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Network topology is the arrangement of the elements (links, nodes, etc.) of a communication network. [1] [2] Network topology can be used to define or describe the arrangement of various types of telecommunication networks, including command and control radio networks, [3] industrial fieldbusses and computer networks.
Structure of cadmium cyanide (Cd(CN) 2), highlighting the interpenetrated structure.Blue = one Cd(CN) 2 substructure, red = other Cd(CN) 2 substructure. An Interpenetrating polymer network (IPN) is a polymer comprising two or more networks which are at least partially interlaced on a polymer scale but not covalently bonded to each other.
If the different relations required for the network structure contrast too greatly it may lead to confusion, delays, and unnecessary increases in complexity. Due to the network structure relying on many different individuals or teams working together independently, effective supervision is needed to avoid shirking or free riding.
A network solid or covalent network solid (also called atomic crystalline solids or giant covalent structures) [1] [2] is a chemical compound (or element) in which the atoms are bonded by covalent bonds in a continuous network extending throughout the material.
There are two main types of neural network. In neuroscience, a biological neural network is a physical structure found in brains and complex nervous systems – a population of nerve cells connected by synapses. In machine learning, an artificial neural network is a mathematical model used to approximate nonlinear functions.
Structured cabling network diagram. Structured cabling is the design and installation of a cabling system that will support multiple hardware uses and be suitable for today's needs and those of the future. With a correctly installed system, current and future requirements can be met, and hardware that is added in the future will be supported [1]
The hierarchical network model is part of the scale-free model family sharing their main property of having proportionally more hubs among the nodes than by random generation; however, it significantly differs from the other similar models (Barabási–Albert, Watts–Strogatz) in the distribution of the nodes' clustering coefficients: as other models would predict a constant clustering ...
The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time. Long short-term memory (LSTM) [1] is a type of recurrent neural network (RNN) aimed at dealing with the vanishing gradient problem [2] present in traditional RNNs.