2022小美赛赛题的移动云盘下载地址 4 [$ k- d0 d4 a& B: }% _& _5 nhttps://caiyun.139.com/m/i?0F5CJAMhGgSJx- v3 F. x+ _# |7 F3 E; d3 H' M
% Q1 r" O: T; x1 B h6 r
2022 / m. J) S2 Z* y1 w0 UCertifificate Authority Cup International Mathematical Contest Modeling " H. \' W! V3 q: Y( }http://mcm.tzmcm.cn9 v1 h3 ~; R6 \+ q; S2 Y
Problem A (MCM)2 C- b4 c* r) d
How Pterosaurs Fly % }' ~; q9 A3 z. kPterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They) C, h4 L0 U- _# ?" q b6 I
existed during most of the Mesozoic: from the Late Triassic to the end of7 E, U" d5 K3 u9 U
the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved7 E0 ]8 l0 @/ |, T' k
powered flflight. Their wings were formed by a membrane of skin, muscle, and ; s& g. |, t9 ?other tissues stretching from the ankles to a dramatically lengthened fourth4 A0 O: i: M$ u2 W
fifinger[1].6 g) x! O' G* B
There were two major types of pterosaurs. Basal pterosaurs were smaller* n s/ V# ?! ?1 L
animals with fully toothed jaws and long tails usually. Their wide wing mem " {+ X! f* }6 E& L( G b) pbranes probably included and connected the hind legs. On the ground, they0 z5 a# a+ P R+ I
would have had an awkward sprawling posture, but their joint anatomy and L" T3 w1 U2 y5 o( {' d! Dstrong claws would have made them effffective climbers, and they may have lived; C7 {% Y9 R4 i4 E/ \
in trees. Basal pterosaurs were insectivores or predators of small vertebrates. . G8 l+ \7 [. s) I/ t& zLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. 5 d/ c/ \0 `8 m6 T3 OPterodactyloids had narrower wings with free hind limbs, highly reduced tails,3 `3 M, P1 U+ c8 W2 H) F/ t
and long necks with large heads. On the ground, pterodactyloids walked well on' q- z- u8 z6 ]9 u& M I, u0 o
all four limbs with an upright posture, standing plantigrade on the hind feet and ; J8 X; @( d3 z! x d) w9 y* G5 nfolding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil ' {) {4 F% W9 T* C+ J& e( C) E: strackways show at least some species were able to run and wade or swim[2].! Q( B6 \9 [1 ?# `& w! Z
Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which c0 }; W+ q; S% icovered their bodies and parts of their wings[3]. In life, pterosaurs would have+ C, c# W8 x4 q
had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug : V# W# ~' P! U' G+ Kgestions were that pterosaurs were largely cold-blooded gliding animals, de 5 S; F+ w/ M9 j3 @$ }riving warmth from the environment like modern lizards, rather than burning $ c5 L; i/ V0 ecalories. However, later studies have shown that they may be warm-blooded1 H1 H1 O6 v( F: J1 A1 m" t
(endothermic), active animals. The respiratory system had effiffifficient unidirec# ` ^! v0 {% ` V, z
tional “flflow-through” breathing using air sacs, which hollowed out their bones8 W) B8 _+ L |: k
to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from * [0 x$ q+ D( r* A! zthe very small anurognathids to the largest known flflying creatures, including/ F6 n, d0 u: `* t2 n. A9 R
Quetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least+ X3 s) j7 e3 m8 G6 e; q; w9 T
nine metres. The combination of endothermy, a good oxygen supply and strong . U) v, C+ r$ [5 E; X/ i1 N1muscles made pterosaurs powerful and capable flflyers. + y# Q7 X' w; C- ~" A+ A0 oThe mechanics of pterosaur flflight are not completely understood or modeled , e* M4 R) H% M. N: S- y1 q. e. J) s1 Fat this time. Katsufumi Sato did calculations using modern birds and concluded2 U/ |! K& w: b2 m0 y. _9 ?3 l
that it was impossible for a pterosaur to stay aloft[6]. In the book Posture,7 K4 ]9 F. g. c4 i# x3 R v
Locomotion, and Paleoecology of Pterosaurs it is theorized that they were able 7 f) g: x4 g7 u! F1 Wto flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].% ]' m. p9 a i% O
However, both Sato and the authors of Posture, Locomotion, and Paleoecology0 k: T% R& s% k/ N C5 ]
of Pterosaurs based their research on the now-outdated theories of pterosaurs6 a0 h0 N/ s+ P1 `3 o: j
being seabird-like, and the size limit does not apply to terrestrial pterosaurs,: f6 [2 B6 x ]+ ]# l
such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that+ O* {) ^, Y' O8 V( J. Z% T
atmospheric difffferences between the present and the Mesozoic were not needed' L2 c& z V. L, a/ d; z; P
for the giant size of pterosaurs[8].+ f9 r% D I# p- l0 N
Another issue that has been diffiffifficult to understand is how they took offff. ! L$ ]* k+ U* L a% VIf pterosaurs were cold-blooded animals, it was unclear how the larger ones ' v i5 w- S8 n6 I% @of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage . o8 j. X! Q2 v% x) Ra bird-like takeoffff strategy, using only the hind limbs to generate thrust for. B8 z6 {2 \) y( V3 P
getting airborne. Later research shows them instead as being warm-blooded! H" V' D) |2 z7 {1 `
and having powerful flflight muscles, and using the flflight muscles for walking as7 r+ I2 \: v g" j/ l3 ^6 b( r, y2 u1 c
quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of' l. Y9 ]! a P5 A7 t! j4 [
Johns Hopkins University suggested that pterosaurs used a vaulting mechanism ; I+ C! n/ \3 f% `) S8 {* {' Nto obtain flflight[10]. The tremendous power of their winged forelimbs would , M! L) Q! z! E# n! q" Wenable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds# e# p" X6 _- j+ A- V P1 w! m
of up to 120 km/h and travel thousands of kilometres[10].6 X; p$ R1 @9 q8 F x( p
Your team are asked to develop a reasonable mathematical model of the7 f: W7 ^( M4 S
flflight process of at least one large pterosaur based on fossil measurements and 4 \# e% h; }5 }$ bto answer the following questions.- {! l& P0 n* Q) @! d
1. For your selected pterosaur species, estimate its average speed during nor! V b7 ?1 D0 c! r8 W$ F2 \+ N& w) i
mal flflight.* i. K( q0 V, G2 P# x) ~3 f4 x0 ]
2. For your selected pterosaur species, estimate its wing-flflap frequency during' \2 q9 w0 t3 ?7 J1 J, z8 T, Y
normal flflight. 2 h s Z0 r( ]& [8 d& \3. Study how large pterosaurs take offff; is it possible for them to take offff like ) Q3 i# H& i6 E. I+ e, k5 }birds on flflat ground or on water? Explain the reasons quantitatively. 6 z$ E8 `) M# n/ RReferences. q2 O' Q7 B4 E/ |) M) `% a
[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight" a( c! b6 |4 r3 H8 z
Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111.1 L; N& b0 R$ X6 o: p4 @& e
2[2] Mark Witton. Terrestrial Locomotion. D4 U* V0 u* k, d Z4 X8 Y
https://pterosaur.net/terrestrial locomotion.php" [7 z- H0 t. U/ [0 ~ z7 J) h
[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs% w& A0 |" ~! M" p& U# u7 `
Were Covered in Fluffffy Feathers. https://www.livescience.com/64324- * r% T9 R* Q, w- q# I/ ypterosaurs-had-feathers.html5 J2 e+ J! r! `0 `1 K* K
[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a N" d! V7 U4 Q4 L- _rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea) # K% _: d) ^9 q3 H3 B- Efrom China. Proceedings of the National Academy of Sciences. 105 (6):5 u9 ?; A: `: D5 L( K) V& G z
1983-87.% T; @( X; w) @/ K- m1 Q m
[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust , @/ X6 B. m& ]+ k9 T* p5 Lskull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4): 2 T8 J& s2 v ^: L: g180-84.! o* D% U- O+ U1 e5 F n8 Q
[6] Devin Powell. Were pterosaurs too big to flfly? : D" K. r! G# }4 i* mhttps://www.newscientist.com/article/mg20026763-800-were-pterosaurs$ I5 r, A3 v- _4 B8 s! U
too-big-to-flfly/ 6 R6 b; z6 Z' _3 J6 ^4 w5 r5 v1 x[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology 0 R0 n+ l6 Y- j# c$ zof pterosaurs. Boulder, Colo: Geological Society of America. p. 60. ( A! ~$ `& k# m+ v[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable ; ?/ V: w/ j- Y' h- a1 A4 l% kair sacs in their wings. 5 x, m5 j5 E; Uhttps://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur$ _6 J! f7 S/ c9 P, E9 R8 a, g9 C5 R
breathing-air-sacs* O1 ^0 {/ n% D% S7 c% m
[9] Mark Witton. Why pterosaurs weren’t so scary after all. : Z7 D6 i' o0 a; R( _0 Rhttps://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils" r. L4 L, C( ?3 h
research-mark-witton8 m% X3 D) t s2 g1 B! k
[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?3 h; G4 b4 i# e: ^$ p+ _8 n
https://www.newscientist.com/article/dn19724-did-giant-pterosaurs ( v! T2 P4 q; m/ q8 D2 D4 ~ Dvault-aloft-like-vampire-bats/ ' Q2 |+ }2 _! j5 O. @0 Q( b5 F, k . h* I+ w9 J0 z" |- w/ G20220 h. j; B* w% ]+ Z, w) A/ n$ u
Certifificate Authority Cup International Mathematical Contest Modeling , a0 ]3 W4 t: B5 }; Ahttp://mcm.tzmcm.cn + }' _! _' {% K# C7 PProblem B (MCM)9 {. k4 Z7 e' ?1 u$ k$ _
The Genetic Process of Sequences9 N- e& c- x. s. k
Sequence homology is the biological homology between DNA, RNA, or protein + ]+ F3 T7 m6 A& i4 Lsequences, defifined in terms of shared ancestry in the evolutionary history of 7 L# r) [7 s/ _- `8 `* slife[1]. Homology among DNA, RNA, or proteins is typically inferred from their 5 X2 R! |& D( R9 ]( Lnucleotide or amino acid sequence similarity. Signifificant similarity is strong" Y- g% `1 g4 ]
evidence that two sequences are related by evolutionary changes from a common , M5 c# \3 m' l% D7 s& y5 \& Zancestral sequence[2]. - M2 ?# s; r+ d# x- ]( q6 YConsider the genetic process of a RNA sequence, in which mutations in nu9 Y% @2 ^+ G/ m6 R. F
cleotide bases occur by chance. For simplicity, we assume the sequence mutation }9 l" O& B* w# tarise due to the presence of change (transition or transversion), insertion and6 T7 [$ c" ^+ B* P# o6 y
deletion of a single base. So we can measure the distance of two sequences by & e+ J- R* M. O# ?3 Bthe amount of mutation points. Multiple base sequences that are close together ) H2 h g- ^( _" z( scan form a family, and they are considered homologous. * m2 j9 F0 O* X8 N, S; H* nYour team are asked to develop a reasonable mathematical model to com 9 T# ` w7 n3 |: ^7 K& [, [plete the following problems.6 l2 i+ j7 A3 }- k( M
1. Please design an algorithm that quickly measures the distance between ) E- W3 w* E5 V0 stwo suffiffifficiently long(> 103 bases) base sequences./ n2 A, n6 {9 o1 Z( o+ q
2. Please evaluate the complexity and accuracy of the algorithm reliably, and3 x8 b, n2 _, I
design suitable examples to illustrate it.6 `9 r% H* | v3 L/ Q+ u
3. If multiple base sequences in a family have evolved from a common an: n5 T; q: L3 Q; m* D+ M
cestral sequence, design an effiffifficient algorithm to determine the ancestral, N ?' x5 |: u( n$ ]( z& ~) Y
sequence, and map the genealogical tree. ) a6 y+ T6 {9 O! f0 i( k+ JReferences$ L ?2 ^ y d5 N' t; L0 g& `
[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re : ]4 T# Z: j4 W# ^view of Genetics. 39: 30938, 2005.4 o# b1 ~* n% b0 Y' `) k/ L
[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, ( j' Z: Q2 ^3 g- b: {et al. “Homology” in proteins and nucleic acids: a terminology muddle and5 z" \& [0 i& u* u H# {
a way out of it. Cell. 50 (5): 667, 1987. & j ^6 m6 v$ }1 ] $ r- J( C: ~. M: `2022 # \0 D/ a$ X3 }# MCertifificate Authority Cup International Mathematical Contest Modeling & p& u1 O3 F4 Q7 |2 thttp://mcm.tzmcm.cn |1 \6 a' E7 N: R8 x: ^/ w
Problem C (ICM)* w0 S+ {- a# H. U* ~
Classify Human Activities R4 u! J- j/ a2 J" W
One important aspect of human behavior understanding is the recognition and/ Z8 d" K9 a2 I' R3 R$ {8 |- B. V. b
monitoring of daily activities. A wearable activity recognition system can im 3 L! W! B; ^% R4 tprove the quality of life in many critical areas, such as ambulatory monitor 5 V; u& w* t. n% @, g% I, Iing, home-based rehabilitation, and fall detection. Inertial sensor based activ 1 e& D, b% ^5 w* [ity recognition systems are used in monitoring and observation of the elderly! i- B; w- B4 {) G3 E
remotely by personal alarm systems[1], detection and classifification of falls[2], 8 h, g- t6 Q5 `4 i' C! |, imedical diagnosis and treatment[3], monitoring children remotely at home or in3 ^0 d( }( U+ c* I( W& r& o0 S
school, rehabilitation and physical therapy , biomechanics research, ergonomics, 3 R* A* _2 W6 B% Z* s* csports science, ballet and dance, animation, fifilm making, TV, live entertain . L: I# I0 v$ y0 I' S: {ment, virtual reality, and computer games[4]. We try to use miniature inertial- s/ N4 b1 t; N2 i$ G1 s( L- G
sensors and magnetometers positioned on difffferent parts of the body to classify! \& Y x. w% J& o3 {7 r- X
human activities, the following data were obtained. * k/ u% u. T- TEach of the 19 activities is performed by eight subjects (4 female, 4 male,- s5 Z! @+ F k2 c- l0 E7 R( F- S
between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes 8 {* q$ b( f7 ]) }7 N- Nfor each activity of each subject. The subjects are asked to perform the activ - n+ p+ `$ V4 z& z0 c% u" eities in their own style and were not restricted on how the activities should be1 N+ }( l" b* N
performed. For this reason, there are inter-subject variations in the speeds and N! d- e8 ~5 o3 b: {* uamplitudes of some activities.) i& ?! q/ K7 `+ b `1 j
Sensor units are calibrated to acquire data at 25 Hz sampling frequency. : S( W7 |8 @" k3 F% h8 ~% @The 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal: d/ r2 \5 M' J( ^ h; b' N; _5 o
segments are obtained for each activity.6 `5 M/ F) T9 v
The 19 activities are:1 E# U8 f2 H* N8 v. `4 a) u
1. Sitting (A1);% h* Y4 W# e) Y' A
2. Standing (A2); # ` m; L, w1 k, S3. Lying on back (A3);& V+ M9 J. x) N1 B8 C
4. Lying on right side (A4);$ e/ G- t, S7 o1 G6 r" K! q- S
5. Ascending stairs (A5); : ~6 K& C- P; Z* t4 X/ ]& H16. Descending stairs (A6);0 G, g0 d% [5 \( T& h& s& E3 z
7. Standing in an elevator still (A7); % V. ?( a+ x( S3 j' U# ?! r0 s/ {8. Moving around in an elevator (A8);7 x9 ?6 |+ q/ i/ M8 ?
9. Walking in a parking lot (A9); ) B* ~& y( h/ A* T10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg: N1 n1 @ k$ n6 \& b) }
inclined positions (A10); # e& M% Q% f5 g: q. O' [: J11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions . J, S: V% U& P3 w(A11); 9 ^, X! N [1 Q7 m7 k# E2 t/ E12. Running on a treadmill with a speed of 8 km/h (A12); ' G P; I) ?% Z) {+ @. G13. Exercising on a stepper (A13);+ }* h+ y- a3 [) N* b* G
14. Exercising on a cross trainer (A14);7 b$ O+ v! u9 E/ F1 X
15. Cycling on an exercise bike in horizontal position (A15);4 e+ U, c) S, ]$ ~) K3 B# X
16. Cycling on an exercise bike in vertical position (A16);% A: p7 w# U S, e
17. Rowing (A17);: T* W) k K% Q3 p
18. Jumping (A18); 3 k* d! x8 G& N9 ?19. Playing basketball (A19). " F3 p: _; h! B# c4 G5 m7 H" G2 mYour team are asked to develop a reasonable mathematical model to solve ) y. H1 u9 w; ?+ r, j) K1 k+ qthe following problems.; ^9 S2 S2 c5 C' l
1. Please design a set of features and an effiffifficient algorithm in order to classify 3 R7 ?8 d, E) E: f7 f! x. wthe 19 types of human actions from the data of these body-worn sensors.8 C, S- N6 M( l' Y
2. Because of the high cost of the data, we need to make the model have3 n9 j- ~$ [$ {" `
a good generalization ability with a limited data set. We need to study / \* {0 j# R; x5 F" Q* A- g- n/ hand evaluate this problem specififically. Please design a feasible method to ' ?) j. I+ i6 A! oevaluate the generalization ability of your model." j3 z4 K* i a8 V+ e @
3. Please study and overcome the overfifitting problem so that your classififi- S# A) ~& u! ?: d6 h
cation algorithm can be widely used on the problem of people’s action s2 O7 B' l( Y2 E L$ Wclassifification. M- j, t# P& i" K \- X. KThe complete data can be downloaded through the following link: + k+ q6 `3 r: F7 o- o* R+ N Z' shttps://caiyun.139.com/m/i?0F5CJUOrpy8oq 2 x" O& U8 J" ^4 a+ p. n# q2Appendix: File structure9 |, e6 H F4 u6 ?; a7 B4 \2 w
• 19 activities (a)) w) m: M" u$ B) H! h5 \
• 8 subjects (p) ( J) _% l- O) n% C- [3 K• 60 segments (s) 0 s- R% K3 k+ q8 }( G• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left + M2 E! V6 b( |; \( Bleg (LL)4 O t7 `6 _0 j( k* A$ j) R0 U; N
• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z ' ~3 d7 v) ?" e2 X' D( p( T4 ~! v! Amagnetometers)4 T( a, @3 O% {! T) }+ V& Q
Folders a01, a02, ..., a19 contain data recorded from the 19 activities. ) _# A, s- E. m, d6 jFor each activity, the subfolders p1, p2, ..., p8 contain data from each of the ; j0 U9 } c0 o: M8 subjects., W& P: Y% ?3 k1 U) ]" V" Q
In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each 8 B* q: S' F" x) V6 }3 Y5 \segment. Q1 @1 Z _: _In each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25 : c0 }* H0 d0 E) k) vHz = 125 rows. & R! {8 \) a# }# _Each column contains the 125 samples of data acquired from one of the2 ~6 T% H/ n# y& g( m
sensors of one of the units over a period of 5 sec. 2 U: w, Q/ W. i; [4 m2 c; m8 m5 JEach row contains data acquired from all of the 45 sensor axes at a particular - P" t8 [& u3 `sampling instant separated by commas.9 c- e* X7 X% I, h% E- v+ ]4 E
Columns 1-45 correspond to:! {! C9 H4 F; H( \+ x K" [
• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,$ \2 n7 a* k# S" r
• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag,& |* b3 z! y4 ~1 v: n8 T
• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag, + f1 ?- R! Y5 f+ Q• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,2 O2 o6 Y! `( G8 g& I" {( [1 T/ j3 A) t
• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag. + n$ n! d k( a5 F+ aTherefore,# K) E% T/ k6 r7 i, p
• columns 1-9 correspond to the sensors in unit 1 (T),; X6 @9 R0 Y. G
• columns 10-18 correspond to the sensors in unit 2 (RA),; G' H- g3 T Z3 V# e
• columns 19-27 correspond to the sensors in unit 3 (LA), 6 d: L4 [* \2 u• columns 28-36 correspond to the sensors in unit 4 (RL), & t3 \' \* N% U0 `$ W P5 e* F• columns 37-45 correspond to the sensors in unit 5 (LL).- L, Y! B: J. K5 S: r
3References 8 @7 x7 ?! Z' i[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic * Z4 b( T9 }8 p$ p( ]$ vdaily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. 2 } B4 l4 C7 a42(5), 679-687, 2004 0 E2 [: l' _& B. m E[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of ! a0 \( ^4 h) U& _, Xlow-complexity fall detection algorithms for body attached accelerometers. d8 Y5 E$ G5 D$ f: ]5 B6 m
Gait Posture 28(2), 285-291, 2008 4 f3 R. [: U% \/ U8 ^[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag/ k# g/ L0 ~- d( I& J
nosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.& t7 ~. r% E+ |6 K8 t
B. 11(5), 553-562, 2007+ e& V: ^* k7 d+ V
[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con& h. I; E0 F5 w+ T& c7 H5 |5 l
trol of a physically simulated character. ACM T. Graphic. 27(5), 2008 & @3 U3 o0 [0 C$ J% M0 }: Y/ { + a" Q4 J( b$ c2022 % ~& w l3 ]+ v7 `8 s+ k/ kCertifificate Authority Cup International Mathematical Contest Modeling7 S; E' `& s; }5 G9 z4 y1 n
http://mcm.tzmcm.cn* n. u8 c6 u: r& F+ w7 a
Problem D (ICM) # M0 Z. `3 J! m* P0 O7 aWhether Wildlife Trade Should Be Banned for a Long) N" }3 ?* S8 X, p6 g# B/ Z
Time 9 b! j# O# ~' K8 RWild-animal markets are the suspected origin of the current outbreak and the , m' L, S$ w' k: Z9 G9 N2002 SARS outbreak, And eating wild meat is thought to have been a source1 d8 m8 c4 v4 c: A6 _6 A" o
of the Ebola virus in Africa. Chinas top law-making body has permanently9 ]0 N2 t) ?9 }5 Q, }
tightened rules on trading wildlife in the wake of the coronavirus outbreak, " G& O( e4 h! ?5 \which is thought to have originated in a wild-animal market in Wuhan. Some9 U4 a; k( t( H% M" }: z, `
scientists speculate that the emergency measure will be lifted once the outbreak( Z& G' Q( ^* \4 k. F
ends. `: I# E% g: e7 ZHow the trade in wildlife products should be regulated in the long term? & {/ u! L0 W& J9 q2 ASome researchers want a total ban on wildlife trade, without exceptions, whereas. g2 T" L/ j! B5 c k
others say sustainable trade of some animals is possible and benefificial for peo( z6 C0 J) T: O0 f- A* W: p' E/ P
ple who rely on it for their livelihoods. Banning wild meat consumption could $ s! W3 ~, }: \( j8 a8 U4 I4 U0 @cost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil # I$ Y0 t2 S O& x$ mlion people out of a job, according to estimates from the non-profifit Society of2 b" \' }0 X8 R7 l" v, ?$ _
Entrepreneurs and Ecology in Beijing.) @& ^ s) N' T: y9 w' i
A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology% N, X) A) ^& ]3 S
in China, chasing the origin of the deadly SARS virus, have fifinally found their - D; h$ g+ @# u Z6 ~9 ]smoking gun in 2017. In a remote cave in Yunnan province, virologists have, D o3 L( z7 m
identifified a single population of horseshoe bats that harbours virus strains with 4 }4 N5 O% E- x* ^: Aall the genetic building blocks of the one that jumped to humans in 2002, killing . X! ^1 \3 ^2 B9 |7 H) E0 n" oalmost 800 people around the world. The killer strain could easily have arisen$ W3 O2 v+ @4 a! U
from such a bat population, the researchers report in PLoS Pathogens on 30 / q8 n1 A# T1 ~5 xNovember, 2017. Another outstanding question is how a virus from bats in : b$ a, N* q0 x6 G) S7 L! RYunnan could travel to animals and humans around 1,000 kilometres away in ! n" t/ _" |6 |! z) rGuangdong, without causing any suspected cases in Yunnan itself. Wildlife ; T; w( S6 |4 c( b6 e6 utrade is the answer. Although wild animals are cooked at high temperature ! o9 U* d2 |$ }& H9 @when eating, some viruses are diffiffifficult to survive, humans may come into contact7 w1 _6 R6 q! f6 X& t# |
with animal secretions in the wildlife market. They warn that the ingredients 8 w7 `* W: a! u! j3 Yare in place for a similar disease to emerge again.1 ? F3 S$ u) J6 L
Wildlife trade has many negative effffects, with the most important ones being:6 g1 S8 s( q9 ` r0 X
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS 1 `, [9 A( _/ youtbreak in 2002.Credit: Matthew Maran/NPL ( G- H G0 k3 a0 w) Y( S: u0 Q7 g• Decline and extinction of populations ; z* _- Y* a# Z6 D' O• Introduction of invasive species 7 s, h1 V, P; z4 t• Spread of new diseases to humans9 H: B5 m u! s1 `4 g, f5 N
We use the CITES trade database as source for my data. This database 6 p* h# q0 g! w/ ?; Bcontains more than 20 million records of trade and is openly accessible. The5 D2 f' N/ Q9 c- S! V$ s0 j
appendix is the data on mammal trade from 1990 to 2021, and the complete $ Z/ \" T8 r8 Jdatabase can also be obtained through the following link: 8 |% k8 ?- ^4 _ q& F1 B* w, u$ lhttps://caiyun.139.com/m/i?0F5CKACoDDpEJ 8 ?! C6 M4 V3 g/ d9 B' W1 n/ ARequirements Your team are asked to build reasonable mathematical mod, X; Y8 P2 N$ ^( p+ V
els, analyze the data, and solve the following problems:( A1 g) R' d- o
1. Which wildlife groups and species are traded the most (in terms of live ' a% H3 T8 f+ q1 qanimals taken from the wild)?+ J1 y n C: D
2. What are the main purposes for trade of these animals?( M$ l/ q* W) c8 w1 R* x4 X- r9 U, R. Y& X
3. How has the trade changed over the past two decades (2003-2022)?# l6 d8 w* q5 [' T, w# p) l5 v
4. Whether the wildlife trade is related to the epidemic situation of major " Q _2 v. U; [4 l1 V7 ainfectious diseases? 6 ?6 c' i4 r- J5 ~/ l25. Do you agree with banning on wildlife trade for a long time? Whether it1 r" j( {3 d/ W/ G+ _
will have a great impact on the economy and society, and why?! A: R/ A" c# y# b
6. Write a letter to the relevant departments of the US government to explain # P# b* t/ k/ @your views and policy suggestions. 2 T1 O L+ @; i _9 y& J. P7 U# u; D
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