LEADER 02535nam 2200565Ia 450 001 9910782462703321 005 20230725183122.0 010 $a1-78684-856-2 010 $a1-281-80624-2 010 $a9786611806248 010 $a0-8261-1632-9 035 $a(CKB)1000000000576890 035 $a(EBL)423231 035 $a(OCoLC)437109614 035 $a(SSID)ssj0000147197 035 $a(PQKBManifestationID)11152744 035 $a(PQKBTitleCode)TC0000147197 035 $a(PQKBWorkID)10016007 035 $a(PQKB)10807977 035 $a(MiAaPQ)EBC423231 035 $a(Au-PeEL)EBL423231 035 $a(CaPaEBR)ebr10265331 035 $a(CaONFJC)MIL180624 035 $a(EXLCZ)991000000000576890 100 $a20020111d2002 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aEnduring issues in American nursing /$fEllen D. Baer [et al.]., editors 210 1$aNew York :$cSpringer Pub. Co.,$d2002. 215 $a1 online resource (393 pages) 311 0 $a0-8261-1373-7 320 $aIncludes bibliographical references and index. 327 $aContents; Contributors; Preface; Section 1: Contemporary Issues in Historical Context; Section 2: Identity: The Meaning of Nursing; Section 3: The Nature of Power and Authority in Nursing; Section 4: The Nature of Nursing Knowledge; Section 5: Conclusion; Appendix: Suggestions for Further Reading; Index 330 $a""Why turn to the past when attempting to build nursing's future?...To make good decisions in planning nursing's future in the context of our complex health care system, nurses must know the history of the actions being considered, the identities and points of view of the major players, and all the stakes that are at risk. These are the lessons of history."". -- from the Introduction. This book presents nursing history in the context of problems and issues that persist to this day. Issues such as professional autonomy, working conditions, relationships with other health professionals, appropri 606 $aNursing$zUnited States$xHistory 606 $aNurses$zUnited States$xHistory 615 0$aNursing$xHistory. 615 0$aNurses$xHistory. 676 $a610.730973 701 $aBaer$b Ellen Davidson$01465413 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782462703321 996 $aEnduring issues in American nursing$93675429 997 $aUNINA LEADER 02291oam 2200445zu 450 001 9910146778903321 005 20210806235834.0 010 $a1-5090-9770-8 035 $a(CKB)1000000000022675 035 $a(SSID)ssj0000453788 035 $a(PQKBManifestationID)12128878 035 $a(PQKBTitleCode)TC0000453788 035 $a(PQKBWorkID)10481690 035 $a(PQKB)11337283 035 $a(NjHacI)991000000000022675 035 $a(EXLCZ)991000000000022675 100 $a20160829d2005 uy 101 0 $aeng 135 $aur||||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$a2005 4th International Workshop on Information Processing in Sensor Networks 210 31$a[Place of publication not identified]$cI E E E$d2005 215 $a1 online resource 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-7803-9201-9 330 $aWe consider a network of distributed sensors, where each sensor takes a linear measurement of some unknown parameters, corrupted by independent Gaussian noises. We propose a simple distributed iterative scheme, based on distributed average consensus in the network, to compute the maximum-likelihood estimate of the parameters. This scheme doesn't involve explicit point-to-point message passing or routing; instead, it diffuses information across the network by updating each node's data with a weighted average of its neighbors' data (they maintain the same data structure). At each step, every node can compute a local weighted least-squares estimate, which converges to the global maximum-likelihood solution. This scheme is robust to unreliable communication links. We show that it works in a network with dynamically changing topology, provided that the infinitely occurring communication graphs are jointly connected. 606 $aInformation networks$vCongresses 606 $aMultisensor data fusion$vCongresses 606 $aSensor networks$vCongresses 615 0$aInformation networks 615 0$aMultisensor data fusion 615 0$aSensor networks 676 $a025.04 801 0$bPQKB 906 $aPROCEEDING 912 $a9910146778903321 996 $a2005 4th International Workshop on Information Processing in Sensor Networks$92511805 997 $aUNINA