DETECTION AND REFACTORING OF BAD SMELL
|
|
- Randolph Morrison
- 5 years ago
- Views:
Transcription
1 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September 2013 DETECTION AND REFACTORING OF BAD SMELL CAUSED BY LARGE SCALE Jang Dexun 1, Ma Pejun 2, Su Xaohong 3, Wang Tantan 4 School Of Computer Scence and Technology, Harbn Insttute of Technology, Harbn, Chna 1 negrocanfly@163.com, 2 slverghost192@163.cn 3 Suxh@ht.edu.cn, 4 Wangtt@ht.edu.cn ABSTRACT Bad smells are sgns of potental problems n code. Detectng bad smells, however, remans tme consumng for software engneers despte proposals on bad smell detecton and refactorng tools. Large Class s a knd of bad smells caused by large scale, and the detecton s hard to acheve automatcally. In ths paper, a Large Class bad smell detecton approach based on class length dstrbuton model and coheson metrcs s proposed. In programs, the lengths of classes are confrmed accordng to the certan dstrbutons. The class length dstrbuton model s generalzed to detect programs after groupng. Meanwhle, coheson metrcs are analyzed for bad smell detecton. The bad smell detecton experments of open source programs show that Large Class bad smell can be detected effectvely and accurately wth ths approach, and refactorng scheme can be proposed for desgn qualty mprovements of programs. KEYWORDS Dstrbuton rule; Class length dstrbuton model; Coheson metrcs; Bad smell detecton; refactorng scheme 1. INTRODUCTION Nowadays, wth the development of software programmng, the number of software analyss tools avalable for detectng bad smells sgnfcantly ncrease. Although these tools are ganng acceptance n practce, a lack of detecton towards some bad smells may decrease the effectveness, such as Long Method, Large Class and Long Parameter Lst [1]. The key of these bad smells s about the structure and components. Software programs are composed of components from every level. The component from hgher level s composed of ones from lower level. Characters compose keywords, whle keywords, varables and operators compose statements. The composton level of object-orented programs s shown n Fgure 1. DOI : /jsea
2 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September 2013 Fgure 1. Composton level of object-orented programs. Large Class [1] bad smell s one classcal bad smells, meanng a class s too large. The cause of large classes may be the large number of nstance varables or methods. Large Class has long hstory, but the detecton s always vague. From the defnton [1], ths bad smell detecton should be acheved by the class length statstcs. Usually the class length s measured by the lnes of code. In practce t s dffcult to confrm a threshold value for detectng one partcular class s too large or not. So t s also dffcult to detect Large Class bad smell partcularly n busness open source programs. The fxed threshold value s not fastdous for Large Class bad smell detecton. In ths paper a detectng method of Large Class bad smell s proposed based on scale dstrbuton. The length of all the classes n one program s extracted, and then dstrbuton model of class scale s bult usng the length of these classes. In dstrbuton model the groups whch are farthest from the dstrbuton curve s consdered to be canddate groups of Large Class bad smell. Furthermore, the coheson metrcs of the classes n these groups are measured to confrm Large Class. The rest of the paper s organzed as follows. Secton 2 presents a short overvew of related work. In Secton 3, the class length dstrbuton model s bult to present the dstrbuton rules of class length. Wth ths model and coheson metrcs presented, the detecton method of Large Class bad smell s proposed n Secton 4. Secton 5 dscusses how to gve proper refactorng scheme. And Secton 6 shows the experment results. The concluson s provded n Secton RELATED WORK In the past decades, a number of studes were conducted for bad smells of programmng codes. Webster [2] ntroduced smells n the context of object-orented programmng codes, and the smells sorted as conceptual, poltcal, codng, and qualty assurance ptfalls. Rel [3] defned 61 heurstcs characterzng good object-orented programmng that enable engneers to assess the qualty of ther systems manually and provde a bass for mprovng desgn and mplementaton. Beck Fowler [1] compled 22 code smells that are desgn problems n source code, and t s the bass of suggestng for refactorngs. Travassos et al. [4] ntroduced a process based on manual nspectons and readng technques to dentfy smells. But manual detecton of bad smells s one tme-consumng process, and s easy to mstake. So researchers pay more attenton n automatc detecton. Marnescu [5] presented a metrc-based approach to detect code smells wth detecton strateges, mplemented n the IPLASMA tool. Tahvldar and Kontoganns [6] used an object-orented metrcs sute consstng of complexty, couplng, and coheson metrcs to detect classes for whch qualty has deterorated and re-engneer detected desgn flaws. A lmtaton of ther approach s that t ndcates the knd 2
3 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September 2013 of the requred transformaton but does not specfy on whch specfc methods, attrbutes, or classes ths transformaton should apply (ths process requres human nterpretaton). O Keeffe and O Cnnede [7] treated object-orented desgn as a search problem n the space of alternatve desgns. Ths s applcaton of search-based approaches to solvng optmzaton problems n software engneerng. Bad smell detectng by metrc needs to be selected proper metrcs and the judgng threshold should be predetermned. Vsualzaton technques are used n some approaches for complex software analyss. These semautomatc approaches are nterestng compromses between fully automatc detecton technques that can be effcent but loose n track of context and manual nspecton that s slow and naccurate [8, 9]. However, they requre human expertse and are thus stll tme-consumng. Other approaches perform fully automatc detecton of smells and use vsualzaton technques to present the detecton results [10, 11]. But vsual detectng results need manual nterventon. Some bad smells relevant to coheson can be detected usng dstance theory. Smon et al. [12] defned a dstance-based metrc to measure the coheson between attrbutes and methods. The nspraton about the approach n ths paper s drawn from the work [12] n the sense that t also employs the Jaccard dstance. However, the approach has proposed several new defntons and processes to get mprovements. The concepton of dstance metrcs s defned not only among enttes (attrbutes and methods) but also between classes. In [13], the dstances between enttes and classes are defned to measure the coheson among them. The bad smell detecton wth dstance theory needs more calculaton. In ths paper the equaton of dstance between one entty and one class has been used for computng the coheson degree of one class. There s less research about bad smell detecton of Large Class. Lu et al [14] proposed a detecton and resoluton sequence for dfferent knds of bad smells to smplfy ther detecton and resoluton, ncludng Large Class bad smell. But Lu pad more attenton to the schedule of detecton rather than Large Class detecton tself, and the specfc detectng process was not provded n the paper. In Large Class bad smell detecton, class sze measures have been ntroduced. When class sze s large, t s seen as Large Class. In bad smell detecton tools, the man way [15] of measurng class sze s to measure the number of lnes of code,.e. NLOC, or the number of attrbutes and methods. PMD[16] and Checkstyle[17] both use NLOC as detecton strategy. The former uses a threshold of 1000 and the second a threshold of The fxed threshold value s not fastdous for Large Class bad smell detecton, and easy to cause false detecton. And n these tools, there s no functon about refactorng of Large Class bad smell. These researches above show that, the detecton of Large Class bad smell s based on fxed threshold comparson. Snce the fxed threshold s selected manually, the objectvty s low. Moreover, the refactorng method s decded manually, and there s no suggeston or scheme about that. 3. THE DISTRIBUTION OF CLASS LENGTH 3.1. Class length dstrbuton appearance In object-orented programs there are a large number of classes. The length of these classes s not the same. In ths paper, t s declared that f the length of one class s larger than the average length of the program, t s called larger class, or smaller class. 3
4 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September 2013 There are some programs wth more classes are the larger classes, whle others are the opposte. Ths depends on the dfferent functon programs should be acheved. And ths s also relevant to dfferent codng habts and programmng styles of developers. From Table1 t s seen that class length statstcs of some open source programs s lsted. Table 1. Class length statstcs of open source programs. Program Number of Class Average Length Larger Class Smaller Class HSQLDB % 70.27% Tyrant % 72.65% Tyrant % 72.90% swngwt % 77.27% Trama % 75% ArgoUML % 80.20% Sprng Frk % 72.31% Azureus_Vuze % 71.82% In Large Class bad smell detectng, the usage of fxed value threshold may cause mstakes: the detecton results of some programs (such as HSQLDB n Table 1) are that most of classes are too large, and from the results of other programs (such as Sprng Frk) there s no Large Class bad smell at all. Besdes that, actually the value of fxed threshold s set manually, wth the lower objectvty. Because of the programs wth dfferent codng habts and programmng styles, the detecton result of Large Class bad smell wth the fxed value threshold s naccurate and less persuasve. In Table 1, the percentage of large classes n programs s lower, and the rato of larger classes and smaller ones s between 1:4 and 3:7. Functonally, common classes usually are desgned to be small and easy to use, partcularly for the frequently used ones. Oppostely, large scale classes are desgned for complex functonalty and computng algorthm. But n programs there are more classes whch are smple and common, and complex classes are less. So for the class length statstcs, smaller classes are majorty, and larger ones are mnorty. Addtonally, n the step of functonal desgn some classes have been desgned to acheve certan functons but these classes are just created but not coded completely. Ths stuaton s obvous partcularly n multple versons comparson of program desgn. Maybe these classes only contan some member varables, comments, or even just class names themselves. Ths knd of unfnshed classes may cause mnorty smaller classes. From the analyss of statstcs and program desgn, the numercal comparson relatonshp of larger classes and smaller classes would be clear. Above all, one conjecture s proposed n ths paper. Conjecture: the class length statstcs of programs confrm to certan dstrbuton rule. And ths dstrbuton rule should be verfed n programs statstcs The verfcaton of certan dstrbuton conjecture The process of curve fttng about the statstcs data of class length s shown n Fgure 2. 4
5 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September Obtan the data Fgure 2. Process of class length statstcs curve fttng. Get the data about the number of classes, the length of each class. The class length s measured by lnes of code. n s the number of classes n the program. The length of class and = 1, 2,, n. C s defned as A, Data statstcs Groupng Accordng to Sturges Equaton, the classes need to be grouped. The Sturges Equaton s N = lg n (1) n s the number of classes. Wth Equaton (1), the classes are dvded nto N groups, named G as, = 1, 2,, N Gettng nterval scope Get the maxmum value A max and mnmum value A mn of each class s length, and the span X. [ A The nterval mn, Amax ] s dvded nto N parts, and the length of sub nterval s m = X / N. So G the nterval of group s [( Amn1) +, m Amn ] + m, = 1, 2,, N. Class number statstcs The number of classes n group Fgure 3: G s defned as P, and the statstcs algorthm s shown as 5
6 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September 2013 Algorthm:Class number statstcs Input: G Output: P, Begn Foreach( G ) Foreach( j = 1, 2,, N ) If( mn mn P ++; EndIf EndFor EndFor End A [( A 1) + j, m A + ] j m ) After the algorthm the vector Curve fttng Graphcal vector Fgure 3. Class number statstcs algorthm. P s valued. The number of group nterval s defned as the data of x axs, and P s defned as the data of y axs. So a seres of ponts s created n the rectangular coordnates to represent the class length statstcs. Curve Fttng Accordng to the pont set of class length statstcs n the rectangular coordnates, get one curve wth the least value of Mean Squared Error (MSE). The process of curve fttng s executed wth all types of statstcal curves. After the curve fttng of the class length statstcs from a large number of open source programs, the Exponental curve s found to be the optmal fttng curve defned as y y A e R0 x = 0 + (2) Through the statstcs data obtanment of large amount of programs, the resduals threshold T s calculated. The value of resduals threshold T s the average of each group MSE n open source programs curve fttng. Ths resduals threshold s used for bad smell detecton. Wth the class length data statstcs of programs to be checked, f the resdual R of group s larger than the resduals threshold, there s Large Class bad smell n ths group, and the bad small classes n ths group s R T. 6
7 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September BAD SMELL DETECTION Usually the quarantne programs are open source programs whch contan a large number of classes. In the detecton method, the nputs are the codes, and the outputs are the bad smell classes Bad smell locaton n group Classes are dvded wth ther length, by Sturges Equaton, and the result s created n a dmensonal vector P = { P 1,P 2, P N }. And ths vector P s ftted wth Exponental curve n the rectangular coordnates. The optmal fttng curve wth least value of MSE s y y ' A e R 0' x = 0 + (3) After curve fttng, the postve resdual R + s: R + = P y (4) Where P s the number of classes n group G, and y s the value of Equaton (3) curve n place. If R + > T, there are bad smell classes n group G, and the number N of bad smell classes s computed n Equaton (5) Bad smell locaton n class N = R T (5) + As the bad smell group locaton above, the bad smell groups may not be the largest groups. Smlarly, the dentfyng method s not to smply select the x largest classes. So t s the key of Large Class bad smell detecton: the detectng bass s not from the metrcs of destnaton class tself (length or others), but from metrcs of all the classes. In ths paper, the bad smell locaton n class s dentfed wth the nner coheson of classes. The coheson metrc s defned wth the entty dstance theory. In entty dstance theory, these concepts should be defned. Defnton 1 (Entty): the entty s the attrbute a or the method m n one class, whch s sgned as E. Defnton 2 (Property Set): the property set s the set of enttes whch have nvokng-relatons wth the gven entty E, and t s sgned as P() E. If one method uses (accesses/calls) one attrbute or another method, they two have nvokng-relatons wth each other. In more detal, P() a contans a tself and all the methods use a, and P() m contans tself and all the attrbutes and methods m uses. Defnton 3(Dstance): the dstance value Dst( E1,) E2 of entty E1 and E 2 s 7
8 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September 2013 ()() P E Dst( E1,) E2 1= ()() P E P E P E (6) Where () P x s the member count of P() x, and the dstance between entty e and class C s the average of the dstances between e and every entty nc : D( e,) C 1 Dst( e,) y, e y EC EC 1 y C = 1 Dst( e,) y e E, y E EC y C C C (7) Where EC s the set of enttes C contans. Defnton 4(Coheson Metrc): the Coheson Metrc value s the rate of the average of the dstance of enttes out of the class and those n the class. Coneson C = e C e C Ds tan ce( e,c) e C Ds tan ce( e,c) e C (8) If the coheson metrc value s smaller, the degree of coheson s lower. So wth the x smallest coheson metrc value, these classes are dentfed to Large Class bad smell. 5. REFACTORING SCHEME In ths secton the classes whch are sure to have Large Class bad smell s refactored. And the refactorng process s Extract Class, whch means the destnaton class should be dvded nto two or more new classes. In practce, the destnaton class would be dvded nto two parts, and the bad smell detecton would be executed agan. The basc dea of refactorng scheme s to dvde the enttes n the destnaton class based on the coheson degree among them. So the key deas are how to represent coheson degree between enttes n classes and how to cluster enttes n classes Coheson degree representaton of enttes n class The coheson degree s represented as the dstance between two enttes. The dstance value of entty E1 ande 2 s shown n Equaton (5). Before clusterng, all the dstances between each two enttes n the destnaton class should be computed accurately. The lower dstance value s, the hgher the coheson degree s. 8
9 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September Enttes clusterng algorthm The agglomeratve clusterng algorthm [18] (whch s a herarchcal clusterng algorthm) s used n ths paper. The process s gven below: 1) Assgn each entty to a sngle cluster, and the dstance value of each two cluster s the dstance of the two enttes; 2) Repeat mergng untl the total cluster number reduces to 2. And the consdered mergng crteron s to merge two clusters wth the lowest dstance value. After mergng once, the dstance to the new mergng cluster s the average of those to last clusters. 3) Output the two clusters (each of them contans several enttes). The agglomeratve clusterng algorthm s gven n Fgure 4: Algorthm:Agglomeratve Clusterng Algorthm Input:ench enttes and ther dstance Output:two new clusters Begn each entty s assgned to be a sngle cluster; Whle(clusterng number s more than 2) merge two clusters A, B wth the lowest dstance value as cluster C; Foreach(any other cluster X n the class) Dst(C,X)=Avg(Dst(A,X),Dst(B,X)); EndFor EndWhle Fgure 4. Agglomeratve clusterng algorthm of refactorng. After the algorthm, accordng to the two new clusters, Extract Class operaton would be executed as refactorng. 6. EXPERIMENTAL RESULTS In ths paper several Java open source programs are used to detect Large Class bad smells. The names of these programs are shown s Table 2: Table 2. Open source programs n Large Class bad smell detecton. Program name Number of classes HSQLDB Tyrant Swng WT Trama 16 ArgoUML 1874 JFreeChart
10 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September Large Class bad smell locaton n group If all the groups of statstcs data have hgh fttng degree (through threshold comparson) after detecton, there s no Large Class bad smell at all. And sometmes the postve resdual s less than 0, so t s detected to be no bad smell. The results of Large Class bad smell group locaton towards the programs n Table 2 s shown n Table 3: Table 3. Results of Large Class bad smell group locaton. Group HSQLDB2.2. Tyrant0.96 SwngWT0.6 Trama ArgoUML Soul In Table 3, the nonzero dgt N means the exstence of Large Class bad smell n ts group, whch s computed n Equaton (5). And the value means the number of Large Class bad smell n the group. If N s equal to zero, there s no Large Class bad smell at all n G group. Besdes, dfferent programs have dfferent numbers of class, so the group number of each program s dfferent wth each other. So maybe there s no dgt n N poston. HSQLDB2.2.4 has only 8 groups, so they are blank spaces n group 8 to group 12. The ArgoUML program has 12 groups, whch s more than any others Large Class bad smell class locaton n class The coheson metrcs of classes n group locaton are computed wth the Equaton (8) to detect whch class/classes have bad smell. As the locaton method proposed n Secton 4.2, N classes were detected as Large Class bad smell wth smallest coheson metrcs. Table 4 shows the coheson metrcs of group 8 class members of Tyrant0.96 program. Wth ths, the classes Creature and GameScreen are both located to be Large Class. 10
11 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September 2013 Table 4. Coheson metrcs of group 8 class members of Tyrant0.96 program Class name Number of lnes Coheson metrc Creature GameScreen Map Table 5 shows the coheson metrcs of group 7 class members n JFreeChart program. After the coheson metrcs computng and analyss, the classes AbstractRenderer, PePlot, CategoryPlot and ChartPanel are dentfed as Large Class. Table 5. Coheson metrcs of group 7 class members of JFreeChart program Class name Number of lnes Coheson metrc AbstractRenderer PePlot DatasetUtltes ChartPanel DateAxs Code refactorng results The classes wth bad smell should be refactored by Extract Class accordng to the enttes dstance and agglomeratve clusterng algorthm. After refactorng the programs should be test agan. Fgure 5 shows the test results of Tyrant0.96 before and after refactorng. (a) Fttng curve before refactorng (b) Fttng curve after refactorng Fgure 5. Comparsons on the results of Tyrant0.96 before and after refactorng. In Fgure 5(a), MSE of the data s , and that s n Fgure 5(b). In Fgure 5(b), the curve has better approxmaton than that n (a). The MSE s less than threshold, so the refactorng s effectve and there s no Large Class bad smell at all. 6.4 Comparsons wth PMD and Checkstyle tools In the secton of related work, the refactorng tools PMD and Checkstyle are ntroduced. PMD and Checkstyle have the ablty for Large Class bad smell detecton, and no refactorng operaton suggeston. As mentoned, n these tools, f the lne number of one class s hgher preset threshold, the class s detected as Large Class. The threshold of PMD for Large Class s 1000, and the 11
12 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September 2013 threshold of Checkstyle s But PMD and Checkstyle cannot provde refactorng schemes for exstng Large Class bad smells. The detectng results from these two refactorng tools are compared wth the approach n ths paper. The results of comparson are shown n Table 6. After manual confrmaton, the precson comparsons of the methods n ths paper and PMD & Checkstype are dsplayed n Table 7. Table 6. Coheson metrcs of group 7 class members of JFreeChart program Detecton Tyrant0.96 JFreeChart tools & methods Large Class Number Large Class Name Large Class Number Large Class Name Method n 2 Creature AbstractRenderer, PePlot, 4 ths paper GameScreen DatasetUtltes, ChartPanel PMD 0 -- AbstractRenderer, PePlot, DatasetUtltes, ChartPanel, DateAxs, ChartFactory, 12 AbstractXYItemRenderer, ContourPlot, ThermometerPlot, AbstractCategoryItemRenderer XYPlot, CategoryPlot Checkstyle XYPlot, CategoryPlot Table 7. Precson comparsons of the methods n ths paper and PMD & Checkstype Program Bad smell detecton Precson (%) Refactorng scheme Precson (%) PMD Checkstyle Ths paper method Ths paper method Tyrant JFreeChart means that the precson rate cannot be computed. From the comparson n Table 7, the method n ths paper s much better than the exstng Large Class bad smell detecton tools. In small scale programs the classes are general small, the potental probablty of Large Class s low, and vce versa. The CLDM s more sutable for larger scale programs Large Class bad smell detecton and refactorng schemes. Small scale programs have less Large Class, so the false postve rate of CLDM s hgher. In addton, because of the dfferent threshold, the detectng Precson and Recall of PMD and Checkstyle are not the same n dfferent scale programs. But t s not sure whch threshold s more effectve for all the programs. 7. CONCLUSION In ths paper the approach of Large Class bad smell detecton and refactorng scheme has been proposed. Fxed-threshold-based detecton method s analyzed to be rgd and error-prone. And a 12
13 Internatonal Journal of Software Engneerng & Applcatons (IJSEA), Vol.4, No.5, September 2013 new model s developed to descrbe the statstc dstrbuton of class length. In ths model, the class groups that are far away from the dstrbuton curve are treated as contanng bad smells potentally. And combnng wth coheson metrc computng, the bad smell classes are confrmed n the class groups. After usng Agglomeratve Clusterng Technque, the scheme of Extract Class s proposed for refactorng. The contrbutons of ths paper are as follows. Frst, the characterstcs of Large Class bad smell are quantfed wth statstcal analyss. Second, the length and coheson metrcs based approach s proposed for Large Class bad smell detecton. ACKNOWLEDGEMENTS Ths research s supported by the Natonal Natural Scence Foundaton of Chna under Grant No and the Research Fund for the Doctoral Program of Hgher Educaton of Chna (Grant No and ). REFERENCES [1] M. Fowler, (1999) Refactorng: Improvng the desgn of exstng code, Addson-Wesley, pp [2] B.F. Webster, (1995) Ptfalls of Object Orented Development, frst M&T Books, Feb. [3] A.J. Rel, (1996) Object-Orented Desgn Heurstcs, Addson-Wesley. [4] G. Travassos, F. Shull, M. Fredercks, & V.R. Basl., (1999) Detectng Defects n Object -Orented Desgns: Usng Readng Technques to Increase Software Qualty, Proceedng of 14th Conference n Object-Orented Programmng, Systems, Languages, and Applcatons, pp [5] R. Marnescu, (2004) Detecton Strateges: Metrcs -Based Rules for Detectng Desgn Flaws, Proceedng of 20th Internatonal Conference n Software Mantenance, pp [6] Ladan Tahvldar & Kostas Kontoganns, (2003) A Metrc -Based Approach to Enhance Desgn Qualty through Meta-Pattern Transformatons, 7th European Conference Software Mantenance and Reengneerng, pp [7] M. O'Keeffe & M. O'Cnnede, (2008) Search -based refactorng: an emprcal study, Journal of software mantenance and evoluton: research and practce,pp [8] K. Dhambr, H. Sahraou & P. Pouln, (2008) Vsual Detecton of Desgn Anomales, Proceedng of 12th European Conference n Software Mantenance and Reeng, pp [9] G. Langeler, H.A. Sahraou & P. Pouln, (2005) Vsualzaton-Based Analyss of Qualty for Large- Scale Software Systems, Proceedng of 20th Internatonal Conference n Automated Software Engneerng, pp [10] M. Lanza & R. Marnescu, (2006) Object -Orented Metrcs n Practce, Sprnger-Verlag. pp [11] E. van Emden & L. Moonen, (2002) Java Qualty Assurance by Detectng Code Smells, Proceedng of 9th Workng Conference n Reverse Engneerng, pp [12] F. Smon, F. Stenbruckner C. Lewerentz, (2001) Metrcs Based Refactorng, Proceedng of 5th European Conference n Software Mantenance and Reengneerng, pp [13] D.X. Jang & P.J. Ma, (2012) Detectng Bad Smells Wth Weght Based Dstance Metrcs Theory, Proceedng of 2nd Internatonal Conference on Instrumentaton, Measurement, Computer, Communcaton and Control, pp [14] H. Lu, Z.Y. Ma & W.Z. Shao, (2012) Schedule of Bad Smell Detecton and Resoluton: A New Way to Save Effort, IEEE Transactons on Software Engneerng, Vol. 38, No. 1, pp [15] D. Fontana, A. Francesca & P.Braone, (2012) Automatc detecton of bad smells n code A n expermental assessment, Journal of Object Technology, Vol. 11, No. 2, pp1-38. [16] [17] [18] J.W. Han & M. Kamber, (2005) Data Mnng Concepts and Technques, Morgan Kaufmann Publshers. 13
High Speed 128-bit BCD Adder Architecture Using CLA
Hgh Speed 128-bt BCD Archtecture Usng CLA J.S.V.Sa Prasanth 1, Y.Yamn Dev 2 PG Student [VLSI&ES], Dept. of ECE, Swamy Vvekananda Engneerng College, Kalavara, Andhrapradesh, Inda 1 Assstant Professor, Dept.
More informationReduced drift, high accuracy stable carbon isotope ratio measurements using a reference gas with the Picarro 13 CO 2 G2101-i gas analyzer
Reduced drft, hgh accuracy stable carbon sotope rato measurements usng a reference gas wth the Pcarro 13 CO 2 G2101- gas analyzer Chrs Rella, Ph.D. Drector of Research & Development Pcarro, Inc., Sunnyvale,
More informationEvaluation of a Center Pivot Variable Rate Irrigation System
Evaluaton of a Center Pvot Varable Rate Irrgaton System Ruxu Su Danel K. Fsher USDA-ARS Crop Producton Systems Research Unt, Stonevlle, Msssspp Abstrat: Unformty of water dstrbuton of a varable rate center
More informationLSSVM Model for Penetration Depth Detection in Underwater Arc Welding Process
ISS 1746-7659, England, UK Journal of Informaton and Computng Scence Vol. 5, o. 4, 2010, pp. 271-278 LSSVM Model for Penetraton Depth Detecton n Underwater Arc Weldng Process WeMn Zhang 1, 2, GuoRong Wang
More informationFirst digit of chosen number Frequency (f i ) Total 100
1 4. ANALYSING FREQUENCY TABLES Categorcal (nomnal) data are usually summarzed n requency tables. Contnuous numercal data may also be grouped nto ntervals and the requency o observatons n each nterval
More informationITRS 2013 Silicon Platforms + Virtual Platforms = An explosion in SoC design by Gary Smith
ITRS 2013 Slcon Platforms + Vrtual Platforms = An exploson n SoC desgn by Gary Smth 2013 2013 Gary Gary Smth Smth EDA, EDA, Inc. Inc. All All Rghts Rghts Reserved. Reserved. 1 The Fve Desgn Constrants
More informationPeak Field Approximation of Shock Wave Overpressure Based on Sparse Data
Peak Feld Approxmaton of Shock Wave Overpressure Based on Sparse Data Yongl Zhang, Taln Han, Yuqun Chen, Enku Zhang, and Xuan Lu Abstract To obtan the shock wave feld dstrbuton, two knds of calber weapons
More informationTerminating Head
Termnatng Head 58246-1 Instructon Sheet for MTA- 100 Receptacle Connectors 408-6929 Usng Dscrete Wre 07 APR 11 Locatng Pawl Feed Slde Tool Base Wre Inserter Adjuster (Inserton Rod) Mass Termnaton Assembly
More informationEngineering Analysis of Implementing Pedestrian Scramble Crossing at Traffic Junctions in Singapore
Engneerng Analyss of Implementng Pedestran Scramble Crossng at Traffc Junctons n Sngapore Dr. Lm Wee Chuan Eldn Department of Chemcal & Bomolecular Engneerng, Natonal Unversty of Sngapore, 4 Engneerng
More informationNumerical Study of Occupants Evacuation from a Room for Requirements in Codes
Numercal Study of Occupants Evacuaton from a Room for Requrements n Codes HL MU JH SUN Unversty of Scence and Technology of Chna State Key Laboratory of Fre Scence Hefe 2300326, CHINA muhl@mal.ustc.edu.cn
More informationThis document is downloaded from DR-NTU, Nanyang Technological University Library, Singapore.
Ths document s downloaded from DR-NTU, Nanyang Technologcal Unversty Lbrary, Sngapore. Ttle capacty analyss usng smulaton Author(s) Ctaton Huang, Shell Yng; Hsu, Wen Jng; He, Yuxong; Song, Tancheng; De
More informationEvolutionary Sets of Safe Ship Trajectories: Evaluation of Individuals
Internatonal Journal on Marne Navgaton and Safety of Sea Transportaton Volume 6 Number 3 September 2012 Evolutonary Sets of Safe Shp Trajectores: Evaluaton of Indvduals R. Szlapczynsk Gdansk Unversty of
More informationDevelopment of Accident Modification Factors for Rural Frontage Road Segments in Texas
Development of Accdent Modfcaton Factors for Rural Frontage Road Segments n Texas Domnque Lord* Zachry Department of Cvl Engneerng & Center for Transportaton Safety Texas Transportaton Insttute Texas A&M
More informationFast Adaptive Coding Unit Depth Range Selection Algorithm for High Efficiency Video Coding
Sensors & Transducers 2014 by IFSA Publshng, S. L. http://www.sensorsportal.com Fast Adaptve Codng Unt Depth Range Selecton Algorthm for Hgh Effcency Vdeo Codng Fang Shuqng, Yu Me, Chen Fen, Xu Shengyang,
More informationMechanical Engineering Journal
56789 Bulletn of the JSME Mechancal Engneerng Journal Vol., o., 6 Measurement of three-dmensonal orentaton of golf club head wth one camera Wataru KIMIZUKA* and Masahde OUKI* * DULOP SPORTS CO. LTD. Waknohama-cho
More informationA Prediction of Reliability of Suction Valve in Reciprocating Compressor
Purdue Unversty Purdue e-pubs nternatonal Compressor Engneerng Conference School of Mechancal Engneerng 1996 A Predcton of Relablty of Sucton Valve n Recprocatng Compressor W. H. You Samsung Electroncs
More informationPedestrian Facilities Planning on Tianjin New Area program
Avalable onlne at www.scencedrect.com ScenceDrect Proceda - Socal and Behavoral Scenc es 96 ( 2013 ) 683 692 13th COTA Internatonal Conference of Transportaton Professonals (CICTP 2013) Pedestran Facltes
More informationEnglish Premier League (EPL) Soccer Matches Prediction using An Adaptive Neuro-Fuzzy Inference System (ANFIS) for
Englsh Premer League (EPL) Soccer Matches Predcton usng An Adaptve Neuro-Fuzzy Inference System (ANFIS) for Amadn, F. I 1 and Ob, J.C. 2 Department of Computer Scence, Unversty of Benn, Benn Cty. Ngera.
More informationSeabed type clustering using single-beam echo sounder time series data
SELECTED TOPICS n POWER SYSTEMS and REMOTE SENSING Seabed type clusterng usng sngle-beam echo sounder tme seres data PETER HUNG, SEÁN MCLOONE Department of Electronc Engneerng, StratAG Natonal Unversty
More informationInvestigation on Hull Hydrodynamics with Different Draughts for 470 Class Yacht
Proceedngs Investgaton on Hull Hydrodynamcs wth Dfferent Draughts for 470 Class Yacht Yong Ma 1, *, Shje Ln 1,2, Yangyng He 1, Xaoshan Le 1,2 and Song Zhang 1,2 1 School of Sports Engneerng and Informaton
More informationCS 2750 Machine Learning. Lecture 4. Density estimation. CS 2750 Machine Learning. Announcements
CS 75 Machne Learnng Lecture 4 ensty estmaton Mlos Hauskrecht mlos@cs.ptt.edu 539 Sennott Square CS 75 Machne Learnng Announcements Homework ue on Wednesday before the class Reports: hand n before the
More informationPERFORMANCE AND COMPENSATION ON THE EUROPEAN PGA TOUR: A STATISTICAL ANALYSIS
PERFORMANCE AND COMPENSATION ON THE EUROPEAN PGA TOUR: A STATISTICAL ANALYSIS C. Barry Pftzner and Chrs Spence, Department of Economcs/Busness, Randolph-Macon College, Ashland, VA, bpftzne@rmc.edu, cspence@rmc.edu
More informationAn intro to PCA: Edge Orientation Estimation. Lecture #09 February 15 th, 2013
An ntro to PCA: Edge Orentaton Estmaton Lecture #09 February 15 th, 2013 Revew: Edges Convoluton wth an edge mask estmates the partal dervatves of the mage surface. The Sobel edge masks are: " #!1 0 1!2
More informationAerator Performance in Reducing Phenomenon of Cavitation in Supercritical Flow in Steep Channel Bed
Engneerng Internatonal Conference UNNES Conservaton 13 Proceedng Aerator Performance n Reducng Phenomenon of Cavtaton n Supercrtcal Flow n Steep Channel Bed Yer Sutopo 1, Bud Wgnyosukarto, Istarto, dan
More informationThe impact of foreign players on international football performance
MPRA Munch Personal RePEc Archve The mpact of foregn players on nternatonal football performance Orhan Karaca Ekonomst Magazne, Research Department October 008 Onlne at http://mpra.ub.un-muenchen.de/11064/
More informationDynamic Analysis of the Discharge Valve of the Rotary Compressor
Purdue Unversty Purdue e-pubs Internatonal Compressor Engneerng Conference School of Mechancal Engneerng 8 Dynamc Analyss of the Dscharge Valve of the Rotary Compressor Bo Huang Shangha Htach Electrcal
More informationExperimental And Numerical Investigation Of The Flow Analysis Of The Water-Saving Safety Valve
Expermental And Numercal Investgaton Of The Flow Analyss Of The Water-Savng Safety Valve Muhammed Safa KAMER s PhD Student n Department of Mechancal Engneerng n Kahramanmaras Sutcu Imam Unversty, Turkey.
More informationA Study on Parametric Wave Estimation Based on Measured Ship Motions
1 A Study on Parametrc Wave Estmaton Based on Measured Shp Motons Ulrk Dam NIELSEN * and Tosho ISEKI ** Abstract The paper studes parametrc wave estmaton based on the wave buoy analogy, and data and results
More informationApplications on openpdc platform at Washington State University
Applcatons on openpdc platform at Washngton State Unversty Chuanln Zhao Ebrahm Rezae Man V. Venkatasubramanan Washngton State Unversty Pullman WA WSU projects OMS - Oscllaton Montorng System Stand-alone
More informationADDITIONAL INSTRUCTIONS FOR ISU SYNCHRONIZED SKATING TECHNICAL CONTROLLERS AND TECHNICAL SPECIALISTS
A ADDITIONAL INSTRUCTIONS FOR ISU SYNCHRONIZED SKATING TECHNICAL CONTROLLERS AND TECHNICAL SPECIALISTS DIFFICULTY GROUPS OF FEATURES 1 DEFINITIONS: Change of Rotaton: Refers to TURNS or LINKING STEPS rotatng
More informationChinese and foreign men s decathlon performance comparison and structural factor correlation test based on SPSS regression model
ISSN : 0974-7435 Volume 10 Issue 3 Chnese and foregn men s decathlon performance comparson and structural factor correlaton test based on SPSS regresson model Pengfe Zhang*, Jngjng Lu Insttute of Physcal
More informationErgonomics Design on Bottom Curve Shape of Shoe-Last Based on Experimental Contacting Pressure Data
Ergonomcs Desgn on Bottom Curve Shape of Shoe-Last Based on Expermental Contactng Pressure Data 1 L Zaran, 2 Sh Ka *1Correspondng Author Wenzhou Vocatonal and Techncal College, lzr_101@sna.com 2 Wenzhou
More informationComprehensive evaluation research of volleyball players athletic ability based on Fuzzy mathematical model
ISSN : 0974-7435 Volume 10 Issue 3 Comprehensve evaluaton research of volleyball players athletc ablty based on Fuzzy mathematcal model Shangbn L, Peyu Zhao, Yngshuang Lu, Lxn Wu* Physcal Educaton Department,
More informationA comparison study on the deck house shape of high speed planing crafts for air resistance reduction
csnak, 2014 Int. J. Nav. Archt. Ocean Eng. (2014) 6:867~875 http://dx.do.org/10.2478/ijnaoe-2013-0218 pissn: 2092-6782, eissn: 2092-6790 A comparson study on the deck house shape of hgh speed planng crafts
More informationNumerical Analysis of Rapid Gas Decompression in Pure Nitrogen using 1D and 3D Transient Mathematical Models of Gas Flow in Pipes
Internatonal Journal of Chemcal and Bologcal Engneerng 6 01 Numercal Analyss of Rapd as Decompresson n Pure Ntrogen usng 1D and 3D Transent Mathematcal Models of as Flow n Ppes Evgeny Burlutsky Abstract
More informationMulti-Criteria Decision Tree Approach to Classify All-Rounder in Indian Premier League
Internatonal Journal of Adanced Scence and Technology Mult-Crtera Decson Tree Approach to Classfy All-Rounder n Indan Premer League Pabtra Kumar Dey 1, Dpendra Nath Ghosh 2, Abhoy Chand Mondal 3 1 Assstant
More informationStudy on coastal bridge under the action of extreme wave
Study on coastal brdge under the acton of extreme wave Bo Huang Bng Zhu Jawe Zhang School of Cvl Engneerng, Southwest Jaotong Unversty, Chengdu 610031, Chna Abstract In order to research the catastrophc
More informationCFD Simulation of R134a and R410A Two-Phase Flow in the Vertical Header of Microchannel Heat Exchanger
Purdue Unversty Purdue e-pubs Internatonal Refrgeraton and Ar Condtonng Conference School of Mechancal Engneerng 2016 CFD Smulaton of R134a and R410A Two-Phase Flow n the Vertcal Header of Mcrochannel
More informationIDENTIFICATION OF TRANSPORTATION IMPROVEMENT PROJECTS IN PHNOM PENH CONSIDERING TRAFFIC CONGESTION LEVEL
Proceedngs of the Eastern Asa Socety for Transportaton Studes, Vol. 5, pp. 1265-1280, 2005 IDENTIFICATION OF TRANSPORTATION IMPROVEMENT PROJECTS IN PHNOM PENH CONSIDERING TRAFFIC CONGESTION LEVEL Sambath
More informationRisk analysis of natural gas pipeline
Rsk analyss of natural gas ppelne Y.-D. Jo 1, K.-S. Park 1, J. W. Ko, & B. J. Ahn 3 1 Insttute of Gas Safety Technology, Korea Gas Safety Corporaton, South Korea Department of Chemcal Engneerng, Kwangwoon
More informationAalborg Universitet. Published in: 9th ewtec Publication date: Document Version Publisher's PDF, also known as Version of record
Aalborg Unverstet Predctablty of the Power Output of Three Wave Energy Technologes n the Dansh orth Sea Chozas, Jula Fernandez; Jensen,. E. Helstrup; Sørensen, H. C.; Kofoed, Jens Peter; Kabuth, Alna Krstn
More informationProduct Information. Gripper for small components MPG-plus
Product Informaton Grpper for small components MPG-plus MPG-plus Grpper for small components More powerful. Faster. Longer fngers. MPG-plus grpper for small components 2-fnger parallel grpper wth smooth
More informationJournal of Chemical and Pharmaceutical Research, 2014, 6(5): Research Article
Avalable onlne www.jocpr.com Journal of Chemcal and Pharmaceutcal Research, 204, 6(5): 520-526 Research Artcle ISS : 0975-7384 CODE(USA) : JCPRC5 Dgtal Electrcal Resstance Tomography System and ts Expermental
More informationPARAMETER OPTIMIZATION OF SEA WATERWAY SYSTEM DREDGED TO THE
ARCHIVES OF TRANSPORT ISSN (prnt): 0866-9546 Volume 40, Issue 4, 2016 e-issn (onlne): 2300-8830 DOI: 10.5604/08669546.1225461 PARAMETER OPTIMIZATION OF SEA WATERWAY SYSTEM DREDGED TO THE SPECIFIED DEPTH
More informationInternational Journal of Engineering and Technology, Vol. 8, No. 5, October Model Systems. Yang Jianjun and Li Wenjin
Internatonal Journal of Engneerng and Technology, Vol. 8, No. 5, October 2016 1 Relablty Optmzaton Desgn of Submarne Free-Runnng Model Systems Yang Janjun and L Wenjn Abstract Wth regard to the relablty
More informationA NEW METHOD FOR IMPROVING SCATTEROMETER WIND QUALITY CONTROL
A NEW METHOD FOR IMPROVING SCATTEROMETER WIND QUALITY CONTROL M. Portabella (, A. Stoffelen (, A. Verhoef (, J. Verspeek ( ( Undad de Tecnología Marna CSIC, Pg. Marítm Barceloneta 37-9, 3 Barcelona, Span
More informationAvailability assessment of a raw gas re-injection plant for the production of oil and gas. Carlo Michelassi, Giacomo Monaci
16 th IMEKO TC4 Symposum Explorng New Fronters of Instrumentaton and Methods for Electrcal and Electronc Measurements Avalablty assessment of a raw gas re-njecton plant for the producton of ol and gas
More informationApplication of fuzzy neural network in the pattern classification of table tennis rotating flight trajectory
Applcaton of fuzzy neural network n the pattern classfcaton of table tenns rotatng flght trajectory Abstract Fe Cheng Shangqu Normal Unversty, Shangqu 476000, Chna In order to mprove the accuracy and effcency
More informationOPTIMIZATION OF PRESSURE HULLS OF COMPOSITE MATERIALS
OPTIMIZATION OF PRESSURE HULLS OF COMPOSITE MATERIALS J.Franco a, A.Corz a*.a.peña b a Materal Composte Group. Unversdad de Cadz. Avda/Ramon Puyol s/n. 11205 Algecras (Span) *alcorz@caltech.es b Calpe
More informationProduct Information. Universal gripper PZN-plus
Product Informaton Unversal grpper PZN-plus PZN-plus Unversal grpper Relable. Robust. Flexble. PZN-plus unversal grpper Unversal 3-Fnger Centrc Grpper wth hgh grppng force and maxmum moments due to mult-tooth
More informationRADIAL STIFFNESS OF A BICYCLE WHEEL AN ANALYTICAL STUDY
ADIAL TIFFE OF A BICYCLE WHEEL - A AALYTICAL TUDY José aría ínguez Dpto. de Físca Aplcada II Facultad de Cenca y Tecnología Unversdad del País Vasco Aptdo. 644 488 Blbao PAI Emal: osemara.mnguez@ehu.es
More informationStructural Gate Decomposition for Depth-Optimal Technology Mapping in LUT-based FPGA
Structural Gate Decomposton for Depth-Optmal Technology Mappng n LUT-based FPGA Abstract Jason Cong and Yean-Yow Hwang Department of Computer Scence Unversty of Calforna, Los Angeles Los Angeles, CA 9004
More informationSchool of Civil Engineering, Shandong University, Jinan , China
2017 Internatonal Conference on Energy, Power and Envronmental Engneerng (ICEPEE 2017) ISBN: 978-1-60595-456-1 Evaluaton on Sustanable Utlzaton of Water Resources n Shandong Provnce Based on Water Footprnt
More informationCrash Frequency and Severity Modeling Using Clustered Data from Washington State
Proceedngs of the IEEE ITSC 2006 2006 IEEE Intellgent Transportaton Systems Conference Toronto, Canada, September 17-20, 2006 WB7.1 Crash Frequency and Severty Modelng Usng Clustered Data from Washngton
More informationInvestigation on Rudder Hydrodynamics for 470 Class Yacht
Proceedngs Investgaton on Rudder Hydrodynamcs for 470 Class Yacht She Ln 1,, Yong Ma, *, Wetao Zheng, Song Zhang 1,, Xaoshan Le 1, and Yangyng He 1, 1 Graduate School of Wuhan Sports Unversty, Wuhan 430079,
More informationComparative Deterministic and Probabilistic Analysis of Two Unsaturated Soil Slope Models after Rainfall Infiltration
Jordan Journal of Cvl Engneerng, Volume 11, No. 1, 2017 Comparatve Determnstc and Probablstc Analyss of Two Unsaturated Sol Slope Models after Ranfall Infltraton Manoj Kr. Sahs 1) and Partha Pratm Bswas
More informationProduct Information. Long-stroke gripper PSH 42
Product Informaton PSH 42 PSH Compact. Flexble. Fully encapsulated. PSH long-stroke grpper 2-fnger parallel grpper wth long jaw stroke and drt-resstant round gudance Feld of applcaton n contamnated work
More informationAutomated External Defibrillators DESIGNED FOR UNEXPECTED HEROES
Automated External Defbrllators DESIGNED FOR UNEXPECTED HEROES HOPE IS IN YOUR HANDS It happens n a splt second. A person collapses the vctm of sudden cardac arrest (SCA) and the clock starts tckng n the
More informationSustainability Enhancement under Uncertainty: A Monte Carlo Based Simulation and System Optimization Method
Sustanablty Enhancement under Uncertanty: A Monte Carlo Based Smulaton and System Optmzaton Method Zheng Lu and Ynlun Huang* Department of Chemcal Engneerng and Materals Scence Wayne State Unversty, Detrot,
More informationMass Spectrometry. Fundamental GC-MS. GC-MS Interfaces
Mass Spectrometry Fundamental GC-MS GC-MS Interfaces Wherever you see ths symbol, t s mportant to access the on-lne course as there s nteractve materal that cannot be fully shown n ths reference manual.
More informationContractor's Material and Test Certificate for Underground Piping
UNDERGROUND PPNG 13-97 Contractors Materal and Test Certfcate for Underground Ppng PROCEDURE Upon completon of work, nspecton and tests shall be made by the contractors representatve and wtnessed by an
More informationCOMPARATIVE ANALYSIS OF WAVE WEATHER WINDOWS IN OPERATION AND MAINTENANCE OF OFFSHORE WIND FARMS AT HSINCHU AND CHANGHUA, TAIWAN
Journal of Marne Scence and Technology, Vol. 5, No. 5, pp. 563-570 (07) 563 DOI: 0.69/JMST-07-0703- COMPARATIVE ANALYSIS OF WAVE WEATHER WINDOWS IN OPERATION AND MAINTENANCE OF OFFSHORE WIND FARMS AT HSINCHU
More informationarxiv: v1 [cs.ne] 3 Jul 2017
Modelng preference tme n mddle dstance trathlons Iztok Fster, 1 Andres Iglesas, 2 Suash Deb, 3, 4 Dušan Fster, 5 and Iztok Fster Jr. 6 1 Unversty of Marbor, Faculty of Electrcal Engneerng and Computer
More informationInternational Journal of Industrial Engineering Computations
Internatonal Journal of Industral Engneerng Computatons 2 (20) 93 202 Contents lsts avalable at GrowngScence Internatonal Journal of Industral Engneerng Computatons homepage: wwwgrowngscencecom/ec A mult-obectve
More informationDecomposition guide Technical report on decomposition
June 2013 Decomposton gude Techncal report on decomposton Erasmus MC Start date of project: 20 Aprl 2012 Duraton: 36 months 1 Table of contents Abstract... 4 Acknowledgements... 5 Introducton... 6 Part
More informationProduct Information. Radial gripper PRG 52
Product Informaton PRG More flexble More powerful. Slm. PRG unversal grpper 180 radal grpper wth powerful 1-shft slotted lnk gear and oval pston. Feld of applcaton For areas of applcaton whch, n addton
More informationA Climbing Robot based on Under Pressure Adhesion for the Inspection of Concrete Walls
A Clmbng Robot based on Under Pressure Adheson for the Inspecton of Concrete Walls K. Berns, C. Hllenbrand, Robotcs Research Lab, Department of Computer Scence, Techncal Unversty of Kaserslautern P.O.
More informationM. Álvarez-Mozos a, F. Ferreira b, J.M. Alonso-Meijide c & A.A. Pinto d a Department of Statistics and Operations Research, Faculty of
Ths artcle was downloaded by: [b-on: Bbloteca do conhecmento onlne UP] On: 29 May 205, At: 02:46 Publsher: Taylor & Francs Informa Ltd Regstered n England and Wales Regstered Number: 072954 Regstered offce:
More informationSports Injuries in School Gaelic Football: A Study Over One Season
Sports njures n School Gaelc Football: A Study Over One Season A. W. S. Watson Sports njures Research Centre, Unversty of Lmerck, Lmerck, reland. Abstract School football njures were studed over the seven
More informationA PROBABILITY BASED APPROACH FOR THE ALLOCATION OF PLAYER DRAFT SELECTIONS IN AUSTRALIAN RULES
Journal of Sports Scence and Medcne (2006) 5, 509-516 http://www.jssm.org Research artcle The 8th Australasan Conference on Mathematcs and Computers n Sport, 3-5 July 2006, Queensland, Australa A PROBABILITY
More informationResearch and Application of Work Roll Contour Technology on Thin Gauge Stainless Steel in Hot Rolling
Send Orders for Reprnts to reprnts@benthamscence.ae The Open Mechancal Engneerng Journal, 215, 9, 111-116 111 Open Access Research and Applcaton of Work Roll Contour Technology on Thn Gauge Stanless Steel
More informationBETHANY TAX INCREMENT FINANCING DISTRICT NO. 1 NOTICE OF TWO PUBLIC HEARINGS
BETHANY TAX NCREMENT FNANCNG DSTRCT NO. 1 NOTCE OF TWO PUBLC HEARNGS On December 12, 2017 at 7:00 P.M., at Bethany Cty Hall, 6700 N.W. 36th Street, Bethany, Oklahoma, the Cty Councl of the Cty of Bethany
More informationJohnnie Johnson, Owen Jones and Leilei Tang. Exploring decision-makers use of price information in a speculative market
Johnne Johnson, Owen Jones and Lele Tang Explorng decson-makers use of prce nformaton n a speculatve market Abstract We explore the extent to whch the decsons of partcpants n a speculatve market effectvely
More informationKeywords: Ordered regression model; Risk perception; Collision risk; Port navigation safety; Automatic Radar Plotting Aid; Harbor pilot.
Modelng perceved collson rsk n port water navgaton Hoong Chor Chn Assocate Professor, Department of Cvl Engneerng, Natonal Unversty of Sngapore, Engneerng Drve, EA #07-03, Sngapore 7576 Emal: cvechc@nus.edu.sg
More informationEvaluating Rent Dissipation in the Spanish Football Industry *
Evaluatng Rent Dsspaton n the Spansh Football Industry * Gudo Ascar Dp. d Economa Poltca e Metod Quanttatv Va S. Felce 5 27100 Pava, Italy Tel: (+39) 0382 506211 Fax: (+39) 0382 304226 gascar@eco.unpv.t
More informationPlanning of production and utility systems under unit performance degradation and alternative resource-constrained cleaning policies
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 Plannng of producton and utlty systems under unt performance degradaton
More informationDriver s Decision Model at an Onset of Amber Period at Signalised Intersections
Probablty to stop 1.00 0.90 0.80 0.70 0.60 0.40 0.30 0-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 75-80 80-85 85-90 90-95 95-100 1st veh n platoon 2nd veh n platoon
More informationENERGY SAVING IN THE HYDRAULIC CIRCUIT FOR AGRICULTURAL TRACTORS: FOCUS ON THE POWER SUPPLY GROUP.
ENERGY SAVING IN THE HYDRAULIC CIRCUIT OR AGRICULTURAL TRACTORS: OCUS ON THE POWER SUPPLY GROUP. Massmo Borgh, Barbara Zardn, rancesco Pntore, Engneerng Department Enzo errar, Unversty of Modena and Reggo
More informationModeling the Performance of a Baseball Player's Offensive Production
Brgham Young Unversty BYU ScholarsArchve All Theses and Dssertatons 006-03-09 Modelng the Performance of a Baseball Player's Offensve Producton Mchael Ross Smth Brgham Young Unversty - Provo Follow ths
More informationGAS-LIQUID INTERFACIAL AREA IN OXYGEN ABSORPTION INTO OIL-IN-WATER EMULSIONS
AS-LIQUID INTERFACIAL AREA IN OXYEN ABSORPTION INTO OIL-IN-WATER EMULSIONS ómez-díaz, D. a, omes, N. b, Texera, J.A. b, Belo, I. b a Department of Chemcal Engneerng, Unversty of Santago de Compostela,
More informationJIMAR ANNUAL REPORT FOR FY 2001 (Project ) Project Title: Analyzing the Technical and Economic Structure of Hawaii s Pelagic Fishery
1 JIMAR ANNUAL REPORT FOR FY 2001 (Project 653540) P.I. Name: PngSun Leung, Khem Sharma and Sam Pooley Project Research Assstant: Naresh Pradhan Project Ttle: Analyzng the Techncal and Economc Structure
More informationFor models: 660 EF/EFO
Installaton Instructons Gas converson kts For models: 660 EF/EFO Part no. 660NGKIT/660LPKIT Warnng: Ths kt must be nstalled by a qualfed nstaller n accordance wth these nstructons and all applcable codes
More informationNonlinear Risk Optimization Approach to Gas Lift Allocation Optimization
pubs.acs.org/iecr Nonlnear Rsk Optmzaton Approach to Gas Lft Allocaton Optmzaton Mahd Khshvand and Ehsan Khamehch* Faculty of Petroleum Engneerng, Amrkabr Unversty of Technology, Tehran, Iran ABSTRACT:
More informationWave Breaking Energy in Coastal Region
ave Breang Energy n Coastal Regon Ray-Qng Ln and Lwa Ln Dept. of Seaeepng Davd Taylor Model Basn NSCCD U.S. Army Engneer Researc and Development Center. INTERODUCTION Huang 006 suggested tat wave breang
More informationInvestigating Reinforcement Learning in Multiagent Coalition Formation
Investgatng Renforcement Learnng n Multagent Coalton Formaton Xn L and Leen-Kat Soh Department of Computer Scence and Engneerng Unversty of Nebrasa-Lncoln 115 Ferguson Hall, Lncoln, NE 66588-0115 {xnl,
More informationEvaluating the Effectiveness of Price and Yield Risk Management Products in Reducing. Revenue Risk for Southeastern Crop Producers * Todd D.
Evaluatng the Effectveness of Prce and Yeld Rsk Management Products n Reducng Revenue Rsk for Southeastern Crop Producers * Todd D. Davs ** Abstract A non-parametrc smulaton model ncorporatng prce and
More informationEquilibrium or Simple Rule at Wimbledon? An Empirical Study
Equlbrum or Smple Rule at Wmbledon? An Emprcal Study Shh-Hsun Hsu, Chen-Yng Huang and Cheng-Tao Tang Revson: March 2004 Abstract We follow Walker and Wooders (200) emprcal analyss to collect and study
More informationSCIENTIFIC COMMITTEE THIRTEENTH REGULAR SESSION. Rarotonga, Cook Islands 9-17 August, 2017
SCIENTIFIC COMMITTEE THIRTEENTH REGULAR SESSION Rarotonga, Cook Islands 9-17 August, 2017 Relatve abundance of yellowfn tuna for the purse sene and handlne fsheres operatng n the Phlppnes Moro Gulf (Regon
More informationMethodology for ACT WorkKeys as a Predictor of Worker Productivity
Methodology for ACT WorkKeys as a Predctor of Worker Productvty The analyss examned the predctve potental of ACT WorkKeys wth regard to two elements. The frst s tme to employment. People takng WorkKeys
More informationProduct Information. Long-stroke gripper PFH-mini
Product Informaton PFH-mn PFH-mn Loadable. Flexble. Relable. PFH-klen long-stroke grpper 2-fnger parallel grpper wth large jaw stroke for large parts and / or a broad range of parts Feld of applcaton clean
More informationJournal of Chemical and Pharmaceutical Research, 2014, 6(3): Research Article
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(3):326-332 Research Artcle ISSN : 0975-7384 CODEN(USA) : JCPRC5 Research on comprehensve evaluaton model-based NBA schedule
More informationRelative Salary Efficiency of PGA Tour Golfers: A Dynamic Review
Relatve Salary Effcency of PGA Tour Golfers: A Dynamc Revew Julo Cesar Alonso Unversdad Ices Julan Benavdes Unversdad Ices Based on one-year sample, Nero (2001) estmated golfers' usng four performance
More informationAalborg Universitet. Published in: 9th ewtec Publication date: Document Version Accepted author manuscript, peer reviewed version
Aalborg Unverstet Predctablty of the Power Output of Three Wave Energy Technologes n the Dansh orth Sea Chozas, Jula Fernandez; Jensen,. E. Helstrup; Sørensen, H. C.; Kofoed, Jens Peter; Kabuth, Alna Krstn
More informationReport No. FHWA/LA.13/508. University of Louisiana at Lafayette. Department of Civil and Environmental Engineering
TECHNICAL REPORT STANDARD PAGE Report No. FHWA/LA.13/508 4. Ttle and Subttle A Comprehensve Study on Pavement Edge Lne Implementaton 7. Author(s) Xaoduan Sun, Ph.D., P.E. Subassh Das 9. Performng Organzaton
More informationSECOND-ORDER CREST STATISTICS OF REALISTIC SEA STATES
SECOND-ORDER CREST STATISTICS OF REALISTIC SEA STATES MARIOS CHRISTOU Shell Internatonal Exploraton and Producton, 2288 GS Rjswjk, The Netherlands. E-mal: maros.chrstou@shell.com PETER TROMANS Ocean Wave
More informationSustainability Profiling of Long-living Software Systems
Sustanablty Proflng of Long-lvng Software Systems Ahmed D Alharth, Mara Spchkova and Margaret Hamlton RMIT Unversty, Melbourne, Australa Emal: {ahmedalharth, maraspchkova,margarethamlton}@rmteduau Abstract
More informationMuscle drain versus brain gain in association football: technology transfer through
Muscle dran versus bran gan n assocaton football: technology transfer through player emgraton and manager mmgraton G. J. Allan a * and J. Moffat b a Correspondng Author: Department of Economcs, Sr Wllam
More informationCost Effective Safety Improvements for Two-Lane Rural Roads
Fnal Techncal Report TNW2008-04 Research Project Agreement No. 61-2394 Cost Effectve Safety Improvements for Two-Lane Rural Roads Ynha Wang Assocate Professor Ngan Ha Nguyen Graduate Research Assstant
More informationCoastal Engineering Technical Note
Coastal Engneerng Techncal Note CETN V-10 Even-Odd Functon Analyss of Shorelne Poston and Volume Change by Jule Dean RoSllt and Nchollls C. Kraus Purpose: To present the background and methodology for
More informationSafety Impact of Gateway Monuments
*Manuscrpt Clck here to vew lnked References Ye, Venezano, and Lord 1 Safety Impact of Gateway Monuments Zhru Ye a,*, Davd Venezano a, Domnque Lord b a Western Transportaton Insttute, Montana State Unversty,
More information