Usage of Pythagorean Triple Sequence in OSPF

Size: px
Start display at page:

Download "Usage of Pythagorean Triple Sequence in OSPF"

Transcription

1 Commuicatios ad Network,,, Published Olie February ( Usage of Pythagorea Triple Sequece i OSPF Simo Tembo, Ke-ichi Yukimatsu, Shohei Kamamura, Takashi Miyamura, Kohei Shiomoto, Atsushi Hiramatsu Departmet of Computer Sciece ad Egieerig, Akita Uiversity, Akita City, Japa NTT Network Service Systems Laboratories, NTT Corporatio, Tokyo, Japa {tembo,yukimatu}@ie.akita-u.ac.jp, {kamamura.shohei, miyamura.takashi, shiomoto.kohei, hiramatsu.atsushi}@lab.tt.co.jp Received November, ; revised December 6, ; accepted December 9, ABSTRACT Shuttig dow a lik for the purposes of a scheduled routie maiteace does cause the forwardig path to chage. If these chages are ot doe i a required order will cause ot oly trasiet micro loops but also a overload i some liks. Curretly, some ISP operators use a graceful lik shutdow procedure by first settig up the Iterior Gateway Protocol (IGP) lik metric to MAX_METRIC ad the shutdow the lik. I this paper, we preset a Pythagorea Triple Metric Sequece as a method to use to shutdow a lik durig such etwork operatios. Coductig a lik shutdow of ay desired lik for maiteace purpose is a very delicate duty that requires extreme care to prevet trasiet loops durig such topological chages. We thus wish to demostrate that there exists a Pythagorea Triple Metric Sequece for ay give lik that ca be used to shutdow a lik durig the routie maiteace by ISPs. Keywords: MAX_METRIC; Lik Shutdow; Pythagorea Triple Metric Sequece; Target Metric; Forwardig Path; Plaed Failures. Itroductio Iteret Service Providers (ISPs) have a obligatio to observe the coditios set i the Service Level Agreemets (SLA) with their customers. Oe of the coditios is to esure that ISPs miimize the duratio of the loss of coectivity. Losses of coectivity are commo occurreces most especially i etworks with larger topologies, although most of the times such losses of coectivity are uavoidable as the ISPs have to shut dow the equipmet for them to be able to coduct the scheduled routie maiteace. The lik metric of a lik ca always be icreased to a larger metric by progressively icreasig the metric of the lik by oe, util the target metric is reached. Therefore the lik ca be shut dow, by icreasig its lik metric util it becomes large that it caot carry packets aymore. Whe the target metric has bee reached, the lik ca the be safely shutdow []. Shuttig dow a lik for the purposes of a scheduled routie maiteace aloe accouts for about % of the total failures []. The shuttig dow of a lik due to these operatios is cosidered as plaed failures. I this paper, we make referece maily to these plaed failures. Some ISP operators do eve use some procedures to coduct a graceful lik shutdow []. The procedure ivolves by first settig up the Iterior Gateway Protocol (IGP) lik metric to MAX_METRIC ad the shutdow the lik that is scheduled for routie maiteace. Shuttig dow a lik for the purposes of a scheduled routie maiteace does cause the forwardig path to chage. For istace, usig a Simple Network model (i Figure ) the forwardig matrices i Figures ad helps to illustrate the effect of shuttig dow a lik o the forwardig path. The forwardig matrix i Figure is the forwardig iformatio bases (FIB) for all the 5 odes whe we have o lik failure. However, i Figure Figure. Simple etwork model all lik metrics equal to. Copyright SciRes.

2 7 S. TEMBO ET AL. NODE TO FROM Figure. Forwardig matrix for simple etwork with o lik failure. NODE TO FROM Figure. Forwardig matrix for simple etwork with a lik betwee odes ad shutdow. whe we shutdow the lik betwee odes ad does cause chages i the FIB, such that ode becomes the ext hop for traffic from ode destied for odes ad. The chages i the forwardig path whe the lik is shutdow, if are ot doe i a required order, will cause ot oly trasiet micro loops but also a overload i some liks. Shuttig dow the lik due to scheduled routie maiteace is cosidered as a predictable failure ad that the effect of such topological chages ca be reduced by correctly choosig updatable order that does ot oly avoids trasiet routig loops but also avoids cogestio ad lik overflow [-8]. I this paper we show that by use of the Pythagorea Triple Properties we ca compute the Pythagorea Triple Metric Sequece that we ca use to cofigure the etwork topology such that the desired lik ca be shutdow. I our Pythagorea Triple Metric Sequece method, we use the Pythagorea Triple Sequece {,, 5} to determie a sequece of metrics as target metrics to use to shutdow a lik. I this research we performed experimets usig the etwork model i Figure i which we assumed that all liks have same IGP metrics of value cofigured to each lik. We the performed experimets usig the etwork topology i Figure i which we assumed that the lik metric of Figure are ot uiform. To evaluate ad validate our Pythagorea Triple Metric Sequece method we directly varied ad cofigured the IGP lik metric of each ad every lik i the etwork topology to {,,,, 5} sequece by cosiderig each lik as a lik desired to be shutdow. For performace evaluatio we have used a Simple Network, COST9, ad HLDA as example of etwork topology models for evaluatio purposes ad whilst for validatio purposes we have used K 5 COMPLETE, as our litmus test etwork topology. I all the cases we use a gravity model type of traffic geeratio accordig to the populatio distributio. I our performace evaluatio we demostrate at what stage of the {,,,, 5} sequece is the shuttig dow possible. We performed simulatios for each ad every lik i each of the etwork topologies to verify whe each lik stops carryig traffic, ad thus validatig the lik beig shutdow. The rest of the paper is orgaized as follows. I Sectio, we itroduce the Pythagorea Triple Sequece Method. We explai the overview of our idea ad itroduce our algorithm. I Sectio, we discuss the experimets ad evaluatio of the results. Fially, i Sectio, we provide the coclusio.. Algorithm to Shutdow a Lik Usig the Pythagorea Triple Sequece.. Overview It has bee proved, that the IGP lik metric of a lik ca always be icreased to a larger metric by progressively icreasig the metric of the lik by oe (see Equatio ()), util the target metric is reached without causig trasiet forwardig loops. m m () m m where is the iitial metric ad is the ew metric. It has bee proved further that the lik ca be shut dow by icreasig its IGP lik metric util it becomes large that it caot carry traffic aymore. It was show that whe the target metric has bee reached, the lik ca the be safely shutdow []. It was illustrated i [], that we ca have a sequece of three elemets to sufficietly provide a loop free covergece: m, m, mt We also kow that curretly, ISP operators must first set the IGP lik metric to MAX_METRIC to gracefully reroute traffic before shuttig dow the lik. A expressio that ca be expaded to a equatio expressed as follows: 6 MAX_METRIC 56 () () I Equatio (), we have a differece of two squares, which is a property of the Pythagorea Triple. It is from this fact that we believe if the property of Equatio () holds for ay give etwork topology the there must exist a Pythagorea Triple that ca be used to shut dow the lik cosiderig the fact that to perform the graceful shut dow, ISPs are curretly usig a Pythagorea property as illustrated i Equatio (). It was illustrated i [5] that there exist ifiitely may right-agled triagles with itegral sides i which the Copyright SciRes.

3 S. TEMBO ET AL. 75 legths of two o-hypoteuse sides differ by. That is, the triple sides as a sequece will be; x, x, y Comparig Equatios ()-(), we ca see some similarities i these expressios, i.e. the first term i Expressio () m x, the secod term m x ad the third term mt y. This aalogy i the Expressios () ad () also provide us with the motivatio to believe that the use of the Pythagorea Triple Sequece is worth explorig. Further, our idea is very much ecouraged by the fact that i the graceful shuttig dow of a lik method a Pythagorea property as illustrated i Equatio () is valid. It was further stated i [5], that there exist positive itegers x ad y such that the legths of the sides of the triagle are x, x ad y respectively fulfils the Pythagoras Theorem, that is: x x y Examples of the Pythagorea triples are {,, 5} ad {,, 9}. The {,, 5} triple ad its multiple {,, 5} are the oly Pythagorea triple that are i arithmetic progressio ad cosecutively icremetig. It is for reaso we have decided to use this particular type of Pythagorea Triples Sequece. We kow that i Arithmetic Sequece the differece betwee oe term ad the ext is a costat as give Equatio (6), a fact that we see i the triple sequece of {,, 5}. aa, da, d, (6) where: a is the first term, ad d is the commo differece betwee the terms... Pythagorea Triples Usig Euclid Formula I mathematics it has bee show that if {a, b, c} is a Pythagorea triple, the so is {ka, kb, kc} for ay positive iteger k, ad that the smallest Pythagorea Triple is {,, 5} whe k. As illustrated i [6], Euclid provided a formula to fid Pythagorea triples from ay two positive itegers m ad, where m. For istace usig Euclid formula i terms of a sequece (see Figure ) we have {a, b, c}: a m b m c m As a Example, Table illustrates the possible Pythagorea triples usig the Euclid formula give i Equa- () (5) (7) (8) (9) m m m Figure. Euclid s parameterizatio. Table. Example of pythagorea triples. m TRIPLE,, 5 5,, 7,, 5 5 9,, 6 5, 6, 6 tios (7)-(9). Further, as a Example usig the Euclid formulas i (7-9), assumig we have triples give as a se- quece m, m, m, ad where m for a to be a positive umber. Thus Equatio () ca be computed as follows: 6 MAX_METRIC 56 Assumig m 56 ad a m 65,55; MAX_METRIC b m 5 c m 65,57 The above example shows that Equatio () fulfils the Euclid formula, agai qualifyig our idea of usig the Pythagorea Triple Sequece Method whe shuttig dow the lik for routie maiteace purpose... The {,, 5} Triple Algorithm As stated earlier i this paper, the {,, 5} triple ad its multiples {,, 5} are the oly triples that are i arithmetic progressio ad cosecutively icremetig. It is these two reasos that we have chose to use this particular type of Pythagorea Triples Sequece i this paper. I our method we show that usig the Pythagorea Triple Metric Sequece {,, 5}, ad give that is the iitial elemet of the sequece {,,,, 5} ad the hece the commo differece of the arithmetic progressio, we ca determie the target lik metric ad directly recofigure to the lik desired to be shutdow. I Table below we illustrate the idea of our algorithm i determiig the target metric. Whe we wish to shutdow ay give lik i ay give topology we cofigur- Copyright SciRes.

4 76 S. TEMBO ET AL. Table. The {,, 5} triple algorithm. PRIMITIVE TRIPLES,, 5,, 5,, 5 6, 8,,, 5 9,, 5,, 5, 6,,, 5,, 5 ig all other liks i the etwork topology with a uiform IGP lik metric which we deote as, except for the lik we wish to shutdow to which we vary its lik metric as per sequece {,,,, 5}.. Results ad Discussio To validate our idea of the Pythagorea Triple Metric Sequece, we have used several differet topologies as etwork models for evaluatio purposes such as ) a Simple Network Figures ad 5; ) COST9 i Figure 6; ) HLDA i Figure 7; ad ) K 5 COMPLETE i Figures 8. We have used gravity model as the traffic coditio accordig to the populatio distributio. The routig algorithm assumed is a miimum cost routig. We have assumed that the lik metric is iversely proportioal to lik capacity... Use of Pythagorea Triple to Shutdow a Lik The value of was varied as show Table to avoid etwork over load. The results show below i Figures 9 ad, illustrate the validity of our method. We validate that by varyig the lik metric of liks as per the values i the sequece {,,,, 5}, it is oly whe the lik metric reaches the values of all or some of the elemets of the {,, 5} sequece that the liks stops to carry traffic. Thus at this poit the lik is cosidered to be safe for shutdow as the lik utilizatio goes dow to zero. The graph i Figure shows that 5% of liks i the Simple Network (i Figure ), ca be shutdow whe the lik metric reaches {} of the {,, 5} sequece. We further observe that ALL liks ca be shut dow after the lik metric reaches {, 5} of the {,, 5} sequece. Therefore we ca say that whe the lik metric of the Simple Network is uiform, 5% of the liks ca be shutdow as per {,, 5} sequece thus makig our algorithm to be valid. Whereas whe we varied the values of the lik metric (see Figure 5) so that we achieved o uiform lik metric sceario, our simula- Figure 5. Simple etwork model all lik metrics NOT equal to. Figure 6. COST9 model ( odes, 5 liks). Figure 7. HLDA model ( odes, 6 liks). Copyright SciRes.

5 S. TEMBO ET AL. 77 Table. Topologies for our performace evaluatio. No. TOPOLOGY Nodes Liks. SIMPLE NETWORK 5 6. K 5 COMPLETE 5. COST9 5. HLDA 6 UTILIZATION Figure 8. K 5 COMPLETE (5 odes, liks) lk - lk LINK METRIC 5 lk - lk - Figure 9. The graph validates that we are able to shutdow some liks i the Simple Network (i Figure ) ad COST9; ALL liks i HLDA ad K 5 COMPLETE after the lik metric reaches {,, 5} of {,,,, 5} sequece. UTILIZATION lk - lk LINK METRIC 5 Figure. The graph validates that other liks i the Simple Network (i Figure ); COST9 ad ALL liks i the Simple Network (i Figure 5) ca be shutdow ONLY after the lik metric reaches {, 5} of {,,,, 5} sequece. tio results show i Figure show that we ca ONLY shutdow the lik after the lik metric reaches the values of {, 5} of the our algorithm {,, 5}. Thus we ca say that NO liks i the Simple Network (i Figure 5), ca be shutdow whe the lik metric reaches {} of the {,, 5} sequece. However, ALL liks ca be shutdow whe the lik metric reaches {, 5} of the {,, 5} sequece. I Figure, below we show the simulatio results whe each ad every lik was each i tur cofigured ad simulated for a lik shutdow i COST9, HLDA ad K 5 COMPLETE topologies usig our algorithm. The validity resposiveess of these etwork topologies to our lik shutdow method is that COST9 respoded favorable to the {,, 5} algorithm is 96%, HLDA is % ad K 5 COMPLETE is also %. These results demostrate that our method is valid to perform a lik shutdow... Use Pythagorea Triple to Shutdow Two Liks To further validate our method we performed experimets o the Simple Network topology (Figure ), usig our algorithm to simultaeously shutdow two liks i the etwork topology. The graphs show i Figures - 5 demostrate that we are ONLY able to simultaeously shutdow TWO liks whe we have a lik metric combiatio of {, 5} or {5, 5}. We have further validated our method by performig similar experimets of simultaeously shuttig dow two liks for COST9, HLDA ad K 5 COMPLETE topologies usig our algorithm. The graphs show i Figures 6 demostrate the resposiveess of the TWO liks shutdow of each topology. The illustratio is that, for COST9, we are able to simultaeously shutdow TWO liks oly whe we have a lik metric combiatio of {, }, {, 5} ad {5, 5}. Whereas i the case of the HLDA model, the resposiveess of our algorithm is % as we are able to simultaeously shutdow TWO liks for all lik metric combiatio. That is, we are able to simultaeously shutdow TWO liks for all lik metric combiatio of {, }, {, }, {, 5} {, }, {, 5} ad {5, 5}. This similar results, is obtaied for the K 5 COM- PLETE, which we have used as our Litmus Test for our algorithm. The results show that the K 5 COMPLETE s performace is % validatio as we are able to simultaeously shutdow TWO liks all the lik metric combiatio of {, }, {, }, {, 5} {, }, {, 5} ad {5, 5}. But as for the case of the Simple Network topology, we are oly able to simultaeously shutdow TWO liks whe we have a Copyright SciRes.

6 78 S. TEMBO ET AL. Liks ca be shutdow at lik metric of {} of the {,, 5} sequece Liks ca be shutdow ONLY whe lik metric reaches {, 5} of the {,, 5} sequece % % Performace % 8% 6% % % % % Simple Network All liks do NOT have SAME Lik Metric Simple Network Model 5% Simple Network - All liks SAME Lik Metric have Figure. Performace compariso of the Simple Network Models, that is whe the value of the lik metric is uiform for all liks ad whe it is ot as show i Figures ad 5. Liks that respods favourably to {,, 5} algorithm Liks that ca be shutdow ONLY whe the lik metric reaches {, 5} of the {,,,, 5} sequece Performace % 9% 8% 7% 6% 5% % % % % % 96% % % 5% 5% % % % SIMPLE NETWORK COST9 HLDA K5 COMPLETE Topology Figure. Performace of shuttig dow a lik.,,, 5,.5. UTILIZATION UITILIZATION lk - lk - lk - lk - lk - lk - lk - lk - lk - lk - lk - lk - Lik ID lk - lk - lk - lk - lk - lk - lk - lk - lk - lk - lk - lk - Lik ID Figure. The graphs illustrate that i the Simple Network (i Figure ); we are ot able to shut dow a lik whe we have lik metric combiatio of {, } ad {, }. lik metric combiatio of {, 5} or {5, 5}. I Figure 7, we show the performace of the TWO liks shutdow usig our algorithm for the Simple Network, COST9, HLDA, ad K 5 COMPLETE topologies. The validity resposiveess of these etwork Figure. The graphs illustrate that i the Simple Network (i Figure ); we are able to shutdow oly ONE lik whe we have lik metric combiatio of {, 5} ad {, }. topologies to the TWO lik shutdow is that the Simple Network respods favorable to the {,, 5} algorithm by %, COST9 respose is 5%, whereas HLDA ad K 5 COMPLETE is % for both of them. The results demostrate that our method is valid to Copyright SciRes.

7 S. TEMBO ET AL. 79 UTILIZATION ,, 5 5, 5 lk - lk - lk - lk - lk - lk - lk - lk - lk - lk - lk - lk - Lik ID Figure 5. The graphs illustrate that i the Simple Network (i Figure ); we are able to shutdow TWO liks whe we have a lik metric combiatio of {, 5} ad {5, 5}. MAXIMUM LOAD No Lik Failure SIMPLE NETWORK HLDA COST9,,, 5, Lik Metric Combiatio K5 COMPLETE, 5 5, 5 Figure 6. Effect of shuttig dow TWO liks o Maximum Load. Performace Performace of {,, 5} algorithm whe perfomig the Two Liks Shutdow % 8% 6% % % % % SIMPLE NETWORK 5% COST9 % HLDA Topology % K5 COMPLETE the fact that the {, 5} is a subset of the Pythagorea Triple Metric {,, 5} Sequece. The similar lik shutdow experimets reveal that were performed for other topologies, as show i figure, reveals that usig our Pythagorea Triple Metric {,, 5} Sequece, the validity is 96% for COST9 model, % for HLDA model ad % for K 5 COM- PLETE. To further validate our method we performed experimets by shuttig dow TWO liks simutaeously for all the four topologies. The results show i Figure 7, summaries our dicoveries that our method still remais valid eve whe we coduct ad shutdow TWO liks simultaeously. To further explai the effect of the wrog combiatio of the lik metric as what effect it has o the performace of the topology, we use Figures 8- of the Simple Network, to illustrate this fact. A example of the compariso of performace is give i Figures 8- of the Simple Network, whe just by a mere swappig of the lik metric of the topology what effect that it has o the Maximum Load. Lets us for istace say we call the model SN whe the lik metric for Lik -/- is equal to, ad for Lik -/- is equal to 5 as show Figure 9. We the call the model SN whe the lik MAXIMUM LOAD No Lik Failure SN- SN-,,, 5,, 5 5, 5 Lik Metric Combiatio Figure 8. Effect o maximum load (for SN ad SN) by mere swappig the lik metric combiatio of the Simple Network. Figure 7. Performace of shuttig dow TWO Liks. perform a TWO liks shutdow... Performace Evaluatio Whe each ad every lik of the Simple Network model, i Figure, was each i tur cofigured ad simulated for a lik shutdow usig our Pythagorea Triple Metric {,, 5} Sequece, 5% of the liks validates our algorithm whilst the other 5% the sequece could oly be shutdow whe their lik metric reached values of {, 5}, the results which we ca still cosider as valid 5 Figure 9. Simple Network (SN) with two Forwardig Trees for trasportig traffic whose destiatio is odes ad (Lik -/- =, Lik -/- = 5). Copyright SciRes.

8 8 S. TEMBO ET AL. 5 Figure. Simple Network (SN) with two Forwardig Trees for trasportig traffic whose destiatio is odes ad SN(Lik -/- = 5, Lik -/- = ). metric for Lik -/- is, ad for Lik -/-is 5 as show i Figure. The chages ca eve be see i the chages of the forwardig trees for traffic destied for odes ad. I Figure 9, the forwardig tree idicates that the lik betwee odes ad is still i use such that for traffic destied for ode from ode ad vice versa cotiues to use lik -/-. Whilst Figure illustrates that the lik betwee ad is shutdow such that the forwardig tree for traffic destied for odes from ode use ode as the ext hop, whereas for the case of traffic destied for ode from ode use ode as the ext hop. Figures ad provide the forwardig matrices for both SN ad SN. Further the overall effect wrog lik combiatio has o performace as show i Figure 8 whe we compare SN graph to that of graph SN. The lik combiatio betwee poits {, } to {, 5}, shows that despite ot achievig the TWO lik shutdow simultaeously which is the objective for this lik combiatio we are already experiecig the icrease i the maximum load as early as the poit {, }. This icrease i maximum load problem proves worst still whe we cosider comparig the performace of the SN, as show i Figure with other topologies this ca be said SN lik combiatio is ideed poor. The K 5 COMPLETE was used as a Litmus Test of our idea ad which from the results we have see that i both experimets, that is a lik shutdow experimets ad a TWO lik shutdow experimets, the Pythagorea Triple Sequece performed % validatio. The additioal explaiatio that we ca give, that ca hopefully provide as some scietific explaatio of the experimetal results our method is by usig properties such as Node Degree, Clusterig Coefficiet ad Expected Path Legth. Whe comparig performace of these topologies usig properties such as Node Degree, Clusterig Co- NODE FROM TO Figure. Forwardig Matrix for a SN Model (i Figure 9). NODE TO FROM Figure. Forwardig Matrix for a SN Model (i Figure ). Performace % 8% 6% % % % Performace of {,, 5} algorithm whe perfomig the Two Liks Shutdow 7% % 5% % % SN SN COST9 HLDA K5 COMPLETE Topology Figure. Compariso of performace of shuttig dow two liks. efficiet (also called the fried of my fried is also my fried ), ad Expected Path Legth (also called Close Cetrality ) [9-], we use Figures -6 to explai these pheomea i compariso to the performace our algorithm. It ca be explaied that the Clusterig Coefficiet (which is ofte discribed as the tedecy for triagles of coectios to appear frequetly i etworks) ad the Average Node Degree for Simple Network are lowest as compared to both COST9 ad HLDA. However whe we compare COST9 ( odes, 5 liks) ad HLDA ( odes, 6 liks) both have the same umber of odes, ad their oly differece betwee them is the extra lik i HLDA topology. This extra lik helps HLDA model to have some improvemets i the Average Node Degree, Clusterig Coefficiet ad Expected Path Legth as we ca see i Figures -6. It could be said that, possibly it is same extra lik that helps HLDA model to respod more favourable eve to our Pythagorea Triple Sequece as compared to COST9. However the major determiat i our algorithm could be said to be the Expected Path Legth, sice it relates more to our assumptio of the Miimum Cost Routig. This fact ca be explaied usig Figure 6. We see Copyright SciRes.

9 S. TEMBO ET AL. 8 Average Node Degree Average Node Degree = [*L/].55.7 SIMPLE NETWORK COST9 HLDA Topology. K5 COMPLETE Figure. Average ode degree of Simple Network, COST- 9, HLDA ad K 5 COMPLETE. Clusterig Coefficiet Clusterig Coefficiet =[(K-)/(K-)].5.55 SIMPLE NETWORK COST9 HLDA Topology.5 K5 COMPLETE Figure 5. Clusterig coefficiet of Simple Network, COST- 9, HLDA ad K 5 COMPLETE. Expected Path Legth SIMPLE NETWORK Expected Path Legth =[log N/log K].58.5 COST9 Topolgy HLDA.6 K5 COMPLETE Figure 6. Expected Path Legth of Simple Network, COST- 9, HLDA ad K 5 COMPLETE. that topologies with miimum Expected Path Legth, that the closer the Expexted Path Legth is to UNITY the better the result is see i respose to the Pythagorea Triple Sequece. The K 5 COMPLETE helps to explai this fact. Whe we compare the all the three properties Expected Path Legth, Clusterig Coefficiet ad Average Node Degree of K 5 COMPLETE to COST9, HLDA ad Simple Network, it is oly the Expeted Path Legth that the K 5 COMPLETE model shows some superiority. This property easily helps to provide the explaatio that the Pythagorea Triple Sequece, is valid to use as a lik shutdow method ad that the determiat pheomea i our method is miimum cost routig resposiveess.. Coclusios We have preseted a ew lik shutdow method for ay give lik desired to be shut dow for routie maiteace. The key idea of our method is the itroductio of the Pythagorea Triple Metric Sequece {,, 5} to use to cofigure a lik that is desired to be shut dow for routie maiteace. Whe a lik is scheduled for routie maiteace the lik ca be cofigured to oe of the metric i the sequece as the target metric before shut dow. I our experimets, each lik was cofigured to {,,,, 5} sequece ad we performed some simulatios, ad it was discovered that it is oly whe the lik metric reached values of {,, 5} were able to perform a lik shutdow. The simulatios result validated our idea of usig the Pythagorea Triple Metric Sequece {,, 5} as a lik shut dow method. I our experimets to performig a lik shutdow, we discovered that usig our Pythagorea Triple Metric {,, 5} Sequece, for the Simple Network model 5% of the liks validates our algorithm whilst the other 5% the sequece could oly be shutdow whe their lik metric reached values of {, 5}, the results which we cosider as valid the fact that the {, 5} is a subset of the Pythagorea Triple Metric {,, 5} Sequece. The similar lik shutdow experimets revealed that as for other topologies, the validity respose is 96% for COST9, % for HLDA ad % for K 5 COM- PLETE. To further validate our algorithm we performed experimets by shuttig dow TWO liks simutaeously for all the four topologies. The validity respose is % for the Simple Network model, COST9 respose is 5%, whereas HLDA ad K 5 COMPLETE is % for both of them. The simulatio results have validated our idea to use the Pythagorea Triple Metric Sequece {,, 5} as a lik shutdow method. We further established that our method has positive resposiveess to miimum cost routig, as we have illustrated that topologies with Expected Path Legth ear UNITY value ted to have good performace. As for future work, we pla to ivestigate further the cosequeces of usig other Pythagorea Triple Sequeces other tha the {,, 5}. REFERENCES [] P. Fracois, M. Shad ad O. Boaveture, Disruptio Free Topology Recofiguratio i OSPF Networks, Proceedigs of IEEE INFOCOM, Achorage, 6- May 7. [] A. Markopoulou, G. Iaaccoe, S. Bhattacharyya, C.-N. Chuah ad C. Doit, Characterizatio of Failures i a IP Backboe, IEEE INFOCOM, Vol.,, pp. 7- Copyright SciRes.

10 8 S. TEMBO ET AL. 7. [] R. Teixeria ad J. Rexford, Maagig Routig Disruptios i Iteret Service Provider Networks, IEEE Commuicatios Magazie, Vol., No., 6, pp doi:.9/mcom [] L. Shi, J. Fu ad X. Fu, Loop-Free Forwardig Table Updates with Miimal Lik Overflow, Proceedig of IEEE ICC, Dresde, -8 Jue 9, pp. -6. [5] C. C. Che ad T. A. Peg, Almost-Isosceles Right- Agled Triagles, Australia Joural of Combiatory, Vol., 995, pp [6] J. Kocik, Clifford Algebras ad Euclid s Parameterizatio of Pythagorea Triples, Advaces i Applied Clifford Algebras, Vol. 7, 7, pp [7] J. Fu, P. Sjödi ad G. Karlsso, Loop-Free Updates of Forwardig Tables, IEEE Trasactios o Network ad Service Maagemet, Vol. 5, No., 8, pp. -5. doi:.9/tnsm.8.8 [8] S. S. W. Lee, P. K. Tseg ad A. Che, Lik Weight Assigmet ad Loop-Free Routig Table Update for Lik State Routig Protocols i Eergy-Aware Iteret, Future Geeratio Computer Systems, Vol. 8, No.,, pp doi:.6/j.future..5. [9] R. Albert ad A.-L. Barabasi, Statistical Mechaics of Complex Networks, Review of Moder Physics, Vol. 7, No.,, pp doi:./revmodphys.7.7 [] M. E. J. Newma, The Structure ad Fuctio of Complex Networks, SIAM Society for Idustrial ad Applied Mathematics, Vol. 5, No.,, pp [] X. F. Wag ad G. Che, Complex Networks-Small- World, Scale-Free ad Beyod, IEEE Circuits ad Systems Magazie, Vol., No.,, pp. 6-. doi:.9/mcas..85 Copyright SciRes.

Quantitative Evaluation of Stress Corrosion Cracking Based on Features of Eddy Current Testing Signals

Quantitative Evaluation of Stress Corrosion Cracking Based on Features of Eddy Current Testing Signals E-Joural of Advaced Maiteace Vol.9-2 (2017) 78-83 Japa Society of Maiteology Quatitative Evaluatio of Stress Corrosio Crackig Based o Features of Eddy Curret Testig Sigals Li WANG 1,* ad Zhemao CHEN 2

More information

Should We Care How Long to Publish? Investigating the Correlation between Publishing Delay and Journal Impact Factor 1

Should We Care How Long to Publish? Investigating the Correlation between Publishing Delay and Journal Impact Factor 1 Should We Care How Log to Publish? Ivestigatig the Correlatio betwee Publishig Delay ad Joural Impact Factor 1 Jie Xu 1, Jiayu Wag 1, Yuaxiag Zeg 2 1 School of Iformatio Maagemet, Wuha Uiversity, Hubei,

More information

Objectives. Sampling Distributions. Overview. Learning Objectives. Statistical Inference. Distribution of Sample Mean. Central Limit Theorem

Objectives. Sampling Distributions. Overview. Learning Objectives. Statistical Inference. Distribution of Sample Mean. Central Limit Theorem Objectives Samplig Distributios Cetral Limit Theorem Ivestigate the variability i sample statistics from sample to sample Fid measures of cetral tedecy for distributio of sample statistics Fid measures

More information

Finite Element Simulation of a Doubled Process of Tube Extrusion and Wall Thickness Reduction

Finite Element Simulation of a Doubled Process of Tube Extrusion and Wall Thickness Reduction World Joural of Mechaics, 13, 3, 5- http://dx.doi.org/1.3/wjm.13.35 Published lie August 13 (http://www.scirp.org/joural/wjm) Fiite Elemet Simulatio of a Doubled Process of Tube Extrusio ad Wall Thickess

More information

Statistics 11 Lecture 18 Sampling Distributions (Chapter 6-2, 6-3) 1. Definitions again

Statistics 11 Lecture 18 Sampling Distributions (Chapter 6-2, 6-3) 1. Definitions again Statistics Lecture 8 Samplig Distributios (Chapter 6-, 6-3). Defiitios agai Review the defiitios of POPULATION, SAMPLE, PARAMETER ad STATISTIC. STATISTICAL INFERENCE: a situatio where the populatio parameters

More information

How is the President Doing? Sampling Distribution for the Mean. Now we move toward inference. Bush Approval Ratings, Week of July 7, 2003

How is the President Doing? Sampling Distribution for the Mean. Now we move toward inference. Bush Approval Ratings, Week of July 7, 2003 Samplig Distributio for the Mea Dr Tom Ilveto FREC 408 90 80 70 60 50 How is the Presidet Doig? 2/1/2001 4/1/2001 Presidet Bush Approval Ratigs February 1, 2001 through October 6, 2003 6/1/2001 8/1/2001

More information

Measures of Spread: Standard Deviation

Measures of Spread: Standard Deviation Measures of Spread: Stadard Deviatio So far i our study of umerical measures used to describe data sets, we have focused o the mea ad the media. These measures of ceter tell us the most typical value of

More information

Practical Basics of Statistical Analysis

Practical Basics of Statistical Analysis Practical Basics of Statistical Aalysis David Keffer Dept. of Materials Sciece & Egieerig The Uiversity of Teessee Koxville, TN 37996-2100 dkeffer@utk.edu http://clausius.egr.utk.edu/ Goveror s School

More information

Statistics Lecture 13 Sampling Distributions (Chapter 18) fe1. Definitions again

Statistics Lecture 13 Sampling Distributions (Chapter 18) fe1. Definitions again fe1. Defiitios agai Review the defiitios of POPULATIO, SAMPLE, PARAMETER ad STATISTIC. STATISTICAL IFERECE: a situatio where the populatio parameters are ukow, ad we draw coclusios from sample outcomes

More information

S3: Ultrasensitization is Preserved for Transient Stimuli

S3: Ultrasensitization is Preserved for Transient Stimuli S3: Ultrasesitizatio is Preserved for Trasiet Stimuli I the followig we show that ultrasesitizatio is preserved (albeit weaeed) upo trasiet stimulatio (e.g. due to receptor dowregulatio) as log as the

More information

23.3 Sampling Distributions

23.3 Sampling Distributions COMMON CORE Locker LESSON Commo Core Math Stadards The studet is expected to: COMMON CORE S-IC.B.4 Use data from a sample survey to estimate a populatio mea or proportio; develop a margi of error through

More information

Estimation and Confidence Intervals

Estimation and Confidence Intervals Estimatio ad Cofidece Itervals Chapter 9 McGraw-Hill/Irwi Copyright 2010 by The McGraw-Hill Compaies, Ic. All rights reserved. GOALS 1. Defie a poit estimate. 2. Defie level of cofidece. 3. Costruct a

More information

Interference Cancellation Algorithm for 2 2 MIMO System without Pilot in LTE

Interference Cancellation Algorithm for 2 2 MIMO System without Pilot in LTE Commuicatios ad Networ, 13, 5, 31-35 http://dx.doi.org/1.436/c.13.53b7 Published Olie September 13 (http://www.scirp.org/oural/c) Iterferece Cacellatio Algorithm for MIMO System without Pilot i LE Otgobayar

More information

Statistical Analysis and Graphing

Statistical Analysis and Graphing BIOL 202 LAB 4 Statistical Aalysis ad Graphig Aalyzig data objectively to determie if sets of data differ ad the to preset data to a audiece succictly ad clearly is a major focus of sciece. We eed a way

More information

Routing-Oriented Update SchEme (ROSE) for Link State Updating

Routing-Oriented Update SchEme (ROSE) for Link State Updating 948 IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. 56, NO. 6, JUNE 28 Routig-Orieted Update SchEme () for Lik State Updatig Nirwa Asari, Gag Cheg, ad Na Wag Abstract Few works have bee reported to address the

More information

Chapter 8 Descriptive Statistics

Chapter 8 Descriptive Statistics 8.1 Uivariate aalysis ivolves a sigle variable, for examples, the weight of all the studets i your class. Comparig two thigs, like height ad weight, is bivariate aalysis. (Which we will look at later)

More information

Appendix C: Concepts in Statistics

Appendix C: Concepts in Statistics Appedi C. Measures of Cetral Tedecy ad Dispersio A8 Appedi C: Cocepts i Statistics C. Measures of Cetral Tedecy ad Dispersio Mea, Media, ad Mode I may real-life situatios, it is helpful to describe data

More information

EDEXCEL NATIONAL CERTIFICATE UNIT 28 FURTHER MATHEMATICS FOR TECHNICIANS OUTCOME 1- ALGEBRAIC TECHNIQUES TUTORIAL 3 - STATISTICAL TECHNIQUES

EDEXCEL NATIONAL CERTIFICATE UNIT 28 FURTHER MATHEMATICS FOR TECHNICIANS OUTCOME 1- ALGEBRAIC TECHNIQUES TUTORIAL 3 - STATISTICAL TECHNIQUES EDEXCEL NATIONAL CERTIFICATE UNIT 8 FURTHER MATHEMATICS FOR TECHNICIANS OUTCOME 1- ALGEBRAIC TECHNIQUES TUTORIAL 3 - STATISTICAL TECHNIQUES CONTENTS Be able to apply algebraic techiques Arithmetic progressio

More information

A Supplement to Improved Likelihood Inferences for Weibull Regression Model by Yan Shen and Zhenlin Yang

A Supplement to Improved Likelihood Inferences for Weibull Regression Model by Yan Shen and Zhenlin Yang A Supplemet to Improved Likelihood Ifereces for Weibull Regressio Model by Ya She ad Zheli Yag More simulatio experimets were carried out to ivestigate the effect of differet cesorig percetages o the performace

More information

Bayesian Sequential Estimation of Proportion of Orthopedic Surgery of Type 2 Diabetic Patients Among Different Age Groups A Case Study of Government

Bayesian Sequential Estimation of Proportion of Orthopedic Surgery of Type 2 Diabetic Patients Among Different Age Groups A Case Study of Government Bayesia Sequetial Estimatio of Proportio of Orthopedic Surgery of Type Diabetic Patiets Amog Differet Age Groups A Case Study of Govermet Medical College, Jammu-Idia Roohi Gupta, Priyaka Aad ad *Rahul

More information

Technical Assistance Document Algebra I Standard of Learning A.9

Technical Assistance Document Algebra I Standard of Learning A.9 Techical Assistace Documet 2009 Algebra I Stadard of Learig A.9 Ackowledgemets The Virgiia Departmet of Educatio wishes to express sicere thaks to J. Patrick Liter, Doa Meeks, Dr. Marcia Perry, Amy Siepka,

More information

Review for Chapter 9

Review for Chapter 9 Review for Chapter 9 1. For which of the followig ca you use a ormal approximatio? a) = 100, p =.02 b) = 60, p =.4 c) = 20, p =.6 d) = 15, p = 2/3 e) = 10, p =.7 2. What is the probability of a sample

More information

CHAPTER 8 ANSWERS. Copyright 2012 Pearson Education, Inc. Publishing as Addison-Wesley

CHAPTER 8 ANSWERS. Copyright 2012 Pearson Education, Inc. Publishing as Addison-Wesley CHAPTER 8 ANSWERS Sectio 8.1 Statistical Literacy ad Critical Thikig 1 The distributio of radomly selected digits from to 9 is uiform. The distributio of sample meas of 5 such digits is approximately ormal.

More information

Measuring Dispersion

Measuring Dispersion 05-Sirki-4731.qxd 6/9/005 6:40 PM Page 17 CHAPTER 5 Measurig Dispersio PROLOGUE Comparig two groups by a measure of cetral tedecy may ru the risk for each group of failig to reveal valuable iformatio.

More information

Sampling Distributions and Confidence Intervals

Sampling Distributions and Confidence Intervals 1 6 Samplig Distributios ad Cofidece Itervals Iferetial statistics to make coclusios about a large set of data called the populatio, based o a subset of the data, called the sample. 6.1 Samplig Distributios

More information

Algorithms for radiotherapy treatment booking

Algorithms for radiotherapy treatment booking Algorithms for radiotherapy treatmet bookig Saja Petrovic *, William Leug *, Xueya Sog * ad Sathaam Sudar # * Automated Schedulig, Optimisatio ad Plaig Research Group, School of Computer Sciece ad IT,

More information

Sec 7.6 Inferences & Conclusions From Data Central Limit Theorem

Sec 7.6 Inferences & Conclusions From Data Central Limit Theorem Sec 7. Ifereces & Coclusios From Data Cetral Limit Theorem Name: The Cetral Limit Theorem offers us the opportuity to make substatial statistical predictios about the populatio based o the sample. To better

More information

Caribbean Examinations Council Secondary Education Certificate School Based Assessment Additional Math Project

Caribbean Examinations Council Secondary Education Certificate School Based Assessment Additional Math Project Caribbea Examiatios Coucil Secodary Educatio Certificate School Based Assessmet Additioal Math Project Does good physical health ad fitess, as idicated by Body Mass Idex, affect the academic performace

More information

STATISTICAL ANALYSIS & ASTHMATIC PATIENTS IN SULAIMANIYAH GOVERNORATE IN THE TUBER-CLOSES CENTER

STATISTICAL ANALYSIS & ASTHMATIC PATIENTS IN SULAIMANIYAH GOVERNORATE IN THE TUBER-CLOSES CENTER March 3. Vol., No. ISSN 37-3 IJRSS & K.A.J. All rights reserved STATISTICAL ANALYSIS & ASTHMATIC PATIENTS IN SULAIMANIYAH GOVERNORATE IN THE TUBER-CLOSES CENTER Dr. Mohammad M. Faqe Hussai (), Asst. Lecturer

More information

Performance Improvement in the Bivariate Models by using Modified Marginal Variance of Noisy Observations for Image-Denoising Applications

Performance Improvement in the Bivariate Models by using Modified Marginal Variance of Noisy Observations for Image-Denoising Applications PROCEEDING OF WORLD ACADEM OF CIENCE, ENGINEERING AND ECHNOLOG VOLUME 5 APRIL 005 IN 307-6884 Performace Improvemet i the Bivariate Models by usig Modified Margial Variace of Noisy Observatios for Image-Deoisig

More information

Chapter 23 Summary Inferences about Means

Chapter 23 Summary Inferences about Means U i t 6 E x t e d i g I f e r e c e Chapter 23 Summary Iferece about Mea What have we leared? Statitical iferece for mea relie o the ame cocept a for proportio oly the mechaic ad the model have chaged.

More information

The relationship between hypercholesterolemia as a risk factor for stroke and blood viscosity measured using Digital Microcapillary

The relationship between hypercholesterolemia as a risk factor for stroke and blood viscosity measured using Digital Microcapillary Joural of Physics: Coferece Series PAPER OPEN ACCESS The relatioship betwee hypercholesterolemia as a risk factor for stroke ad blood viscosity measured usig Digital Microcapillary To cite this article:

More information

Modified Early Warning Score Effect in the ICU Patient Population

Modified Early Warning Score Effect in the ICU Patient Population Lehigh Valley Health Network LVHN Scholarly Works Patiet Care Services / Nursig Modified Early Warig Score Effect i the ICU Patiet Populatio Ae Rabert RN, DHA, CCRN, NE-BC Lehigh Valley Health Network,

More information

Rheological Characterization of Fiber Suspensions Prepared from Vegetable Pulp and Dried Fibers. A Comparative Study.

Rheological Characterization of Fiber Suspensions Prepared from Vegetable Pulp and Dried Fibers. A Comparative Study. ANNUAL TRANSACTIONS OF THE NORDIC RHEOLOGY SOCIETY, VOL. 3, 5 Rheological Characterizatio of Fiber Suspesios Prepared from Vegetable Pulp ad Dried Fibers. A Comparative Study. Elea Bayod, Ulf Bolmstedt

More information

Automatic reasoning evaluation in diet management based on an Italian cookbook

Automatic reasoning evaluation in diet management based on an Italian cookbook Automatic reasoig evaluatio i diet maagemet based o a Italia cookbook Luca Aselma, aselma@di.uito.it Alessadro Mazzei, mazzei@di.uito.it Adrea Piroe, adrea.piroe@di.uito.it Departmet of Computer Sciece,

More information

INTERNATIONAL STUDENT TIMETABLE SYDNEY CAMPUS

INTERNATIONAL STUDENT TIMETABLE SYDNEY CAMPUS INTERNATIONAL STUDENT TIMETABLE KEY TERM DATES Term Iductio Day Term Dates Public Holidays Studet Fees 2013/14 Holiday Periods* Commece Util* Commece Util ALL studets 1, 2014 Fri 24 Ja Fri 24 Ja Su 6 Apr

More information

Energy Efficient Ethernet Passive Optical Networks (EPONs) in Access Networks

Energy Efficient Ethernet Passive Optical Networks (EPONs) in Access Networks NEW ASPECTS of APPLIED INFORMATICS, BIOMEDICAL ELECTRONICS & INFORMATICS ad COMMUNICATIONS Eergy Efficiet Etheret Passive Optical Networks (EPONs) i Access Networks Yig Ya ad Lars Dittma Departmet of Photoics

More information

Concepts Module 7: Comparing Datasets and Comparing a Dataset with a Standard

Concepts Module 7: Comparing Datasets and Comparing a Dataset with a Standard Cocepts Module 7: Comparig Datasets ad Comparig a Dataset with a Stadard Idepedece of each data poit Test statistics Cetral Limit Theorem Stadard error of the mea Cofidece iterval for a mea Sigificace

More information

Intro to Scientific Analysis (BIO 100) THE t-test. Plant Height (m)

Intro to Scientific Analysis (BIO 100) THE t-test. Plant Height (m) THE t-test Let Start With a Example Whe coductig experimet, we would like to kow whether a experimetal treatmet had a effect o ome variable. A a imple but itructive example, uppoe we wat to kow whether

More information

Plantar Pressure Difference: Decision Criteria of Motor Relearning Feedback Insole for Hemiplegic Patients

Plantar Pressure Difference: Decision Criteria of Motor Relearning Feedback Insole for Hemiplegic Patients 22 4th Iteratioal Coferece o Bioiformatics ad Biomedical Techology IPCBEE vol.29 (22) (22) IACSIT Press, Sigapore Platar Pressure Differece: Decisio Criteria of Motor Relearig Feedback Isole for Hemiplegic

More information

Guidance on the use of the Title Consultant Psychologist

Guidance on the use of the Title Consultant Psychologist Guidace o the use of the Title Cosultat Psychologist If you have problems readig this documet ad would like it i a differet format, please cotact us with your specific requiremets. Tel: 0116 2254 9568;

More information

Reporting Checklist for Nature Neuroscience

Reporting Checklist for Nature Neuroscience Correspodig Author: Mauscript Number: Mauscript Type: Galea NNA48318C Article Reportig Checklist for Nature Neurosciece # Figures: 4 # Supplemetary Figures: 2 # Supplemetary Tables: 1 # Supplemetary Videos:

More information

Copy of: Proc. IEEE 1998 Int. Conference on Microelectronic Test Structures, Vol.11, March 1998

Copy of: Proc. IEEE 1998 Int. Conference on Microelectronic Test Structures, Vol.11, March 1998 Copy of: Proc. IEEE 998 It. Coferece o Microelectroic Test Structures, Vol., March 998 Wafer Level efect esity istributio Usig Checkerboard Test Structures Christopher Hess, Larg H. Weilad Istitute of

More information

How important is the acute phase in HIV epidemiology?

How important is the acute phase in HIV epidemiology? How importat is the acute phase i HIV epidemiology? Bria G. Williams South Africa Cetre for Epidemiological Modellig ad Aalysis (SACEMA), Stellebosch, Wester Cape, South Africa Correspodece should be addressed

More information

5/7/2014. Standard Error. The Sampling Distribution of the Sample Mean. Example: How Much Do Mean Sales Vary From Week to Week?

5/7/2014. Standard Error. The Sampling Distribution of the Sample Mean. Example: How Much Do Mean Sales Vary From Week to Week? Samplig Distributio Meas Lear. To aalyze how likely it is that sample results will be close to populatio values How probability provides the basis for makig statistical ifereces The Samplig Distributio

More information

Outline. Neutron Interactions and Dosimetry. Introduction. Tissue composition. Neutron kinetic energy. Neutron kinetic energy.

Outline. Neutron Interactions and Dosimetry. Introduction. Tissue composition. Neutron kinetic energy. Neutron kinetic energy. Outlie Neutro Iteractios ad Dosimetry Chapter 16 F.A. Attix, Itroductio to Radiological Physics ad Radiatio Dosimetry Neutro dosimetry Thermal eutros Itermediate-eergy eutros Fast eutros Sources of eutros

More information

5.1 Description of characteristics of population Bivariate analysis Stratified analysis

5.1 Description of characteristics of population Bivariate analysis Stratified analysis Chapter 5 Results Page umbers 5.1 Descriptio of characteristics of populatio 121-123 5.2 Bivariate aalysis 123-131 5.3 Stratified aalysis 131-133 5.4 Multivariate aalysis 134-135 5.5 Estimatio of Attributable

More information

Chapter 21. Recall from previous chapters: Statistical Thinking. Chapter What Is a Confidence Interval? Review: empirical rule

Chapter 21. Recall from previous chapters: Statistical Thinking. Chapter What Is a Confidence Interval? Review: empirical rule Chapter 21 What Is a Cofidece Iterval? Chapter 21 1 Review: empirical rule Chapter 21 5 Recall from previous chapters: Parameter fixed, ukow umber that describes the populatio Statistic kow value calculated

More information

The Suicide Note: Do unemployment rates affect suicide rates? Author: Sarah Choi. Course: A World View of Math and Data Analysis

The Suicide Note: Do unemployment rates affect suicide rates? Author: Sarah Choi. Course: A World View of Math and Data Analysis The Suicide Note: Do uemploymet rates affect suicide rates? Author: Sarah Choi Course: A World View of Math ad Data Aalysis Istructors: Dr. Joh R. Taylor, Mrs. Desiré J. Taylor ad Mrs. Christia L. Turer

More information

Maximum Likelihood Estimation of Dietary Intake Distributions

Maximum Likelihood Estimation of Dietary Intake Distributions CARD Workig Papers CARD Reports ad Workig Papers 8-1992 Maximum Likelihood Estimatio of Dietary Itake Distributios Jeffrey D. Helterbrad Iowa State Uiversity Follow this ad additioal works at: http://lib.dr.iastate.edu/card_workigpapers

More information

Routing. Spring 2018 CS 438 Staff, University of Illinois 1

Routing. Spring 2018 CS 438 Staff, University of Illinois 1 Routig Sprig 08 S 438 Staff, Uiversity of Illiois Routig hicago Sprigfield hampaig tourist appears ad gruts, hicago? Which way do you poit? aville Idiaapolis Effigham St. Louis Sprig 08 S 438 Staff, Uiversity

More information

JUST THE MATHS UNIT NUMBER STATISTICS 3 (Measures of dispersion (or scatter)) A.J.Hobson

JUST THE MATHS UNIT NUMBER STATISTICS 3 (Measures of dispersion (or scatter)) A.J.Hobson JUST THE MATHS UNIT NUMBER 8.3 STATISTICS 3 (Measures of dispersio (or scatter)) by A.J.Hobso 8.3. Itroductio 8.3.2 The mea deviatio 8.3.3 Practica cacuatio of the mea deviatio 8.3.4 The root mea square

More information

DISTRIBUTION AND PROPERTIES OF SPERMATOZOA IN DIFFERENT FRACTIONS OF SPLIT EJACULATES*

DISTRIBUTION AND PROPERTIES OF SPERMATOZOA IN DIFFERENT FRACTIONS OF SPLIT EJACULATES* FERTILITY AND STERILITY Copyright 1972 by The Williams & Wilkis Co. Vol. 23, No.4, April 1972 Prited i U.S.A. DISTRIBUTION AND PROPERTIES OF SPERMATOZOA IN DIFFERENT FRACTIONS OF SPLIT EJACULATES* R. ELIASSON,

More information

Gamma and inverse Gaussian frailty models: A comparative study

Gamma and inverse Gaussian frailty models: A comparative study Iteratioal Joural of Mathematics ad Statistics Ivetio (IJMSI) E-ISSN: 3 4767 P-ISSN: 3-4759 Volume 4 Issue 4 April. 06 PP-40-05 Gamma ad iverse Gaussia frailty models: A comparative study Samia A. Adham,

More information

! A data structure representing a list. ! A series of dynamically allocated nodes. ! A separate pointer (the head) points to the first

! A data structure representing a list. ! A series of dynamically allocated nodes. ! A separate pointer (the head) points to the first Liked Lists Week 8 Gaddis: Chater 17 CS 5301 Fall 2015 Itroductio to Liked Lists! A data structure reresetig a list! A series of dyamically allocated odes chaied together i sequece - Each ode oits to oe

More information

Person Identification by Using AR Model for EEG Signals

Person Identification by Using AR Model for EEG Signals Perso Idetificatio by Usig AR Model for EEG Sigals Gelareh Mohammadi, Parisa Shoushtari, Beham Molaee Ardekai ad Mohammad B. Shamsollahi Abstract A direct coectio betwee ElectroEcephaloGram (EEG) ad the

More information

Real-Time Noise Cancelling Approach on Innovations-Based Whitening Application to Adaptive FIR RLS in Beamforming Structure

Real-Time Noise Cancelling Approach on Innovations-Based Whitening Application to Adaptive FIR RLS in Beamforming Structure Real-Time Noise Cacellig Approach o Iovatios-Based Whiteig Applicatio to Adaptive FIR RLS i Beamformig Structure 7 Jisoo Jeog Faculty of Biomedical Egieerig ad Health Sciece, Uiversiti Tekologi Malaysia,

More information

Study No.: Title: Rationale: Phase: Study Period: Study Design: Centres: Indication: Treatment: Objectives: Primary Outcome/Efficacy Variable:

Study No.: Title: Rationale: Phase: Study Period: Study Design: Centres: Indication: Treatment: Objectives: Primary Outcome/Efficacy Variable: UM27/189/ The study listed may iclude approved ad o-approved uses, formulatios or treatmet regimes. The results reported i ay sigle study may ot reflect the overall results obtaied o studies of a product.

More information

Ovarian Cancer Survival

Ovarian Cancer Survival Dairy Products, Calcium, Vitami D, Lactose ad Ovaria Cacer: Results from a Pooled Aalysis of Cohort Studies Stephaie Smith-Warer, PhD Departmets of Nutritio & Epidemiology Harvard School of Public Health

More information

Improving the Bioanalysis of Endogenous Bile Acids as Biomarkers for Hepatobiliary Toxicity using Q Exactive Benchtop Orbitrap?

Improving the Bioanalysis of Endogenous Bile Acids as Biomarkers for Hepatobiliary Toxicity using Q Exactive Benchtop Orbitrap? Troy Voelker, Mi Meg Tadem Labs, Salt Lake City, UT Kevi Cook, Patrick Beett Thermo Fisher Scietific, Sa Jose, CA Improvig the Bioaalysis of Edogeous Bile Acids as Biomarkers for Hepatobiliary Toxicity

More information

Objectives. Types of Statistical Inference. Statistical Inference. Chapter 19 Confidence intervals: Estimating with confidence

Objectives. Types of Statistical Inference. Statistical Inference. Chapter 19 Confidence intervals: Estimating with confidence Types of Statistical Iferece Chapter 19 Cofidece itervals: The basics Cofidece itervals for estiatig the value of a populatio paraeter Tests of sigificace assesses the evidece for a clai about a populatio.

More information

Primary: To assess the change on the subject s quality of life between diagnosis and the first 3 months of treatment.

Primary: To assess the change on the subject s quality of life between diagnosis and the first 3 months of treatment. Study No.: AVO112760 Title: A Observatioal Study To Assess The Burde Of Illess I Prostate Cacer Patiets With Low To Moderate Risk Of Progressio Ratioale: Little data are available o the burde of illess

More information

YOUR BEST DAYS START WITH BETTER PROTECTION FROM LOWS. *,1,2

YOUR BEST DAYS START WITH BETTER PROTECTION FROM LOWS. *,1,2 YOUR BEST DAYS START WITH BETTER PROTECTION FROM LOWS. *,1,2 Oly SmartGuard TM from MiiMed takes actio for you whe you eed it most. MiiMed 530G system HIT THE ROAD. WORRY LESS ABOUT GOING LOW. *,1,3 37.5%

More information

ARTICLE IN PRESS. Journal of Theoretical Biology

ARTICLE IN PRESS. Journal of Theoretical Biology Joural of Theoretical Biology 26 (29) 227 239 Cotets lists available at ScieceDirect Joural of Theoretical Biology joural homepage: www.elsevier.com/locate/yjtbi A ew mathematical model for the homeostatic

More information

Supplementary Information for: Use of Fibonacci numbers in lipidomics. Enumerating various classes of fatty acids

Supplementary Information for: Use of Fibonacci numbers in lipidomics. Enumerating various classes of fatty acids Supplemetary Iformatio for: Use of Fiboacci umbers i lipidomics Eumeratig various classes of fatty acids Stefa Schuster *,1, Maximilia Fichter 1, Severi Sasso 1 Dept. of Bioiformatics, Friedrich Schiller

More information

Children and adults with Attention-Deficit/Hyperactivity Disorder cannot move to the beat

Children and adults with Attention-Deficit/Hyperactivity Disorder cannot move to the beat 1 SUPPLEMENTARY INFORMATION Childre ad adults with Attetio-Deficit/Hyperactivity Disorder caot move to the beat Frédéric Puyjariet 1, Valeti Bégel 1,2, Régis Lopez 3,4, Delphie Dellacherie 5,6, & Simoe

More information

Chem 135: First Midterm

Chem 135: First Midterm Chem 135: First Midterm September 30 th, 2013 Please provide all aswers i the spaces provided. You are ot allowed to use a calculator for this exam, but you may use (previously disassembled) molecular

More information

l A data structure representing a list l A series of dynamically allocated nodes l A separate pointer (the head) points to the first

l A data structure representing a list l A series of dynamically allocated nodes l A separate pointer (the head) points to the first Liked Lists Week 8 Gaddis: Chater 17 CS 5301 Srig 2018 Itroductio to Liked Lists l A data structure reresetig a list l A series of dyamically allocated odes chaied together i sequece - Each ode oits to

More information

GOALS. Describing Data: Numerical Measures. Why a Numeric Approach? Concepts & Goals. Characteristics of the Mean. Graphic of the Arithmetic Mean

GOALS. Describing Data: Numerical Measures. Why a Numeric Approach? Concepts & Goals. Characteristics of the Mean. Graphic of the Arithmetic Mean GOALS Describig Data: umerical Measures Chapter 3 Dr. Richard Jerz Calculate the arithmetic mea, weighted mea, media, ad mode Explai the characteristics, uses, advatages, ad disadvatages of each measure

More information

Lecture Outline. BIOST 514/517 Biostatistics I / Applied Biostatistics I. Paradigm of Statistics. Inferential Statistic.

Lecture Outline. BIOST 514/517 Biostatistics I / Applied Biostatistics I. Paradigm of Statistics. Inferential Statistic. BIOST 514/517 Biostatistics I / Applied Biostatistics I Kathlee Kerr, Ph.D. Associate Professor of Biostatistics iversity of Washigto Lecture 11: Properties of Estimates; Cofidece Itervals; Stadard Errors;

More information

Development Report of Powerful Acoustic Computing Environment

Development Report of Powerful Acoustic Computing Environment Developmet Report of Powerful Acoustic Computig Eviromet Takayuki asumoto echaical CAE Divisio VPD Group Cyberet Systems Co. td. 006 ANSYS Ic. ANSYS Ic. Proprietary Ageda Part: The Curret Status of Numerical

More information

An Automatic Denoising Method with Estimation of Noise Level and Detection of Noise Variability in Continuous Glucose Monitoring

An Automatic Denoising Method with Estimation of Noise Level and Detection of Noise Variability in Continuous Glucose Monitoring Preprit, 11th IFAC Symposium o Dyamics ad Cotrol of Process Systems, icludig Biosystems Jue 6-8, 16. NTNU, Trodheim, Norway A Automatic Deoisig Method with Estimatio of Noise Level ad Detectio of Noise

More information

GSK Medicine: Study Number: Title: Rationale: Study Period: Objectives: Indication: Study Investigators/Centers: Research Methods:

GSK Medicine: Study Number: Title: Rationale: Study Period: Objectives: Indication: Study Investigators/Centers: Research Methods: The study listed may iclude approved ad o-approved uses, mulatios or treatmet regimes. The results reported i ay sigle study may ot reflect the overall results obtaied o studies of a product. Bee prescribig

More information

Basic Requirements. of meeting cow herd production and profitability goals for the beef cattle enterprise.

Basic Requirements. of meeting cow herd production and profitability goals for the beef cattle enterprise. Basic Requiremets It is imperative that cattle producers have a adequate uderstadig of the basic utriet requiremets of the cow herd to make iformed ad effective utritio-related decisios. by Matt Hersom,

More information

COMBINATORIAL ON/OFF MODEL FOR OLFACTORY CODING. A.Koulakov 1, A.Gelperin 2, and D.Rinberg 2

COMBINATORIAL ON/OFF MODEL FOR OLFACTORY CODING. A.Koulakov 1, A.Gelperin 2, and D.Rinberg 2 COMBINATORIAL ON/OFF MODEL FOR OLFACTORY CODING A.Koulakov, A.Gelperi 2, ad D.Riberg 2. Cold Sprig Harbor Laboratory, Cold Sprig Harbor, NY, 724 USA 2. Moell Chemical Seses Ctr., Philadelphia, PA, 904

More information

Improved Ratio and Regression Estimators of the Mean of a Sensitive Variable in Stratified Sampling

Improved Ratio and Regression Estimators of the Mean of a Sensitive Variable in Stratified Sampling tatistics ad Applicatios {I 45-7395 (olie)} Volume 5 os. &, 07 (ew eries), pp 63-78 Improved Ratio ad Regressio Estimators of te Mea of a esitive Variable i tratified amplig Geeta Kaluca, Rita ousa, at

More information

PDSS: The decision support system of diabetic patient for Public Health

PDSS: The decision support system of diabetic patient for Public Health Proceedigs of the 3rd Iteratioal Coferece o Idustrial Applicatio Egieerig 5 PDSS: The decisio support system of diabetic patiet for Public Health Bejapuk Jogmuewai, Kailas Bumrugchat, Papo kaewhi Iformatics

More information

Chapter - 8 BLOOD PRESSURE CONTROL AND DYSLIPIDAEMIA IN PATIENTS ON DIALYSIS

Chapter - 8 BLOOD PRESSURE CONTROL AND DYSLIPIDAEMIA IN PATIENTS ON DIALYSIS Chapter - BLOOD PRESSURE CONTROL AND DYSLIPIDAEMIA IN PATIENTS ON DIALYSIS S. Prasad Meo Hooi Lai Seog Lee Wa Ti Suita Bavaada ST REPORT OF THE MALAYSIAN DIALYSIS AND TRANSPLANT REGISTRY SECTION.: BLOOD

More information

Stress Distribution within the Osteochondral Lesions of the Talus with Nonshoulder-type and Shoulder-type

Stress Distribution within the Osteochondral Lesions of the Talus with Nonshoulder-type and Shoulder-type Stress Distributio withi the Osteochodral Lesios of the Talus with Noshoulder-type ad Shoulder-type Departmet of Orthopaedic Surgery, Fukuoka Uiversity Faculty of Medicie, JAPAN Tomoobu Hagio, MD, PhD,

More information

International Journal of Scientific & Engineering Research, Volume 5, Issue 2, February-2014 ISSN

International Journal of Scientific & Engineering Research, Volume 5, Issue 2, February-2014 ISSN ISSN 2229-5518 72 Search For a Biomarker For The Diagosis Ad Progosis of Rheumatoid Arthritis Prasath G, Suil Rao Padmaraj, Vijayakumar T & Riju Mathew Abstract Rheumatoid Arthritis (RA) is oe of the commoest

More information

Standard deviation The formula for the best estimate of the population standard deviation from a sample is:

Standard deviation The formula for the best estimate of the population standard deviation from a sample is: Geder differeces Are there sigificat differeces betwee body measuremets take from male ad female childre? Do differeces emerge at particular ages? I this activity you will use athropometric data to carry

More information

Body Mass Index and Disability Pension in Middle-Aged Men Non-Linear Relations

Body Mass Index and Disability Pension in Middle-Aged Men Non-Linear Relations Iteratioal Joural of Epidemiology O Iteratioal Epridemlotoglcal Associatio 199 Vol. 25, No. 1 Prited i Great Britai Body Mass Idex ad Disability Pesio i Middle-Aged Me No-Liear Relatios NILS-OVE MANSSON,*

More information

DEGRADATION OF PROTECTIVE GLOVE MATERIALS EXPOSED TO COMMERCIAL PRODUCTS: A COMPARATIVE STUDY OF TENSILE STRENGTH AND GRAVIMETRIC ANALYSES

DEGRADATION OF PROTECTIVE GLOVE MATERIALS EXPOSED TO COMMERCIAL PRODUCTS: A COMPARATIVE STUDY OF TENSILE STRENGTH AND GRAVIMETRIC ANALYSES Califoria State Uiversity, Sa Berardio CSUSB ScholarWorks Electroic Theses, Projects, ad Dissertatios Office of Graduate Studies 9-2014 DEGRADATION OF PROTECTIVE GLOVE MATERIALS EXPOSED TO COMMERCIAL PRODUCTS:

More information

VAPREVENT SYSTEM. Evidence-based innovation in oral care

VAPREVENT SYSTEM. Evidence-based innovation in oral care VAPREVENT SYSTEM Evidece-based iovatio i oral care V A P Vetilator Associated Peumoia (VAP) is the secod most commo hospital-acquired ifectio. 1 It is deadlier tha either cetral lie-associated bloodstream

More information

Favorable domain size in proteins Dong Xu 1 * and Ruth Nussinov 1,2

Favorable domain size in proteins Dong Xu 1 * and Ruth Nussinov 1,2 Research Paper 11 Favorable domai size i proteis Dog Xu 1 * ad Ruth Nussiov 1,2 Backgroud: It has bee observed that sigle-domai proteis ad domais i multidomai proteis favor a chai legth i the rage 1 15

More information

Estimation of changes in instantaneous aortic blood flow by the analysis of arterial blood pressure

Estimation of changes in instantaneous aortic blood flow by the analysis of arterial blood pressure Estimatio of chages i istataeous aortic blood flow by the aalysis of arterial blood pressure The MIT Faculty has made this article opely available. lease share how this access beefits you. Your story matters.

More information

INTRODUCTION PLAN:

INTRODUCTION PLAN: 8.06.7 FEBS Workshop o Molecular Life Sciece Educatio Kauas, 26-27 th Jue, 207 DRY PRACTICALS-CASE-BASED DISCUSSIONS A MODEL OF A BIOCHEMISTRY DRY PRACTICAL FOR TEACHING LIVER FUNCTIONS AND BILIRUBIN METABOLISM

More information

7 A complex molecular system: surfactant micelle

7 A complex molecular system: surfactant micelle 7 A complex molecular system: surfactat micelle formatio 7.1 Itroductio I this chapter we give a real life example of a theorectical study of a complex molecular system i the form of self-assemblig surfactats.

More information

The Nutritional Density Ratio Dilemma: Developing a Scale for Nutritional Value Paul D. Q. Campbell

The Nutritional Density Ratio Dilemma: Developing a Scale for Nutritional Value Paul D. Q. Campbell The Nutritioal Desity Ratio Dilemma: Developig a Scale for Nutritioal Value Paul D. Q. Campbell Shared by Paul D. Q. Campbell The author(s) would appreciate your feedback o this article. Click the yellow

More information

GSK Medicine Study Number: Title: Rationale: Study Period: Objectives: Primary Secondary Indication: Study Investigators/Centers: Research Methods

GSK Medicine Study Number: Title: Rationale: Study Period: Objectives: Primary Secondary Indication: Study Investigators/Centers: Research Methods The study listed may iclude approved ad o-approved uses, formulatios or treatmet regimes. The results reported i ay sigle study may ot reflect the overall results obtaied o studies of a product. Before

More information

INTEGRATED MECHANISMS OF K + LOSS IN MYOCARDIAL ISCHEMIA: A SIMULATION STUDY

INTEGRATED MECHANISMS OF K + LOSS IN MYOCARDIAL ISCHEMIA: A SIMULATION STUDY 1 of 4 INTEGRATED MECHANISMS OF K + LOSS IN MYOCARDIAL ISCHEMIA: A SIMULATION STUDY B. Rodríguez, J. M. Ferrero (Jr) LIB- DIE- Uiversidad Politécica de Valecia (Spai) Abstract-Durig the early phase of

More information

Meningococcal B Prevention Tools for Your Practice

Meningococcal B Prevention Tools for Your Practice Meigococcal B Prevetio Tools for Your Practice NAPNAP MeB Facts for HCPs Fast Facts Although ucommo, MeB is potetially fatal. 1 MeB symptoms progress quickly; death ca occur i 24 hours or less. MeB accouts

More information

Comparison of speed and accuracy between manual and computer-aided measurements of dental arch and jaw arch lengths in study model casts

Comparison of speed and accuracy between manual and computer-aided measurements of dental arch and jaw arch lengths in study model casts Compariso of speed ad accuracy betwee maual ad computeraided measuremets (Diah Wibisoo, et.al.) Compariso of speed ad accuracy betwee maual ad computeraided measuremets of detal arch ad jaw arch legths

More information

Fracture of underwater notched structures

Fracture of underwater notched structures Egieerig Solid Mechaics 4 (2016) 43-52 Cotets lists available at GrowigSciece Egieerig Solid Mechaics homepage: www.growigsciece.com/esm Fracture of uderwater otched structures M.M. Mirsayar a* ad Behrouz

More information

WALLENPAUPACK. Lake Wallenpaupack Operations during the April 2005 Flood

WALLENPAUPACK. Lake Wallenpaupack Operations during the April 2005 Flood LAKE WALLENPAUPACK Lake Wallepaupack Operatios durig the April 2005 Flood Lake Performace Summary March-April Storm Evet LAKE WALLENPAUPACK Over 14 BG of water stored betwee March 28 ad April 3 icludig

More information

COMPARISON OF A NEW MICROCRYSTALLINE

COMPARISON OF A NEW MICROCRYSTALLINE Br. J. cli. Pharmac. (1979), 8, 59-64 COMPARISON OF A NEW MICROCRYSTALLINE DICOUMAROL PREPARATION WITH WARFARIN UNDER ROUTINETREATMENTCONDITIO D. LOCKNER & C. PAUL Departmet of Medicie, Thrombosis Uit,

More information

Research Article The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes

Research Article The Research of Improved Grey GM (1, 1) Model to Predict the Postprandial Glucose in Type 2 Diabetes BioMed Research Iteratioal Volume 201, Article ID 37052, pages http://dx.doi.org/.1155/201/37052 Research Article The Research of Improved Grey GM (1, 1) Model to Predict the Postpradial Glucose i Type

More information

Pilot and Exploratory Project Support Grant

Pilot and Exploratory Project Support Grant KEY DATES LETTERS OF INTENT DUE November 3, 2014 5:00 pm est FULL PROPOSAL INVITATIONS November 17, 2014 FULL PROPOSAL DEADLINE Jauary 15, 2015 5:00 pm est NOTIFICATION OF AWARDS April, 2015 Pilot ad Exploratory

More information

Effect of Preparation Conditions of Activated Carbon Prepared from Rice Husk by ZnCl 2 Activation for Removal of Cu (II) from Aqueous Solution

Effect of Preparation Conditions of Activated Carbon Prepared from Rice Husk by ZnCl 2 Activation for Removal of Cu (II) from Aqueous Solution Iteratioal Joural of Egieerig & Techology IJET-IJENS Vol:10 No:06 8 Effect of Preparatio Coditios of Activated Carbo Prepared from Rice Husk by ZCl Activatio for Removal of Cu (II) from Aqueous Solutio

More information