Electrostatic force class 8 ncert. .pptx

Poikilocytosis, different types, abnormalutirs

Bragg Brentano Alignment for D4 with LynxEye

Small Intestine

Antigen overview

Collaborative team recommendation involves selecting users with certain skills to form a team who will, more likely than not, accomplish a complex task successfully. To automate the traditionally tedious and error-prone manual process of team formation, researchers from several scientific spheres have proposed methods to tackle the problem. In this tutorial, while providing a taxonomy of team recommendation works based on their algorithmic approaches to model skilled users in collaborative teams, we perform a comprehensive and hands-on study of the graph-based approaches that comprise the mainstream in this field, then cover the neural team recommenders as the cutting-edge class of approaches. Further, we provide unifying definitions, formulations, and evaluation schema. Last, we introduce details of training strategies, benchmarking datasets, and open-source tools, along with directions for future works.

Science-Technology Quiz (School Quiz 2024)

The rapid assembly of the first supermassive black holes is an enduring mystery. Until now, it was not known whether quasar ‘feeding’ structures (the ‘hot torus’) could assemble as fast as the smaller-scale quasar structures. We present JWST/MRS (rest-frame infrared) spectroscopic observations of the quasar J1120+0641 at z = 7.0848 (well within the epoch of reionization). The hot torus dust was clearly detected at λrest ≃ 1.3 μm, with a black-body temperature of  K, slightly elevated compared to similarly luminous quasars at lower redshifts. Importantly, the supermassive black hole mass of J1120+0641 based on the Hα line (accessible only with JWST), MBH = 1.52 ± 0.17 × 109 M⊙, is in good agreement with previous ground-based rest-frame ultraviolet Mg II measurements. Comparing the ratios of the Hα, Paα and Paβ emission lines to predictions from a simple one-phase Cloudy model, we find that they are consistent with originating from a common broad-line region with physical parameters that are consistent with lower-redshift quasars. Together, this implies that J1120+0641’s accretion structures must have assembled very quickly, as they appear fully ‘mature’ less than 760 Myr after the Big Bang.

Identifying Indian wood involves recognizing key characteristics such as grain patterns, color, texture, hardness, and specific anatomical features. These identification keys include observing the wood's pores, growth rings, and resin canals, as well as its scent and weight. Understanding these features is essential for accurate wood identification, which is crucial for various applications in carpentry, furniture making, and conservation. Additionally, the application of Convolutional Neural Networks (CNN) in wood identification has revolutionized this field. CNNs can analyze images of wood samples to identify species with high accuracy by learning and recognizing intricate patterns and features. This technological advancement not only enhances the precision of wood identification but also accelerates the process, making it more efficient for industry professionals and researchers alike.

1,1 and 1,2 Migratory insertion reactions