LEADER 00978nam0-22003371i-450- 001 990000427740403321 005 20001010 010 $a88-204-8746-2 035 $a000042774 035 $aFED01000042774 035 $a(Aleph)000042774FED01 035 $a000042774 100 $a20001010d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aGIS per la gestione degli edifici scolastici$fTullio Calosci , Maria Antonietta Esposito 210 $aMilano$cAngeli$d1996 215 $a124 p.$d23 cm$d 225 1 $aCostruire l'ambiente$eprocedure e strumenti$v269.1 610 0 $aScuole 676 $a727 700 1$aCalosci,$bTullio$0334357 702 1$aEsposito,$bMaria Antonietta$f<1954- > 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000427740403321 952 $a08 AE 18$b543$fDINED 959 $aDINED 996 $aGIS per la gestione degli edifici scolastici$9116988 997 $aUNINA DB $aING01 LEADER 06711nam 22006972 450 001 9910785495303321 005 20151005020621.0 010 $a1-107-21867-5 010 $a0-511-85175-8 010 $a1-282-91852-4 010 $a9786612918520 010 $a0-511-91806-2 010 $a0-511-78086-9 010 $a0-511-91527-6 010 $a0-511-91904-2 010 $a0-511-91348-6 010 $a0-511-91708-2 035 $a(CKB)2670000000057925 035 $a(EBL)585303 035 $a(OCoLC)689996445 035 $a(SSID)ssj0000434099 035 $a(PQKBManifestationID)11300703 035 $a(PQKBTitleCode)TC0000434099 035 $a(PQKBWorkID)10396248 035 $a(PQKB)11546369 035 $a(UkCbUP)CR9780511780868 035 $a(MiAaPQ)EBC585303 035 $a(Au-PeEL)EBL585303 035 $a(CaPaEBR)ebr10433594 035 $a(CaONFJC)MIL291852 035 $a(PPN)261338129 035 $a(EXLCZ)992670000000057925 100 $a20100519d2011|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aCancer symptom science $emeasurement, mechanisms, and management /$fedited by Charles S. Cleeland, Michael J. Fisch [and] Adrian Dunn$b[electronic resource] 210 1$aCambridge :$cCambridge University Press,$d2011. 215 $a1 online resource (xvii, 356 pages) $cdigital, PDF file(s) 225 0 $aCambridge medicine Cancer symptom science 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 $a0-521-86901-3 320 $aIncludes bibliographical references and index. 327 $aMachine generated contents note: Preface; Foreword; Part I. Introduction: 1. Introduction to Cancer Symptom Science Charles S. Cleeland, Adrian J. Dunn and Michael J. Fisch; 2. Researching the mechanisms underlying the symptoms of patients with cancer Adrian J. Dunn; 3. Cytokines, sickness behavior: a model for cancer symptoms Steven S. Zalcman, Randall T. Woodruff, Ruchika Mohla and Allan Siegal; Part II. Cancer Symptom Mechanisms and Models: Clinical and Basic Science: 4. The clinical science of cancer pain assessment and management Russell K. Portenoy and Victor T. Chang; 5. Pain: basic science: 5a. Mechanisms of disease-related pain in cancer: insights from the study of bone tumors Patrick W. Mantyh and Juan Miguel Jimenez Andrade; 5b. Neuropathic pain: basic science Patrick M. Dougherty and Haijun Zhang; 6. Cognitive dysfunction: is chemobrain real? Christina A. Meyers and Jeffrey S. Wefel; 7. Cognitive impairment: basic science Perry N. Fuchs, Jessica A. Boyette-Davis and Adrian J. Dunn; 8. Depression in cancer: pathophysiology at the mind-body interface Andrew H. Miller, Michael A. Burke and Charles L. Raison; 9. Depressive illness: basic science: 9a. Animal models of depressed mood and sickness behavior Adrian J. Dunn; 9b. From inflammation to sickness and depression: the cytokine connection Robert Dantzer and Keith W. Kelly; 10. Cancer-related fatigue: clinical science Xin Shelley Wang; 11. Developing translational animal models of cancer-related fatigue Mary W. Meagher; 12. Cancer anorexia/weight loss syndrome Aminah Jatoi and Nisha Lassi; 13. Appetite loss/cachexia: basic science Tristin D. Brisbois-Clarkson, Wendy V. Wismer and Vickie E. Baracos; 14. Sleep and its disorders: clinical science Sofia Ancoli-Israel and Lianqi Liu; 15. Sleep and its disorders Mark R. Opp and Luca Imeri; 16. Proteins and symptoms Bang-Ning Lee and James M. Reuben; 17. Genetic approaches to treating and preventing symptoms in patients with cancer Quiling Shi and Charles S. Cleeland; 18. Functional imaging of symptoms T. Dorina Papageorgiou, Edward F. Jackson and Javier O. Valenzuela; 19. High-dose therapy and posttransplantation symptom burden: striking a balance Sergio A. Giralt and Loretta A. Williams; Part III. Clinical Perspectives in Symptom Management and Research: 20. Promoting symptom research in cooperative groups Lynne I. Wagner and David Cella; 21. Practical aspects of symptom management in patients with cancer Richard T. Lee and Michael J. Fisch; Part IV. Symptom Measurement: 22. Symptom measurement by patient report Charles S. Cleeland and Tito R. Mendoza; 23. The economics of cancer-related symptoms: valuing supportive care interventions Lesley-Ann Miller and Jane C. Weeks; 24. Longitudinal models for symptoms Diane L. Fairclough; 25. Bayesian adaptive design: a new approach to test the effectiveness of symptom-reducing agents using patient-reported outcomes Valen E. Johnson and Tito R. Mendoza; Part V. Government and Industry Perspectives: 26. Promoting cancer symptom science research Ann O'Mara and Maria Sgambati; 27. Developing symptom management drugs Joanna M. Brell and Lori M. Minasian; 28. Cancer-related symptoms: issues for consideration in drug and therapeutic biological product label claims in the United States Jane A. Scott; 29. Symptom research: looking ahead Charles S. Cleeland, Adrian J. Dunn and Michael J. Fisch; Index. 330 $aCancer Symptom Science is the first interdisciplinary compilation of research on the mechanisms underlying the expression of cancer-related symptoms. It presents innovations in clinical, animal and in vitro research, research methods in brain imaging, and statistical-descriptive approaches to understanding the mechanistic basis of symptom expression. This volume also provides perspectives from patients, government and industry. By collecting and synthesizing the developing threads of new approaches to understanding cancer-related symptoms, the book promotes a pioneering framework for merging behavioral and biological disciplines to clarify mechanisms of symptom evolution, incorporating new technologies, testing novel agents for symptom control, and improving patient functioning and quality of life both during and after cancer treatment. With an expert editorial team led by Charles S. Cleeland, an internationally-recognized leader in cancer pain assessment and treatment, this is essential reading for surgical, clinical and medical oncologists, academic researchers, and pharmaceutical companies developing new agents to control symptom expression. 606 $aCancer$xPathophysiology 606 $aSymptoms 615 0$aCancer$xPathophysiology. 615 0$aSymptoms. 676 $a616.99/4071 702 $aCleeland$b Charles S. 702 $aFisch$b Michael J.$f1964- 702 $aDunn$b Adrian J. 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910785495303321 996 $aCancer symptom science$93829291 997 $aUNINA LEADER 05731nam 2200829 a 450 001 9910815808903321 005 20200520144314.0 010 $a9781118735572 010 $a1118735579 010 $a9781118691861 010 $a1118691865 010 $a9781118735640 010 $a1118735641 035 $a(CKB)2550000001102599 035 $a(EBL)1315864 035 $a(SSID)ssj0000917900 035 $a(PQKBManifestationID)11483966 035 $a(PQKBTitleCode)TC0000917900 035 $a(PQKBWorkID)10893234 035 $a(PQKB)10562601 035 $a(DLC) 2013017551 035 $a(Au-PeEL)EBL1315864 035 $a(CaPaEBR)ebr10734639 035 $a(CaONFJC)MIL505082 035 $a(CaSebORM)9781118735572 035 $a(MiAaPQ)EBC1315864 035 $a(OCoLC)841518549 035 $a(OCoLC)869214322 035 $a(OCoLC)ocn869214322 035 $a(Perlego)999757 035 $a(EXLCZ)992550000001102599 100 $a20130805d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDemand-driven forecasting $ea structured approach to forecasting /$fCharles W. Chase, Jr 205 $a2nd ed. 210 $aHoboken, N.J. $cWiley$dc2013 215 $a1 online resource (386 p.) 225 1 $aWiley & SAS Business Series 300 $aIncludes index. 311 08$a9781118669396 311 08$a1118669398 311 08$a9781299738317 311 08$a1299738311 320 $aIncludes bibliographical references and index. 327 $aDemand-Driven Forecasting; Contents; Foreword; Preface; Acknowledgments; About the Author; Chapter 1 Demystifying Forecasting: Myths versus Reality; DATA COLLECTION, STORAGE, AND PROCESSING REALITY; ART-OF-FORECASTING MYTH; END-CAP DISPLAY DILEMMA; REALITY OF JUDGMENTAL OVERRIDES; OVEN CLEANER CONNECTION; MORE IS NOT NECESSARILY BETTER; REALITY OF UNCONSTRAINED FORECASTS, CONSTRAINED FORECASTS, AND PLANS; NORTHEAST REGIONAL SALES COMPOSITE FORECAST; HOLD-AND-ROLL MYTH; THE PLAN THAT WAS NOT GOOD ENOUGH; PACKAGE TO ORDER VERSUS MAKE TO ORDER; "DO YOU WANT FRIES WITH THAT?"; SUMMARY; NOTES 327 $aChapter 2 What Is Demand-Driven Forecasting?TRANSITIONING FROM TRADITIONAL DEMAND FORECASTING; WHAT'S WRONG WITH THE DEMAND-GENERATION PICTURE?; FUNDAMENTAL FLAW WITH TRADITIONAL DEMAND GENERATION; RELYING SOLELY ON A SUPPLY-DRIVEN STRATEGY IS NOT THE SOLUTION; WHAT IS DEMAND-DRIVEN FORECASTING?; WHAT IS DEMAND SENSING AND SHAPING?; CHANGING THE DEMAND MANAGEMENT PROCESS IS ESSENTIAL; COMMUNICATION IS KEY; MEASURING DEMAND MANAGEMENT SUCCESS; BENEFITS OF A DEMAND-DRIVEN FORECASTING PROCESS; KEY STEPS TO IMPROVE THE DEMAND MANAGEMENT PROCESS 327 $aWHY HAVEN'T COMPANIES EMBRACED THE CONCEPT OF DEMAND-DRIVEN?Key Points; SUMMARY; NOTES; Chapter 3 Overview of Forecasting Methods; UNDERLYING METHODOLOGY; DIFFERENT CATEGORIES OF METHODS; HOW PREDICTABLE IS THE FUTURE?; SOME CAUSES OF FORECAST ERROR; SEGMENTING YOUR PRODUCTS TO CHOOSE THE APPROPRIATE FORECASTING METHOD; New Products Quadrant; Niche Brands Quadrant; Growth Brands Quadrant; Harvest Brands Quadrant; SUMMARY; NOTE; Chapter 4 Measuring Forecast Performance; "WE OVERACHIEVED OUR FORECAST, SO LET'S PARTY!"; PURPOSES FOR MEASURING FORECASTING PERFORMANCE 327 $aSTANDARD STATISTICAL ERROR TERMSSPECIFIC MEASURES OF FORECAST ERROR; OUT-OF-SAMPLE MEASUREMENT; FORECAST VALUE ADDED; SUMMARY; NOTES; Chapter 5 Quantitative Forecasting Methods Using Time Series Data; UNDERSTANDING THE MODEL-FITTING PROCESS; INTRODUCTION TO QUANTITATIVE TIME SERIES METHODS; QUANTITATIVE TIME SERIES METHODS; MOVING AVERAGING; EXPONENTIAL SMOOTHING; SINGLE EXPONENTIAL SMOOTHING; HOLT'S TWO-PARAMETER METHOD; HOLT'S-WINTERS' METHOD; WINTERS' ADDITIVE SEASONALITY; Multiplicative versus Additive Seasonality; SUMMARY; NOTES; Chapter 6 Regression Analysis; REGRESSION METHODS 327 $aSIMPLE REGRESSIONCORRELATION COEFFICIENT; COEFFICIENT OF DETERMINATION; MULTIPLE REGRESSION; DATA VISUALIZATION USING SCATTER PLOTS AND LINE GRAPHS; CORRELATION MATRIX; MULTICOLLINEARITY; ANALYSIS OF VARIANCE; F-TEST; ADJUSTED R2; PARAMETER COEFFICIENTS; t-TEST; P-VALUES; VARIANCE INFLATION FACTOR; DURBIN-WATSON STATISTIC; INTERVENTION VARIABLES (OR DUMMY VARIABLES); REGRESSION MODEL RESULTS; KEY ACTIVITIES IN BUILDING A MULTIPLE REGRESSION MODEL; CAUTIONS ABOUT REGRESSION MODELS; SUMMARY; NOTES; Chapter 7 ARIMA Models; PHASE 1: IDENTIFYING THE TENTATIVE MODEL; Stationarity 327 $aAnalysis of the Autocorrelation Plots 330 $aAn updated new edition of the comprehensive guide to better business forecasting Many companies still look at quantitative forecasting methods with suspicion, but a new awareness is emerging across many industries as more businesses and professionals recognize the value of integrating demand data (point-of-sale and syndicated scanner data) into the forecasting process. Demand-Driven Forecasting equips you with solutions that can sense, shape, and predict future demand using highly sophisticated methods and tools. From a review of the most basic forecasting methods to the most a 410 0$aWiley and SAS business series. 606 $aEconomic forecasting 606 $aBusiness forecasting 606 $aForecasting 615 0$aEconomic forecasting. 615 0$aBusiness forecasting. 615 0$aForecasting. 676 $a330.01/12 700 $aChase$b Charles$0967720 712 02$aWiley Online Library (Servicio en línea) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910815808903321 996 $aDemand-driven forecasting$93925524 997 $aUNINA