Staffing & Scheduling

Analysis of HIV infection among voluntary blood donors based on HIV ELISA and nucleic acid detection.

OBJECTIVE: To analyze the epidemiological characteristics of human immunodeficiency virus (HIV) infection among voluntary blood donors and provide a foundation for improving the donor recruitment strategies and developing a more scientific and effective HIV screening strategy.

How to enhance employee engagement in the AI era: An fsQCA-based study.

BackgroundThe ongoing fourth industrial revolution, characterized by the integration of intelligent machines, presents a transformative shift in the nature of work. Unlike past industrial revolutions that reshaped job dynamics, the current reliance on intelligent machines introduces new complexities. It is challenging to determine whether the adoption of intelligent machines fosters active engagem...

Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) mod...

Optimizing load scheduling and data distribution in heterogeneous cloud environments using fuzzy-logic based two-level framework.

Cloud environment handles heterogeneous services, data, and users collaborating on different technologies and resource scheduling strategies. Despite ...

Imaging-guided bioresorbable acoustic hydrogel microrobots.

Micro- and nanorobots excel in navigating the intricate and often inaccessible areas of the human body, offering immense potential for applications su...

Diagnosis of infections using artificial intelligence techniques versus standard microscopy in a reference laboratory.

Diagnosing malaria using standard techniques is time-consuming. With limited staffing in many laboratories, this may lead to delays in reporting. Inno...

Flexibility and resilience in equity-centered research: lessons learned conducting a randomized controlled trial of a family-based substance use prevention program for American Indian families.

Meaningful and effective community engagement lies at the core of equity-centered research, which is a powerful tool for addressing health disparities...

MolGraph: a Python package for the implementation of molecular graphs and graph neural networks with TensorFlow and Keras.

Molecular machine learning (ML) has proven important for tackling various molecular problems, such as predicting molecular properties based on molecul...

Effectiveness of unilateral lower-limb exoskeleton robot on balance and gait recovery and neuroplasticity in patients with subacute stroke: a randomized controlled trial.

BACKGROUND: Impaired balance and gait in stroke survivors are associated with decreased functional independence. This study aimed to evaluate the effe...

Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning.

Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as ...

MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-Ray Self-Supervised Representation Learning.

Self-supervised learning (SSL) reduces the need for manual annotation in deep learning models for medical image analysis. By learning the representati...

Effects of Robot-Assisted Gait Training on Balance and Fear of Falling in Patients With Stroke: A Randomized Controlled Clinical Trial.

OBJECTIVE: The aim of this study was compare the effects of combined training, which included robot-assisted gait training in addition to traditional ...

Cohort profile: AI-driven national Platform for CCTA for clinicaL and industriaL applicatiOns (APOLLO).

PURPOSE: Coronary CT angiography (CCTA) is well established for the diagnostic evaluation and prognostication of coronary artery disease (CAD). The gr...

Using deep learning and word embeddings for predicting human agreeableness behavior.

The latest advancements of deep learning have resulted in a new era of natural language processing. The machines now possess an unparallel ability to ...

Predefined-Time Convergent Kinematic Control of Robotic Manipulators With Unknown Models Based on Hybrid Neural Dynamics and Human Behaviors.

This article proposes a model-free kinematic control method with predefined-time convergence for robotic manipulators with unknown models. The predefi...

Site-Invariant Meta-Modulation Learning for Multisite Autism Spectrum Disorders Diagnosis.

Large amounts of fMRI data are essential to building generalized predictive models for brain disease diagnosis. In order to conduct extensive data ana...

D4A: An efficient and effective defense across agnostic adversarial attacks.

Recent studies show that Graph Neural Networks (GNNs) are vulnerable to structure adversarial attacks, which draws attention to adversarial defenses i...

Understanding ecosystem services of detailed forest and wetland types using remote sensing and deep learning techniques in Northern China.

Spanning both temperate and sub-frigid zones, Northeast China boasts typical boreal forests and abundant wetland resources. Because of these attribute...

Application of social media communication for museum based on the deep mediatization and artificial intelligence.

Based on deep mediatization theory and artificial intelligence (AI) technology, this study explores the effective improvement of museums' social media...

Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current Status and Future Directions.

The integration of artificial intelligence (AI) into healthcare is becoming increasingly mainstream. Leveraging digital technologies, such as AI and d...