Fractional-order integrator A fractional-order integrator or just simply fractional integrator is an integrator device that calculates the fractional-order integral or derivative (usually called a differintegral) of an input. Differentiation or integration is a real or complex parameter. The fractional integrator is useful in fractional-order control where the history of the system under control is important to the control system output. Overview[edit] The differintegral function, includes the integer order differentiation and integration functions, and allows a continuous range of functions around them. Digital devices[edit] Digital devices have the advantage of being versatile, and are not susceptible to unexpected output variation due to heat or noise. A solution to this problem is the Coopmans approximation, which allows old data to be forgotten more gracefully (though still with exponential decay, rather than with the power law decay of a purely analog device). Analog devices[edit] can be constructed.
John Maynard Smith John Maynard Smith[a] FRS (6 January 1920 – 19 April 2004) was a British theoretical and mathematical evolutionary biologist and geneticist.[1] Originally an aeronautical engineer during the Second World War, he took a second degree in genetics under the well-known biologist J. B. S. Haldane. Biography[edit] Early years[edit] John Maynard Smith was born in London, the son of the surgeon Sidney Maynard Smith, but following his father's death in 1928, the family moved to Exmoor, where he became interested in natural history. On leaving school, Maynard Smith joined the Communist Party of Great Britain and started studying engineering at Trinity College, Cambridge. Second degree[edit] Maynard Smith, having decided that aircraft were “noisy and old-fashioned”,[3] then took a change of career, entering University College London (UCL) to study fruit fly genetics under Haldane. University of Sussex[edit] Evolution and the Theory of Games[edit] He was elected a Fellow of the Royal Society in 1977.
Eusociality Eusociality (Greek eu: "good/real" + "social"), the highest level of organization of animal sociality, is defined by the following characteristics: cooperative brood care (including brood care of offspring from other individuals), overlapping generations within a colony of adults, and a division of labor into reproductive and non-reproductive groups.[1][2] The division of labor creates specialized behavioral groups within an animal society which are sometimes called castes. Eusociality is distinguished from all other social systems because individuals of at least one caste lose the ability to perform at least one behavior characteristic of individuals in another caste.[2][3] Eusociality is mostly observed and studied in Hymenoptera (ants, bees, and wasps) and Isoptera (termites).[1] For example, a colony has caste differences; a queen and king take the roles as the sole reproducers and the soldiers and workers work together to create a living situation favorable for the brood. E. O.
Fractional-order control Fractional-order control (FOC) is a field of control theory that uses the fractional-order integrator as part of the control system design toolkit. The use of fractional calculus (FC) can improve and generalize well-established control methods and strategies. [1] The fundamental advantage of FOC is that the fractional-order integrator weights history using a function that decays with a power-law tail. The effect is that the effects of all time are computed for each iteration of the control algorithm. In fact, the fractional integral operator is different from any integer-order rational transfer function , in the sense that it is a non-local operator that possesses an infinite memory and takes into account the whole history of its input signal.[2] Fractional-order control shows promise in many controlled environments that suffer from the classical problems of overshoot and resonance, as well as time diffuse applications such as thermal dissipation and chemical mixing. See also[edit]
DNA replication Biological process In molecular biology,[1][2][3][4] DNA replication is the biological process of producing two identical replicas of DNA from one original DNA molecule.[5] DNA replication occurs in all living organisms acting as the most essential part of biological inheritance. This is essential for cell division during growth and repair of damaged tissues, while it also ensures that each of the new cells receives its own copy of the DNA.[6] The cell possesses the distinctive property of division, which makes replication of DNA essential. DNA structure[edit] DNA exists as a double-stranded structure, with both strands coiled together to form the characteristic double helix. The pairing of complementary bases in DNA (through hydrogen bonding) means that the information contained within each strand is redundant. DNA polymerase[edit] Replication process[edit] Initiation[edit] Pre-replication complex[edit] Preinitiation complex[edit] Elongation[edit] DNA polymerase has 5′–3′ activity. P. [edit]
Francis Heylighen Belgian cyberneticist (born 1960) Francis Paul Heylighen (born 27 September 1960) is a Belgian cyberneticist investigating the emergence and evolution of intelligent organization. He presently works as a research professor at the Vrije Universiteit Brussel (the Dutch-speaking Free University of Brussels), where he directs the transdisciplinary research group on "Evolution, Complexity and Cognition"[1][2] and the Global Brain Institute. Biography[edit] Francis Heylighen was born on September 27, 1960 in Vilvoorde, Belgium. In 1983 he started working as a researcher for the Belgian National Fund for Scientific Research (NFWO). In 1989 Valentin Turchin and Cliff Joslyn founded the Principia Cybernetica Project, and Heylighen joined a year later. Work[edit] His research focuses on the emergence and evolution of complex, intelligent organization. Basic ideas[edit] This broad variety of work is held together by two basic principles. Principia Cybernetica[edit] The Global Brain[edit] See also[edit]
Genetic algorithm Competitive algorithm for searching a problem space Methodology[edit] Optimization problems[edit] In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. A typical genetic algorithm requires: a genetic representation of the solution domain,a fitness function to evaluate the solution domain. Once the genetic representation and the fitness function are defined, a GA proceeds to initialize a population of solutions and then to improve it through repetitive application of the mutation, crossover, inversion and selection operators. Initialization[edit] Selection[edit] Genetic operators[edit] Heuristics[edit] Termination[edit] Limitations[edit] Variants[edit]
Adaptation and Natural Selection The aim of the book is to "clarify certain issues in the study of adaptation and the underlying evolutionary processes."[3] Though more technical than a popular science book, its target audience is not specialists but biologists in general and the more advanced students of the topic. It was mostly written in the summer of 1963 when Williams utilized the University of California, Berkeley's library.[3] Contents[edit] See also[edit] References[edit] ^ Pinker, S. (1994). External links[edit] Adaptation and Natural Selection, Princeton University Press Ecosystem Community of living organisms together with the nonliving components of their environment Ecosystems provide a variety of goods and services upon which people depend. Ecosystem goods include the "tangible, material products" of ecosystem processes such as water, food, fuel, construction material, and medicinal plants. Ecosystem services, on the other hand, are generally "improvements in the condition or location of things of value". These include things like the maintenance of hydrological cycles, cleaning air and water, the maintenance of oxygen in the atmosphere, crop pollination and even things like beauty, inspiration and opportunities for research. Many ecosystems become degraded through human impacts, such as soil loss, air and water pollution, habitat fragmentation, water diversion, fire suppression, and introduced species and invasive species. Definition Origin and development of the term G. Processes External and internal factors Primary production Energy flow Decomposition Examples
Good regulator Theorem in cybernetics as making the regulator a 'model' of the system. With regard to the brain, insofar as it is successful and efficient as a regulator for survival, it must proceed, in learning, by the formation of a model (or models) of its environment. The theorem is general enough to apply to all regulating and self-regulating or homeostatic systems. The theorem does not explain what it takes for the system to become a good regulator. See also[edit] References[edit] ^ R.