2022小美赛赛题的移动云盘下载地址 9 y! y8 N8 V$ b) ?' @
https://caiyun.139.com/m/i?0F5CJAMhGgSJx! r0 ^+ ^" `: F M2 W4 E
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2022 4 S6 L2 ^1 O6 N wCertifificate Authority Cup International Mathematical Contest Modeling3 n1 P4 k! t! ]8 w- b2 j
http://mcm.tzmcm.cn 2 V5 k5 ]( N$ z4 eProblem A (MCM)/ D e. j; {0 Y( P; a: d, B
How Pterosaurs Fly6 o" @4 K% H- h1 w7 h2 {( F
Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They # F" t% Q" B" Xexisted during most of the Mesozoic: from the Late Triassic to the end of " k5 G* h# C% |the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved1 s- T$ [) F6 j' V: f7 i7 a" Z" _! I
powered flflight. Their wings were formed by a membrane of skin, muscle, and $ V3 f) b5 m" l H' D$ O0 ? x( Bother tissues stretching from the ankles to a dramatically lengthened fourth ) |8 N+ W& S- O) c* R$ g5 Ufifinger[1].6 s) w$ L( h# `# b* t- z' @: c; @5 f& I
There were two major types of pterosaurs. Basal pterosaurs were smaller 7 h9 H7 z9 r( Xanimals with fully toothed jaws and long tails usually. Their wide wing mem( r. n. c: U( H' t6 X' v8 x; v
branes probably included and connected the hind legs. On the ground, they- u& S, Z: e2 E9 N( G* O7 V
would have had an awkward sprawling posture, but their joint anatomy and & T* j" L0 ], C! P% ~strong claws would have made them effffective climbers, and they may have lived6 I( X S9 ^0 a+ o. Y
in trees. Basal pterosaurs were insectivores or predators of small vertebrates. + \6 V! p/ h9 \- u9 X; H7 N9 U8 q% VLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. " B. f3 f0 \6 m) Y2 IPterodactyloids had narrower wings with free hind limbs, highly reduced tails, # e3 Z O: T, ?' }and long necks with large heads. On the ground, pterodactyloids walked well on & B" H% K/ Z& F4 Oall four limbs with an upright posture, standing plantigrade on the hind feet and5 U- }' F2 d0 X" J
folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil7 q2 q0 ~& X3 s) o( L( ~6 O
trackways show at least some species were able to run and wade or swim[2].4 V9 B* @4 q& Y8 d3 _
Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which 7 j f2 l8 E- u) P9 o0 {4 K4 |$ a& Lcovered their bodies and parts of their wings[3]. In life, pterosaurs would have 7 B( [) ]! k( Z% [# Ihad smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug9 f; L; w& G& ]9 p- |! h
gestions were that pterosaurs were largely cold-blooded gliding animals, de 3 P+ D; y0 q+ }% D+ S0 briving warmth from the environment like modern lizards, rather than burning& L& g3 n# d+ P" W% M! l" G3 K' G
calories. However, later studies have shown that they may be warm-blooded6 ?! U( w$ s1 a
(endothermic), active animals. The respiratory system had effiffifficient unidirec( ^3 i$ X1 Q- u% @# e9 F$ B! l/ K
tional “flflow-through” breathing using air sacs, which hollowed out their bones9 T- z9 Z: l/ j4 V% W% [
to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from 9 {* |, X- l( u- g v' ^" uthe very small anurognathids to the largest known flflying creatures, including 0 H/ M: C4 \9 dQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least + R, i) T) m. Z4 G. R9 tnine metres. The combination of endothermy, a good oxygen supply and strong& {5 H+ b, x6 J& k3 A+ a
1muscles made pterosaurs powerful and capable flflyers. ( _3 a- R2 z2 X, c: ` p& |/ _The mechanics of pterosaur flflight are not completely understood or modeled " | ~) W, f- x. G* G! Q1 w2 ^6 h7 kat this time. Katsufumi Sato did calculations using modern birds and concluded % l) l6 @9 ]/ V: k4 Othat it was impossible for a pterosaur to stay aloft[6]. In the book Posture, , }5 Q0 U, [* L* x! V% nLocomotion, and Paleoecology of Pterosaurs it is theorized that they were able, H! ^1 X9 V6 J
to flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].# Q3 {8 v1 n7 y3 Y) _
However, both Sato and the authors of Posture, Locomotion, and Paleoecology ) @- c0 y3 r0 ?5 \of Pterosaurs based their research on the now-outdated theories of pterosaurs / Z# W: ~& C0 d! o- \9 G7 E7 Lbeing seabird-like, and the size limit does not apply to terrestrial pterosaurs,- e- b5 t9 P6 T/ e
such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that. [- z4 G! ?/ c# ^: D# _
atmospheric difffferences between the present and the Mesozoic were not needed ' f" `7 B9 q, Y9 }4 Q/ R8 Gfor the giant size of pterosaurs[8].$ |* V- A+ W s; J: ` `. _7 M
Another issue that has been diffiffifficult to understand is how they took offff.3 B. v% I% i, Z- m0 M' j6 \) u& s
If pterosaurs were cold-blooded animals, it was unclear how the larger ones* N) j" u( d! N! D- r; _
of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage 4 K( Y) h0 x H! U% ]* Ta bird-like takeoffff strategy, using only the hind limbs to generate thrust for 7 D- n6 R$ ?& u1 ]2 y/ w k6 H2 hgetting airborne. Later research shows them instead as being warm-blooded / g: I% w% G3 @" Kand having powerful flflight muscles, and using the flflight muscles for walking as( X4 W8 J7 X- {* |
quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of 6 ?. y. a9 w+ Y9 v% u( j8 _/ e' qJohns Hopkins University suggested that pterosaurs used a vaulting mechanism8 b7 m" ]& j# b. E' u
to obtain flflight[10]. The tremendous power of their winged forelimbs would# b* M1 c) {" e1 o
enable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds 3 e' j' ?+ d7 |+ m5 Y" i4 dof up to 120 km/h and travel thousands of kilometres[10].4 Y, ~& z7 `7 ?$ o$ ~
Your team are asked to develop a reasonable mathematical model of the + F4 q: Z- g H% }6 `flflight process of at least one large pterosaur based on fossil measurements and $ N+ _7 ^7 u$ j s" z9 \2 Y" Zto answer the following questions.2 N+ R, O# t$ |& }& [6 G4 ^2 ^6 {3 o
1. For your selected pterosaur species, estimate its average speed during nor6 l' U6 i Z8 n+ R9 j
mal flflight.! p. C$ D. ^8 _8 X
2. For your selected pterosaur species, estimate its wing-flflap frequency during) F+ y1 O6 \3 t- ^2 s* I
normal flflight. 9 g9 r S& W+ Q9 l% f- K# r6 v4 s3. Study how large pterosaurs take offff; is it possible for them to take offff like) W( h3 C [' ]2 @1 V
birds on flflat ground or on water? Explain the reasons quantitatively. $ [' A6 Z& Z9 l1 L9 a+ PReferences , J. g! @2 t5 Z0 l Y. G[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight & B7 {9 J( Z, o) \! LMembrane. Acta Palaeontologica Polonica. 56 (1): 99-111. , x; u/ H9 N* X8 V2[2] Mark Witton. Terrestrial Locomotion.# k/ ? _1 a+ @; c4 b
https://pterosaur.net/terrestrial locomotion.php 8 L0 H3 L; \6 p9 o1 Z[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs 5 S G% U/ t7 AWere Covered in Fluffffy Feathers. https://www.livescience.com/64324-' W; F% ^( a% p" P
pterosaurs-had-feathers.html $ ? t5 \3 r, h2 P2 L8 Z2 z/ t8 X. c[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a# T8 }: j3 o0 x: M6 _: |
rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)" B# ~! v5 g. D: f9 E) l/ C4 m" _
from China. Proceedings of the National Academy of Sciences. 105 (6):; L; H h3 b# `7 h F1 H" ^
1983-87.4 [7 z& Q$ x. W3 w
[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust) N! _ R7 @* G* ]7 f1 w" f
skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4): ; l6 h7 I; m9 {+ l2 @: L+ \180-84. S z3 @* v1 _5 \1 ~4 T
[6] Devin Powell. Were pterosaurs too big to flfly? / J5 G4 |# L4 e4 l4 ^4 Y5 M; G( ^https://www.newscientist.com/article/mg20026763-800-were-pterosaurs7 b4 h. o1 ?% |- a# Y, I
too-big-to-flfly/ $ G2 r9 K: ^2 J( S[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology 1 T5 `' j0 w" c& Fof pterosaurs. Boulder, Colo: Geological Society of America. p. 60. 9 k1 D0 w9 O; j( P[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable 2 U J! L! z% mair sacs in their wings.' v" V" |+ u% k1 c# h5 o
https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur4 Q* T( A" A+ i
breathing-air-sacs A- A$ o1 @, R) i0 z, ~[9] Mark Witton. Why pterosaurs weren’t so scary after all. Z7 S6 G/ U3 C5 w7 I& ]+ |- j, a( Jhttps://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils # v# o" L2 C3 `research-mark-witton( ^3 L5 J; \0 _3 k5 B+ j
[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?5 o# H7 l+ ] T9 z4 }6 I* E
https://www.newscientist.com/article/dn19724-did-giant-pterosaurs % P. X$ b4 }8 m9 e; rvault-aloft-like-vampire-bats/7 D, C6 ?- J- y4 J/ w
0 D* a$ K3 I" ?5 c2022' M8 m: F9 G5 v$ m7 ~9 l$ Z- e4 ?
Certifificate Authority Cup International Mathematical Contest Modeling& @. D4 w* x7 O( P
http://mcm.tzmcm.cn$ ]4 L& g$ W1 X
Problem B (MCM)4 T6 t; Z' y7 ~9 Q
The Genetic Process of Sequences7 m% r4 B4 q$ J# Q, a9 z" T% `& z5 Y
Sequence homology is the biological homology between DNA, RNA, or protein " Y$ i0 L# E( b3 F. usequences, defifined in terms of shared ancestry in the evolutionary history of 9 j) S3 d7 U! L: P' ?1 ^9 m/ Mlife[1]. Homology among DNA, RNA, or proteins is typically inferred from their3 R' Q# L m4 H- D
nucleotide or amino acid sequence similarity. Signifificant similarity is strong . @9 J5 T; U$ p: F. levidence that two sequences are related by evolutionary changes from a common9 u" x8 G) y1 F
ancestral sequence[2]. " b% Y! F; i5 v) [5 o: tConsider the genetic process of a RNA sequence, in which mutations in nu, J6 G: w, M" S! M, U
cleotide bases occur by chance. For simplicity, we assume the sequence mutation* x$ r; y5 Z4 ~" P. a
arise due to the presence of change (transition or transversion), insertion and ! u, R. l' x6 c0 R% Ddeletion of a single base. So we can measure the distance of two sequences by/ M+ M8 N- q" |) \
the amount of mutation points. Multiple base sequences that are close together/ r* Y' S" U9 g
can form a family, and they are considered homologous.) c t, a" P3 U- A, |5 B2 e. {
Your team are asked to develop a reasonable mathematical model to com% ^; g0 n) G( `- ~; L3 n) `
plete the following problems.' M5 E$ K' F/ K
1. Please design an algorithm that quickly measures the distance between- T7 C {; Z8 P" ]. Q7 x8 ?# u
two suffiffifficiently long(> 103 bases) base sequences.+ s Y* r" z% U: C+ w# v) b8 Z0 X9 p7 ]( k
2. Please evaluate the complexity and accuracy of the algorithm reliably, and % z5 U- v* l3 X0 q/ |* vdesign suitable examples to illustrate it. + b8 H9 G* v& e) L" O3. If multiple base sequences in a family have evolved from a common an & O6 `# ?! v% L( ^" ^& `* ?cestral sequence, design an effiffifficient algorithm to determine the ancestral- ?9 o* H. Y5 P
sequence, and map the genealogical tree. $ C- N, O, R' Q8 gReferences/ m1 Q' O0 H$ M7 ^# p Y
[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re 4 t9 q) `2 R* q+ C7 i+ p& M/ kview of Genetics. 39: 30938, 2005.2 @6 U G( z" N9 R9 ]' K
[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, . c% r$ `4 B4 {2 K7 N w4 [* Ret al. “Homology” in proteins and nucleic acids: a terminology muddle and+ f- i7 {7 b) U7 {3 `" I. _
a way out of it. Cell. 50 (5): 667, 1987.. Y+ j7 g7 _' W. G* c" A
: r- R& F2 C5 I9 F# N9 g- J20228 ?+ t* H' @5 Z- s5 m& H* t( a
Certifificate Authority Cup International Mathematical Contest Modeling. B @* A n$ Y1 l4 _3 O& }0 H
http://mcm.tzmcm.cn ( `: H6 K0 d O/ k6 F% nProblem C (ICM) 5 m) |5 R8 a `- L0 h% P0 B) OClassify Human Activities 5 H, \9 p7 T4 F1 O% _# R/ lOne important aspect of human behavior understanding is the recognition and 0 O+ j& R+ L+ g, k) B3 jmonitoring of daily activities. A wearable activity recognition system can im' E. a3 I3 S! \1 b: i" ?& k7 ^
prove the quality of life in many critical areas, such as ambulatory monitor4 O* Z* A1 T }
ing, home-based rehabilitation, and fall detection. Inertial sensor based activ6 z0 ^. c+ M3 t9 n5 u- Y6 k
ity recognition systems are used in monitoring and observation of the elderly 8 m# @1 m/ a# m a: o1 b8 X) yremotely by personal alarm systems[1], detection and classifification of falls[2], 6 r% y8 R7 x- ` b6 ]medical diagnosis and treatment[3], monitoring children remotely at home or in - F5 c0 B& n6 l/ Z) Yschool, rehabilitation and physical therapy , biomechanics research, ergonomics, ' p% m5 g9 Q1 d" Gsports science, ballet and dance, animation, fifilm making, TV, live entertain9 U9 o& j6 ]2 C* L; c
ment, virtual reality, and computer games[4]. We try to use miniature inertial Z6 K! w0 v$ @4 {7 T
sensors and magnetometers positioned on difffferent parts of the body to classify ) _0 D9 R2 w8 G8 \) m7 rhuman activities, the following data were obtained." [& Q: t( |) Q+ t$ A. A
Each of the 19 activities is performed by eight subjects (4 female, 4 male, ( P4 Y$ b; P1 {+ Pbetween the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes4 a- |) \2 \& B
for each activity of each subject. The subjects are asked to perform the activ / w7 b( _+ k+ u6 rities in their own style and were not restricted on how the activities should be ! _' W) N$ q4 O& W: d' a& \( g7 iperformed. For this reason, there are inter-subject variations in the speeds and3 H4 l4 {6 [+ _6 D8 i& O9 G# ^; y6 z
amplitudes of some activities. 6 |# h1 ~% G5 M3 eSensor units are calibrated to acquire data at 25 Hz sampling frequency. t R/ W" R/ q$ I1 g9 B
The 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal $ ~1 H5 C" t* B6 M& x- T, ]# \segments are obtained for each activity., |. N9 `0 F! F3 |' E# S
The 19 activities are: . z& Z. p5 C! T) ~0 _1. Sitting (A1); 5 X9 I. `( }) j( ], d& Q2. Standing (A2); $ E/ S# S+ _% D$ C4 }9 r3. Lying on back (A3); - C/ l+ H# m$ t5 U. c4. Lying on right side (A4);5 f4 S6 w9 Z l; X
5. Ascending stairs (A5); " R7 X( G6 m. B5 M* N* t, v16. Descending stairs (A6); % C4 X( m$ W! J2 D, D5 Y7. Standing in an elevator still (A7); . [* _! B/ p$ d0 F+ g8. Moving around in an elevator (A8); 5 y6 u" X$ j+ z. @0 d# S1 P- H2 x9. Walking in a parking lot (A9); 1 M- o3 d% e7 t& `1 r* ]10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg & X# X0 }5 }3 b: @% sinclined positions (A10);! Z0 |! s( ~) S, o' T
11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions1 v: Q4 {2 l) |' ^' u
(A11);$ r$ O2 D* C/ B9 L7 b6 K
12. Running on a treadmill with a speed of 8 km/h (A12); ) y9 I& N/ L( E6 c' A: {" j/ ~13. Exercising on a stepper (A13); # C: ?& N( @ i! h( C14. Exercising on a cross trainer (A14); , E: d) e1 D# L" h15. Cycling on an exercise bike in horizontal position (A15); r; l5 h0 T* J4 n% b16. Cycling on an exercise bike in vertical position (A16);/ m" o9 J/ N S! N
17. Rowing (A17); ! Y+ [ v9 D! ~6 y* |18. Jumping (A18); 8 o" i7 W& p) v( v19. Playing basketball (A19). 9 a7 J: Q6 O2 {6 I) V) {- {Your team are asked to develop a reasonable mathematical model to solve7 {" E$ |, \; n0 X
the following problems., s, f& U' N% N" e) b& P" ~
1. Please design a set of features and an effiffifficient algorithm in order to classify 6 U0 t: o6 Q" s2 `# Cthe 19 types of human actions from the data of these body-worn sensors. . Z+ T3 d* z( I0 [8 l2. Because of the high cost of the data, we need to make the model have5 g7 }" Q: B, g/ v/ E3 t
a good generalization ability with a limited data set. We need to study 3 q9 } `! Q$ h' M; Mand evaluate this problem specififically. Please design a feasible method to 5 L& I( w: C6 a( d t7 x1 c8 ^ Wevaluate the generalization ability of your model.' e6 N7 M6 w4 M
3. Please study and overcome the overfifitting problem so that your classififi-( {/ ?9 b: {# i) G9 `7 G7 b; j
cation algorithm can be widely used on the problem of people’s action2 Y0 Q+ d1 j! x2 U# Q) y, D" a
classifification. + h) p' a8 J) |, C' eThe complete data can be downloaded through the following link: : D3 J/ h, v6 @6 s5 thttps://caiyun.139.com/m/i?0F5CJUOrpy8oq 9 f2 `" v' N% v( B! f2Appendix: File structure " ~( U# { u9 R+ L0 h" e• 19 activities (a)5 o$ \7 \/ v2 s/ O2 O1 H
• 8 subjects (p) 5 |2 j6 {6 [" M% R! P* h• 60 segments (s) 9 K! t# o6 s+ @0 Z' N/ r8 u• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left ; w. P5 I* o) pleg (LL) ; r/ ]; C2 p! l5 F8 n- d' @% P; o( v• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z 0 o- T9 z) e5 l/ `magnetometers)7 J" d C( j: W/ L) Y, w2 z) c
Folders a01, a02, ..., a19 contain data recorded from the 19 activities. . z$ l2 p1 _: M( T+ T: I$ QFor each activity, the subfolders p1, p2, ..., p8 contain data from each of the 3 ^1 u% a+ P( P4 g8 subjects.( P5 w' ^9 @0 u' v2 f
In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each , A4 B( T/ I9 Nsegment.: F s W4 c1 N8 ~( u2 l) c
In each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25 7 p$ u! v& n) g& ^Hz = 125 rows.; O- o. C, o, f, p
Each column contains the 125 samples of data acquired from one of the; t; ^9 j0 |+ E! y! C- J& m
sensors of one of the units over a period of 5 sec." b. v8 H- ~& w/ X' y& u6 D" @" S
Each row contains data acquired from all of the 45 sensor axes at a particular4 @4 v. D: V0 f+ {5 p" \% m, {
sampling instant separated by commas.7 u" @8 T$ F* R: ]8 z+ |# Q
Columns 1-45 correspond to:/ _" w7 L/ w6 s
• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,$ [9 _* k! c5 `1 q
• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag, 1 w/ V1 I# R# }1 D5 U3 I3 f: I) D• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag,$ @" O" Y/ \2 n- e0 o
• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,: s4 Y+ R3 _/ V! B: i
• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag. 4 p: H) Y6 B) M# |* M6 VTherefore, ; m( k! z! Z. `+ u• columns 1-9 correspond to the sensors in unit 1 (T),8 p, j8 p! Z' v
• columns 10-18 correspond to the sensors in unit 2 (RA),2 o |7 s W. M0 k$ f$ R
• columns 19-27 correspond to the sensors in unit 3 (LA),0 Q: [3 |/ o: x8 f8 f
• columns 28-36 correspond to the sensors in unit 4 (RL), k: `" V. q) E7 {! |2 U• columns 37-45 correspond to the sensors in unit 5 (LL). 3 k8 H3 ]1 l. H( y3References + o" z+ r4 X. o# }! d) Y[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic& P0 f# T8 n# Z& q
daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. ( j! c& H. z. ], P* m0 z( X w42(5), 679-687, 2004" K' O* T0 L6 S2 J
[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of 8 M& z0 V" W u# [5 h1 flow-complexity fall detection algorithms for body attached accelerometers. 2 |0 V5 `( J YGait Posture 28(2), 285-291, 2008" x9 b" \' N& j& [3 x Q
[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag - S: W1 r, Q5 H% {/ b+ V8 Y* rnosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.6 h. u- h; Y( f2 n o$ `
B. 11(5), 553-562, 2007 . H3 \1 j# a0 }! p: c8 p) ?) G[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con ! P; e8 [+ M! q( z) n" vtrol of a physically simulated character. ACM T. Graphic. 27(5), 2008: x0 d$ ?6 @/ C% v O( I
4 n# f h+ K9 H4 @2 f6 ^
2022 ) m2 k1 M" S2 s4 ?Certifificate Authority Cup International Mathematical Contest Modeling! w& E- q: W# i! q7 \
http://mcm.tzmcm.cn % k- d1 q+ F4 l' ~- K) uProblem D (ICM)$ ^# n3 z/ B7 i9 O1 z
Whether Wildlife Trade Should Be Banned for a Long ' j! O% |) H3 |* A4 X4 \8 c4 nTime8 I% J- b- s' z
Wild-animal markets are the suspected origin of the current outbreak and the' o5 Q. l. @# q& F7 E) j
2002 SARS outbreak, And eating wild meat is thought to have been a source6 V2 y5 c, ~0 J3 H6 x) p
of the Ebola virus in Africa. Chinas top law-making body has permanently- F+ J& y$ _0 S
tightened rules on trading wildlife in the wake of the coronavirus outbreak, . F- y+ s0 X" c, r* iwhich is thought to have originated in a wild-animal market in Wuhan. Some + q( k( ]9 Q1 t# B5 _scientists speculate that the emergency measure will be lifted once the outbreak 4 @; @. s! c) V1 Z5 q/ [1 iends. ) t4 U' _2 T8 @' m l* [1 c' V: RHow the trade in wildlife products should be regulated in the long term?* e9 h0 P- g# @ w3 q8 D% k, p
Some researchers want a total ban on wildlife trade, without exceptions, whereas8 E; L3 i1 P/ W
others say sustainable trade of some animals is possible and benefificial for peo; r' A: _8 w, \% M- L5 M* P6 l
ple who rely on it for their livelihoods. Banning wild meat consumption could ! A0 B2 k4 x3 Xcost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil2 @4 i; }9 c _4 ~
lion people out of a job, according to estimates from the non-profifit Society of . N1 x$ ]" ?; {4 S d* f9 c6 i4 p# JEntrepreneurs and Ecology in Beijing.1 E# |* z/ }! r, ^
A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology9 Z: Q( q% Q( a2 Q
in China, chasing the origin of the deadly SARS virus, have fifinally found their# w: h! Z( Z8 }" I% S2 D+ w
smoking gun in 2017. In a remote cave in Yunnan province, virologists have # U# L& b9 `" z# midentifified a single population of horseshoe bats that harbours virus strains with9 K3 ^$ }; y+ q# \5 k" O
all the genetic building blocks of the one that jumped to humans in 2002, killing% j6 {8 W: i% t" g: ?
almost 800 people around the world. The killer strain could easily have arisen - g: a, X2 t5 d% E* H, y {. H- hfrom such a bat population, the researchers report in PLoS Pathogens on 30 ' E/ N: B! J. v. C( DNovember, 2017. Another outstanding question is how a virus from bats in : o% y; u) H* p0 h3 W9 @/ ~% ~Yunnan could travel to animals and humans around 1,000 kilometres away in K6 I5 K* E! X2 Z! D' |6 U$ L; a1 s/ @Guangdong, without causing any suspected cases in Yunnan itself. Wildlife ! _- u. U. u) O( ]2 |trade is the answer. Although wild animals are cooked at high temperature, u. c- |6 r! z- ` x- M
when eating, some viruses are diffiffifficult to survive, humans may come into contact ' X2 ]- h6 c/ i% swith animal secretions in the wildlife market. They warn that the ingredients2 K1 \1 p3 c' c
are in place for a similar disease to emerge again. 6 Z2 x6 U/ A T3 YWildlife trade has many negative effffects, with the most important ones being:; C, \, [8 D9 e1 k
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS q# D6 u) A& t. G+ routbreak in 2002.Credit: Matthew Maran/NPL, i9 I5 {" @6 U2 A- k
• Decline and extinction of populations 6 ]6 P- J( u2 m/ O• Introduction of invasive species 6 V& N5 g' q; }, W5 B3 H! o$ j• Spread of new diseases to humans 8 j. ^- e o- c$ b4 z5 ?We use the CITES trade database as source for my data. This database+ b' j- e/ s/ j/ B- k
contains more than 20 million records of trade and is openly accessible. The8 N7 V6 p# m- ]1 e
appendix is the data on mammal trade from 1990 to 2021, and the complete! W1 \; s3 W A7 A T
database can also be obtained through the following link:& s3 P X# k/ ]: N4 {2 q
https://caiyun.139.com/m/i?0F5CKACoDDpEJ' D0 u0 f$ j4 D6 O4 W
Requirements Your team are asked to build reasonable mathematical mod 8 M, o! _- c7 [) y- m4 S, j; H4 g$ Wels, analyze the data, and solve the following problems: 0 m9 j9 _0 y4 f0 h" T% x1. Which wildlife groups and species are traded the most (in terms of live/ Y- l! e( L; e$ ]5 Z& I$ N
animals taken from the wild)?" H5 o {2 _: T9 e
2. What are the main purposes for trade of these animals?: |5 {2 v; z, P8 |% ]
3. How has the trade changed over the past two decades (2003-2022)? " C: F- x# ^! s% L4. Whether the wildlife trade is related to the epidemic situation of major! ~: d2 K- }# g' f& x$ A
infectious diseases? 7 S1 a8 c( k. Z; n! K. j5 x25. Do you agree with banning on wildlife trade for a long time? Whether it % t! X2 t* K/ G$ C# t8 f: Ywill have a great impact on the economy and society, and why?2 z: G) H% E! |2 E. o
6. Write a letter to the relevant departments of the US government to explain # w' P9 }5 N# Q8 u" e, lyour views and policy suggestions. % k% i T# q1 c/ i7 e+ k# n( M$ A$ B# r+ K: |/ k2 h( q3 w
7 S( E" V; f P, c: |2 g