Das Buch "Poisonous plants in Chandrapur District of Maharashtra" ist eine Doktorarbeit des Autors Dr. Mukund B. Shende unter dem Titel "A study on poisonous plants from Chandrapur District of Maharshtra State" im Zeitraum von 2010 bis 2014 auf dem Gebiet der Botanik und Toxikologie. Da die gr nen Pflanzen eine Reihe von Sekund rmetaboliten wie Alkaloide, Terpenoide, Phenole und cyanogene Glykoside produzieren. Der Zweck dieser Produkte ist wahrscheinlich die Verteidigung gegen Mikroben und Tiere. Viele dieser sekund ren Stoffwechselprodukte sind giftig f r den Menschen und andere Lebewesen. Die vorliegende Studie befasst sich mit solchen giftigen angiospermischen Pflanzen im Distrikt Chandrapur in Maharashtra, Indien. Der wichtige Teil dieser Studie befasst sich mit cynogenen Pflanzen. Diese Pflanzen enthalten cyanogene Glykoside und sind daher in der Lage, Cyanwasserstoff in kleinen und gr eren Mengen von 5 bis 800 ppm zu produzieren und werden dementsprechend als toxische und nicht-toxische cynogene Pflanzen kategorisiert. Insgesamt wurden 422 Pflanzen vom Autor untersucht. Davon wurden 331 Pflanzen als cynogene Pflanzen eingestuft. Diese Studie ist n tzlich auf dem Gebiet der Toxikologie, Umweltwissenschaft, Landwirtschaft, menschlichen Ern hrung, Medizin und Pflanzenvielfalt.
Le livre "Poisonous plants in Chandrapur District of Maharashtra" est un travail de doctorat de l'auteur Dr. Mukund B. Shende sous le titre "A study on poisonous plants from Chandrapur District of Maharshtra State" during the period of 2010 to 2014 in the field of Botany and Toxicology. Comme les plantes vertes produisent un certain nombre de m tabolites secondaires comme les alcalo des, les terp no des, les ph noliques et les glycosides cyanog niques. Le but de ces produits est probablement la d fense contre les microbes et les animaux. Beaucoup de ces m tabolites secondaires sont toxiques pour l'homme et les autres organismes vivants. La pr sente tude porte sur ces plantes angiospermes toxiques dans le district de Chandrapur du Maharashtra, en Inde. La partie importante de cette tude porte sur les plantes cynog nes. Ces plantes contiennent des glycosides cyanog nes et sont donc capables de produire du cyanure d'hydrog ne en petite et grande quantit , de 5 800 ppm, et sont donc class es comme des plantes cynog nes toxiques et non toxiques. Au total, 422 plantes ont t tudi es par l'auteur. Sur ce total, 331 plantes ont t trouv es comme tant des plantes cynog nes. Cette tude est utile dans le domaine de la toxicologie, des sciences de l'environnement, de l'agriculture, de la nutrition humaine, de la m decine et de la diversit des plantes.
By George B. Dantzig LINEAR PROGRAMMING The Story About How It Began: Some legends, a little about its historical sign- cance, and comments about where its many mathematical programming extensions may be headed. Industrial production, the ?ow of resources in the economy, the exertion of military e?ort in a war, the management of ?nances—all require the coordination of interrelated activities. What these complex undertakings share in common is the task of constructing a statement of actions to be performed, their timing and quantity(calledaprogramorschedule), that, ifimplemented, wouldmovethesystem from a given initial status as much as possible towards some de?ned goal. While di?erences may exist in the goals to be achieved, the particular processes, and the magnitudes of e?ort involved, when modeled in mathematical terms these seemingly disparate systems often have a remarkably similar mathematical str- ture. The computational task is then to devise for these systems an algorithm for choosing the best schedule of actions from among the possible alternatives. The observation, in particular, that a number of economic, industrial, ?nancial, and military systems can be modeled (or reasonably approximated) by mathem- ical systems of linear inequalities and equations has given rise to the development of the linear programming ?eld.
Linear programming represents one of the major applications of mathematics to business, industry, and economics. It provides a methodology for optimizing an output given that is a linear function of a number of inputs. George Dantzig is widely regarded as the founder of the subject with his invention of the simplex algorithm in the 1940's. This second volume is intended to add to the theory of the items discussed in the first volume. It also includes additional advanced topics such as variants of the simplex method; interior point methods (early and current methods), GUB, decomposition, integer programming, and game theory. Graduate students in the fields of operations research, industrial engineering and applied mathematics will find this volume of particular interest.
Linear programming represents one of the major applications of mathematics to business, industry, and economics. It provides a methodology for optimizing an output given that is a linear function of a number of inputs. George Dantzig is widely regarded as the founder of the subject with his invention of the simplex algorithm in the 1940's. This second volume is intended to add to the theory of the items discussed in the first volume. It also includes additional advanced topics such as variants of the simplex method; interior point methods (early and current methods), GUB, decomposition, integer programming, and game theory. Graduate students in the fields of operations research, industrial engineering and applied mathematics will find this volume of particular interest.
By George B. Dantzig LINEAR PROGRAMMING The Story About How It Began: Some legends, a little about its historical sign- cance, and comments about where its many mathematical programming extensions may be headed. Industrial production, the ?ow of resources in the economy, the exertion of military e?ort in a war, the management of ?nances—all require the coordination of interrelated activities. What these complex undertakings share in common is the task of constructing a statement of actions to be performed, their timing and quantity(calledaprogramorschedule), that, ifimplemented, wouldmovethesystem from a given initial status as much as possible towards some de?ned goal. While di?erences may exist in the goals to be achieved, the particular processes, and the magnitudes of e?ort involved, when modeled in mathematical terms these seemingly disparate systems often have a remarkably similar mathematical str- ture. The computational task is then to devise for these systems an algorithm for choosing the best schedule of actions from among the possible alternatives. The observation, in particular, that a number of economic, industrial, ?nancial, and military systems can be modeled (or reasonably approximated) by mathem- ical systems of linear inequalities and equations has given rise to the development of the linear programming ?eld.
