Yadav Rekha (32 results)

Condition: New. pp. 332.

Condition: New. pp. 332.

Condition: New. pp. 332.

Hardcover. Condition: New. ISBN:9789359194738.

Buch. Condition: Neu. Neuware - Principles of Sustainable Aquaculture: A Systematic Approach is a book that explores the foundations and future of aquaculture. This delves into sustainable practices, addressing the environmental, economic, and social dimensions of aquaculture systems. It provides insights into efficient resource management, innovative farming techniques, and ecological balance. Beginning with the basics of aquaculture-such as species selection, water quality, and feed strategies, it gradually progresses to advanced topics like integrated systems and policy frameworks. Emphasizing a systematic approach, it offers practical solutions for minimizing environmental impact while maximizing productivity. This book is an essential resource for students, researchers, and farmers passionate about fostering a sustainable aquaculture industry.

Condition: New.

Taschenbuch. Condition: Neu. TOXICITY STUDY OF FUNGAL ISOLATES FROM MAIZE STRAWS IN RATS | MYCOROXIS FUNGAL ISOLATES OF MAIZE STRAWS IN RATS | Rekha Yadav (u. a.) | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207477883 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Condition: New.

Taschenbuch. Condition: Neu. Neuware -The present study was undertaken to evaluate the toxic features of fungal contaminated meadow grass that had caused the toxicity and even death of cattle in Baihongal and in and around villages in Belgaum district of Karnataka and to confirm the effects of mycotoxins produced by these isolates in rats. The affected animals were exhibiting clinical signs of toxicity characterized by loss of body condition, anorexia, ataxia, bleeding from orifices and eventually death. The present study was undertaken to evaluate the toxicity of fungal contaminated meadow grass in rats and the species of fungi have been isolated and identified as Aspergillus niger, Aspergillus terreus , Rhizoctonia bataticola and Rhizopus stolonifer. The fungal infected wheat material was analyzed for the presence mycotoxins by LC-MS/MS method. The A.niger infected wheat material showed the presence of aflatoxin G1 and aflatoxin G2 , Fumonisin B1& B2 citrinin and beauricin. The A.terreus infected wheat material showed the presence of citrinin and aflatoxin. The Rhizoctonia bataticola infected wheat material showed the presence of Ochratoxin-A and citrinin.Books on Demand GmbH, Überseering 33, 22297 Hamburg 260 pp. Englisch.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Hardback. Condition: New. New copy - Usually dispatched within 4 working days.

Condition: New.

Condition: New. In.

Condition: New.

Hardcover. Condition: Brand New. 240 pages. 9.18x6.12x9.45 inches. In Stock.

Condition: New.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Chahar SeemaSeema Completed a Master of Science ( Soil Science & Agricultural Chemistry) From Banaras Hindu University, Varanasi, UP (India). Currently she doing Doctoral degree In Soil Sc.& Agril. Chemistry with A study on the use o.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 260 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 260 pp. Englisch.

Hardcover. Condition: new. Hardcover. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of Technology Computer Aided Design (TCAD). It provides the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 260 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The present study was undertaken to evaluate the toxic features of fungal contaminated meadow grass that had caused the toxicity and even death of cattle in Baihongal and in and around villages in Belgaum district of Karnataka and to confirm the effects of mycotoxins produced by these isolates in rats. The affected animals were exhibiting clinical signs of toxicity characterized by loss of body condition, anorexia, ataxia, bleeding from orifices and eventually death. The present study was undertaken to evaluate the toxicity of fungal contaminated meadow grass in rats and the species of fungi have been isolated and identified as Aspergillus niger, Aspergillus terreus , Rhizoctonia bataticola and Rhizopus stolonifer. The fungal infected wheat material was analyzed for the presence mycotoxins by LC-MS/MS method. The A.niger infected wheat material showed the presence of aflatoxin G1 and aflatoxin G2 , Fumonisin B1& B2 citrinin and beauricin. The A.terreus infected wheat material showed the presence of citrinin and aflatoxin. The Rhizoctonia bataticola infected wheat material showed the presence of Ochratoxin-A and citrinin.

Hardcover. Condition: new. Hardcover. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of Technology Computer Aided Design (TCAD). It provides the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering. 226 pp. Englisch.

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering.

Hardcover. Condition: new. Hardcover. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of technology computer-aided design (TCAD). It provides various algorithms of machine learning, such as regression, decision tree, support vector machine, K-means clustering and so forth. This book also highlights semiconductor materials and their uses in multi-gate devices and the analog and radio-frequency (RF) behaviours of semiconductor devices with different materials.Features:Focuses on semiconductor materials and the use of machine learning to facilitate understanding and decision-makingCovers RF and noise analysis to formulate the frequency behaviour of semiconductor devices at high frequencyExplores pertinent biomolecule detection methodsReviews recent methods in the field of machine learning for semiconductor materials with real-life applicationsExamines the limitations of existing semiconductor materials and steps to overcome the limitations of existing TCAD softwareThis book is aimed at researchers and graduate students in semiconductor materials, machine learning and electrical engineering. Machine Learning for Semiconductor Materials studies recent techniques and methods of machine learning to mitigate the use of Technology Computer Aided Design (TCAD). It provides the various algorithms of machine learning such as regression, decision tree, support vector machine and k-means clustering and so forth. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &Uumlber den AutorrnrnSeema ha completato un Master of Science (scienza del suolo e chimica agraria) dalla Banaras Hindu University, Varanasi, UP (India). Attualmente sta facendo il dottorato in Soil Sc.& Agril. Chemistry con uno studio sull us.