Egyptian blue (CaCuSi4O10) is exfoliated into 2D nanosheets of high monodispersity down to monolayers by a mild surfactant-assisted procedure. These nanosheets are fluorescent in the near infrared (NIR) and useful for (bio)photonics.

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Background CT head is commonly performed in the setting of delirium and altered mental status (AMS), with variable yield. We aimed to evaluate the yield of CT head in hospitalized patients with delirium and/or AMS across a variety of clinical settings and identify factors associated with abnormal imaging. Methods We included studies in adult hospitalized patients, admitted to the emergency department (ED) and inpatient medical unit (grouped together) or the intensive care unit (ICU). Patients had a diagnosis of delirium/AMS and underwent a CT head that was classified as abnormal or not. We searched Medline, Embase and other databases (informed by PRISMA guidelines) from inception until November 11, 2021. Studies that were exclusively performed in patients with trauma or a fall were excluded. A meta‐analysis of proportions was performed; the pooled proportion of abnormal CTs was estimated using a random effects model. Heterogeneity was determined via the I2 statistic. Factors associated with an abnormal CT head were summarized qualitatively. Results Forty‐six studies were included for analysis. The overall yield of CT head in the inpatient/ED was 13% (95% CI: 10.2%–15.9%) and in ICU was 17.4% (95% CI: 10%–26.3%), with considerable heterogeneity (I2 96% and 98% respectively). Heterogeneity was partly explained after accounting for study region, publication year, and representativeness of the target population. Yield of CT head diminished after year 2000 (19.8% vs. 11.1%) and varied widely depending on geographical region (8.4%–25.9%). The presence of focal neurological deficits was a consistent factor that increased yield. Conclusion Use of CT head to diagnose the etiology of delirium and AMS varied widely and yield has declined. Guidelines and clinical decision support tools could increase the appropriate use of CT head in the diagnostic etiology of delirium/AMS.

Clustering is the process of arranging comparable data elements into groups. One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithm’s strategy has a direct influence on the clustering results. This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages. This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments. The outcomes are described in terms of big datasets. The focus of this study is on clustering algorithms with the WEKA data mining tool. Clustering is the process of dividing a big data set into small groups or clusters. Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics. It’s a data-modeling technique that provides a clear image of your data. Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets. KEYWORDS: data clustering, weka , k-means, hierarchical clustering

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