting the mechanical and thermal properties of sustainable vin...

Deep learning enhanced ALPS reveals genetic and environmental factors of brain glymphatic function.

BACKGROUND: The glymphatic system plays a critical role in brain waste clearance and health. Diffusion tensor imaging along the perivascular space (DT...

Subphenotypes in acute respiratory distress syndrome: A scoping review across clinical, biological, computational, imaging, omics, and artificial intelligence approaches.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome with high mortality. Subphenotyping may identify more homogeneous g...

The autonomy paradox in AI-generated content adoption: Creative-specific alternative to TAM model in China's micro-short drama industry.

In China's booming micro-short drama industry, Artificial Intelligence Generated Content (AIGC) presents creators with an 'autonomy paradox': improvin...

Evolution of root nodule symbiosis via paleopolyploidy and modular pathway rewiring.

The evolution of root nodule symbiosis (RNS), a key innovation for plant nitrogen acquisition, has long been studied but lacks a mechanistic, gene-lev...