Results 51 to 60 of about 938,196 (259)

Psychological Safety Among Interprofessional Pediatric Oncology Teams in Germany: A Nationwide Survey

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background Psychological safety (PS) is essential for teamwork, communication, and patient safety in complex healthcare environments. In pediatric oncology, interprofessional collaboration occurs under high emotional and organizational demands. Low PS may increase stress, burnout, and adverse events.
Alexandros Rahn   +4 more
wiley   +1 more source

A Novel K-Means Clustering Method for Locating Urban Hotspots Based on Hybrid Heuristic Initialization

open access: yesApplied Sciences, 2022
With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulating ...
Yiping Li   +4 more
doaj   +1 more source

Characterizing Parental Concerns About Lasting Impacts of Treatment in Children With B‐Acute Lymphoblastic Leukemia

open access: yesPediatric Blood &Cancer, EarlyView.
ABSTRACT Background B‐acute lymphoblastic leukemia (B‐ALL) is the most common pediatric cancer, and while most children in high‐resource settings are cured, therapy carries risks for long‐term toxicities. Understanding parents’ concerns about these late effects is essential to guide anticipatory support and inform evolving therapeutic approaches ...
Kellee N. Parker   +7 more
wiley   +1 more source

k-Means+++: Outliers-Resistant Clustering

open access: yesAlgorithms, 2020
The k-means problem is to compute a set of k centers (points) that minimizes the sum of squared distances to a given set of n points in a metric space. Arguably, the most common algorithm to solve it is k-means++ which is easy to implement and provides a
Adiel Statman   +2 more
doaj   +1 more source

Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data

open access: yes, 2019
The k-means clustering is one of the most popular clustering algorithms in data mining. Recently a lot of research has been concentrated on the algorithm when the dataset is divided into multiple parties or when the dataset is too large to be handled by ...
A Likas   +13 more
core   +1 more source

Revealing the structure of land plant photosystem II: the journey from negative‐stain EM to cryo‐EM

open access: yesFEBS Letters, EarlyView.
Advances in cryo‐EM have revealed the detailed structure of Photosystem II, a key protein complex driving photosynthesis. This review traces the journey from early low‐resolution images to high‐resolution models, highlighting how these discoveries deepen our understanding of light harvesting and energy conversion in plants.
Roman Kouřil
wiley   +1 more source

Spherical k-Means Clustering

open access: yesJournal of Statistical Software, 2012
Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency.
Kurt Hornik   +3 more
doaj  

Sparse Multi-View K-Means Clustering

open access: yesIEEE Access
In machine learning, k-means clustering is an unsupervised leaning technique to partition the data into k clusters that are homogeneous within the cluster and heterogeneous between clusters.
Miin-Shen Yang, Shazia Parveen
doaj   +1 more source

Organoids in pediatric cancer research

open access: yesFEBS Letters, EarlyView.
Organoid technology has revolutionized cancer research, yet its application in pediatric oncology remains limited. Recent advances have enabled the development of pediatric tumor organoids, offering new insights into disease biology, treatment response, and interactions with the tumor microenvironment.
Carla Ríos Arceo, Jarno Drost
wiley   +1 more source

The LINEX Weighted k-Means Clustering

open access: yesJournal of Statistical Theory and Applications (JSTA)
LINEX weighted k-means is a version of weighted k-means clustering, which computes the weights of features in each cluster automatically. Determining which entity is belonged to which cluster depends on the cluster centers.
Narges Ahmadzadehgoli   +2 more
doaj   +1 more source

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