标题: 2022年第十一届认证杯数学中国数学建模国际赛（小美赛）赛题发布 [打印本页] 作者: ilikenba 时间: 2022-12-2 08:01 标题: 2022年第十一届认证杯数学中国数学建模国际赛（小美赛）赛题发布 2022小美赛赛题的移动云盘下载地址 # w' D+ r7 T. _7 d$ b
https://caiyun.139.com/m/i?0F5CJAMhGgSJx. ?0 H7 t' z( A
4 J1 `6 B2 X) n5 H `9 f: b20226 ^- u7 E: g+ S
Certifificate Authority Cup International Mathematical Contest Modeling ; D# Y$ i5 Q4 z# u/ B6 Zhttp://mcm.tzmcm.cn 2 [% Q! T7 G, m* ]0 V* L8 HProblem A (MCM)" h/ K m6 X/ R3 M4 A+ i
How Pterosaurs Fly4 ]1 O( ?, `$ @) I0 R! x' x
Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They7 i$ `' F" b; n1 R5 V E
existed during most of the Mesozoic: from the Late Triassic to the end of 0 s# `0 v; @6 f( i& Y7 E* [2 N- mthe Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved ( `. f/ P- ^3 @# ~powered flflight. Their wings were formed by a membrane of skin, muscle, and r" m* r* \! `& R
other tissues stretching from the ankles to a dramatically lengthened fourth ( P8 K% o, |) Q3 A" Y/ H3 F3 o @fifinger[1].% x/ e9 H! M% X w. ~* J& |
There were two major types of pterosaurs. Basal pterosaurs were smaller! h$ Y! B" |2 h. q0 V. s
animals with fully toothed jaws and long tails usually. Their wide wing mem % U+ m7 f/ C) T1 H. F% l1 {. @branes probably included and connected the hind legs. On the ground, they2 B# E( v3 W0 b' D: `
would have had an awkward sprawling posture, but their joint anatomy and8 o- ~1 ]* h2 M3 ~% |) t- F' }
strong claws would have made them effffective climbers, and they may have lived' p' j& P4 O) u3 T
in trees. Basal pterosaurs were insectivores or predators of small vertebrates. 7 e o. Z: |% [6 s3 @Later pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. 3 t( n* }. q3 F2 N0 PPterodactyloids had narrower wings with free hind limbs, highly reduced tails,/ Z( @7 E! r: l# b7 W- }! l3 {
and long necks with large heads. On the ground, pterodactyloids walked well on B2 Q6 V) i( |+ t* q$ S
all four limbs with an upright posture, standing plantigrade on the hind feet and0 P! ]* J6 {$ n( q8 ^2 M& i; @
folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil8 M( s% r( |- Y4 o; ~
trackways show at least some species were able to run and wade or swim[2].% ^. w8 i4 C7 g( Z
Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which % U/ d4 s6 T1 J" O( o: J" J: v; tcovered their bodies and parts of their wings[3]. In life, pterosaurs would have4 e& g3 D- K7 r6 i4 ?3 ?
had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug) A1 b+ L) j- w' y
gestions were that pterosaurs were largely cold-blooded gliding animals, de 2 S( O) ~" J0 q. e+ mriving warmth from the environment like modern lizards, rather than burning! S+ }3 w% A0 i8 j* M
calories. However, later studies have shown that they may be warm-blooded2 s& A7 O5 p+ `/ c
(endothermic), active animals. The respiratory system had effiffifficient unidirec . G$ Q) I8 f1 v' g3 _$ rtional “flflow-through” breathing using air sacs, which hollowed out their bones# `! s- C9 T8 z' N( W
to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from2 o- }6 ?. a6 r# x; q! w
the very small anurognathids to the largest known flflying creatures, including # n& x. l# F( d/ ^9 R J$ vQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least l! F/ y& J7 C; \% C' X3 Ynine metres. The combination of endothermy, a good oxygen supply and strong `# v( M4 Z/ k2 Z- y# g- k& O8 n+ f
1muscles made pterosaurs powerful and capable flflyers.( C& g: N2 |% j
The mechanics of pterosaur flflight are not completely understood or modeled @8 j8 C) \5 n( u4 D: I+ \at this time. Katsufumi Sato did calculations using modern birds and concluded 0 y* p% x6 ~: lthat it was impossible for a pterosaur to stay aloft[6]. In the book Posture,+ C/ l9 r( @4 r" y( }; g+ V
Locomotion, and Paleoecology of Pterosaurs it is theorized that they were able $ W: g. q+ V; M3 R+ L! x' ato flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].8 E1 U+ q1 M: `" X* x+ q
However, both Sato and the authors of Posture, Locomotion, and Paleoecology , z9 U( e* v% ~+ }, `of Pterosaurs based their research on the now-outdated theories of pterosaurs 7 V3 M& ~$ L) s% F% b# gbeing seabird-like, and the size limit does not apply to terrestrial pterosaurs, ! w8 N% h& l+ }8 H2 Gsuch as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that 8 J: \ _- i; ^2 G9 D/ H7 yatmospheric difffferences between the present and the Mesozoic were not needed( Y( c2 f& U9 g J
for the giant size of pterosaurs[8].0 t% _1 e2 R2 n7 I# z8 x- V) q d
Another issue that has been diffiffifficult to understand is how they took offff. . \( f, t& A/ K/ h' O5 x% \If pterosaurs were cold-blooded animals, it was unclear how the larger ones& B* b+ o( h, p7 c/ x
of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage6 E) w4 D I8 m) V- O
a bird-like takeoffff strategy, using only the hind limbs to generate thrust for 7 D, _# p/ @$ }getting airborne. Later research shows them instead as being warm-blooded1 P5 {- D5 R" E4 F
and having powerful flflight muscles, and using the flflight muscles for walking as 3 d* ~& R2 h) m) c5 L7 ?+ Oquadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of. a$ o( Q+ m, I. e5 y
Johns Hopkins University suggested that pterosaurs used a vaulting mechanism 3 n# Y2 ]7 B, j T' `- [to obtain flflight[10]. The tremendous power of their winged forelimbs would, q8 ~1 K$ G9 n8 I! ^
enable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds % d& D; y+ n) x6 dof up to 120 km/h and travel thousands of kilometres[10]. # e: j4 V1 E4 s" _' N2 C: cYour team are asked to develop a reasonable mathematical model of the$ b3 s4 W4 `' o
flflight process of at least one large pterosaur based on fossil measurements and 7 Q: B# E# h' i, D6 L# g8 S9 zto answer the following questions.- v% ]+ `7 I" |' o( u
1. For your selected pterosaur species, estimate its average speed during nor 1 c# X- Y# ~2 i/ g4 }mal flflight.) \4 B. @8 L* d
2. For your selected pterosaur species, estimate its wing-flflap frequency during 7 m' s) e2 t* }9 \ V7 e, Qnormal flflight.- N+ q; L' G) f& `* a
3. Study how large pterosaurs take offff; is it possible for them to take offff like + n" x& t: j1 M# Ubirds on flflat ground or on water? Explain the reasons quantitatively." |" B, m6 i4 C
References! C, M6 Y0 D, I0 ]$ [
[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight 1 d; i7 [! z/ X5 A( j0 s+ NMembrane. Acta Palaeontologica Polonica. 56 (1): 99-111. C' o/ _) R0 h( Z0 h# L
2[2] Mark Witton. Terrestrial Locomotion./ o: k- L& }2 T3 z3 z' h
https://pterosaur.net/terrestrial locomotion.php / L4 C' S& }$ N[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs" @& M( I, z. V" j; P3 N5 N; R
Were Covered in Fluffffy Feathers. https://www.livescience.com/64324-9 `5 Z" L* H& g/ _+ k
pterosaurs-had-feathers.html * y2 k7 v/ |& T; v[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a ( M) p! X0 @' `0 L7 J, e% {3 j/ [rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea) # C0 }7 _9 R6 C' gfrom China. Proceedings of the National Academy of Sciences. 105 (6):8 }) p; ?6 J5 R. p5 i
1983-87. 8 @' }. l2 ^3 C- N+ }9 k q[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust 6 `# v( `3 w9 z: m8 i+ H0 P4 Yskull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4): . r% U+ @2 m: U180-84. ' N3 }* Y4 V9 p* g" f+ m; w[6] Devin Powell. Were pterosaurs too big to flfly? 7 a% R8 g+ C1 R% R! @https://www.newscientist.com/article/mg20026763-800-were-pterosaurs 6 p; |3 {' t9 O) l" Itoo-big-to-flfly/4 P& u$ E1 K. |; ^
[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology) w0 z l5 k$ u8 W/ L5 L4 l. J
of pterosaurs. Boulder, Colo: Geological Society of America. p. 60. # N* ~1 f% X; s/ t) {, n[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable+ g" @# s! j7 |6 [- @
air sacs in their wings. 9 j) w/ Z2 Z0 {3 F9 [https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur' _2 h8 G% R. t- b3 K
breathing-air-sacs 2 _0 g* z0 o+ R* V[9] Mark Witton. Why pterosaurs weren’t so scary after all. 9 N; c1 n$ p8 K! ?' ~6 b: Qhttps://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils( D. d: S. {/ [ Y7 d
research-mark-witton ) h. q( K& [! R, G5 Z1 ]0 y3 N[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats? ; o# n P! B( k; k3 uhttps://www.newscientist.com/article/dn19724-did-giant-pterosaurs6 D; c" h x& z( Q3 A
vault-aloft-like-vampire-bats/ . a9 _& t E) H, D) V0 d+ q$ r! } 8 Y8 w" T" K' r( s, @! E- k( a2022 8 k, i3 J" t/ f* ECertifificate Authority Cup International Mathematical Contest Modeling * _6 L- x" r% A5 V) Xhttp://mcm.tzmcm.cn : c' Z" L/ ?* u3 t9 w& MProblem B (MCM) - j# ~5 A; B7 a# G: B5 q/ wThe Genetic Process of Sequences C! W# {; ]' }: Z* F; v2 J4 @2 `1 I
Sequence homology is the biological homology between DNA, RNA, or protein 3 E: @& |* h7 ?' n ysequences, defifined in terms of shared ancestry in the evolutionary history of # Q' N9 e* V9 R, p* w$ M0 _" ?+ C: blife[1]. Homology among DNA, RNA, or proteins is typically inferred from their 0 h6 B3 B& X2 L- x, ~: nnucleotide or amino acid sequence similarity. Signifificant similarity is strong 5 _& O, b" a. b: H5 Wevidence that two sequences are related by evolutionary changes from a common; g) p, _2 c' f& [: f& Z
ancestral sequence[2].% F" w7 { q, {6 G! u7 }
Consider the genetic process of a RNA sequence, in which mutations in nu4 R! ]- H+ t& r4 e. @9 j4 n
cleotide bases occur by chance. For simplicity, we assume the sequence mutation8 [8 M1 ^, u& H( {6 M" c5 P
arise due to the presence of change (transition or transversion), insertion and 0 e) V* S; A1 r. I h+ \deletion of a single base. So we can measure the distance of two sequences by * s/ X* j! C+ o- Y) R! m+ v" f5 dthe amount of mutation points. Multiple base sequences that are close together / V7 l* e9 f0 Q0 g% L6 ]can form a family, and they are considered homologous.* d- |$ I+ w* g3 f' ]. E
Your team are asked to develop a reasonable mathematical model to com 3 J8 Z5 u; T3 n' {8 N) Nplete the following problems.8 ?9 j& M8 A8 z, e/ j
1. Please design an algorithm that quickly measures the distance between6 b; N" P! |( ~* _4 y+ s. H3 F, ^/ _
two suffiffifficiently long(> 103 bases) base sequences. p" ?& G _3 y' d) f4 y
2. Please evaluate the complexity and accuracy of the algorithm reliably, and - ?: V" Z6 O% |9 [3 K& mdesign suitable examples to illustrate it. . @- R, ~( ?2 D; d, t" r5 Z3. If multiple base sequences in a family have evolved from a common an $ B4 ~5 K8 S! }9 u& vcestral sequence, design an effiffifficient algorithm to determine the ancestral & B. x& S' _$ T5 c r& lsequence, and map the genealogical tree. 1 u1 n' Z# C: \8 p& q1 x# AReferences9 O+ f% N7 N' t
[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re " C( K! @: g: z* _4 H) ]view of Genetics. 39: 30938, 2005.( C7 w' }8 w4 _7 o: ]
[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, # G4 C2 V6 K& C' Jet al. “Homology” in proteins and nucleic acids: a terminology muddle and) i; P ^% {5 o }2 B
a way out of it. Cell. 50 (5): 667, 1987. 1 v/ S$ W& o. k/ }. m 4 }, ?2 a, b+ \% A: h2022 , P- ^. |, F4 `9 a+ aCertifificate Authority Cup International Mathematical Contest Modeling $ p" |1 y2 m; g, y. ]3 Yhttp://mcm.tzmcm.cn1 W: P; j z9 R2 ]; z
Problem C (ICM) m+ s4 i. D6 X& D; S
Classify Human Activities2 B+ O0 h2 Q- w5 Z& w) Q
One important aspect of human behavior understanding is the recognition and ]0 V: E5 V! H$ W6 a- J1 V
monitoring of daily activities. A wearable activity recognition system can im 5 u4 J; o0 x* y! u) S3 y/ eprove the quality of life in many critical areas, such as ambulatory monitor0 ~7 ^0 O6 a q# }
ing, home-based rehabilitation, and fall detection. Inertial sensor based activ 4 @3 Z$ o* Z/ T. Sity recognition systems are used in monitoring and observation of the elderly* h5 V3 f2 Q# L
remotely by personal alarm systems[1], detection and classifification of falls[2],* s$ l( ]+ f. F, c$ m9 ~5 ]1 C
medical diagnosis and treatment[3], monitoring children remotely at home or in9 Q7 X6 C8 \# v7 u- Y
school, rehabilitation and physical therapy , biomechanics research, ergonomics,: `9 s3 G. c4 N% d1 {/ c
sports science, ballet and dance, animation, fifilm making, TV, live entertain2 M, _: g1 M( }3 [
ment, virtual reality, and computer games[4]. We try to use miniature inertial ! P, m2 O8 K4 Vsensors and magnetometers positioned on difffferent parts of the body to classify 5 U# j" F2 [) }human activities, the following data were obtained. - ] b8 C& K1 ]4 W4 M8 eEach of the 19 activities is performed by eight subjects (4 female, 4 male,: y5 ~0 y8 X1 V: e6 O( I
between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes& k4 p- o& i0 }% F: f
for each activity of each subject. The subjects are asked to perform the activ2 }* m9 c* {7 z
ities in their own style and were not restricted on how the activities should be % O9 N) b. L5 f% J4 O7 F uperformed. For this reason, there are inter-subject variations in the speeds and ( p$ _* v# R4 Jamplitudes of some activities./ R1 S* M; r) a& S8 _' `1 M- f" c
Sensor units are calibrated to acquire data at 25 Hz sampling frequency.. W# w6 j- ~8 c% b
The 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal, E4 i' q; n- I. o0 c- r/ F2 x5 h% K9 F- c
segments are obtained for each activity. . o1 M' X0 S5 { \) w+ mThe 19 activities are:% D" [% j: Z% E/ @+ U
1. Sitting (A1); % K/ s& m2 ?: h2 q7 f; \" n2. Standing (A2); 9 B/ {: w/ j7 n& V5 F7 ?# R3. Lying on back (A3); & w6 |' ]% ]0 a, U) r$ w" f! k4. Lying on right side (A4);+ o' \3 N1 w1 p* P8 x6 W
5. Ascending stairs (A5); ; q9 H5 f, U+ |16. Descending stairs (A6); 7 P! S0 ^+ @) S1 S" V: l1 S' m7. Standing in an elevator still (A7);, c. s9 I7 U; A, t6 ?
8. Moving around in an elevator (A8);9 m& k8 L. W6 R# O
9. Walking in a parking lot (A9); 0 M; I0 L# M2 L! Z' U10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg . y: o/ j6 i5 x( Sinclined positions (A10); 2 C, J- Y) v9 z" N. V' W3 s5 l11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions) P( f8 x9 N* t0 o4 P" _
(A11);2 W. C$ L4 ?0 D N9 ?5 v% M
12. Running on a treadmill with a speed of 8 km/h (A12);9 E, p4 M" M0 L6 w5 s" G4 t
13. Exercising on a stepper (A13); ' |% C) s5 ?( ?3 E Z$ Q, U! I* k: \14. Exercising on a cross trainer (A14);1 [1 K2 }3 C) [- y
15. Cycling on an exercise bike in horizontal position (A15);% c8 u1 s3 v4 f5 T
16. Cycling on an exercise bike in vertical position (A16);2 k+ ]' h J5 |% f( S
17. Rowing (A17); 2 @( A) N8 W5 V' `1 F# [18. Jumping (A18);! U1 m" u$ A+ C& F- M. l0 H5 V% S
19. Playing basketball (A19). 8 i* W) b5 r7 OYour team are asked to develop a reasonable mathematical model to solve$ @% K: \1 C. M( U$ s& g
the following problems. $ m6 J7 U' x. A% w7 [+ I3 I1. Please design a set of features and an effiffifficient algorithm in order to classify / T2 i1 ^+ n- P9 q, q/ C6 Cthe 19 types of human actions from the data of these body-worn sensors. # I; S: e4 z4 G2. Because of the high cost of the data, we need to make the model have 7 a- u! g6 f4 }3 Sa good generalization ability with a limited data set. We need to study- g4 K5 F0 T9 |2 f$ @1 F; E
and evaluate this problem specififically. Please design a feasible method to 7 u0 n1 n( X& H# ^evaluate the generalization ability of your model. , y+ x4 C5 Q$ j* z0 M8 d3. Please study and overcome the overfifitting problem so that your classififi-) K& K I0 l6 S1 B: B# l
cation algorithm can be widely used on the problem of people’s action' K3 _1 @- [ Q" q/ k
classifification. 0 A) i# p5 _" ?5 |/ ?The complete data can be downloaded through the following link: + x5 ?; ?( {; }* L0 e9 qhttps://caiyun.139.com/m/i?0F5CJUOrpy8oq/ I) G3 a" T1 G( K& v. F) h& x6 p
2Appendix: File structure& J; p* m2 q0 Y3 {: K! Y( n# @
• 19 activities (a), F7 k( D, M6 Z. r5 d
• 8 subjects (p) ) ~. {- g. t6 u6 p2 g• 60 segments (s) Z8 C/ I1 @: h" I" w; R
• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left 1 g" w' K9 n2 s5 uleg (LL)$ ]: G1 H. m4 n: ^$ p
• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z7 r/ b( V5 e) q H. c5 H, O
magnetometers)6 O1 H1 m4 q r# ^0 C: k
Folders a01, a02, ..., a19 contain data recorded from the 19 activities.; i) }, G: u! B: B' x; r
For each activity, the subfolders p1, p2, ..., p8 contain data from each of the ; Y; p2 u W$ Q$ x) ?, R9 [8 subjects. 2 M C3 v- N$ ^' X( B3 w! I3 a' qIn each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each , {8 T( P- Q' R5 Asegment.& t4 F3 l2 J# P& t
In each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25 ( {- \" @9 \- I% {5 p8 OHz = 125 rows. 1 B+ S! \2 @ g& T. h* U; AEach column contains the 125 samples of data acquired from one of the0 p) I1 R. F3 R
sensors of one of the units over a period of 5 sec.+ R7 j3 w, w+ U; h1 w! D, g9 v
Each row contains data acquired from all of the 45 sensor axes at a particular 9 k4 e2 H7 m ]& usampling instant separated by commas.2 n( P* N' |& k) ]! i
Columns 1-45 correspond to: 1 J' v! _ A, E. u( E' d• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag, + H& Y0 M& s+ v4 q: y• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag,! M" r5 j' l! |+ N2 i3 f
• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag,) _. g, b5 _, G; V2 }
• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,& K8 H9 J8 j, W" ]! d" D1 p
• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag. 1 K/ `4 s: f; s. gTherefore,5 v2 G+ w x3 L; S* _
• columns 1-9 correspond to the sensors in unit 1 (T), 6 J7 v. I; k7 T. k( @• columns 10-18 correspond to the sensors in unit 2 (RA), F6 s# B( }4 X• columns 19-27 correspond to the sensors in unit 3 (LA),% K9 T F8 B1 t4 l8 I
• columns 28-36 correspond to the sensors in unit 4 (RL),1 K0 j0 V: z2 i) o
• columns 37-45 correspond to the sensors in unit 5 (LL)./ W* d, y; U& s
3References ]& j8 o7 K% N0 O8 p" T& N! \[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic * z5 Z8 m9 u' P+ [- C* c& Ndaily movements using a triaxial accelerometer. Med. Biol. Eng. Comput., U2 z3 k1 N% k1 f( |
42(5), 679-687, 2004- x+ s& B( q, W" \% z
[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of . x4 y* O( \6 N3 jlow-complexity fall detection algorithms for body attached accelerometers. / ?' c5 k3 U0 q. a' {Gait Posture 28(2), 285-291, 2008 $ z. y0 D: ], M. L. V[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag& I' u4 b, }# I1 w5 [3 [2 p4 A& ?. }5 s2 q
nosis method for intelligent wearable sensor system. IEEE T. Inf. Technol. 1 y5 X8 y5 g6 r, V2 J% yB. 11(5), 553-562, 2007 7 D0 r: a% E' Z* r$ u6 q+ `[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con w. O" L. r5 z7 u; _ Ftrol of a physically simulated character. ACM T. Graphic. 27(5), 2008 0 Y K' k/ f5 N" s3 U7 ]9 K; V4 r) `# N1 [$ R
2022! p' n5 U; ^. n3 t8 `
Certifificate Authority Cup International Mathematical Contest Modeling * F6 ~/ P( I5 g& w9 Ohttp://mcm.tzmcm.cn % c' g; ^* R2 i7 m( lProblem D (ICM)( k9 r7 M* t2 \# ^
Whether Wildlife Trade Should Be Banned for a Long: s/ S8 U5 {, Y, }/ T% M& E% ]
Time $ X( W& i+ @) AWild-animal markets are the suspected origin of the current outbreak and the % C2 D) W- c: {+ V3 ?7 W1 H ^2002 SARS outbreak, And eating wild meat is thought to have been a source7 j" Q4 q. C) p$ L
of the Ebola virus in Africa. Chinas top law-making body has permanently4 M, L5 P6 L |
tightened rules on trading wildlife in the wake of the coronavirus outbreak, 0 r! {; h3 D+ f. m; {which is thought to have originated in a wild-animal market in Wuhan. Some ; G9 o I( ]: y. w& p7 iscientists speculate that the emergency measure will be lifted once the outbreak. f6 o i0 X4 ], p, m: b( Q
ends.7 }# y+ J: x# j. C& R
How the trade in wildlife products should be regulated in the long term?1 h( e0 ^8 c6 [$ K7 o7 P
Some researchers want a total ban on wildlife trade, without exceptions, whereas+ Q& \+ V* F# x. A( q( ^2 D
others say sustainable trade of some animals is possible and benefificial for peo! M% o3 v1 n+ r% |' T' {- r
ple who rely on it for their livelihoods. Banning wild meat consumption could 2 s" x- P# l* e1 E# Ycost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil- C1 L7 n) e8 X( W/ K' h
lion people out of a job, according to estimates from the non-profifit Society of# X' T4 q$ P- `8 E5 A2 J& J
Entrepreneurs and Ecology in Beijing.$ J( e* b. [5 p& `. P ?; f- t) G! \5 A
A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology / P R; v$ `5 ` nin China, chasing the origin of the deadly SARS virus, have fifinally found their 9 I& U6 C" n( u: K/ ]' tsmoking gun in 2017. In a remote cave in Yunnan province, virologists have" T9 I" Y/ u$ D/ e5 Y- x
identifified a single population of horseshoe bats that harbours virus strains with7 Z0 Z1 f) M# g3 u( u1 ^( w
all the genetic building blocks of the one that jumped to humans in 2002, killing ( J7 ]' I! n( K. H" k6 g: p! Calmost 800 people around the world. The killer strain could easily have arisen* c, C. S, E; C% s/ I. F/ @
from such a bat population, the researchers report in PLoS Pathogens on 30 4 ]- ~, r( a% d) g7 {November, 2017. Another outstanding question is how a virus from bats in / J, x: D8 Y& z# [0 B% ?Yunnan could travel to animals and humans around 1,000 kilometres away in& q2 P( ^$ i- N6 Q2 A
Guangdong, without causing any suspected cases in Yunnan itself. Wildlife( ?7 V) [8 V( o" ^. a* p( o6 X
trade is the answer. Although wild animals are cooked at high temperature, e- a6 g) X3 A' i" W8 w+ \( J9 {
when eating, some viruses are diffiffifficult to survive, humans may come into contact# ]4 F! _' ?0 c& f/ V2 C" j/ x
with animal secretions in the wildlife market. They warn that the ingredients ( \8 Y8 W- Y# ?$ a3 C! N/ A% Zare in place for a similar disease to emerge again. 9 ~; Z! ?2 j, u. i7 E+ wWildlife trade has many negative effffects, with the most important ones being:8 N$ u! I" S: E/ |% R4 i
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS 5 f" V! Z: R, T2 uoutbreak in 2002.Credit: Matthew Maran/NPL! m! S6 \9 B& q
• Decline and extinction of populations 6 Y& V6 A5 `/ L; w3 F7 ?1 u• Introduction of invasive species" V. F% u% h7 B" h5 n; B
• Spread of new diseases to humans& W1 Q! T+ y* y' {' v' F9 {0 F
We use the CITES trade database as source for my data. This database, X/ r0 R# t9 `
contains more than 20 million records of trade and is openly accessible. The% W! j# f( j+ Y. B& {0 s+ i
appendix is the data on mammal trade from 1990 to 2021, and the complete # q z! B ]1 p$ C6 g3 Qdatabase can also be obtained through the following link:' d7 t Z# s1 }4 a
https://caiyun.139.com/m/i?0F5CKACoDDpEJ 0 G% i, H/ k5 ], SRequirements Your team are asked to build reasonable mathematical mod* B- Y! @* J3 C$ A: k6 q9 \! f
els, analyze the data, and solve the following problems: + r' l& ]5 P; ?3 z1. Which wildlife groups and species are traded the most (in terms of live * h' X8 l( g. l+ Sanimals taken from the wild)?& _& b. p# @$ P; p! u
2. What are the main purposes for trade of these animals? 3 U9 L' W& Z+ L9 f3. How has the trade changed over the past two decades (2003-2022)? / ?5 E; ^/ j7 B! M. L6 y4. Whether the wildlife trade is related to the epidemic situation of major8 c* o( ~& W! M5 T1 L
infectious diseases? ; c& i, T0 q- {9 P* m& E5 T# f25. Do you agree with banning on wildlife trade for a long time? Whether it $ P8 u' B; O, a8 Kwill have a great impact on the economy and society, and why?" ~7 a; X7 ]2 y! ]. O& x
6. Write a letter to the relevant departments of the US government to explain , q' C: k7 E, z) z3 qyour views and policy suggestions." d. h& v) y, q1 C
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