89,312 research outputs found
Amiodarone and reperfusion ventricular fibrillation
No full text[No abstract available]Ayoub CM, 2009, EUR J ANAESTH, V26, P1056, DOI 10.1097-EJA.0b013e32832f0dfb; NANAS JN, 1995, CIRCULATION, V91, P451; SAMANTARAY A, 2009, J CARDIOTHORAC VASC1
Coagulation of highly turbid suspensions using magnesium hydroxide: Effects of slow mixing conditions
No full textLaboratory experiments were carried out to study the effects of slow mixing conditions on magnesium hydroxide floc size and strength and to determine the turbidity and total suspended solid (TSS) removal efficiencies during coagulation of highly turbid suspensions. A highly turbid kaolin clay suspension (1,213 ± 36 nephelometric turbidity units (NTU)) was alkalized to pH 10.5 using a 5 M NaOH solution; liquid bittern (LB) equivalent to 536 mg-L of Mg2+ was added as a coagulant, and the suspension was then subjected to previously optimized fast mixing conditions of 100 rpm and 60 s. Slow mixing speed (20, 30, 40, and 50 rpm) and time (10, 20, and 30 min) were then varied, while the temperature was maintained at 20.7 ± 1 °C. The standard practice for coagulation-flocculation jar test ASTM D2035-13 (2013) was followed in all experiments. Relative floc size was monitored using an optical measuring device, photometric dispersion analyzer (PDA 2000). Larger and more shear resistant flocs were obtained at 20 rpm for both 20- and 30-min slow mixing times; however, given the shorter duration for the former, the 20-min slow mixing time was considered to be more energy efficient. For slow mixing camp number (Gt) values in the range of 8,400-90,000, it was found that the mixing speed affected floc size and strength more than the time. Higher-turbidity removal efficiencies were achieved at 20 and 30 rpm, while TSS removal efficiency was higher for the 50-rpm slow mixing speed. Extended slow mixing time of 30 min yielded better turbidity and TSS removal efficiencies at the slower speeds. © 2014 Springer-Verlag Berlin Heidelberg.American Public Health Association (APHA) American Water Works Association (AWWA) and Water Environment Federation (WEF), 2012, STANDARD METHODS EXA; [Anonymous], 2013, D203513 ASTM; Ayoub GM, 2013, WATER AIR SOIL POLL, V224, DOI 10.1007-s11270-012-1379-y; Ayoub GM, 2000, WATER RES, V34, P640, DOI 10.1016-S0043-1354(99)00162-1; Ayoub G.M., 2000, INT J ENVIRON STUD, V58, P85, DOI 10.1080-00207230008711318; Ayoub GM, 2001, J ENVIRON ENG-ASCE, V127, P196, DOI 10.1061-(ASCE)0733-9372(2001)127:3(196); Ayoub GM, 1999, WATER ENVIRON RES, V71, P443, DOI 10.2175-106143097X122031; Ayoub GM, 2002, ADV ENVIRON RES, V6, P277, DOI 10.1016-S1093-0191(01)00058-2; Barbot E, 2010, CHEM ENG J, V156, P83, DOI 10.1016-j.cej.2009.10.001; CORNWELL DA, 1983, J AM WATER WORKS ASS, V75, P470; Ebeling JM, 2003, AQUACULT ENG, V29, P23, DOI 10.1016-S0144-8609(03)00029-3; Fitzpatrick CSB, 2004, WATER SCI TECHNOL, V50, P171; Ghernaout D, 2012, DESALIN WATER TREAT, V44, P15, DOI 10.5004-dwt.2012.2186; Gregory J, 2009, ADV COLLOID INTERFAC, V147-48, P109, DOI 10.1016-j.cis.2008.09.003; Gregory J, 2004, WATER SCI TECHNOL, V50, P163; Gregory J, 2001, WATER SCI TECHNOL, V44, P231; Jarvis P, 2004, WATER SCI TECHNOL, V50, P63; Jarvis P., 2005, Reviews in Environmental Science and Bio-Technology, V4, P1, DOI 10.1007-s11157-005-7092-1; Jarvis P, 2005, WATER RES, V39, P3121, DOI 10.1016-j.watres.2005.05.022; Judkins JF, 1987, WATER POLLUT CONTROL, V50, P2446; Kan CC, 2002, COLLOID SURFACE A, V203, P1, DOI 10.1016-S0927-7757(01)01095-0; Kan CC, 2002, J WATER SUPPLY RES T, V51, P77; KANG LS, 1995, J ENVIRON ENG-ASCE, V121, P893, DOI 10.1061-(ASCE)0733-9372(1995)121:12(893); LEENTVAAR J, 1982, WATER RES, V16, P655, DOI 10.1016-0043-1354(82)90087-2; Lin JL, 2008, CHEMOSPHERE, V72, P189, DOI 10.1016-j.chemosphere.2008.01.062; Liu T, 2011, WATER RES, V45, P4260, DOI 10.1016-j.watres.2011.05.037; Manning AJ, 1999, MAR GEOL, V160, P147, DOI 10.1016-S0025-3227(99)00013-4; MUYIBI SA, 1995, WATER RES, V29, P2689, DOI 10.1016-0043-1354(95)00133-6; Photometric Dispersion Analyser PDA, 2000, OP MAN; Rossini M, 1999, WATER RES, V33, P1817, DOI 10.1016-S0043-1354(98)00367-4; Semerjian L, 2003, ADV ENVIRON RES, V7, P389, DOI 10.1016-S1093-0191(02)00009-6; Solomentseva I, 2007, COLLOID SURFACE A, V298, P34, DOI 10.1016-j.colsurfa.2006.12.016; Spicer PT, 1998, POWDER TECHNOL, V97, P26, DOI 10.1016-S0032-5910(97)03389-5; Xiao F, 2010, DESALINATION, V250, P902, DOI 10.1016-j.desal.2008.12.050; Yeung A, 1997, J COLLOID INTERF SCI, V196, P113, DOI 10.1006-jcis.1997.5140; Yu WZ, 2011, CHEM ENG J, V171, P425, DOI 10.1016-j.cej.2011.03.098; Yukselen MA, 2004, INT J MINER PROCESS, V73, P251, DOI 10.1016-S0301-7516(03)00077-2; Zhao JH, 2012, DESALIN WATER TREAT, V45, P153, DOI 10.5004-dwt.2012.3232; Zouboulis AI, 2005, J CHEM TECHNOL BIOT, V80, P1136, DOI 10.1002-jctb.13000
The effect of fast mixing conditions on the coagulation-flocculation process of highly turbid suspensions using liquid bittern coagulant
No full textThe effect of fast mixing on floc formation and pollutant removal, using magnesium hydroxide as a coagulant, was investigated through characterization of relative strength and size of the formed flocs while operating at different mixing speeds and mixing times using a dynamic optical monitoring apparatus, and photometric dispersion analyzer (PDA2000). The parameters investigated included fast mixing speed (80, 100, and 120 rpm) and time (20, 40, and 60 s). Highly turbid kaolin clay suspensions (1213 ± 36 NTU) were alkalized using sodium hydroxide (NaOH) to pH values of 10.51 ± 0.02 at temperatures 20.7 ± 0.1°C, and liquid bittern (LB) was used as a coagulant. Fast mixing time had a clear effect on the flocs resistance to applied shear during the slow mixing phase. For all fast mixing times, 120 rpm caused the formation of largest flocs. Stronger flocs, indicated by the least change in flocculation index with time, required 60 s to form at all fast mixing speeds. Turbidity and TSS removal efficiencies are not only dependent on fast mixing speed but also on fast mixing time, such that higher fast mixing speeds were required for shorter mixing times. © 2014 © 2014 Balaban Desalination Publications. All rights reserved.Ayoub GM, 2000, WATER RES, V34, P640, DOI 10.1016-S0043-1354(99)00162-1; Ayoub G.M., 2000, INT J ENVIRON STUD, V58, P85, DOI 10.1080-00207230008711318; Ayoub GM, 2001, J ENVIRON ENG-ASCE, V127, P196, DOI 10.1061-(ASCE)0733-9372(2001)127:3(196); Ayoub GM, 1999, WATER ENVIRON RES, V71, P443, DOI 10.2175-106143097X122031; Ayoub GM, 2002, ADV ENVIRON RES, V6, P277, DOI 10.1016-S1093-0191(01)00058-2; CORNWELL DA, 1983, J AM WATER WORKS ASS, V75, P470; Gregory J, 2009, ADV COLLOID INTERFAC, V147-48, P109, DOI 10.1016-j.cis.2008.09.003; Gregory J, 2004, WATER SCI TECHNOL, V50, P163; Hubbard A.T., 2002, FINE DAY FLOCCUATION, P2197; Jarvis P., 2005, Reviews in Environmental Science and Bio-Technology, V4, P1, DOI 10.1007-s11157-005-7092-1; Jarvis P, 2005, WATER RES, V39, P3121, DOI 10.1016-j.watres.2005.05.022; Jefferson B, 2004, WATER SCI TECHNOL, V50, P47; Kan CC, 2002, COLLOID SURFACE A, V203, P1, DOI 10.1016-S0927-7757(01)01095-0; Kan CC, 2002, J WATER SUPPLY RES T, V51, P77; Li T, 2006, POWDER TECHNOL, V168, P104, DOI 10.1016-j.powtec.2006.07.003; Liu T, 2011, WATER RES, V45, P4260, DOI 10.1016-j.watres.2011.05.037; Rossini M, 1999, WATER RES, V33, P1817, DOI 10.1016-S0043-1354(98)00367-4; Semerjian L, 2003, ADV ENVIRON RES, V7, P389, DOI 10.1016-S1093-0191(02)00009-6; Sheng WY, 2006, DRY TECHNOL, V24, P1271, DOI 10.1080-07373930600840377; Solomentseva I, 2007, COLLOID SURFACE A, V298, P34, DOI 10.1016-j.colsurfa.2006.12.016; Spicer PT, 1998, POWDER TECHNOL, V97, P26, DOI 10.1016-S0032-5910(97)03389-5; Xu W., 2011, J WATER SUSTAINABILI, V1, P45; Yu WZ, 2011, CHEM ENG J, V171, P425, DOI 10.1016-j.cej.2011.03.098; Yu WZ, 2010, ENVIRON SCI TECHNOL, V44, P6371, DOI 10.1021-es1007627; Yukselen MA, 2004, INT J MINER PROCESS, V73, P251, DOI 10.1016-S0301-7516(03)00077-2; Yukselen MA, 2004, J CHEM TECHNOL BIOT, V79, P782, DOI 10.1002-jctb.1056; Zhao JH, 2012, DESALIN WATER TREAT, V45, P153, DOI 10.5004-dwt.2012.3232; Zouboulis AI, 2005, J CHEM TECHNOL BIOT, V80, P1136, DOI 10.1002-jctb.13000
Precipitation softening: A pretreatment process for seawater desalination
No full textReduction of membrane fouling in reverse osmosis systems and elimination of scaling of heat transfer surfaces in thermal plants are a major challenge in the desalination of seawater. Precipitation softening has the potential of eliminating the major fouling and scaling species in seawater desalination plants, thus allowing thermal plants to operate at higher top brine temperatures and membrane plants to operate at a reduced risk of fouling, leading to lower desalinated water costs. This work evaluated the use of precipitation softening as a pretreatment step for seawater desalination. The effectiveness of the process in removing several scale-inducing materials such as calcium, magnesium, silica, and boron was investigated under variable conditions of temperature and pH. The treatment process was also applied to seawater spiked with other known fouling species such as iron and bacteria to determine the efficiency of removal. The results of this work show that precipitation softening at a pH of 11 leads to complete elimination of calcium, silica, and bacteria; to very high removal efficiencies of magnesium and iron (99.6 and 99.2 percent, respectively); and to a reasonably good removal efficiency of boron (61 percent). © 2013 Springer-Verlag Berlin Heidelberg.Al-Rawajfeh AE, 2011, HEAT TRANST ENG, V33, P272; Al-Rawajfeh AE, 2013, CHEM PROCESS ENG-INZ, V34, P253, DOI 10.2478-cpe-2013-0021; Al-Rawajfeh AE, 2012, MEMBR WATER TREAT, V3, P253; Al-Rawajfeh Aiman E., 2008, Recent Patents on Chemical Engineering, V1; Al-Rawajfeh AE, 2008, CHEM ENG COMMUN, V195, P998, DOI 10.1080-00986440801906922; Al-Sofi MAK, 1999, DESALINATION, V126, P61, DOI 10.1016-S0011-9164(99)00155-1; American Public Health Association (APHA) American Water Works Association (AWWA) and Water Environment Federation (WEF), 2012, STANDARD METHODS EXA; AYOUB GM, 1992, WATER RES, V26, P817, DOI 10.1016-0043-1354(92)90013-T; AYOUB GM, 1986, J WATER POLLUT CON F, V58, P924; AYOUB GM, 1986, WATER RES, V20, P1265, DOI 10.1016-0043-1354(86)90157-0; Bonnelye V, 2007, DESALINATION, V205, P200, DOI 10.1016-j.desal.2006.04.045; Brehant A., 2003, Water Science and Technology: Water Supply, V3, P437; Brehant A, 2002, DESALINATION, V144, P353, DOI 10.1016-S0011-9164(02)00343-0; Choi YH, 2009, DESALINATION, V247, P137, DOI 10.1016-j.desal.2008.12.019; Comstock SEH, 2011, WATER RES, V45, P4855, DOI 10.1016-j.watres.2011.06.035; Dalvi AGI, 2000, DESALINATION, V132, P217, DOI 10.1016-S0011-9164(00)00153-3; DEMAIO A, 1983, DESALINATION, V45, P197; El Din AMS, 2005, DESALINATION, V177, P241, DOI 10.1016-j.desal.2004.09.030; El-Manharawy S, 2003, DESALINATION, V153, P109, DOI 10.1016-S0011-9164(02)01110-4; Fernandez-Alvarez G, 2010, DESALINATION, V263, P264, DOI 10.1016-j.desal.2010.06.068; Gabelich CJ, 2011, DESALINATION, V272, P36, DOI 10.1016-j.desal.2010.12.050; Gilron J, 2000, DESALINATION, V127, P271, DOI 10.1016-S0011-9164(00)00016-3; Greenlee LF, 2009, WATER RES, V43, P2317, DOI 10.1016-j.watres.2009.03.010; Hassan AM, 1998, DESALINATION, V118, P35, DOI 10.1016-S0011-9164(98)00079-4; Hussain MA, 2007, US Patent, Patent No. [7,198,722, 7198722]; IRVING LAURENCE, 1926, JOUR MARINE BIOL ASSOC, V14, P441; Isaias NP, 2001, DESALINATION, V139, P57, DOI 10.1016-S0011-9164(01)00294-6; Koseoglu H, 2008, DESALINATION, V223, P126, DOI 10.1016-j.desal.2007.01.189; Koseoglu H, 2008, DESALINATION, V227, P253, DOI 10.1016-j.desal.2007.06.029; LECHEVALLIER MW, 1988, APPL ENVIRON MICROB, V54, P2492; Antony A, 2011, J MEMBRANE SCI, V383, P1, DOI 10.1016-j.memsci.2011.08.054; Matin A, 2011, DESALINATION, V281, P1, DOI 10.1016-j.desal.2011.06.063; MAVIS JD, 1975, IND ENG CHEM PROC DD, V14, P204, DOI 10.1021-i260055a002; Melian-Martel N, 2012, DESALINATION, V305, P44, DOI 10.1016-j.desal.2012.08.011; Mohammadesmaeili F, 2010, WATER RES, V44, P6021, DOI 10.1016-j.watres.2010.07.070; Morse JW, 2007, CHEM REV, V107, P342, DOI 10.1021-cr050358j; Nadav N, 2005, DESALINATION, V185, P121, DOI 10.1016-j.desal.2005.03.075; Parks JL, 2007, J ENVIRON ENG-ASCE, V133, P149, DOI 10.1061-(ACSE)0733-9372(2007)133:2(149); Pearce GK, 2008, DESALINATION, V222, P66, DOI 10.1016-j.desa1.2007.05.029; Rahardianto A, 2007, J MEMBRANE SCI, V289, P123, DOI 10.1016-j.memsci.2006.11.043; Rahardianto A, 2010, DESALINATION, V264, P256, DOI 10.1016-j.desal.2010.06.018; Rincon AG, 2004, APPL CATAL B-ENVIRON, V51, P283, DOI 10.1016-j.apcatb.2004.03.007; Sanciolo P, 2012, DESALINATION, V295, P43, DOI 10.1016-j.desal.2012.03.015; Sheikholeslami R, 2001, DESALINATION, V139, P83, DOI 10.1016-S0011-9164(01)00297-1; Sheikholeslami R, 2011, DESALINATION, V278, P259, DOI 10.1016-j.desal.2011.05.034; Subramani A, 2012, SEP PURIF TECHNOL, V88, P138, DOI 10.1016-j.seppur.2011.12.010; Valavala Ramesh, 2011, Environmental Engineering Research, V16, P205; Vedavyasan CV, 2007, DESALINATION, V203, P296, DOI 10.1016-j.desal.2006.04.012; Wildebrand C, 2007, DESALINATION, V204, P448, DOI 10.1016-j.desal.2006.03.547; Zebger I, 2003, POLYM DEGRAD STABIL, V80, P293, DOI 10.1016-S0141-3910(03)00013-20
Machine-Learning-Assisted Failure Prediction in Microwave Networks based on Equipment Alarms
No full textModern microwave networks must cope with strict Quality of Services (QoS) requirements, such as low latency, high bandwidth and high availability. As network failures can affect service availability, failure management is crucial for service maintenance and, recently, application of Machine Learning (ML) for automated failure management is becoming pervasive. In particular, ML promises to deliver predictive maintenance capabilities, where failure occurrence is anticipated thanks to ML prediction capabilities. In this study we developed two workflows, based on a modular ML implementation, capable of short- and long-horizon failure predictions, while taking into consideration computational complexity constraint. As input data, we used real alarms coming from deployed equipment of a nation-wide microwave network. Our ML-based failure-prediction system learns from human experience through labelled data, performs alarms forecasting, detects future failure occurrence and identifies failure root causes. In our numerical results, we compare the prediction performance of different ML models in terms of various standard ML performance metrics. Overall accuracy over 95% is achieved in all prediction scenario simulated within an hour, suggesting that microwave network operators can gain actual operational benefits by deploying this framework in realworld infrastructures
Adsorption of arsenate on untreated dolomite powder
No full textRaw dolomite powder was evaluated for its efficiency in adsorbing As(V) from water. An experimental setup comprised of a fluidized dolomite powder bed was used to assess the impact of various test variables on the efficiency of removal of As(V). Test influents including distilled water (DW), synthetic groundwater (SGW) and filtered sewage effluent (FSE) were employed to assess the effect of influent parameters on the adsorption process and the quality of the effluent generated. Dolomite exhibited good As(V) removal levels for distilled water (92percent) and synthetic ground water (84percent) influents at all initial As(V) concentrations tested (0.055-0.600 ppm). Breakthrough of dolomite bed occurred after 45 bed volumes for DW and 20 bed volumes for SGW influents with complete breakthrough taking place at more than 300 bed volumes. As(V) removal from FSE influents was relatively unsuccessful as compared to the DW and SGW influents. Partial removal in the order of 32percent from filtered sewage effluent at initial concentration of 0.6 mg-L started at 75 bed volumes and gradually stopped at 165 bed volumes. Varying degrees of As(V) adsorption capacities were observed by the different test influents employed, which indicate that the adsorption of As(V) is adversely affected by competing species, mainly sulfates and phosphates present in the influent. The adsorptive behavior of dolomite was described by fitting data generated from the study into the Langmuir and Freundlich isotherm models. Both models described well the adsorption of dolomite. The average isotherm adsorptive capacity was determined at 5.02 μg-g. Regeneration of the dolomite bed can be achieved with the use of caustic soda solution at a pH of 10.5. © 2007 Elsevier B.V. All rights reserved.Ahn JS, 2003, WATER RES, V37, P2478, DOI 10.1016-S0043-1354(02)00637-1; American Public Health Association (APHA), 1999, STAND METH EX WAT WA; *ASTM, 2000, STAND TEST METH PART; Ayoub GM, 2001, WATER ENVIRON RES, V73, P478, DOI 10.2175-106143001X139533; Ayoub GM, 2006, WATER ENVIRON RES, V78, P353, DOI 10.2175-106143005X90001; CLIFFORD DA, 1998, P 3 INT C ARS EXP HL; DeMarco MJ, 2003, WATER RES, V37, P164, DOI 10.1016-S0043-1354(02)00238-5; Genc H, 2003, J PHYS IV, V107, P537, DOI 10.1051-jp4:20030359; Genc H, 2003, J COLLOID INTERF SCI, V264, P327, DOI 10.1016-S0021-9797(03)00447-8; Genc-Fuhrman H, 2004, ENVIRON SCI TECHNOL, V38, P2428, DOI 10.1021-es035207h; Genc-Fuhrman H, 2004, J COLLOID INTERF SCI, V271, P313, DOI 10.1016-j.jcis.2003.10.011; HADDAD F, 1990, THESIS U BEIRUT LEBA; HERING JG, 2002, ENV CHEM ARSENIC, P167; Joshi A, 1996, J ENVIRON ENG-ASCE, V122, P769, DOI 10.1061-(ASCE)0733-9372(1996)122:8(769); KALINIAN H, 1991, THESIS AM U BERIUT; KARSCHUNKE K, 2000, P 26 WEDC C DHAK BAN, P221; Katsoyiannis IA, 2004, WATER RES, V38, P17, DOI 10.1016-j.watres.2003.09.011; Khan AH, 2000, J ENVIRON SCI HEAL A, V35, P1021; Lakshmipathiraj P, 2006, J HAZARD MATER, V136, P281, DOI 10.1016-j.jhazmat.2005.12.015; MCCAULOU DR, 1994, J CONTAM HYDROL, V15; Mc-Ghee T.J., 1991, WATER SUPPLY SEWERAG; Mokashi SA, 2002, LETT APPL MICROBIOL, V34, P258, DOI 10.1046-j.1472-765x.2002.01083.x; MURCOTT S, 1999, ARS BANGL GROUND WAT; NOKOLAIDIS NP, 2003, WATER RES, V37, P1417; Petrusevski B, 2002, WA SCI TECHNOL, V2, P127; POKHREL D, 2005, RADIOACTIVE WASTE MA, V9, P152; Pokrovsky OS, 1999, GEOCHIM COSMOCHIM AC, V63, P3133, DOI 10.1016-S0016-7037(99)00240-9; Ramaswami A, 2001, WATER RES, V35, P4474, DOI 10.1016-S0043-1354(01)00168-3; Selvin N, 2002, WA SCI TECHNOL, V2, P11; Smith E, 2002, J ENVIRON QUAL, V31, P557; SUBRAMANIAN KS, 1996, P 1995 WAT QUAL TECH, P1063; Thirunavukkarasu O. S., 2002, URBAN WATER, V4, P415, DOI 10.1016-S1462-0758(02)00029-8; Thirunavukkarasu OS, 2003, WATER AIR SOIL POLL, V142, P95, DOI 10.1023-A:1022073721853; Thirunavkukkarasu OS, 2001, WATER QUAL RES J CAN, V36, P55; Vaishya RC, 2003, J WATER SUPPLY RES T, V52, P299; Viraraghavan T, 1999, WATER SCI TECHNOL, V40, P69, DOI 10.1016-S0273-1223(99)00432-1; *WHO, 2001, 224 ENV HLTH CRIT; Wilkie JA, 1996, COLLOID SURFACE A, V107, P97, DOI 10.1016-0927-7757(95)03368-8; ZALDIVAR R, 1974, BEITR PATHOL, V151, P38467
Traffic-Adaptive Re-Configuration of Programmable Filterless Optical Networks
No full textIn view of incoming 5G mobile communication, network operators must upgrade their network capacity while capping capital and operational expenditures. Filterless Optical Networks (FONs) are emerging as a cost-effective technology as they eliminate costly active switching elements, the Reconfigurable Optical Add-Drop multiplexers (ROADMs) based on Wavelength Selective Switch (WSS), by replacing them with passive devices as optical power splitters/combiners. However, eliminating active switching and filtering components enforces signal broadcast on all the outputs of the passive splitters, resulting in the transmission of optical signals over unintended links and hence in higher spectrum occupation with respect to wavelength-switched optical networks (WSONs) based on active devices. To mitigate spectrum waste, FONs can be augmented by deploying programmable optical switches, which increase network flexibility as they allow re-configuration of fiber trees established in FONs to accommodate demands. This filterless network is referred to as Programmable FON (P-FON). In this paper, we propose a traffic-adaptive heuristic algorithm, namely Adapt P-FON, for the re-configuration of programmable optical switches in FONs. The algorithm performs routing and spectrum assignment for traffic demands and also provides optimized configuration of programmable optical switches such that the overall spectrum utilization in the network is minimized. We evaluate the advantages of P-FONs, in terms of spectrum utilization and equipment cost, against FONs and WSON scenarios. Results show that P-FONs have significant advantages in terms of spectrum utilization in comparison to FONs (up to 60%), and, at the same time, cost savings (up to 90%), considering cost of splitters, WSSs and programmable switches, in comparison to WSON
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Efficient Online Virtual Machines Migration for Alert-Based Disaster Resilience
No full textSeveral recent weather-based disasters had very negative impacts on cloud networks, causing Data Center (DC) shutdown, consequent data-loss and intolerable downtime of cloud services. This has put the reactive disaster-resilient design of cloud networks on top the agenda of several cloud DC operators. DC operators are investigating approaches to avoid downtime of cloud services in case a DC is affected by a disaster. Thanks to virtualization most cloud services run on Virtual Machines (VMs) hosted by DCs, so it is possible to keep these services alive if the VMs are evacuated (namely, migrated) before the disaster from a DC affected by the disaster to a DC in a safe location, in an online technique. This technique is known as online 'VM migration', which results without or with a minimal service downtime. In this paper, we present an Integer Linear Programming (ILP) model for efficient online VMs migration in case of an alerted disaster (e.g., most weather-based disasters, as hurricanes) such as to avoid service downtime. The ILP performs scheduling and assigns route and bandwidth to the migration of VMs towards a safe DC within an alert time, with the objective of maximizing the number of VMs migrated and minimizing service downtime, network resource occupation and migration duration. We present a comparative analysis of offline and online migration strategies such as to quantify the trade-off between downtime, network resource utilization and migration duration. Moreover, we investigate the impact of the memory dirtying rate on the online migration process, i.e., the number of VMs evacuated and network resource occupation
Survivable Virtual Network Mapping in Filterless Optical Networks
No full textToday's optical networks must meet the unprecedented capacity requirements of 5G communications and provide such capacity under strict cost constraints. Filterless Optical Networks (FONs) (i.e., optical networks where optical nodes are solely based on passive splitters and combiners) are emerging as an outstanding solution to reduce network cost while supporting capacity growth. Due to FONs' specific design criteria (the network topology must be divided into edge-disjoint filterless fiber trees), traditional network problems, such as, e.g., routing and wavelength assignment and virtual network mapping, shall be tackled adopting distinct approaches with respect to state-of-the-art filtered optical networks networks. In this paper, we investigate the problem of survivable virtual network mapping (SVNM) in FONs. We propose an Integer Linear Programming model to establish fiber trees and provide survivable mapping of virtual networks, while minimizing cost of additional equipment and spectrum. We show that joint optimization of filterless trees and survivable mapping significantly decreases transceivers and spectrum cost compared to a disjoint solution where the tree establishment does not consider SVNM constraints
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