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Synthesis, Characterization, and Remediation Application of Iron Oxide Nanoparticles
Published in Pankaj Chowdhary, Abhay Raj, Contaminants and Clean Technologies, 2020
Sandhya Singh, Gaurav Hitkari, Gulam Abbas
Sol-gel methods commonly refer to the condensation and hydrolysis of metal alkoxide precursors, foremost to dispersal of metal oxide particles into ‘sol’. The ‘sol’ is then dehydrated or ‘gelled’ by the exclusion of solvent or through chemical reaction. Formally, water is applied as a solvent but the precursors may also be hydrolyzed into base or acid. Basic catalysis turns materials into colloidal gel, whereas acid catalysis produces a polymeric nature of the gel (Lam et al. 2008). The rates of condensation and hydrolysis are significant parameters that affect the properties of the finishing products. Reduced particle sizes are obtained at slower and additional controlled hydrolysis rates. The particle size and shape also depend upon the nature of solution, pH, and temperature (Tavakoli, Sohrabi, and Kargari 2007). Magnetic arrangement in this process relies on the phases made and the particle volume fraction and is highly susceptible to the size dissemination and spreading of the particles (Tavakoli, Sohrabi, and Kargari 2007). This technique offers selected advantages such as (i) the opportunity to obtain materials with a predetermined structure according to experimental conditions; (ii) the probability to achieve unpolluted amorphous phases, monodisperse, and good regulator of the particle size; (iii) the control of the nanostructure and the uniformity of the reaction products; and (iv) the probability to insert molecules that preserve their stability and properties inside the sol-gel matrix.
Analysing Tactical Cognitive Systems: Theories, Models and Methods
Published in Peter Berggren, Staffan NäHlinder, Erland Svensson, Assessing Command and Control Effectiveness, 2017
The term dynamic system refers to an object driven by external input signals u(t) for every value of time t which, as a response, produces a set of output signals y(t) for every t. Ashby (1956) and Brehmer (1992), among others, have shown that most complex systems have real-time, dynamic properties. The system output at a given time not only depends on the input value at that specific moment, but also on earlier input values. A good regulator of a system also has to implement a model of the system that can be (that is to be) controlled. Put somewhat differently, Ashby’s law of requisite variety (Ashby 1956) states that the variety of a controller (also called regulator) of a dynamic system has to be equal to, or greater than, the variety of the system itself.
Developing resilience potentials
Published in Erik Hollnagel, Safety-II in Practice, 2017
There is a second and more serious obstacle, namely, the question of what it more precisely is that determines how an organisation performs and how well it does relative to some criterion or criteria – such as safety, quality, productivity, customer satisfaction, ensuring availability, etc. This is, clearly, not unrelated to the first obstacle, especially if the model of the organisation goes beyond the mere structure and includes something about the ‘causes’ and the ‘mechanisms’. The understanding of what determines performance is crucial because it is a necessary condition for management and control. Cybernetics formulated that in the 1950s as the Law of Requisite Variety (Ashby, 1956). The Law of Requisite Variety is concerned with the problem of regulation or control and expresses the principle that the variety of a controller should match the variety of the system to be controlled. Effective control is therefore not possible if the controller has less variety than the system. This has also been expressed by saying that ‘every good regulator of a system must be a model of that system’ (Conant and Ashby, 1970).
Articulating the new urban water paradigm
Published in Critical Reviews in Environmental Science and Technology, 2021
Manuel Franco-Torres, Briony C. Rogers, Robin Harder
A core theorem of cybernetics, states that "every good regulator of a system must be a model [a replica] of that system" (Conant & Ashby, 1970, p. 89). Accordingly, the old urban water paradigm’s methodology proposes an UWS that projects the stationarity and simplicity of its context and problems, while the new paradigm’s methodology promotes an UWS that mimics the complexity and dynamism of its context and problems.