...The Ambiguity of l-eadership' Uni!e6ity of Califo.nia, Berkeley .o..epr o/ te.d.tthtp zt. zddrcsed ta) the dn. rhe rtue ot whethet lpade6htp.lle.\ ory.niz.tonzl pe4ot6.h.e. zhd l.) the pt6e\ ol rl..rina l..de^, whi.,r tequehly enphetizer ory2niznonzll,t1 elevzit ctit.tia Leade6hip L. pro.et ot .atibuing .au.dtio^ to indi!id!21 \o.izl z.to6 stldy ot l? adeB a! tynbols .nd oI the uo.e$ ol ztr tbu n E 1..d" e fr i Eht 6e Ftod u.t tve Pro6,emr wnh rhe ItrfRtY PftrftR bquny ol \ d.hnn'on a^d nez'ur.neht. tb ^h Ledeahrp nas lo' lome rme b€.h. mijo, so('al a.d olg.n,.al'onal Fy.holoEy. 'n LrndenyinB m!.h ol rhk rcra(h hn been th€ a$o6pron th.r leadeEhlp is caueally r€lared ro o,8a^izarionalp€rtorma.@ ThrouShanan,lyai! ol l€.dedh,p nyles, b€havio6/ o' (h.r.crernri(t (dep€nd ng on rhe ih€orcIk.l p€^pe.riv€ .holenl, rhe.r8um€nr h.!been m.de more€l' 'hal re.riv€ lead.c can be *l€.r.d or tained or, alrernarively, the !iru.rion .:. be .oniiSured ro prolid€ lor enh.n(ed le.der and orBadzatio.al rop,( Ihree probl.mi wnh enph.ri!on rB50' n l.:d.6nip t (on.eprr.. b€ po!€d: (.):mbi8uny in d€ii (o.(. ir*ll; lb) nlionand he.tureme.tol '€ lhe quenio. ol wheih.r l.deRhip h.s di(.mibh .rl.ci! oh o.Banil.tion.l our@mB; .id k) rh€ qledio. pro.€$ in r!..€$io. ro Lrd.trhip pornion!, whi(h teq uendy ! !e! o4.niz.rio.r lly i €l€r.nr (rirerir and *hi.h h8 iopli..taon!lot norm.rive lheori.s ol l€.d.Ehip. The.raurunl herc isrh le.deBhip i!oli.t re5rprih.rily&. phenomenolosic.l...
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...J A Collaborative Approach to Fall Prevention By ANGELA MERRETT, PATRICIA THOMAS, ANNE STEPHENS, ROLA MOGHABGHAB and MARILYN GRUNEIR The four partner organizations formally recognized their commitment and shared vision through a letter of agreement, which specified a framework, definitions and terms for their respective roles and responsibilities. The letter of agreement was intended to foster a sense of belonging among the group and identify leadership and governance for the project. The two site coordinators were responsible for preparing and coordinating the ethical review applications and letters of support. The team decided to use the existing practices of community service providers. To facilitate referrals of participating patients, the team first identified the existing service pathways. The hospital medical director distributed an overview document to inform emergency department physicians of the project, and the clinical nurse specialist reviewed the referral process, protocols and documentation with nursing staff. ore than ever, health-care providers need to communicate with each other to stay informed about the services clients receive. Working in collaboration is essential to the delivery of effective, efficient and timely care (D'Amour, Ferrada-Videla, San Martin Rodriguez, & Beaulieu, 2005; Interprofessional Care Steering Committee, 2007). We all had roles on the Geriatric Emergency ManagementFalls Intervention Team (GEM-FIT) project, which was aimed...
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...University of Hull | Supply Chain Planning and Control-Individual assignment: Pony Group | | | | 4/13/2013 | 56130 Model leader: Riccardo Mogre | Student number: 201100320 Word count: 2,662 Contents 1. Introduction 2 2. Section 1: Demand forecast 2 2.1. Moving average 2 2.2. Simple Exponential Smoothing 3 2.3. Holt’s Model 4 2.4. Winter’s Model 5 2.5. Demand forecast for XYZ 8 3. Section 2: Aggregate planning 9 3.1. Aggregate planning Question 1 10 3.2. Aggregate planning Question 2 11 4. Section 4: Pricing and revenue management 12 Appendix 15 1. Introduction This assignment is based on the market information of Pony group, an electronic manufacturer, to calculate and forecast the future development of this company. The topic is divided into three sections, the first part is to forecast the demand for next four months of Pony LCD TV screen, the second section is to identify the optimal production schedule for the cell phones market. The last section is to identify the optimal price for pony handheld consoles. 2. Section 1: Demand forecast In this section, it will provide the demand forecast for next four months based on the historical demand data. There are four forecast methods used in this part, which are moving average, simple exponential smoothing, Holt’s model, and Winter’s model respectively. Firstly, I will figure out the MAPE (mean absolute percentage error) of all these four methods, and by comparing...
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...TIME SERIES Contents Syllabus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Models for time series 1.1 Time series data . . . . . . . . . . . . . 1.2 Trend, seasonality, cycles and residuals 1.3 Stationary processes . . . . . . . . . . 1.4 Autoregressive processes . . . . . . . . 1.5 Moving average processes . . . . . . . . 1.6 White noise . . . . . . . . . . . . . . . 1.7 The turning point test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii iii iv 1 1 1 1 2 3 4 4 5 5 5 6 6 7 7 8 9 9 9 12 13 13 15 16 17 17 17 18 19 2 Models of stationary processes 2.1 Purely indeterministic processes . . . . . . 2.2 ARMA processes . . . . . . . . . . . . . . 2.3 ARIMA processes . . . . . . . . . . . . . . 2.4 Estimation of the autocovariance function 2.5 Identifying a MA(q) process . . . . . . . . 2.6 Identifying an AR(p) process . . . . . . . . 2.7 Distributions of the ACF and PACF . . . 3 Spectral methods...
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...First Case, Due October 12, 2O1Z f,ichard lvey Sttrcst the University of Iry:srcrn Onaario f lTrin*r hrcy lHl2t0g YOUlilc trf,AN tNC. Dave shaw aN E izabu, M.A. Grzsby ravised fltis case identifying i$anratkm to pr-otect corfrctenti@ f,*:Y'-Ww.y!,W!!!-y:ye!:rfl tttuorrate etther amive soarv o pror,il or imftadire hautin{ cf a'iriaagrera i situation. {wnw t'dect -The unionvitts g!fr &ore, wrltbn ctautia lrlalwney roi ctiJtrjussio/,. rhe authors (b kt,,td to iaiii m" w a"* td u*n*i]#ri'ffi]ffil;i;eH;{; X,# #X# Eusi,ness, The univetsilv ot l,yeslen, mail casr,s@,iwy.urrc.ca. Richxd lvey *laol of 6.,s,;'/Ess Fo udation prohibits arry.form of repr(tr,tfi//ion,s*rage ar tra'r'mittat without its written pemixion. Reprdtntian af lhis lnatenat is not averad imdr,r iunaiuatlon oy *y r4s organiziiia;--iirib, wa"orr*qussf p€"r"$s,ott ta GProch{E maienats, w,fiad tvey Fuhlishing, nicnurb "i*i'rcfi* uiry-6cri*t i aaslress FourdaBorr, clb Rldlag lvoy$dmo; of ontano,'Lod, dtrtEtio, ca;aia,-i6;;'3;il; phone (Stg) 66t'32a}; tux (slg) ofgzslness Foutflatian 6613{yi2; e_ Copyright@ 2012, Rrc/rard I*y Se*a/ Uersbn- 20t2_0$4t 11:tclothtug 51016'fotrng lrda& Irrc-* and was arxicus to lorow how the and to assess tbal performance. I-g-t,#U tlo I*-lo had just finished his first hectic year as owntr and opamor of his .orr,p*y hrd p;;dd fmar:cially HrurtEr graduald with a ruriversity holours degrec ia businEss aonr...
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...BRUNEL BUSINESS SCHOOL COVERSHEET FOR ONLINE COURSEWORK SUBMISSIONS |Module Code |MG2138 | |Module Title |Marketing Channels and Logistics (A 2012/3 YEAR) | |Module leader |Michael Bourlakis | |Student ID number |1138368 | |Student name | | |Degree Programme (e.g. Business and Management (General)) |Business and Management (Marketing) | |I understand that the School does not tolerate plagiarism. Plagiarism is the knowing or reckless presentation of another | |person’s thoughts, writings, inventions, as one’s own. It includes the incorporation of another person’s work from published | |or unpublished sources, without indicating that the material is derived from those sources. It includes the use of material | |obtained from the internet. (Senate Regulations 6.46) | | ...
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...f Name:/,fwn/ue Student lM'o3 n: //? w COURSE DATE VENUE DURATION 1. 2. 3. 4. 5. : CBB1023/CC81023 PHYSICAL CHEM|STRY (Friday) : 15 July2011 : TestRoomBlockN : 20:00 22:00hrs (120minutes) TNSTRUCT|QNS ALL TESTEES TO write NAME,lD & SIGNATURE top of EVERY pageof the Testsheets. on ThisTestSheets contains FIVE(5) aUESTIONS withtotalof max.20 markseach. Answerin inkALL questions the provided on spaces.Eachsub-question countto max of 4 markseach. Answer all quantitative questions to mustbe NUMERICAL VALUESnotonlyequation. Forthe purpose thistest,convert results S.l. UNITS of all to ONLY. A/ofe; (6) Thereare S_lX PAGESin thisTestSheets including coverpase this SELECTED CONSTANTS, CONVERSION FACTORS MATHEMATICAL & RELATIONSHIPS Constants A:0.509 e : 1. 60 2 1 7 6 1 0 - 1e x C eo: 8.85419 10-12 C2m-l x J-r er= 78.54(water) Ne:6.02214x 1023 mol-r tr - 3.141592 R: 8 . 3 1 4 4 7 l K-m ol -r r Data A,(C) = 12.01 A,(H): 1.0079 A,(O): 16.00 A.(N): 14.01 A,(Na):22.99 cp,,'[co26)]: +37.11 K-l J mol-' cp,,'[Flzo(t)]: +75.291 K-t J mol'' +46.4J K-r mol'r cp,n'[Na*(aq)]--84 Cp,"[CHTCOO-(aq)]: J K-r mol-' : cp,,'[NaHCos(s)] +53.00 K-r J mol-' : A1H'[COz(S)]-393.51 mol-r kJ : -285.83 mol-r A1I{'[H2O(/)] kJ AfI'[Na*(aq)]: -240.12 mol-' kJ A1H'[CH3C (aq)]: + I 50.6kJ OO' mol-' : ArH'[NaHCOr(s)] -23L00kJ mol'' pm r(K *): 1 51 r(C l -): 1 81 pm Conversion I a tm= 1 01325 Pa I b a r: 1 05 Pa lTl,cp,,', /trrH** lorU^: pMp/p(fl * //P(a)l I = %Eizibi log yn: -lz*z-lA1/' lP: vrlp lp: Mlp/p nolo: nBlB...
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...Question 1: Forecasting (9 marks) Each Friday, Smith Machine Parts (SMP) LTD has to forecast the next week’s demand for brackets #357, #358 and #359. For many years, this has been done reasonably well by Joe Smith, who recently retired. When asked how he made his forecasts, Joe said (between puffs on his corn cob pipe), “Well, I looks at the bubbles on me beer, and I rubs me lucky rabbit foot, and the forecast comes to me maid.” SMP has asked three of its analysts to propose scientific forecasting systems to replace old Joe. The forecasters and the proposed methods are as follows: A. Allen: three-week moving average B. Black: exponential smoothing with ( = 0.2 C. Clark: simple linear regression using the previous 6 weeks of data Each of the three forecasters has been assigned one bracket. The demand for the past six weeks (in thousands of brackets) is given below, as well as the forecasts obtained with the three suggested methods: |Bracket #357 (three-week moving average): | |Bracket #358 (exp. smoothing with ( = 0.2): | |Week # |Demand |Forecast | |Week # |Demand |Forecast | | | | | | | | | |1 | |- | |1 ...
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...| | Prepared by: SHRUTI GULATI | | | | | | DEXELEX To assess the existing clinical practice of INTENSIVISTS & ANAESTHESIOLOGISTS for the management of sedation and analgesia while taken into consideration “dexmedetomidine” ACKNOWLEDGEMENT I would like to express my sincere gratitude towards RANBAXY Laboratories Limited, Mumbai for selecting me as a summer trainee and giving me an opportunity to work on this project and also for providing a good working ambience for successful completion of this project. I also thank the MARKETING RESEARCH TEAM for giving me an opportunity to conduct market research for DEXELEX belonging to INTENSIVA division of RANBAXY. I would like to express my special gratitude towards my guide Mr. Shoreb Khanduri, who throughout my project has been teacher and supporter for me showing me the right direction during my project. I am highly grateful to all summer interns and friends for their support and cooperation, owing to which I have successfully completed and compiled this report Last but not least, I would like to express my sincere thanks to my institute NIPER for providing me all kind of support in my summer project placement at RANBAXY Laboratories Limited. Shruti Gulati Certificate This is to certify that the dissertation submitted in partial fulfillment for the degree of M.B.A. in Pharmaceutical Management of National Institute of Pharmaceutical Education and Research (NIPER) is a result of the bonafide research work...
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...Ind. Eng. Chem. Res. 2010, 49, 8657–8669 8657 Single-Stage Scheduling of Multiproduct Batch Plants: An Edible-Oil Deodorizer Case Study Songsong Liu,† Jose M. Pinto,‡ and Lazaros G. Papageorgiou*,† Centre for Process Systems Engineering, Department of Chemical Engineering, UniVersity College London, Torrington Place, London WC1E 7JE, U.K., and Process Systems R&D, Praxair Inc., 39 Old Ridgebury Rad, Danbury, Connecticut 06810 This article considers the short-term scheduling of a single-stage batch edible-oil deodorizer that can process multiple products in several product groups. Sequence-dependent changeovers occur when switching from one product group to another. Based on the incorporation of products into product groups, mixed integer linear programming (MILP) models are proposed for two scenarios, with and without backlogs. Then, the models are successfully applied to a real-world case with 70 product orders over a 128-h planning horizon. Compared with a literature model developed for a similar problem, the proposed models exhibit significantly better performance. 1. Introduction In the past decade, a large number of optimization models and approaches have been proposed for batch scheduling and planning. A number of reviews on the planning and scheduling of batch processes have been presented in the literature.1-6 Initially, discrete-time formulation models using the state-task network7 (STN) or resource-task network8 (RTN) were used for batch scheduling problems...
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...REPORT ON HEURISTICS SOLUTION FOR COMPLEX CHEMICAL CARGOES Contents CHAPTER 1- INTRODUCTION 5 1.1 Background 5 CHAPTER 2 - LITERATURE REVIEW 7 2.1 Classification Scheme 7 2.2 Modes of shipping 7 CHAPTER 3 - APPLICATION AND MODEL 9 3.1 Description of the project 9 3.2 Objective of this project 10 3.3 Model Formulation 11 3.4 Mixed-integer Linear Programming Formulation 14 CHAPTER 4 - SOLUTION ALGORITHM 15 4.1 Problem complexity 15 4.2 Heuristic Algorithm 15 4.3 Test case 18 CHAPTER 5 - COMPUTATIONAL RESULT 21 CHAPTER 6 - CONCLUSION 24 6.1 Conclusion and Recommendation 24 6.2 Future Research 24 REFERENCES 26 APPENDIX B – Various ship routing and scheduling problem 29 APPENDIX C – Basic notation for this project 33 CHAPTER 1- INTRODUCTION 1.1 Background Logistics plays an integral role in companies dealing with tangible goods, both finished and semi-finished products. This ranges from electronic products to computer software and even liquid chemicals. Companies need to ensure their products reach their customers in the right quantity, at the right time and the lowest cost to maximize their profits. Logistics component of a company’s operation can be kept in house, if it is considered a key function; otherwise can be out sourced to an established logistics company, such as Federal Express (FedEx). Goods can be delivered via air, sea or land freight. Long distance deliveries use mainly air or sea fright, with land fright...
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...Table of Contents Section 1 – Motor and Load Basics.................................................................................................... 9 AC Motors........................................................................................................................................... 11 NEMA Design Types........................................................................................................................ 12 Motor Synchronous Speed............................................................................................................... 12 3-Phase Motor Connections - NEMA ............................................................................................... 13 Rotor Inertia – NEMA....................................................................................................................... 16 3-Phase Motor Connections – IEC Nomenclature............................................................................ 17 Rotor Inertia – IEC ........................................................................................................................... 18 AC Motor Operation above Base Speed .......................................................................................... 19 Synchronous Motors ........................................................................................................................ 20 Wound Rotor........................................................................................
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