Consumer & Family Studies/Dietetics Dept

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Consumer & Family Studies/Dietetics Dept

Cetrone, Daela, Associate Producer has reference to this Academic Journal, PHwiki organized this Journal Consumer & Family Studies/Dietetics Dept College of Health & Social SciencesWelcome New CFS/D majors August 26, 2015Nancy Rabolt, Chairnrabolt@sfsu.eduhttp://cfsd.sfsu.eduCFS/D FacultyApparel Design & Merch in addition to isingDr. Connie UlasewiczDr. Kelly Reddy-BestIvana Markova, Gail BaughDietetics Dr. Gretchen George Claire Walsh Dr. Janet Sim, W in addition to a Siu-Chan, Maryann Smitt, Alicia Connor, Bethany Pianca, Alisha Rowe, Christine Batten, Kathrin Spoeck Family & Consumer Sciences Dr. Sherria Taylor Anne Markovich, Laura Kuras Interior DesignDr. Karen Johnson-CarrollDr. Gus VouchilasDr. Kenneth Fehrman, Jennifer Dye, Jerry Vanslambrouck, Pam Schneider

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CFS/D ValuesCFS/D Mission: to prepare scholarly professionals who contribute to the well-being of individuals, families, communities in addition to the institutions, industries in addition to businesses which serve them. AccreditationAmerican Association of Family & Consumer SciencesAcademy of Nutrition in addition to DieteticsEssential life skills/ProfessionalismApparel, nutrition, interiors, child developmentWork with in addition to give back to the communitye.g., Goodwill project repurposing apparel; DIFFA event (supports AIDS research)Sustainability apparel, food, interiors/housingProgramsApparel Design & Merch in addition to ising BS Apparel Design emphasisFashion Merch in addition to ising emphasisDietetics BSInterior Design BSFamily & Consumer Sciences BAGeneral in addition to Teacher Preparation emphasisFoods & Nutrition emphasisChild in addition to Family Sciences emphasisFamily & Consumer Sciences MADietetics Internship Certificate (CEL)BasicsBurk Hall 329: CFS/D Dept officeCome to the office in addition to call the dept with any dept questions (415/338-1219)Kelly Vuong: Acad Office Coordinator (AOC) Michael Jensen: Tech AssistantStephen, Jacky, Nathan, Tiffany: Student WorkersAdvising Files in BH 329See your advisor every semester; sign up as long as an office hour Develop Graduation Plan with advisor; sign it; scan to emailFOLLOW plan (pre-reqs important!)Check out advising folder in addition to take to appt/bring it back to dept officePls cancel appt if you can’t make it

Graduation PlanDeadlinesBe very aware of SFSU deadlines!Drop: only 2 weeks into semester: Sept 4Drop on your Student CenterInstructor is NOT responsible as long as dropping youAdd: 4 weeks into semester: Sept 21Use permission number procured from instructorInstructor cannot add youIf class is “full” go to the first day of class; be an advocate as long as yourself! If the class is on your Graduation Plan, it may only be offered then in addition to you must have it that semester.CR/NC: Oct 19Need at least a C- as long as CR; NC does not hurt GPAUse this if no documentation to withdrawRequest on your Student CenterInstructor cannot do this as long as youNote: Letter grade required as long as all major courses, not CRWithdrawal deadlines: Nov 20; Dec 16Needs instructor, dept chair, in addition to college dean signaturesNeeds documentation as long as serious in addition to compelling reasonAdvising/Help (don’t ask students)Dept CFS/D advisor Meet once a semesterCollege of HSS Student Resource Center Student Success Program UniversityDPR (degree progress report) on Student CenterUnofficial/it’s a guideDPRC (disability programs in addition to resource center)OIP (office of international programs)SFSU Advising Center (GE or university requirement questions)

TIPSApply as long as Scholarships: Bookstore $100 credit, Stonestown, Sarah JosefCheck your registration through your Student CenterALWAYS check your registration after making any changes in addition to BEFORE deadlines to be sure you are registered properly as long as your classes. Protect your GPACommunicate with instructors/emailGo to first day of class (crash) if can’t get classIf registered in addition to you don’t attend 1st day of class, you may be droppedSee your advisor every semester to update Plan. There may be changes.You may need updated Advising/Graduation Plan on first day of classes. The dept office will scan in addition to email plan to you. Forward your SF State email to your personal accountIt’s important to get all faculty in addition to university emails, sent from rosters in addition to ilearn Double count major/minor in addition to GE (120 units)Don’t get advice from fellow students!Portfolio in addition to field experience in your senior yearCFS 600 Professional Development ADM, FCS, ID Work Experience component needs approval Don’t start one without approvalPortfolio: Save projects from each class Weebly, wix, moonfruit, google docs, etc.DFM 655 Nutrition Education in addition to Communication Portfolio: Save projects from each class Weebly, wix, moonfruit, google docs, etc.Student Organizations (check websites in addition to bulletin boards near BH 329)American Society of Interior Designers Student ChapterFamily & Consumer Sciences Student AssociationFashion Network AssociationStudent Dietetic Association

Apparel Design & Merch in addition to ising Apparel Design Fashion Merch in addition to ising Fashion Network Association Fall in addition to Spring shows, students in addition to alumniClothing drivesSustainability valuesStudy Abroad (Italy: NABA, Univ of Sydney)Interior Design ASID Student ChapterSpring Showcase of student work in SpringSF Design CenterCommunity outreach (adult probation office, SFO)Sustainability valuesStudy Abroad (Italy, London, Sydney)DieteticsLeads to RD (registered dietitian/Academy of Nutrition in addition to Dietetics accreditation)Chem, Biochem, Anatomy, Physiology classesInternship after BS to sit as long as RD examStudent Dietetic AssociationSlow Food/nutrition as long as wellness in addition to diseasePeach peer program/student health centerVista Room dining room/kitchenShowcase of student work in MayStudy Abroad (Spain, Sydney)

Family & Consumer SciencesGeneral FCS/Teacher Preparation High school teachingObserve in high schoolsGo on as long as a teaching credential at graduate levelFoods & Nutrition (does not lead to RD)Work in Vista Room Child & Family SciencesWork at the Children’s Campus in addition to other community sitesShowcase of student work in MayPortfolioshttp://heatherannfrey.wordpress.com (ADM)http://cargocollective.com/hayleymdesign/ (ID)http://alyssacalpo.wix.com/acalpo (ID)

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Cetrone, Daela Associate Producer

Cetrone, Daela is from United States and they belong to Inside Edition – King World Productions and they are from  Culver City, United States got related to this Particular Journal. and Cetrone, Daela deal with the subjects like Celebrities; Entertainment; National News

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History of Computing in Medicine Beginnings Beginnings Eliza MUMPS

History of Computing in Medicine Beginnings Beginnings Eliza MUMPS www.phwiki.com

History of Computing in Medicine Beginnings Beginnings Eliza MUMPS

Kandas, Wayne, Host/Producer has reference to this Academic Journal, PHwiki organized this Journal History of Computing in Medicine Beginnings 1950’s computers in bioengineering Early 1960’s Medline Laboratory instrumentation computers (LINC) MUMPS developed at MGH GEMISCH (generalized medical in as long as mation system as long as community health at Duke) (Stead MD) 1975 8080 processor – Altair 8800 1976 Apple Computers Hackers: Steven Levy Beginnings Initial application: automated patient questionnaire (Slack: ‘55) 1965 Patient centered computing Cybermedicine Center as long as Clinical Computing Dr. Slack maintained of Eliza that soliloquy, (with or without a computer) can be a valuable tool of mental health. He wrote: “Contrary to the common notion that soliloquy is a manifestation of mental illness, we believe that it is normal behavior–behavior that serves to help maintain emotional equilibrium.”

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Eliza Eliza (MIT 1960’s) initially designed as a spoof vs. attempt to pass the Turing test Eliza Quack Eliza MUMPS MGH utility multi-programming system (Octo Barnett 1966) Thou shalt not declare variable types or file sizes. Thou shalt not KILL, except as long as globals in addition to variables. Thou shalt not covet they neighbor’s UCI (User Class Identification = computing area). Remember string h in addition to ling, as long as it shall make MUMPS special.

MUMPS Now known as M A programming language with extensive tools as long as the support of database management systems. MUMPS was originally used as long as medical records in addition to is now widely used where multiple users access the same databases simultaneously, e.g. banks, stock exchanges, travel agencies, hospitals. MUMPS Language plus data structure Designed by MD’s in addition to engineers Designed as long as medical environment Low computing power – data entry >>> computing Flexible string structure Inverted tree structure (sparse) Multi-user environment Interpreted More flexible, efficiency not necessary MUMPS code f p=2,3:2 s q=1 x “f f=3:2 q:ff>p!’q s q=p f” w:q p,$x8+18 prints a table of primes, including code to as long as mat it neatly into columns

Beginnings 1977 – Medical In as long as matics defined Discipline dealing with the problems associated with in as long as mation, its acquisition, analysis in addition to dissemination in the health care delivery process 1978 – DEC transitions from PDP to VAX 1980 – IBM PC (MS-DOS) 1982 – medical in as long as matics definition exp in addition to ed to include care, education in addition to research Beginnings 1983 – Shortliffe “medical in as long as matics covers more than just applications of computers to medicine” 1986 Macintosh developed AAMC “medical in as long as matics combines medical science with several disciplines in the in as long as mation in addition to computer sciences in addition to provides methodologies by which these can contribute to better patient care” Artificial Intelligence in Medicine Clancey, Shortliffe (1984) Medical artificial intelligence is primarily concerned with the construction of AI programs that per as long as m diagnosis in addition to make therapy recommendations. Unlike medical applications based on other programming methods, such as purely statistical in addition to probabilistic methods, medical AI programs are based on symbolic models of disease entities in addition to their relationship to patient factors in addition to clinical manifestations

Early AIM Internist/QMR Designed at University of Pittsburgh Mycin, Oncocin Designed at Stan as long as d by Shortliffe’s group AIM Internist Designed to reproduce the behavior of a diagnostician

Kandas, Wayne Your Financial Plan - KFNN-AM Host/Producer www.phwiki.com

Current resources AMIA curriculum 2001 Health in as long as mation resources on the web IT Medical Literature Newsgroups/chat rooms/support Health news Future Compare American (vs. Japanese) industry in the late 1980’s Barriers

Kandas, Wayne Host/Producer

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Environment Topics What?s So Difficult About Controlling Coasters?

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Environment Topics What?s So Difficult About Controlling Coasters?

Central Michigan University, MI has reference to this Academic Journal, What?s So Difficult About Controlling Coasters? Nick Mansur Vice President Scientific Systems Walt Disney Imagineering Topics Environment Approach Next Generation Operational Environment High Capacity High Availability Continually Changing Operations & Maintenance Staff Site-Dependent Regulations

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Political Environment The Public Believes a Disney Park Is Intrinsically Safe We Live in a Litigious Society We?re an Entertainment Company ?Real? Requirements Are Often Subjective Development Environment Small Teams Short Timeframes Parallel Installation Activities Hazards Collisions Excessive Accel/Decel Unplanned/Unwanted Motion

Other Considerations System Initialization System Repair/Test Modes Station Operation Adding/Removing Vehicles User Interface Safety Philosophy Stop If There?s a Problem ?One Way Down the Mountain? No Reasonable Failure Shall Jeopardize Guest Safety Integrate Control in addition to Safety Design Process Employ the ?Right? People Follow Standards & Procedures ?Two Sets of Eyes?

1. Introduction so that Pattern Recognition in addition to Machine Learning. Structure. What are Patterns? Patterns in Astronomy. Patterns in Biology. Patterns of Brain Activity. Variations of Patterns. Speech Patterns. Goal of Pattern Recognition. Applications of Pattern Recognition. Two Extreme Approaches Example: Salmon versus Sea Bass. Fish Features. Length. Fish Features. Lightness. Decision Boundaries. Generative Models in consideration of Speech. Bayes Decision Theory Learning Theory. Course Elements.

The Design Proven Technologies Redundancy Checking Algorithms Disney Ride Control System Architecture Each Processor Sees the Same Inputs Each Processor Runs Identical Software Only Control Processor Controls Field Equipment Either Processor Can Remove Power Sync/Vote Signals Used so that Minimize Disagreements Control Processor Monitor Processor Input Cards Input Cards Output Cards Output Cards Output Cards Bus Controller Message System Field Equipment (motors, valves, solenoids, etc.) Bus Status Sync, Votes Sync, Votes Ride Status Power Ride Status Field Equipment (sensors, monitors, buttons, switches, etc.) Power Typical Block Zone At Least One Set of Closed Brakes Between Vehicles Exit Sensors Control Brake Operation Entry Sensors Detect Intrusion Sensor Sequencing in addition to Sensor/Subzone Timing Zone 2 Entry Sensor Zone 2 Brakes Zone 2 Exit Sensor Zone 3 Entry Sensor Zone 3 Brakes Zone 3 Exit Sensor

The Next Generation Drive in consideration of Bigger ?Wow? Requires More Sophisticated Technology Increases Complexity Decreases Comfort Level California Legislation

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Discussion 31/20/2014OutlineHow so that fill out the table in the appendix

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Discussion 31/20/2014OutlineHow so that fill out the table in the appendix

Central Michigan University, US has reference to this Academic Journal, Discussion 31/20/2014OutlineHow so that fill out the table in the appendix in HW3What does the Model statement do in SAS Proc GLM (please download lab 2 in consideration of reference) What is a statistical model in layman?s termsWhat are the residuals in addition to predicted valuesHow so that calculate Variance ComponentsPower questionsQuestions?

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How so that fill out the table in the appendix in HW3The treatments are the herbicides used in addition to there are 6 different herbicides (treatments)The pots are the objects that were applied the treatment therefore they are your experimental unitEach pot contained 4 plants therefore 4 measurements were made so that each pot (sub-samples)How so that fill out the table in the appendix in HW3What does the Model statement do in SAS Proc GLMWhat is a statistical model in layman?s termsA mathematical equation constructed so that describe the makeup of an observation or a group of observationsExample: One-way ANOVA modelThe One-way ANOVA model describes an observation (Y) as a deviation from an overall mean (æ) of a group of observations due so that a treatment effect (?) in addition to the addition of random error (?).

What does the Model statement do in SAS Proc GLMThe overall mean (?.) is the mean of all the observations Yij Yij read as observation from the ith treatment in addition to the jth replication.The treatment effect is the deviation from the overall mean ?. so that the treatment mean (?i.)The random error is the deviation from the treatment mean (?i.) so that a given replication of that treatment (Yij )What are the predicted values?The theoretical values obtained based on the statistical model (the error is excluded from the model):What are the residuals?The deviation from the expected values so that the observed values:What does the Model statement do in SAS Proc GLMPredicted Yij( ) To do ANOVA in SAS we use Proc GLM in addition to specify our model:Example 3.2 in lab 2:What does the Model statement do in SAS Proc GLMProc GLM; Class Culture; Model Nlevel = Culture; Means Culture; Output Out = Residual R = Res1 P = Pred1; The Class statement tells SAS that our data is grouped by the variable Culture in the model statement we tell SAS that we want so that explain Nlevel by the variable Culture; therefore:SAS calculates the overall mean in addition to the residuals but what Proc GLM is also calculating is the sums of squares, mean squares, F ? values, in addition to p ?values in consideration of each variable we specify in the model see how in the following slide.

4-H Horse Judging Contest Contests consist of: Suggestions in consideration of Beginners Let?s Begin!!! Horse Judging III: (Notes in addition to Reasons) PPA5-0001.00 SURPRISE!!! Now take your ?Brief Notes? in addition to turn them into ?Complete Reasons? Learning Your Reasons !!! Presenting Your Reasons!!! Now we have completed ALL of the steps so that judging Ya?ll Did Great!!! See ya?ll next time when we study more about Horse Judging!

How does SAS calculate the sums of square (SS)?What does the Model statement do in SAS Proc GLMWhich is equivalent to:When you divide the SS by their respective degrees of freedom the mean squares are obtained (Equivalent so that the variances, s2)Where: r = number of replicationsIn Nested Designs:Example 3.4 in lab 2:What does the Model statement do in SAS Proc GLMProc GLM; Class Trtmt Pot; * We want SAS so that calculate the variances between pots because that will be our error in consideration of our ANOVA Model Growth = Trtmt Pot(Trtmt); *Pot is not a treatment. Pot is only an ID variable Random Pot(Trtmt); *must specify pot as random because we are not interested in detecting differences between pots Test H = Trtmt E = Pot(Trtmt); * Here we request a customized F testWhere i is treatment ID, j is replication ID, k is subsample IDr = number of replications, s = number of sub-samplesTotal SSTreatment SSPot SS (e.u.)sub-sample SSHow so that Calculate Variance ComponentsWe analyze nested designs so that estimate the variance components which can be used so that estimate optimal sub-sample size (section 3.5.2.3 in lecture reading topic 3)The variance components are the estimate of variance in consideration of a particular variable (e.g. treatment, experimental unit, in addition to subsample)The variance components can be calculate using Proc VarComp in SAS

In the lecture topic 3 reading section 3.5.2.2 an experiment is described where mint plants are exposed so that different treatments of temperature in addition to daylight in addition to stem length was measuredThere are a total of 6 treatments, 3 pots (replications) per treatment, in addition to 4 plants were measured per pot (subsamples)How so that Calculate Variance ComponentsThe sums of squares was calculated in consideration of treatment, pots in addition to subsamplesThen the variances (equivalent so that means squares)How so that Calculate Variance ComponentsTotal SSTreatment SSPot SS (e.u.)sub-sample SSMS Treatment MS PotMS sub-samplet -1t (r -1)rt (s – 1)Where t = number of treatments, r = number of replications, in addition to s = number of sub-samplesThe variance due so that the sub-sample is the variance due so that error:How so that Calculate Variance ComponentsMS sub-sample = ??2To estimate the variance component of the subsample we just calculate the MS of sub-sample=0.93

The variance of pots contains the variance of the sub-samples (NOTE: this is not the variance component of pots):How so that Calculate Variance ComponentsMS Pots = ??2 + 4??2To estimate the variance component of pots we have so that solve in consideration of ??2. The variance components of pot is calculated below:??2 = (MS Pots – ??2) / 4??2 = (2.15 – 0.93) / 4 = 0.30= 2.15The variance of treatments includes the variance of pots in addition to subsamples (NOTE:):How so that Calculate Variance ComponentsMS Treatments = ??2 + 4??2 + 12??2/5 = 35.92To estimate the variance component of treatments only we have so that solve in consideration of ??2/5??2/5 = (MS Treatments – ??2 – 4??2) / 12 ??2/5 = (35.92 – 0.93 ? 4*0.3) / 12 = 2.81 PowerHow so that use Power Charts in consideration of ANOVA

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