6 w* E$ {8 R* l: D0 U2022 4 K W2 c& W- C) o" _Certifificate Authority Cup International Mathematical Contest Modeling 9 Y$ [: Q# F/ shttp://mcm.tzmcm.cn ) \# a( b( e0 EProblem A (MCM) 1 D7 q' {' X0 q; ~! r9 t% [3 qHow Pterosaurs Fly0 T$ E& _: f% F' V- Y+ A5 { ^7 a
Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They 7 G5 K9 `% Z; v' Xexisted during most of the Mesozoic: from the Late Triassic to the end of7 D9 t' f3 b' [( n/ Q7 L; T
the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved6 z7 w" k, V" X" y' N3 T
powered flflight. Their wings were formed by a membrane of skin, muscle, and* Q6 j; W R: I
other tissues stretching from the ankles to a dramatically lengthened fourth # \; Y( Z/ q9 M9 x- ?* `fifinger[1]. # Y9 U, ?9 T, A" CThere were two major types of pterosaurs. Basal pterosaurs were smaller 8 `" A( V3 Q6 O* ]animals with fully toothed jaws and long tails usually. Their wide wing mem ! o: A8 w' e* x' \7 b4 Bbranes probably included and connected the hind legs. On the ground, they3 Z, u' a" |/ F9 y( w2 c& X
would have had an awkward sprawling posture, but their joint anatomy and' Z; i! q* Z6 q X+ [; Y
strong claws would have made them effffective climbers, and they may have lived3 m% C! r% n- N+ O8 s4 r, s+ s
in trees. Basal pterosaurs were insectivores or predators of small vertebrates. # c' n8 A( B7 ]* l# rLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. / |4 {5 V) f" M6 nPterodactyloids had narrower wings with free hind limbs, highly reduced tails,& e, i @; b' j- v' Y3 t7 ^+ a
and long necks with large heads. On the ground, pterodactyloids walked well on 5 M; }. d% [2 X2 G( Z/ sall four limbs with an upright posture, standing plantigrade on the hind feet and : x# n1 P9 b% x! \( v, }. `# Y- Efolding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil 1 w6 t$ f, D0 K# r( [% ftrackways show at least some species were able to run and wade or swim[2]. X+ I1 _) p# S$ e9 a4 q) VPterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which * b# a7 T- o+ vcovered their bodies and parts of their wings[3]. In life, pterosaurs would have# t; l2 w+ N# r9 A, R" ^
had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug " {$ |% h$ g0 t1 W6 z4 L) t3 ]gestions were that pterosaurs were largely cold-blooded gliding animals, de 1 D% l4 o( d/ H9 A- {6 o( Nriving warmth from the environment like modern lizards, rather than burning, R# G! X ^$ M" V) g4 x
calories. However, later studies have shown that they may be warm-blooded 7 U4 ^) w% K: H. `# p(endothermic), active animals. The respiratory system had effiffifficient unidirec 6 x: i* s. W. a' Utional “flflow-through” breathing using air sacs, which hollowed out their bones% m/ L& C( k; G# B
to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from 5 y2 I/ M$ N e# { ythe very small anurognathids to the largest known flflying creatures, including , R4 [0 }/ _: W0 [1 _' GQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least6 d" n4 D: m. v
nine metres. The combination of endothermy, a good oxygen supply and strong. l+ t5 x/ D4 [8 f2 f8 N
1muscles made pterosaurs powerful and capable flflyers.1 B2 X9 Y4 D Q; c" h
The mechanics of pterosaur flflight are not completely understood or modeled0 e2 ^& \$ U4 `. T1 l% W( d8 H. @) r
at this time. Katsufumi Sato did calculations using modern birds and concluded 9 i7 t* j4 S# Z) O# jthat it was impossible for a pterosaur to stay aloft[6]. In the book Posture, $ Y0 y) y% q- O# q9 Y* E6 Z3 K8 _4 NLocomotion, and Paleoecology of Pterosaurs it is theorized that they were able " B# e3 ^9 P- O7 ` Vto flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7]. ! T$ [0 H" E$ G: X5 V$ O b( h0 c% AHowever, both Sato and the authors of Posture, Locomotion, and Paleoecology1 A# D1 |; m$ {' E [5 ~
of Pterosaurs based their research on the now-outdated theories of pterosaurs$ y: z4 E' X# U* F( w; h! {9 Q
being seabird-like, and the size limit does not apply to terrestrial pterosaurs, L" O2 @3 E+ A) {0 T
such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that + ^% c& S: ?/ v! j0 q/ latmospheric difffferences between the present and the Mesozoic were not needed1 F9 _0 L- S- A0 A0 c
for the giant size of pterosaurs[8]. - v8 R9 W( ~( MAnother issue that has been diffiffifficult to understand is how they took offff.+ k/ ~% M2 l; r8 ?, O/ F5 s% v0 i$ L
If pterosaurs were cold-blooded animals, it was unclear how the larger ones) {# D! r+ A+ }$ M! W
of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage; z( k+ o3 E# G& c. m' H
a bird-like takeoffff strategy, using only the hind limbs to generate thrust for 1 i; s# e1 R- \' Q0 H( ?, Xgetting airborne. Later research shows them instead as being warm-blooded * t" _$ |) D2 g! Q+ g9 |: W) N' aand having powerful flflight muscles, and using the flflight muscles for walking as 3 h9 O6 |1 `5 f" Gquadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of2 c0 b0 u: Y% m! H. U: ~
Johns Hopkins University suggested that pterosaurs used a vaulting mechanism ; c6 q2 P$ _0 M* c! `; e5 M% K1 qto obtain flflight[10]. The tremendous power of their winged forelimbs would / f1 C" s9 c' }4 `. Yenable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds s) }1 Z- P" e2 `' z8 z3 D' bof up to 120 km/h and travel thousands of kilometres[10]. ' \6 L) z- w4 l! n/ EYour team are asked to develop a reasonable mathematical model of the) [! r3 e: S! h. A* _% d
flflight process of at least one large pterosaur based on fossil measurements and 6 \! n8 i" L8 x2 B! Rto answer the following questions.6 Y4 F$ N- v |2 P0 E) G
1. For your selected pterosaur species, estimate its average speed during nor ! e3 x$ c; H M% J! b2 W! o0 ~mal flflight.1 \. K% C+ M; q
2. For your selected pterosaur species, estimate its wing-flflap frequency during& G- u5 u4 Q9 h& D# S
normal flflight.# n. x. ^/ X& g/ Z: L3 ^5 A8 h
3. Study how large pterosaurs take offff; is it possible for them to take offff like- L" w4 R7 @2 p3 X/ N! P; t9 Z- w" w/ B
birds on flflat ground or on water? Explain the reasons quantitatively.' D. A- |, z. `0 f2 w
References % F4 V2 o5 ~6 }5 f' g[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight + L5 x. U' z7 k/ O# t. G0 ~Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111. ! {8 W! p" r: ^) e5 e/ P9 }2[2] Mark Witton. Terrestrial Locomotion.( T) l r7 h- Z/ |% w$ J
https://pterosaur.net/terrestrial locomotion.php" ` j9 B4 R6 D( A1 f
[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs 8 j' N$ M9 J3 X7 }& x( RWere Covered in Fluffffy Feathers. https://www.livescience.com/64324- ( u; e' W! B; k: _; Npterosaurs-had-feathers.html" h9 L% s$ s3 |# w" G7 Q
[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a 0 I( D, J7 b5 r1 S5 ]rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)4 F7 Q0 r- U) d1 M* u
from China. Proceedings of the National Academy of Sciences. 105 (6): " o+ m7 |% G) s% r7 j1983-87.' Y6 S8 e8 v, V! G% L2 u8 Y! U: ? U
[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust 5 M! T; U, x) P3 z9 J! `4 xskull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4):5 i: J$ c% i( h# e; h5 @
180-84. " c# `8 A9 C+ Z b/ l[6] Devin Powell. Were pterosaurs too big to flfly?, G* [9 e; w1 q: t0 @. y* d9 a* m9 u' ]
https://www.newscientist.com/article/mg20026763-800-were-pterosaurs + a+ p* K: F5 r% `/ J; Rtoo-big-to-flfly/7 ~0 C0 I$ j- P& n' z) V% J) W
[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology1 E9 R* h; x2 {, W
of pterosaurs. Boulder, Colo: Geological Society of America. p. 60.# x! e5 ]2 [) Q; F& p7 z
[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable 3 {$ Q: }& c7 r8 c) b9 M$ Cair sacs in their wings.1 h( }3 q( B) h$ d2 l& Z8 `, w+ J
https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur1 w, H0 L- U8 p+ ~
breathing-air-sacs 7 A) b% Q$ y3 B[9] Mark Witton. Why pterosaurs weren’t so scary after all.0 k7 y- ?- @! @' r' d+ ^
https://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils( X+ N8 s7 P; |$ q( B5 ]
research-mark-witton 3 |2 K7 w' P3 }+ \) N# p! N9 k3 o[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?; u! m! {7 v8 u8 r9 U
https://www.newscientist.com/article/dn19724-did-giant-pterosaurs 5 l" h" @0 p) k! ^vault-aloft-like-vampire-bats/ 3 m3 Z" q! G9 g7 {& K ' s$ V3 x+ _7 g, ~' { j2022 t# b6 p' V7 {3 J! i$ q3 a9 p
Certifificate Authority Cup International Mathematical Contest Modeling 7 S* G/ U# J4 h; L! k9 x9 @http://mcm.tzmcm.cn ! S- k& t7 G9 u5 ^Problem B (MCM) : g! q" O/ X. S' [The Genetic Process of Sequences! v+ l7 O; B; r3 w0 W2 v0 Z
Sequence homology is the biological homology between DNA, RNA, or protein . @3 L+ J$ v0 `8 bsequences, defifined in terms of shared ancestry in the evolutionary history of! V' }3 y) Y- d: g, U0 F
life[1]. Homology among DNA, RNA, or proteins is typically inferred from their3 [) ~6 N- N) i0 w+ y. T+ C
nucleotide or amino acid sequence similarity. Signifificant similarity is strong - |; h: V( {: B6 s% Z: U& b. e9 fevidence that two sequences are related by evolutionary changes from a common; u/ A" ~6 A, ]$ m
ancestral sequence[2].3 F/ M0 t) D+ X* B" p, [
Consider the genetic process of a RNA sequence, in which mutations in nu" F! }6 J- Y9 W0 r
cleotide bases occur by chance. For simplicity, we assume the sequence mutation 7 z `7 b v: R# U# {4 ?arise due to the presence of change (transition or transversion), insertion and0 A. f( F1 K, @
deletion of a single base. So we can measure the distance of two sequences by 5 h0 a+ W7 P! R8 Z: i6 S0 t& @the amount of mutation points. Multiple base sequences that are close together {2 q; K1 R2 @ wcan form a family, and they are considered homologous. & }( u! W% |* g2 J8 n3 `6 pYour team are asked to develop a reasonable mathematical model to com* @ b {3 T! Y! k k! B D
plete the following problems.4 B C+ E) V1 [2 g
1. Please design an algorithm that quickly measures the distance between 6 j: Y9 i* [& Htwo suffiffifficiently long(> 103 bases) base sequences.. i% J5 s& O' C9 G
2. Please evaluate the complexity and accuracy of the algorithm reliably, and, R; E& P) R, i8 k
design suitable examples to illustrate it.* Z$ k7 w4 D( o1 J5 ]
3. If multiple base sequences in a family have evolved from a common an0 r9 u8 ~+ ?. ]
cestral sequence, design an effiffifficient algorithm to determine the ancestral * k B3 D6 q H1 k# [# B3 \sequence, and map the genealogical tree.' r3 V t4 o8 P! @- ]$ w
References 4 Y9 E, ]$ W8 q0 A[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re8 h) p/ M( c$ r2 Q& b, r9 p
view of Genetics. 39: 30938, 2005. : M0 f: g7 {; r0 R: U/ S[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, ; d4 v2 ` s) H" r9 |1 Tet al. “Homology” in proteins and nucleic acids: a terminology muddle and 4 L0 m: W; R1 ^! L/ F! e5 J1 Na way out of it. Cell. 50 (5): 667, 1987.% }! g1 } ~5 R! {1 ~0 K2 h) g
- j8 v5 o1 q8 Q& b1 O20223 U2 W2 c" }: s7 v C$ D$ j% M: _
Certifificate Authority Cup International Mathematical Contest Modeling# g1 _( g: X6 u/ C" k! |
http://mcm.tzmcm.cn8 @% N+ `* z* | m
Problem C (ICM)' v! O Z: u8 l- h& s ~1 h
Classify Human Activities ; J7 X, q/ Z4 rOne important aspect of human behavior understanding is the recognition and ) e$ F1 X5 d2 B; x: \+ _: Jmonitoring of daily activities. A wearable activity recognition system can im % W6 s* U2 O5 }2 C: s$ Lprove the quality of life in many critical areas, such as ambulatory monitor) C V! A. B& ?
ing, home-based rehabilitation, and fall detection. Inertial sensor based activ1 t8 S, O2 w7 x9 i& _2 P
ity recognition systems are used in monitoring and observation of the elderly & u" l; Q2 u' [8 J1 T6 n* Uremotely by personal alarm systems[1], detection and classifification of falls[2],6 X) V. t6 m1 L: @2 T9 F
medical diagnosis and treatment[3], monitoring children remotely at home or in 9 D$ S1 L2 y7 H3 ~1 r; @ e2 Lschool, rehabilitation and physical therapy , biomechanics research, ergonomics, ( ?* `% W# x2 [/ v. `7 i2 Qsports science, ballet and dance, animation, fifilm making, TV, live entertain8 L" B) m& [9 ~ r& u/ ^
ment, virtual reality, and computer games[4]. We try to use miniature inertial 3 h8 f, H) O& ~3 x) fsensors and magnetometers positioned on difffferent parts of the body to classify- y' _- L* y6 {8 _$ U0 t% u
human activities, the following data were obtained. ; g8 {9 c/ l! y/ q& [Each of the 19 activities is performed by eight subjects (4 female, 4 male, ' g. r( a& E9 d4 R. P: cbetween the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes1 {* }& u/ A9 L# M0 F+ {- T
for each activity of each subject. The subjects are asked to perform the activ ( a5 M2 R8 ?( ^* U, |6 H$ mities in their own style and were not restricted on how the activities should be 5 f- f' m9 x% r8 j( U4 F! ]2 xperformed. For this reason, there are inter-subject variations in the speeds and9 R& P9 E; {4 `& }( k1 }( p9 W
amplitudes of some activities. H' w6 p% D2 l$ QSensor units are calibrated to acquire data at 25 Hz sampling frequency. $ e7 I$ R& ]; r0 m- Q5 u! w# HThe 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal : i+ K' f+ N ?5 Q4 Msegments are obtained for each activity.( k c9 {1 ?% ?! F' h, g
The 19 activities are:; s" q; n2 D3 H G% y
1. Sitting (A1); # f; }: f6 D/ D3 a' T2. Standing (A2);/ W1 J; d+ d4 p6 L
3. Lying on back (A3); * U) g. b+ O" @0 P+ z4. Lying on right side (A4);, K0 F) y# v$ o3 ` x6 Q
5. Ascending stairs (A5);9 R# D1 @) w$ |! f- X
16. Descending stairs (A6);" Q* y0 O" [: [3 k* K( j! g
7. Standing in an elevator still (A7); * Q/ e3 L" o7 q: E. _ i8. Moving around in an elevator (A8); * ?0 J. r" b7 h: L9. Walking in a parking lot (A9); 9 o W, Z" K* L( \. g10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg! p& g0 S3 x- ^
inclined positions (A10);! B k0 a/ K% D. L3 I; n/ @3 T
11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions . x% _* c2 }/ n! I(A11); + p O3 C P2 c6 m% O12. Running on a treadmill with a speed of 8 km/h (A12);9 d1 W7 L- Q+ }; h; i8 r) x# H
13. Exercising on a stepper (A13); / E5 c O9 y" j$ r5 G2 B8 M( s14. Exercising on a cross trainer (A14);; L6 [4 C5 h, `5 n/ h6 G: a
15. Cycling on an exercise bike in horizontal position (A15);: L: W) L4 t( c/ v4 J
16. Cycling on an exercise bike in vertical position (A16); " b% T% G2 M' E3 F9 d+ G17. Rowing (A17);3 i1 J r3 Q. M
18. Jumping (A18); # U- b! H1 ]+ b% s. M. |3 W; u19. Playing basketball (A19). + E+ g3 a) o1 u# R( N/ ^& K& |6 {Your team are asked to develop a reasonable mathematical model to solve. w& D+ t% M5 I5 E& c- l0 j3 x* A
the following problems.: @& k: I1 o3 I
1. Please design a set of features and an effiffifficient algorithm in order to classify . z- @' }2 {% Wthe 19 types of human actions from the data of these body-worn sensors. q8 E4 K. f: [$ q5 a2. Because of the high cost of the data, we need to make the model have/ c$ w* l G' a( F5 H% Q
a good generalization ability with a limited data set. We need to study) D7 ]9 J4 s/ A+ q
and evaluate this problem specififically. Please design a feasible method to . X, I& v8 X$ B! q* Jevaluate the generalization ability of your model. * W* ~- A' F2 {3. Please study and overcome the overfifitting problem so that your classififi-, k5 E! B4 Q. n+ @% i
cation algorithm can be widely used on the problem of people’s action 3 n! S1 n4 w/ ^7 Q2 iclassifification.5 {! t% c L9 a
The complete data can be downloaded through the following link: # ?7 L# n4 x5 ~4 t* I& ahttps://caiyun.139.com/m/i?0F5CJUOrpy8oq % X. @8 h% Z, A( c% m0 q8 u2Appendix: File structure ; L2 Z% |5 G9 P, y1 l- e• 19 activities (a) i. z0 s1 B$ j+ r5 b- v' V
• 8 subjects (p) + N' N* |% `3 }; z" W$ T6 t4 J& ~• 60 segments (s)# V6 [& [( s) y/ p( [
• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left 5 O5 o, ?5 v% x# D" c- E% Aleg (LL) 4 n. \3 o" Z$ ?2 i7 j- H0 @7 [• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z- B* ]6 Q1 s" R6 ~( |
magnetometers) K f' k4 D& W. |. `
Folders a01, a02, ..., a19 contain data recorded from the 19 activities.) ~% {$ f: @) w/ h
For each activity, the subfolders p1, p2, ..., p8 contain data from each of the ( A& T& ` e% @0 B& O+ G! P8 subjects.- V4 N7 k% k( v( ^4 M- F' m$ W; B
In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each * i& i2 ~5 H" H! [! `) Lsegment. 9 a, Q; Y: c7 c2 O9 z6 kIn each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25% }! r* h6 q% I6 Z5 F# E Q& q8 [
Hz = 125 rows. 4 F* r' ^3 |6 c6 o2 l8 y6 I" ZEach column contains the 125 samples of data acquired from one of the4 l2 \1 Q! ` O* E3 z
sensors of one of the units over a period of 5 sec. ( X9 c/ N# o* d# h( h( t# A0 t4 O& [Each row contains data acquired from all of the 45 sensor axes at a particular, j" b% q5 P" a8 f
sampling instant separated by commas. : \! Z4 r7 Z1 }( S! [* _Columns 1-45 correspond to: 9 i1 P) N1 o% O: x \• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,. ]' u' _8 k4 ~% v) `% {9 |" U
• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag, 2 F, X- b6 c+ _2 e- `0 S• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag, % o; u6 v$ b5 _/ D0 s6 p' q• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag, 8 L* u( |* z X s% |, @• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag. 1 e8 A6 z5 @; Q& s! o4 Q2 \. MTherefore,* S, S6 d7 C/ u) B7 z$ f. S
• columns 1-9 correspond to the sensors in unit 1 (T),/ ~% W9 C+ d a
• columns 10-18 correspond to the sensors in unit 2 (RA),6 v _6 H- H9 C( m; \4 Y
• columns 19-27 correspond to the sensors in unit 3 (LA), + K' ~3 E8 o7 e3 F• columns 28-36 correspond to the sensors in unit 4 (RL),! o" P- \6 R9 k) a1 n" _) [
• columns 37-45 correspond to the sensors in unit 5 (LL)./ T) m' ~' _. Y9 ~6 v( R8 t
3References 9 g# ?+ Q5 }8 o, o1 v, e1 A[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic9 A% H3 L. v7 Q) h8 o! R3 c
daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput.! U9 S& L! w5 T. J
42(5), 679-687, 2004 8 r- z- L- _% X! |; }9 _[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of ' d/ z" e- K* c. j# \) ~low-complexity fall detection algorithms for body attached accelerometers./ K9 G0 E8 @( I% z
Gait Posture 28(2), 285-291, 2008 ' y. r0 u4 y E* {4 h1 L X/ C1 _[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag " e* g* ?/ C/ L4 Hnosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.) c+ Y& ^9 b3 a! b2 f
B. 11(5), 553-562, 2007& W4 ?! F: F0 U8 [+ W, T' ^9 S' j
[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con9 e6 @0 e4 T; C! ~" R1 }
trol of a physically simulated character. ACM T. Graphic. 27(5), 20089 P2 E: X. s3 Y/ ]
% r" x+ H. r# e) c8 J20227 A0 Z; ~! D7 Z0 o" l# g. _4 n# V( B
Certifificate Authority Cup International Mathematical Contest Modeling " r% P5 _ Y" yhttp://mcm.tzmcm.cn + |3 F+ m- h8 P7 `1 qProblem D (ICM) ) R- ?7 w8 d$ V# L! mWhether Wildlife Trade Should Be Banned for a Long( p3 @: r' J) K0 W3 _' j% S
Time # q2 a* b, e- u( |( C. \Wild-animal markets are the suspected origin of the current outbreak and the! j4 b4 H+ e7 T6 R
2002 SARS outbreak, And eating wild meat is thought to have been a source . X( m" o6 b2 n! Lof the Ebola virus in Africa. Chinas top law-making body has permanently ; {3 z. m3 E' M! _6 ztightened rules on trading wildlife in the wake of the coronavirus outbreak,0 N+ r c% \3 W! i" P o+ g
which is thought to have originated in a wild-animal market in Wuhan. Some: J- w6 O$ G/ e; @4 K
scientists speculate that the emergency measure will be lifted once the outbreak, c3 J5 i4 E9 H" y! M
ends. & O5 i9 g% M1 n; B5 `+ O, \, uHow the trade in wildlife products should be regulated in the long term? & b+ Q4 e I) b* I4 k7 v% | }Some researchers want a total ban on wildlife trade, without exceptions, whereas& X0 P/ o( F4 v$ k/ W! O
others say sustainable trade of some animals is possible and benefificial for peo & ]1 |; E1 K' Aple who rely on it for their livelihoods. Banning wild meat consumption could ) |2 i" u1 c8 i* l- y1 |; Z/ ncost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil 9 a1 J2 o- a4 r% Q7 V* hlion people out of a job, according to estimates from the non-profifit Society of ( B2 t7 C& i' r" A" q OEntrepreneurs and Ecology in Beijing.+ I- c0 U5 K: v8 I/ e2 z
A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology$ O- R0 g& e t( F9 ?' v6 R
in China, chasing the origin of the deadly SARS virus, have fifinally found their 6 N* y2 y- |- @5 X4 W: jsmoking gun in 2017. In a remote cave in Yunnan province, virologists have & O& [1 h! M7 fidentifified a single population of horseshoe bats that harbours virus strains with: J0 _4 p) \' z, H' T/ G9 c9 I. n
all the genetic building blocks of the one that jumped to humans in 2002, killing: K9 Y9 @ P" v+ f
almost 800 people around the world. The killer strain could easily have arisen4 t" a% k: o4 A
from such a bat population, the researchers report in PLoS Pathogens on 30 + u/ U* D9 g1 v% F# d& y" n& pNovember, 2017. Another outstanding question is how a virus from bats in 0 \5 o9 N+ p8 B" v* EYunnan could travel to animals and humans around 1,000 kilometres away in$ C/ z. `3 f- i. d! ]1 F
Guangdong, without causing any suspected cases in Yunnan itself. Wildlife- ?- r, N; X$ H9 d g) N
trade is the answer. Although wild animals are cooked at high temperature 3 v5 V6 ]6 }% x5 N" D& [when eating, some viruses are diffiffifficult to survive, humans may come into contact 5 a. \* [# h' fwith animal secretions in the wildlife market. They warn that the ingredients0 p! a9 f, J- |) y1 |
are in place for a similar disease to emerge again.) ]% p0 L: X M) Y
Wildlife trade has many negative effffects, with the most important ones being:: D7 v7 B3 c# _% N7 o
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS 2 i5 P4 T5 Z9 d8 Boutbreak in 2002.Credit: Matthew Maran/NPL 0 k0 M6 _0 q. [: J0 s% @: P- i• Decline and extinction of populations p# L3 i" V: m6 e+ \2 d, e; f
• Introduction of invasive species" b% b! W! U3 F X9 |+ [% i! a$ Q
• Spread of new diseases to humans ' e2 k* n: w! X0 `We use the CITES trade database as source for my data. This database: b4 A1 x! c' }# h
contains more than 20 million records of trade and is openly accessible. The8 h/ Y c) u- g6 m$ ^$ M+ N& A6 ~' I
appendix is the data on mammal trade from 1990 to 2021, and the complete6 T2 X" ~/ m4 | g
database can also be obtained through the following link: % S0 u( V: w0 g8 s: j$ v" [. Mhttps://caiyun.139.com/m/i?0F5CKACoDDpEJ# T0 F# [+ k- z! i7 w
Requirements Your team are asked to build reasonable mathematical mod# b0 ~7 d) t6 t. Q" z# [
els, analyze the data, and solve the following problems: 9 ^3 R" S3 C3 Y& \2 h1. Which wildlife groups and species are traded the most (in terms of live + M% w! e2 |' g. p q. ^3 I/ Y$ V5 aanimals taken from the wild)? ) d' z% G' h# H8 M% d$ q9 C9 V2. What are the main purposes for trade of these animals? ! F$ K+ w3 N" I# S3. How has the trade changed over the past two decades (2003-2022)? % `7 g/ ~, a# X l. p4. Whether the wildlife trade is related to the epidemic situation of major5 t9 H9 F2 S2 B
infectious diseases?; F5 [: x2 O2 v7 i% D* h! S0 K; a
25. Do you agree with banning on wildlife trade for a long time? Whether it / y; I1 @( Z% N6 l# D' swill have a great impact on the economy and society, and why?" ~1 c: A0 s3 p3 D& R6 j
6. Write a letter to the relevant departments of the US government to explain 3 } l4 G q7 P3 vyour views and policy suggestions.6 f2 {+ [" v" F2 J/ @
z! |; P' N5 T3 D$ u 9 G8 B3 D8 h0 {& U! d$ { A. O% p$ m S: y* y' g+ q3 u3 a/ t. {) v1 U. @$ Q$ O6 l
