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 " N" r8 e+ R, v: g) e" @8 Z/ d2 ~
https://caiyun.139.com/m/i?0F5CJAMhGgSJx% v' z Y2 e/ S$ h
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Certifificate Authority Cup International Mathematical Contest Modeling + y4 H) D6 l" q6 I( h* Ohttp://mcm.tzmcm.cn0 A5 V3 J! G3 E
Problem A (MCM) $ k& p: |7 P; Y D( V0 L/ R: pHow Pterosaurs Fly M* W2 o( I) `% Q7 j3 \Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They 6 z* q; H9 {- [6 @8 A' N+ {existed during most of the Mesozoic: from the Late Triassic to the end of% i8 v; F% p* f0 T6 D) l. K
the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved 0 W6 b _, i$ @( I5 K bpowered flflight. Their wings were formed by a membrane of skin, muscle, and( d- ]7 n* c9 l' E% ?% S
other tissues stretching from the ankles to a dramatically lengthened fourth9 L7 [3 s1 A+ j; j( O' H! m
fifinger[1]. w9 O; J# t1 A7 v: ^8 Y
There were two major types of pterosaurs. Basal pterosaurs were smaller ! B& H; S$ o: B4 z Eanimals with fully toothed jaws and long tails usually. Their wide wing mem p! U6 |3 c' |) p8 ^branes probably included and connected the hind legs. On the ground, they0 k7 d# d* C6 S; M" R( i; {
would have had an awkward sprawling posture, but their joint anatomy and + N2 U' U) _3 [$ U0 x. Istrong claws would have made them effffective climbers, and they may have lived 5 U8 U" x! V$ I# B# jin trees. Basal pterosaurs were insectivores or predators of small vertebrates. % G3 q$ }! v1 XLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. 9 `! U/ M: }; ?! N2 A1 X: GPterodactyloids had narrower wings with free hind limbs, highly reduced tails,2 v, [: j- y/ \" L& A
and long necks with large heads. On the ground, pterodactyloids walked well on* U! [, M2 d0 U; B) W& c
all four limbs with an upright posture, standing plantigrade on the hind feet and8 |/ S5 Y3 {0 o# s
folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil S) A% R1 I/ {
trackways show at least some species were able to run and wade or swim[2]. 6 t l- A5 g# f/ N$ C- q: Q0 k/ iPterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which ^& \$ i( y K7 W
covered their bodies and parts of their wings[3]. In life, pterosaurs would have* m' B5 f* }) D
had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug 7 c$ m0 Y) h. q) b wgestions were that pterosaurs were largely cold-blooded gliding animals, de2 P! {0 S- B: _7 g! I d
riving warmth from the environment like modern lizards, rather than burning) A* k6 B1 I9 P8 s! v
calories. However, later studies have shown that they may be warm-blooded: @8 L6 V2 v5 ]; M( I9 |7 b
(endothermic), active animals. The respiratory system had effiffifficient unidirec 8 J7 q# K5 ]; h. c8 {* Etional “flflow-through” breathing using air sacs, which hollowed out their bones % T4 U. c& l$ eto an extreme extent. Pterosaurs spanned a wide range of adult sizes, from ! M) C5 F8 d1 C5 r Cthe very small anurognathids to the largest known flflying creatures, including/ j6 `. ?5 ]2 _% j
Quetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least 9 P% ~; N0 {! o4 Tnine metres. The combination of endothermy, a good oxygen supply and strong $ W, z. p) G% h' [# A1muscles made pterosaurs powerful and capable flflyers. $ w+ ^4 \ M5 s: c7 H1 j- P9 EThe mechanics of pterosaur flflight are not completely understood or modeled0 [5 T1 }9 e1 G% H
at this time. Katsufumi Sato did calculations using modern birds and concluded j7 G+ T) t3 Z. q% Z0 N U
that it was impossible for a pterosaur to stay aloft[6]. In the book Posture, 8 [ a9 ?6 `7 ^$ {' S+ s. h/ tLocomotion, and Paleoecology of Pterosaurs it is theorized that they were able/ ]. Y& q' y) w
to flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7]. 9 f7 Q0 X4 t! W0 m; v1 C$ xHowever, both Sato and the authors of Posture, Locomotion, and Paleoecology . ?/ u% ?6 S% K8 |" {of Pterosaurs based their research on the now-outdated theories of pterosaurs) R' j& N+ h4 c
being seabird-like, and the size limit does not apply to terrestrial pterosaurs,$ O' @+ R }2 b7 b
such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that r4 x v* R q5 |" x0 y$ Aatmospheric difffferences between the present and the Mesozoic were not needed* k" C8 h0 s6 m# t( X- e7 y
for the giant size of pterosaurs[8]. 9 y& b9 t6 j: A* a; C2 H" q/ WAnother issue that has been diffiffifficult to understand is how they took offff. H) t7 W" B$ I- cIf pterosaurs were cold-blooded animals, it was unclear how the larger ones 0 D r9 V* W' C/ f/ O. z" x" v, Qof enormous size, with an ineffiffifficient cold-blooded metabolism, could manage X9 t/ V$ m# Wa bird-like takeoffff strategy, using only the hind limbs to generate thrust for4 X7 u3 _0 O4 y. C: C- h
getting airborne. Later research shows them instead as being warm-blooded3 {7 f2 V7 C5 o) l2 g
and having powerful flflight muscles, and using the flflight muscles for walking as! H% \* d$ U. M+ X6 h% }" j
quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of * S. b+ g) S. @4 c9 g& O9 z) FJohns Hopkins University suggested that pterosaurs used a vaulting mechanism6 L' y' l6 w" c. T8 ]
to obtain flflight[10]. The tremendous power of their winged forelimbs would% n" B+ W/ s+ H+ x
enable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds3 I7 `+ _1 Q- n& `) `
of up to 120 km/h and travel thousands of kilometres[10]. ( N: H" \+ e6 N4 f( X* S# xYour team are asked to develop a reasonable mathematical model of the8 c8 V5 m+ Q J, S& W; q
flflight process of at least one large pterosaur based on fossil measurements and : u7 _( \: M3 D1 ito answer the following questions.& j" x( z$ `" P* O s5 ^( d
1. For your selected pterosaur species, estimate its average speed during nor 7 k- y% P) J5 B3 z, p. G8 c dmal flflight. 2 n, P4 G) o. Q# m/ _1 U( E2. For your selected pterosaur species, estimate its wing-flflap frequency during4 ]9 G* c* e& K; _+ A n& C
normal flflight. + F e8 r4 I) J2 I2 @3. Study how large pterosaurs take offff; is it possible for them to take offff like" X, t' I! Y! e' Q
birds on flflat ground or on water? Explain the reasons quantitatively.2 t$ |. z! L( B" q r+ u
References ) F6 H! ~+ T; ^$ g5 }[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight$ b- `5 w! d$ J
Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111. " b4 I( M% I7 q' C* u2[2] Mark Witton. Terrestrial Locomotion.8 Q1 }8 i5 c2 R
https://pterosaur.net/terrestrial locomotion.php ( R/ d- I- Y N4 f0 _, l[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs! S* N# x9 `' s# t; b: [% q
Were Covered in Fluffffy Feathers. https://www.livescience.com/64324- 2 x2 V' t9 J) |pterosaurs-had-feathers.html4 {& |! r- b1 y1 N
[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a4 n& W0 Z6 ]- W7 o0 ?* O5 ~
rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)% h$ R% W5 `7 Q. a( S
from China. Proceedings of the National Academy of Sciences. 105 (6):# u$ T% J" S; X( `5 [0 n1 `
1983-87. , S( {4 v1 M V. w+ y[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust 9 g6 O/ m1 V* ^# m; Z& I; m! Cskull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4): 7 n' H) R6 O: G180-84. . s: M4 d. j4 x5 \( M$ g[6] Devin Powell. Were pterosaurs too big to flfly?# j$ f' A4 A* O5 k( }& C3 Q
https://www.newscientist.com/article/mg20026763-800-were-pterosaurs 9 p U" q9 B0 J3 }/ D( |5 ]: Xtoo-big-to-flfly/ 5 o$ ]; f- i) f3 e- m[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology ' Y1 d, n5 E4 F/ G' x5 zof pterosaurs. Boulder, Colo: Geological Society of America. p. 60. - \4 {( ^$ e8 w' b% m9 C* D) |* S[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable $ n+ W/ u2 L3 N l c8 P' kair sacs in their wings. : }1 n X, i: h( U; Hhttps://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur - P, ~% A- t! C0 c; v% M& J2 C3 pbreathing-air-sacs 8 x; V- F. w! R1 q# |. @[9] Mark Witton. Why pterosaurs weren’t so scary after all. ! l7 U5 E& M ] N" V1 }. U6 _https://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils1 Q0 [: n' O r* _, h! z
research-mark-witton ) z' T5 ]) Q6 |0 d" m+ e- O, p$ G[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?7 J/ k6 R" a8 e- _+ q8 h* C
https://www.newscientist.com/article/dn19724-did-giant-pterosaurs- D4 n, [. D4 q) Z, S6 K* M
vault-aloft-like-vampire-bats/ ! J+ y+ U) j4 K3 T% e+ o* n' M7 b4 w }* F" Y
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Certifificate Authority Cup International Mathematical Contest Modeling + Q+ c v. _: b: ^4 a" Fhttp://mcm.tzmcm.cn " x$ b5 }1 d1 t# X) _$ l$ }Problem B (MCM) ' m! K; q1 ~ a, \7 x- C' y) @+ P' iThe Genetic Process of Sequences ' A8 i4 I a( p% E U# c3 H; LSequence homology is the biological homology between DNA, RNA, or protein 4 L) I* [: Z* msequences, defifined in terms of shared ancestry in the evolutionary history of q: e0 e% \& @( f5 X5 ulife[1]. Homology among DNA, RNA, or proteins is typically inferred from their 4 o/ D9 M z* o8 l/ K" D! snucleotide or amino acid sequence similarity. Signifificant similarity is strong ; c0 ~: ~4 h4 q4 I" I9 v3 ~$ u4 uevidence that two sequences are related by evolutionary changes from a common5 Q/ Y( V4 A! o5 F& B9 p
ancestral sequence[2]. ( a, _; N( k6 j( J/ x+ MConsider the genetic process of a RNA sequence, in which mutations in nu V8 c" A) w" T5 \" N9 D( {8 c
cleotide bases occur by chance. For simplicity, we assume the sequence mutation % u: T4 J/ O1 a* v# Larise due to the presence of change (transition or transversion), insertion and ( h2 X7 _: L+ i9 h% `6 Y5 ]: @' jdeletion of a single base. So we can measure the distance of two sequences by- t/ ~* t# M5 s) w1 x8 M/ _
the amount of mutation points. Multiple base sequences that are close together % z# _) y U' c* J9 C9 Y% Hcan form a family, and they are considered homologous./ b$ ]9 i9 d( G+ \
Your team are asked to develop a reasonable mathematical model to com : z. Y+ G0 v8 J% i) hplete the following problems. + r" K- y2 [- y; H& N1. Please design an algorithm that quickly measures the distance between3 T2 _9 W7 e5 k3 _- H
two suffiffifficiently long(> 103 bases) base sequences.4 c3 r+ K2 I) F, _7 P
2. Please evaluate the complexity and accuracy of the algorithm reliably, and2 F$ P0 x! E- p7 o9 Y
design suitable examples to illustrate it. : u; Z; h( n5 y" E3. If multiple base sequences in a family have evolved from a common an - G) C/ |# |* E! |: ?8 y/ Z- acestral sequence, design an effiffifficient algorithm to determine the ancestral * t. p( ~; Q: B; D" zsequence, and map the genealogical tree. ! \! ~- d. r- M% yReferences 2 a& p4 q7 `1 m3 a( E/ v& \[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re / Q3 N- c$ ~; Y' ]" o( ]. Wview of Genetics. 39: 30938, 2005. 9 P, e7 Q! p) p2 h0 u `- l[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE,) V. L4 m* G) U; ]" }
et al. “Homology” in proteins and nucleic acids: a terminology muddle and. S. ^. V" x4 v
a way out of it. Cell. 50 (5): 667, 1987. 0 r0 j5 ]+ D% p6 G5 j9 D5 g4 b. m3 l6 T. t! q" \
20227 {+ P, b& Q" z: x; t7 | K0 T
Certifificate Authority Cup International Mathematical Contest Modeling) q" L0 h _" \6 l* H8 D3 `6 P2 W
http://mcm.tzmcm.cn6 H/ b2 {$ O6 _% x" C( W- D
Problem C (ICM) 0 ?7 j! H& b. k) B$ x w% u) c, JClassify Human Activities & y. C X% k5 F3 D# p# w0 WOne important aspect of human behavior understanding is the recognition and3 L5 I8 Z! X% H) [& ]
monitoring of daily activities. A wearable activity recognition system can im 4 J/ n# m! a4 p Mprove the quality of life in many critical areas, such as ambulatory monitor / n; Z; e$ j6 i a% f3 Hing, home-based rehabilitation, and fall detection. Inertial sensor based activ $ V: }/ n# A. ]6 @0 r% R) _ity recognition systems are used in monitoring and observation of the elderly0 O# t% X' j; C/ W0 b! \ d& p9 v6 D$ v
remotely by personal alarm systems[1], detection and classifification of falls[2], $ c& I8 P: F: h) V2 Ymedical diagnosis and treatment[3], monitoring children remotely at home or in % M! i1 y8 T$ W7 E [( E1 W9 bschool, rehabilitation and physical therapy , biomechanics research, ergonomics,& l8 h4 ~6 Q+ ] n/ b l1 t+ M
sports science, ballet and dance, animation, fifilm making, TV, live entertain9 A( a+ y7 i# E/ G0 k4 ~% k- Z, _
ment, virtual reality, and computer games[4]. We try to use miniature inertial " ]* Q2 T) c4 F1 B- d3 G% w/ Osensors and magnetometers positioned on difffferent parts of the body to classify: g& p( O0 n( Q& J# C
human activities, the following data were obtained. 5 K& q H$ @4 `+ U/ g- AEach of the 19 activities is performed by eight subjects (4 female, 4 male, $ {) y7 z" x$ [between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes ; q! V2 W# X9 F/ `" Y1 Z. G( v lfor each activity of each subject. The subjects are asked to perform the activ7 l4 ]! E q# d7 B, T8 Z. ]# h ]2 w" [
ities in their own style and were not restricted on how the activities should be ) ]! W& ]% P! G- u6 [5 m$ Tperformed. For this reason, there are inter-subject variations in the speeds and , S2 _) m; O, P% oamplitudes of some activities.* R! i L; o4 ~0 U% [
Sensor units are calibrated to acquire data at 25 Hz sampling frequency. 0 T0 c$ s/ ^' l. v8 HThe 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal* _" n8 Q$ h M4 L0 a4 E
segments are obtained for each activity. 2 {8 y/ X# q& M, C& fThe 19 activities are:, [1 F7 E _ q* A6 j
1. Sitting (A1);8 N5 N+ d8 u# l% ? q( H- p
2. Standing (A2); : H+ h+ k( |4 C. A; y3 A$ \) }3. Lying on back (A3);9 ]2 a5 B. l' `; t
4. Lying on right side (A4);9 f1 Q0 @& z1 `* U8 A
5. Ascending stairs (A5);% ^1 }" i3 ?% p$ \9 [4 C
16. Descending stairs (A6);, P: B- Y- f2 G: V2 a: N
7. Standing in an elevator still (A7);& I- }( m, x! ?% E) U4 ?
8. Moving around in an elevator (A8);7 i# M' y. M( }, I, G
9. Walking in a parking lot (A9); / x! F+ ?& s2 f% U$ p& p p10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg 3 G: I/ N$ @& r: Cinclined positions (A10);! c0 b+ a* w8 L# c7 k# E
11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions 2 ?, x& a4 H/ C0 A/ A(A11);) X& K; v( D! ?. C* q
12. Running on a treadmill with a speed of 8 km/h (A12);* _' u& f/ b+ L+ ^" G
13. Exercising on a stepper (A13);9 u' ^ k& G- ^: ]1 a
14. Exercising on a cross trainer (A14); 1 A9 f6 l9 o" h% [" U0 x& z5 u15. Cycling on an exercise bike in horizontal position (A15); 6 n/ r" f) _$ n6 ~4 [/ _16. Cycling on an exercise bike in vertical position (A16); % L7 s# L/ s/ ]! E17. Rowing (A17);5 F- n8 z U9 ^) u, g
18. Jumping (A18);! [9 l8 w4 a; y1 ^2 D) H' T
19. Playing basketball (A19). & B3 k) ^5 k2 |3 s$ J+ t5 j% uYour team are asked to develop a reasonable mathematical model to solve9 z* K* F5 C) x' j6 @' q
the following problems. 3 S- G ~ n& Q6 E9 E& n) \! O1. Please design a set of features and an effiffifficient algorithm in order to classify! C2 x" B) }) M3 x0 M
the 19 types of human actions from the data of these body-worn sensors.7 g7 p5 U1 X1 s6 Y/ H( Y
2. Because of the high cost of the data, we need to make the model have! U: w0 x/ K) q4 M# ~( j# v
a good generalization ability with a limited data set. We need to study' c. l% z6 E0 ]5 ~. O
and evaluate this problem specififically. Please design a feasible method to! Q* \/ e: P$ U3 i( I2 B
evaluate the generalization ability of your model. " t* T" B. V+ m2 R3. Please study and overcome the overfifitting problem so that your classififi-: C, Q( l- o7 s# {* I8 y
cation algorithm can be widely used on the problem of people’s action : E4 w0 Z" `- Dclassifification. / Y" l: b ?1 C; ]; S _The complete data can be downloaded through the following link: 4 o" |$ L9 U/ F" Dhttps://caiyun.139.com/m/i?0F5CJUOrpy8oq }/ E( v0 w5 W3 v6 W1 a8 e' v2Appendix: File structure [5 G6 r# ?. L1 Z
• 19 activities (a)6 M2 C1 T9 d4 C0 g/ Y$ L3 B
• 8 subjects (p)8 S" l4 F, b# x: c0 P4 z
• 60 segments (s) / D, n7 y8 F; _* H" R5 k; Z• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left# k. {3 W' ?9 H* b
leg (LL)9 Z: i0 v$ w$ z9 n( l, f3 L
• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z 4 B2 [# M: e6 I) Z6 y( k* ]magnetometers)$ w) E7 h! r# J2 Q8 n; R; i
Folders a01, a02, ..., a19 contain data recorded from the 19 activities.( D8 X8 c6 Y$ x
For each activity, the subfolders p1, p2, ..., p8 contain data from each of the6 A8 j7 n3 x2 {# n. K
8 subjects.$ m B( l0 D% @& \) A5 d# E
In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each+ O* l( N, Z7 M9 H
segment. ! v3 \- ^) m: O/ `- Y/ MIn each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 254 W R, u/ o; W$ S! m
Hz = 125 rows. 6 {; z. T+ A6 GEach column contains the 125 samples of data acquired from one of the & N- W: b& T/ G0 q& K T$ B$ ]: S- O$ psensors of one of the units over a period of 5 sec.6 q- A2 O% f. K4 X+ _: z3 p$ ^
Each row contains data acquired from all of the 45 sensor axes at a particular ' P) G5 q7 C6 {/ ^sampling instant separated by commas.9 r7 b3 |. d0 l/ L8 l' c2 G
Columns 1-45 correspond to: ' Q& v& I/ I% g) V• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,6 I$ @8 F6 L1 I0 J6 k4 V; @" C
• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag, ! Q* R: ^% f1 p: I; L• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag,5 }2 g2 j) Q' d$ U8 e0 o# q
• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,2 \% ~7 V5 I: P' _9 K
• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag.; Q, F) S- Z0 ^) P& |
Therefore, & g* ` c8 }& c/ o- S% O• columns 1-9 correspond to the sensors in unit 1 (T),* `8 T9 ?- s+ L$ q
• columns 10-18 correspond to the sensors in unit 2 (RA),) J( k! A% z, j2 O+ H
• columns 19-27 correspond to the sensors in unit 3 (LA), # B( X1 i* o9 ~9 u• columns 28-36 correspond to the sensors in unit 4 (RL), $ t6 j$ ^% [$ u3 O. ?• columns 37-45 correspond to the sensors in unit 5 (LL). / f5 n' \$ w! l3References ; Y6 y' ]) o/ D) m7 y[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic+ @* |. j" ], ]# D+ o3 X
daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput., Q0 a- Z: A& Q2 W- O/ A* h
42(5), 679-687, 20042 m8 U7 h! }( l/ A H! X
[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of( h2 J3 b% u2 N7 R1 z
low-complexity fall detection algorithms for body attached accelerometers. Y6 y0 h0 h- f) u' V# Q v6 AGait Posture 28(2), 285-291, 2008 1 Z0 L/ M7 ]* V( S[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag% e, {& h& s" o' z1 x/ s* |
nosis method for intelligent wearable sensor system. IEEE T. Inf. Technol." @. H6 i/ u# t
B. 11(5), 553-562, 2007, N5 F( G, @ a# U7 r- D% w
[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con, a! r3 Y3 T8 Z: N" d- u0 j
trol of a physically simulated character. ACM T. Graphic. 27(5), 2008 v$ E/ I" l. J! Q! X m z% b' n5 P/ u; L& z$ w8 V' z2022* I2 I0 }8 ~; H* D4 g( g* l
Certifificate Authority Cup International Mathematical Contest Modeling* A4 ]. ?' e# v9 S
http://mcm.tzmcm.cn2 d5 ?! R! R6 T N' w* J
Problem D (ICM)# g, _' I" p+ f1 n$ a% {
Whether Wildlife Trade Should Be Banned for a Long - p+ ? @! l+ |! z/ e2 U" iTime # `# w- ?3 T# e6 wWild-animal markets are the suspected origin of the current outbreak and the ; A; y( Q5 R0 f8 b2002 SARS outbreak, And eating wild meat is thought to have been a source . Q# s, D6 W. M- mof the Ebola virus in Africa. Chinas top law-making body has permanently0 B# q# y/ }9 i% [9 u
tightened rules on trading wildlife in the wake of the coronavirus outbreak, , H1 G( a, j: m9 M* Swhich is thought to have originated in a wild-animal market in Wuhan. Some! k. p7 z# N9 P2 r Q2 s3 i8 T6 p
scientists speculate that the emergency measure will be lifted once the outbreak2 ?& i8 Q- ~; }6 ]
ends. 0 Y3 f8 a3 b7 r, F) K. N8 v5 ]How the trade in wildlife products should be regulated in the long term? . ~. W; @$ U) DSome researchers want a total ban on wildlife trade, without exceptions, whereas5 K+ c1 H+ ^: o; d; F2 i
others say sustainable trade of some animals is possible and benefificial for peo 8 |$ a+ b! w: R& F; aple who rely on it for their livelihoods. Banning wild meat consumption could8 d2 c" p, x% S0 d
cost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil2 p$ I, _. O! u; ~
lion people out of a job, according to estimates from the non-profifit Society of# _& [4 }& j- X- r
Entrepreneurs and Ecology in Beijing.+ R E, t& N' B- Z2 P* ]0 K
A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology/ \5 B8 A$ I7 c. j
in China, chasing the origin of the deadly SARS virus, have fifinally found their % V# x% P7 E! n* Usmoking gun in 2017. In a remote cave in Yunnan province, virologists have ) J9 n, s2 s( h) Pidentifified a single population of horseshoe bats that harbours virus strains with " `/ f% [* K, K5 h" ^7 z$ Qall the genetic building blocks of the one that jumped to humans in 2002, killing $ Y3 k% D: l9 Y% L7 [7 y5 Palmost 800 people around the world. The killer strain could easily have arisen( L1 k: Q) o7 l( W/ `- N' p
from such a bat population, the researchers report in PLoS Pathogens on 30 4 F% q- Z. a* }% C8 [4 sNovember, 2017. Another outstanding question is how a virus from bats in 9 c* m+ Q! n6 m) i7 TYunnan could travel to animals and humans around 1,000 kilometres away in 0 V# H8 l. Y4 l/ N OGuangdong, without causing any suspected cases in Yunnan itself. Wildlife 6 p. C @! S% K( z1 ptrade is the answer. Although wild animals are cooked at high temperature ; G/ U+ Q, c2 x% Owhen eating, some viruses are diffiffifficult to survive, humans may come into contact- X" E: Z; z. y) U3 U7 }: M+ `
with animal secretions in the wildlife market. They warn that the ingredients 6 i8 L1 i1 U4 k! i2 k6 o9 e# p+ J$ tare in place for a similar disease to emerge again. * X: L) w1 j2 J" ~4 w) \Wildlife trade has many negative effffects, with the most important ones being:1 @' `3 P* l2 A2 Y: W% x' ? Y
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS $ m; S4 X8 ^! Q! L M9 p9 j( B# goutbreak in 2002.Credit: Matthew Maran/NPL v& E1 v2 k, y5 n5 y5 a. b. F7 j8 \
• Decline and extinction of populations: U* c3 Q3 ?+ o
• Introduction of invasive species + j6 w: m4 P8 C$ u$ K• Spread of new diseases to humans. ~2 \' n+ m0 F I ]% p
We use the CITES trade database as source for my data. This database 5 y- {: P; k. ^1 G U1 pcontains more than 20 million records of trade and is openly accessible. The6 _3 T8 D# p% q! n1 K5 K8 t a
appendix is the data on mammal trade from 1990 to 2021, and the complete 5 ], ?0 X+ C2 e- ~" Cdatabase can also be obtained through the following link:: m7 S3 n+ Y" @2 ^5 g8 i( e& } N, ?
https://caiyun.139.com/m/i?0F5CKACoDDpEJ 1 _3 M6 R6 i5 {/ [7 yRequirements Your team are asked to build reasonable mathematical mod u+ y+ I. |" R$ J& [9 P
els, analyze the data, and solve the following problems: 2 C2 F$ f. T2 v$ t1. Which wildlife groups and species are traded the most (in terms of live& c2 ^8 ~2 L0 Y- @+ [/ j
animals taken from the wild)?% L! ^- X" r# W& }
2. What are the main purposes for trade of these animals? $ y( h. c' {& |; W9 ]2 a# J4 k3. How has the trade changed over the past two decades (2003-2022)? % R# F' ~: _' s: |( g9 z. E9 Z4. Whether the wildlife trade is related to the epidemic situation of major3 u- G' `3 w+ m' E* w* G% W5 d
infectious diseases? 7 y [1 H* @% A$ ], H25. Do you agree with banning on wildlife trade for a long time? Whether it , k7 y! y) h- y# L8 Wwill have a great impact on the economy and society, and why? # a$ s2 B; ]! _6. Write a letter to the relevant departments of the US government to explain 3 n! H' p8 b. e0 V( a% myour views and policy suggestions.8 C) n' {9 d1 _
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