# |5 T: E& u3 B. V2022 6 B) T2 t) N; a6 P; {, @. X# OCertifificate Authority Cup International Mathematical Contest Modeling " E. `% F9 v( o7 D. lhttp://mcm.tzmcm.cn 2 h) J3 n6 s! B* T9 b4 fProblem A (MCM) 7 a' X* {; ~) K+ ^7 q( x- KHow Pterosaurs Fly + G, o+ \" {; ]Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They2 o! y$ J; Z6 K, J- a; O* Z; i) W
existed during most of the Mesozoic: from the Late Triassic to the end of' J; u3 a% q# x$ ]' p
the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved ! M4 B$ M: k4 L- Npowered flflight. Their wings were formed by a membrane of skin, muscle, and ; x' g* E. ]' l% y- e/ aother tissues stretching from the ankles to a dramatically lengthened fourth6 c" [/ L7 I4 `, e' ]* u
fifinger[1].- G9 H- a8 a8 {) p) |. R
There were two major types of pterosaurs. Basal pterosaurs were smaller3 [/ @# `( M0 d) H8 P
animals with fully toothed jaws and long tails usually. Their wide wing mem2 a- j# q: ]9 z
branes probably included and connected the hind legs. On the ground, they& t S: X% J1 T9 X, t( M% e* G
would have had an awkward sprawling posture, but their joint anatomy and1 w" r# T; E- Z) N9 s
strong claws would have made them effffective climbers, and they may have lived6 {( b( @& O* `, o8 ^
in trees. Basal pterosaurs were insectivores or predators of small vertebrates.3 [) ?6 E1 |: G5 ?2 ?! a$ _
Later pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. # B1 g/ w: H ?& p1 ?& u5 x# }* T* PPterodactyloids had narrower wings with free hind limbs, highly reduced tails,6 f& Y1 l' C: D" x9 j0 ~; [( Z( |
and long necks with large heads. On the ground, pterodactyloids walked well on2 K5 ~) L% t L' q1 k, E* k: e
all four limbs with an upright posture, standing plantigrade on the hind feet and5 t2 u) e# a; p$ z$ E3 {0 S
folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil) Q5 G* D2 S* S2 f
trackways show at least some species were able to run and wade or swim[2].9 [" S; i; Y1 X2 p' X. Q0 V: v
Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which' i! o6 E3 j6 k
covered their bodies and parts of their wings[3]. In life, pterosaurs would have" c' |3 n6 O( }, Q
had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug7 X G- t; }! f9 S- C8 ^8 q! I
gestions were that pterosaurs were largely cold-blooded gliding animals, de% h6 S& f$ V3 g. F3 T% B: s+ I
riving warmth from the environment like modern lizards, rather than burning . [. m& m- i E" Bcalories. However, later studies have shown that they may be warm-blooded, ^# S6 n$ X+ n/ r4 A
(endothermic), active animals. The respiratory system had effiffifficient unidirec" _: D) b( F4 B" G
tional “flflow-through” breathing using air sacs, which hollowed out their bones: @! z" z; G- A3 u: \! |' e N
to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from + [4 R K, O: H2 tthe very small anurognathids to the largest known flflying creatures, including ( P, K2 s" z+ a/ EQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least& w0 p1 |/ G- {' i- ] X" f) y
nine metres. The combination of endothermy, a good oxygen supply and strong ) H. Y. U4 u$ S0 k' P0 I7 \: c1muscles made pterosaurs powerful and capable flflyers.0 b; A6 e# X4 E, Z& O a$ F
The mechanics of pterosaur flflight are not completely understood or modeled $ Z: ]% G: C1 t% M) H" vat this time. Katsufumi Sato did calculations using modern birds and concluded0 D; P! H5 B* k# s x2 h
that it was impossible for a pterosaur to stay aloft[6]. In the book Posture,; g: y. J2 h" i" R. L
Locomotion, and Paleoecology of Pterosaurs it is theorized that they were able8 t, D% q0 D2 E# j5 l" N- T
to flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].% p5 [- [- Z. h7 W& Y# \& E
However, both Sato and the authors of Posture, Locomotion, and Paleoecology , g/ f; `" N( |) [. jof Pterosaurs based their research on the now-outdated theories of pterosaurs. h G& d4 |4 {+ {3 a" M
being seabird-like, and the size limit does not apply to terrestrial pterosaurs,& x# z4 g# H0 i% k! e; L
such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that 2 }# |9 l* O; e* b) Aatmospheric difffferences between the present and the Mesozoic were not needed : ?4 e) ]/ z# H: G4 sfor the giant size of pterosaurs[8].1 t, v9 w9 z# {' S! [, ?) ~0 `
Another issue that has been diffiffifficult to understand is how they took offff. 7 B3 E! O, O4 l% f$ \If pterosaurs were cold-blooded animals, it was unclear how the larger ones 9 _6 O7 S" ~3 q' f0 B2 Wof enormous size, with an ineffiffifficient cold-blooded metabolism, could manage) g A+ p6 R3 ~ d0 V
a bird-like takeoffff strategy, using only the hind limbs to generate thrust for - ^( F F1 k/ A( n4 W6 ygetting airborne. Later research shows them instead as being warm-blooded) Q! _7 k7 N @6 b* \3 [& @
and having powerful flflight muscles, and using the flflight muscles for walking as/ n; T& D3 u8 D' i. U
quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of X% [$ [1 f1 W' v# y
Johns Hopkins University suggested that pterosaurs used a vaulting mechanism) E" S* ~- G3 l& P- k3 g
to obtain flflight[10]. The tremendous power of their winged forelimbs would* ]. G. d8 p" U$ r
enable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds : O4 e7 ?5 E( w' z; E! t/ Tof up to 120 km/h and travel thousands of kilometres[10]. 8 u* F6 Z5 B5 l" F6 ]9 c wYour team are asked to develop a reasonable mathematical model of the1 O' X9 `9 {9 J. d4 @4 h$ u5 K, h
flflight process of at least one large pterosaur based on fossil measurements and+ D. z+ k( T8 J- k
to answer the following questions.! d. s+ W6 q/ O8 L
1. For your selected pterosaur species, estimate its average speed during nor! r" v' J9 F) J9 l
mal flflight.1 s) x% D9 L8 Z( c8 n; q
2. For your selected pterosaur species, estimate its wing-flflap frequency during ; e, {/ R9 g, W6 ~" \normal flflight.1 L$ A8 ^* z: O3 @; ~: N
3. Study how large pterosaurs take offff; is it possible for them to take offff like + ^: U" [1 l# Ybirds on flflat ground or on water? Explain the reasons quantitatively.; M+ ]' r5 `+ c( M
References2 v) `9 @( ]4 X
[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight8 P0 u9 j" e' c' |" k+ c$ T7 P1 P- x
Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111.# P' q/ J8 E( T* D
2[2] Mark Witton. Terrestrial Locomotion. $ {- i/ _4 l% v% Y: Bhttps://pterosaur.net/terrestrial locomotion.php# y5 y/ |6 d7 t: Q9 H$ C; v2 g
[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs - N; R& z0 S, U6 i" PWere Covered in Fluffffy Feathers. https://www.livescience.com/64324-: u. G$ G, y3 }+ e9 a% K i
pterosaurs-had-feathers.html% ?( l3 X& `$ p- m2 E: Z, M( p7 n
[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a) G3 V& M* o" z' e( ^: O, T Q
rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)- r7 Z" |, w' s/ a, l% ^
from China. Proceedings of the National Academy of Sciences. 105 (6):' s3 c! @* A) |" U1 l3 F( S
1983-87. ; B7 V/ n! v* x. t[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust % U2 U, j; G0 L8 B8 pskull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4): / ^5 E1 }- @! p# Y2 x$ U. {180-84.' ]: }. a+ j6 r0 t# R# s
[6] Devin Powell. Were pterosaurs too big to flfly? / O" V3 e8 R! b' S; rhttps://www.newscientist.com/article/mg20026763-800-were-pterosaurs $ j7 ]2 h( u4 B! Y& K5 etoo-big-to-flfly/ # T+ C4 R9 L2 |% R$ K! E' I# c[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology . k8 n, @( B# G% `of pterosaurs. Boulder, Colo: Geological Society of America. p. 60.8 \" w# n- ?8 k7 {3 W
[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable . \. M6 j- q2 q' N0 n) qair sacs in their wings.- `6 k s( o, j' I) }: z) V' }
https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur: a3 F. U2 i0 _; m' Y! h, B% M8 W, i" a
breathing-air-sacs + Y0 d: m: h3 K- f3 l[9] Mark Witton. Why pterosaurs weren’t so scary after all.) x& x0 \% b: Y4 E- w6 U' Z
https://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils e- N) L0 a' v j2 ]2 i
research-mark-witton" j- K" }6 e# L2 u) j% g2 j
[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats? 4 ?/ [+ l9 Z& T' D- Yhttps://www.newscientist.com/article/dn19724-did-giant-pterosaurs # _* A6 h/ H5 O$ Z3 |2 Bvault-aloft-like-vampire-bats/ : P) B5 h6 [6 d; }# |3 [6 H4 R $ \+ H0 x" b8 }1 p2 R2022 / ]1 [: \) W) `9 e- A! pCertifificate Authority Cup International Mathematical Contest Modeling 0 y2 B5 f$ ]. z ^: Hhttp://mcm.tzmcm.cn. w$ a% A. i1 F4 |/ h5 P
Problem B (MCM) k& g0 {, P$ J( L6 p' g4 {/ G1 G* r
The Genetic Process of Sequences ; `$ t3 t. _9 T' ESequence homology is the biological homology between DNA, RNA, or protein9 q; @ y0 l* W+ T) f3 D2 u6 p
sequences, defifined in terms of shared ancestry in the evolutionary history of( t6 q5 s# L! k* O
life[1]. Homology among DNA, RNA, or proteins is typically inferred from their% o! Q7 Q6 J( ?
nucleotide or amino acid sequence similarity. Signifificant similarity is strong $ p- ]1 P, F! }& A. R6 wevidence that two sequences are related by evolutionary changes from a common / A' g$ y+ U0 V3 E1 e T* T% xancestral sequence[2].4 R' x; y, C- Z# |4 P0 w. V
Consider the genetic process of a RNA sequence, in which mutations in nu " E& f7 q* L2 V% N$ k! m, ucleotide bases occur by chance. For simplicity, we assume the sequence mutation + ^% q1 i3 a0 N1 d$ @arise due to the presence of change (transition or transversion), insertion and. T. d! f* ? E9 k
deletion of a single base. So we can measure the distance of two sequences by " a. _& s: d2 R6 x8 Mthe amount of mutation points. Multiple base sequences that are close together' V7 u; J/ s- x+ c0 @
can form a family, and they are considered homologous.0 P+ y: Z: I8 G& k$ Z. R
Your team are asked to develop a reasonable mathematical model to com9 m0 f! \% i4 p% T1 C
plete the following problems.+ H4 h, I+ \* w4 N: N1 O2 b
1. Please design an algorithm that quickly measures the distance between( t6 |* D$ {# G8 s7 ^- }
two suffiffifficiently long(> 103 bases) base sequences.! b. T$ c4 \7 q6 `0 w9 a3 [& G
2. Please evaluate the complexity and accuracy of the algorithm reliably, and* d* z) v( M1 h
design suitable examples to illustrate it. ' F" v% T: @8 g. n2 H" _+ ~3. If multiple base sequences in a family have evolved from a common an% x! C* Q5 t' |2 Q7 D! D
cestral sequence, design an effiffifficient algorithm to determine the ancestral$ p+ i+ I$ {5 D1 K( b! ^
sequence, and map the genealogical tree. / b3 z% n4 j2 j8 @ O- B( v; ZReferences+ h2 \* B1 H( R. u- l a2 F; h
[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re 7 R+ N( S% i- ~( Bview of Genetics. 39: 30938, 2005.) a, H+ `- x; b) _9 ^' ~
[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE,2 d2 ^& s5 ^$ R9 f3 O0 j7 }
et al. “Homology” in proteins and nucleic acids: a terminology muddle and . [. }% z w3 x6 E- f! @$ v! na way out of it. Cell. 50 (5): 667, 1987.$ n' p" G, }; Y
/ D/ K5 z: F$ ^6 y2022" Y* R9 {" Q1 |7 q0 e1 q
Certifificate Authority Cup International Mathematical Contest Modeling z G* I# n+ O+ s9 I! lhttp://mcm.tzmcm.cn' T! j4 O7 }" Q9 |5 M, }; I5 I
Problem C (ICM) " r- H) r* q4 g/ ]# r( aClassify Human Activities' e$ f6 H) W& t6 |" \9 s) P7 D1 j1 Z
One important aspect of human behavior understanding is the recognition and , ^; O. ^4 s p3 Omonitoring of daily activities. A wearable activity recognition system can im8 l1 X5 X" ^9 I& N
prove the quality of life in many critical areas, such as ambulatory monitor # |+ D/ ~6 S( |4 s, d$ iing, home-based rehabilitation, and fall detection. Inertial sensor based activ ( w( W8 t: O$ }8 O9 }8 Fity recognition systems are used in monitoring and observation of the elderly @, h' }5 |3 Y( z5 g) Rremotely by personal alarm systems[1], detection and classifification of falls[2],. A- h4 p! V& Y
medical diagnosis and treatment[3], monitoring children remotely at home or in ; l3 ~* |7 c4 E+ U X* o1 ^2 Aschool, rehabilitation and physical therapy , biomechanics research, ergonomics, 5 E0 E( b4 b9 C" O' Ksports science, ballet and dance, animation, fifilm making, TV, live entertain " j. Q: O2 m) p6 ~" Iment, virtual reality, and computer games[4]. We try to use miniature inertial 2 S. M2 a: ~# e# p6 z" t4 tsensors and magnetometers positioned on difffferent parts of the body to classify; c6 ^# _' ^; ~$ w0 p
human activities, the following data were obtained. M. }8 v- ^ {# s6 Y
Each of the 19 activities is performed by eight subjects (4 female, 4 male,7 d! K6 u" ?# l. G) J- r1 j1 @
between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes) ]- g0 V. B# p% t
for each activity of each subject. The subjects are asked to perform the activ1 z2 V: d5 Y5 j9 h1 s" H
ities in their own style and were not restricted on how the activities should be3 k: v5 h6 [# t I+ c* K
performed. For this reason, there are inter-subject variations in the speeds and% x# o* c' \) p7 J
amplitudes of some activities. 7 z' g6 b5 f, kSensor units are calibrated to acquire data at 25 Hz sampling frequency." N6 q r: X! H- Y
The 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal, q3 A. _1 L2 J" f# f) p
segments are obtained for each activity.1 o. A* V6 z; {( o% a
The 19 activities are:" q6 s( |) J! @4 z/ B
1. Sitting (A1);" u0 M5 q! q) K& F# m+ G2 w3 P3 z
2. Standing (A2);& W6 Z3 P! `; g7 @4 [
3. Lying on back (A3);. j, G- n' J, \" x7 d3 r: x6 U& ]: f
4. Lying on right side (A4); / y: P1 K f4 ^5. Ascending stairs (A5);; T5 C& ]- z+ v; \: O" O* ]: }
16. Descending stairs (A6);4 A+ |) }# d7 |9 ^8 h V% I
7. Standing in an elevator still (A7); & X; n0 _" Q; p9 B& }& Y" Y5 e% A8. Moving around in an elevator (A8);4 g/ e+ Y! f" w
9. Walking in a parking lot (A9); ! M# i y, j3 C% j. G1 a" _ N6 Y( [10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg * |- d- Z; t# Tinclined positions (A10); 6 {1 @6 ?+ m- {% E11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions* O- J! p$ ~% ^0 m" u
(A11); 1 @" Q s3 Z6 S( L& ]& R12. Running on a treadmill with a speed of 8 km/h (A12); }8 C$ Y- q+ _13. Exercising on a stepper (A13);) |% k8 e" }# X/ K9 U! Y
14. Exercising on a cross trainer (A14); 4 }3 L; R# R6 Q* g6 P3 l8 I15. Cycling on an exercise bike in horizontal position (A15); - {) E4 {1 k9 L% t# f4 F( K16. Cycling on an exercise bike in vertical position (A16); 2 F4 b$ a4 r3 u8 F: k6 u: A17. Rowing (A17);2 m" d6 g* B4 b3 M( @
18. Jumping (A18);' x+ ?2 s3 `$ F. `. V* A1 q
19. Playing basketball (A19). + |* c% B/ E( }3 q1 EYour team are asked to develop a reasonable mathematical model to solve0 G0 G7 L% C0 N3 Q( M4 W
the following problems.( U' K& y1 V1 _9 o
1. Please design a set of features and an effiffifficient algorithm in order to classify : w: ?1 l4 y4 R, {+ U0 D, ]7 Dthe 19 types of human actions from the data of these body-worn sensors.: |# g' u* L# n
2. Because of the high cost of the data, we need to make the model have : y- _* f2 d' _3 ?6 \/ c! K. F! p+ Wa good generalization ability with a limited data set. We need to study " B# a% k2 d R0 _3 Jand evaluate this problem specififically. Please design a feasible method to - f1 E- W7 @" Hevaluate the generalization ability of your model. 2 m" s$ r( R* H3 V- a% h- L3. Please study and overcome the overfifitting problem so that your classififi-* @; q, O% U2 ?8 \8 V3 b; \ c, a
cation algorithm can be widely used on the problem of people’s action7 Y( X' B7 z0 L9 o; C4 T( G. o6 l0 f
classifification.8 n* B0 e, i( U& E5 H3 ~' J
The complete data can be downloaded through the following link:. b$ t) B0 V4 I5 G
https://caiyun.139.com/m/i?0F5CJUOrpy8oq0 ] t6 b+ }( m; l, V. f- X
2Appendix: File structure ; T% S+ X# o) _/ n, h' O7 ^ [+ d• 19 activities (a) 9 `; ~& C s- g: }- i2 r% _• 8 subjects (p) 9 G4 V$ p% l% L% [& P+ e1 V: B; I1 Q+ L• 60 segments (s)! D2 H" e% i" C: L1 b, |8 L
• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left " G, p0 j4 ]; x5 J' b2 qleg (LL)* B2 U1 h2 ]) H0 x7 N
• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z 1 o+ s1 T7 E4 Z- @magnetometers) 9 J: A1 D8 Z, T/ E& |$ G dFolders a01, a02, ..., a19 contain data recorded from the 19 activities. " a1 x& n( Q- Y9 R+ n$ F4 f+ f- eFor each activity, the subfolders p1, p2, ..., p8 contain data from each of the% \, u, ], V4 C6 b& n* r0 j, A
8 subjects. # d3 s0 D: N( n8 E. J1 ^1 x; pIn each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each9 M3 J% b7 k' ?" q) L" [! [
segment. 2 i9 K- D/ p a* H+ c# H7 }5 g* ^7 Y7 bIn each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 254 ]$ @" i% S% r
Hz = 125 rows. , w) a ^& L# E" e; D) F* U uEach column contains the 125 samples of data acquired from one of the# `4 q4 j$ o4 I, m/ y, e" y7 C# D
sensors of one of the units over a period of 5 sec.8 w' W, m4 K" F; C, \. c
Each row contains data acquired from all of the 45 sensor axes at a particular & G2 \- _0 X- S+ b4 i+ Fsampling instant separated by commas., d- K7 B4 @6 Q9 t$ c& e9 J
Columns 1-45 correspond to: 0 ~) A! }% w3 h, A% J' [5 |8 k• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag, 5 X+ j8 R, v: C7 P" a) V/ s• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag, 1 Y6 N' E/ b) I( _# c• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag,3 n( k" a! S8 q1 K, [7 _5 s \. `
• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,7 z/ G O7 P6 j J' N3 S
• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag.& J0 f* s+ I, e4 B& o$ a; d: q
Therefore, ; V J: j2 C+ X5 o! v• columns 1-9 correspond to the sensors in unit 1 (T), ) s9 |3 ^& u0 d• columns 10-18 correspond to the sensors in unit 2 (RA),9 n: T; x8 e( C( J6 B" e
• columns 19-27 correspond to the sensors in unit 3 (LA), 6 L7 M. h, Y7 z8 Q- G• columns 28-36 correspond to the sensors in unit 4 (RL),) q' J% o/ l4 N. [: C! O! l
• columns 37-45 correspond to the sensors in unit 5 (LL). ' E) p) R0 ]: z4 k! q3References 1 r: f3 I. J6 t( g+ U* H[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic5 y6 a5 B+ L' \
daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. 3 X% o$ S8 N8 Z7 F& p8 N) w42(5), 679-687, 2004 - P9 }5 e* g# X& o. e[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of; C5 \" q3 q7 ]4 H* J6 Y
low-complexity fall detection algorithms for body attached accelerometers.& x7 l2 A: ]2 f; M! i l& U
Gait Posture 28(2), 285-291, 2008 + O$ k& V: _: r- S[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag; ], D3 u: K' r1 }$ @
nosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.7 c4 P) G$ ~! M2 ^) c @9 L
B. 11(5), 553-562, 2007 # @5 K" w* ~: f- b! j[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con " ?9 Y, G+ ^; f4 utrol of a physically simulated character. ACM T. Graphic. 27(5), 20087 p" h- B! S/ v% [5 x5 r3 R
2 f6 i( b/ L. O- a( F2022! C. l2 u) U8 g+ ?, l) \% A2 w
Certifificate Authority Cup International Mathematical Contest Modeling _( F' U( _7 l# {# P
http://mcm.tzmcm.cn# ^5 |, p/ u5 Y
Problem D (ICM)# v* B7 U! B- [5 Z) H
Whether Wildlife Trade Should Be Banned for a Long / c0 l' Y, Y* M9 D" UTime0 u2 e6 u9 F$ z. M' ?0 ?) o( E
Wild-animal markets are the suspected origin of the current outbreak and the2 i5 H( i3 z! l3 o+ `1 N
2002 SARS outbreak, And eating wild meat is thought to have been a source( Z* ~7 V: u+ F
of the Ebola virus in Africa. Chinas top law-making body has permanently' S6 k$ S6 F4 E7 k6 Y8 a7 E
tightened rules on trading wildlife in the wake of the coronavirus outbreak,% Q* z$ S. o! H$ F! j. u
which is thought to have originated in a wild-animal market in Wuhan. Some" d- j% e: m9 p$ a# z
scientists speculate that the emergency measure will be lifted once the outbreak3 W: l) b/ Y5 z5 W
ends. 1 k' z( X* H6 @6 b W1 {4 K4 rHow the trade in wildlife products should be regulated in the long term? - x$ G) `, i& O' {5 R0 DSome researchers want a total ban on wildlife trade, without exceptions, whereas$ e8 r8 x" x5 N- m; i) N6 q
others say sustainable trade of some animals is possible and benefificial for peo# c/ G0 M8 B0 t
ple who rely on it for their livelihoods. Banning wild meat consumption could : O% G4 Y( x& T7 Y+ m( ^' i& @, Ccost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil ( I( T2 G+ P9 n1 A3 P! o( xlion people out of a job, according to estimates from the non-profifit Society of ) L5 {; ]' }1 X' r4 u: z# IEntrepreneurs and Ecology in Beijing.( V, n: R+ @( ^. v# t
A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology 1 a7 N$ D' @% hin China, chasing the origin of the deadly SARS virus, have fifinally found their3 I8 Z! f2 R9 T7 m$ }, g
smoking gun in 2017. In a remote cave in Yunnan province, virologists have 1 \+ Y/ }1 N, R' B% W. Tidentifified a single population of horseshoe bats that harbours virus strains with$ K, \6 t% `# u
all the genetic building blocks of the one that jumped to humans in 2002, killing5 ]+ ^( r; q; d1 ^7 G$ a' M
almost 800 people around the world. The killer strain could easily have arisen3 T+ Y2 {6 p' J2 G
from such a bat population, the researchers report in PLoS Pathogens on 30 7 } b; V* v- }" hNovember, 2017. Another outstanding question is how a virus from bats in $ @: A4 w' t* x. ]! i( ~" K FYunnan could travel to animals and humans around 1,000 kilometres away in ( r% z9 f5 M# k! Z" S( R+ U. KGuangdong, without causing any suspected cases in Yunnan itself. Wildlife - E! @; v1 S5 t( Ctrade is the answer. Although wild animals are cooked at high temperature* K! G$ x0 B, d2 f( @
when eating, some viruses are diffiffifficult to survive, humans may come into contact) m: i, b8 l! [; S
with animal secretions in the wildlife market. They warn that the ingredients+ D# [, n* G7 M
are in place for a similar disease to emerge again. & y0 i. R5 V3 b+ L; U9 b& J9 QWildlife trade has many negative effffects, with the most important ones being:0 M% F! x& e+ @2 D, J8 h, G
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS 9 a9 Z( s% F2 q0 {+ Routbreak in 2002.Credit: Matthew Maran/NPL " R1 F S' }' U) N6 d• Decline and extinction of populations ! K" _ D) L( M" }• Introduction of invasive species " M/ M% Q6 u4 s0 s& u- F• Spread of new diseases to humans, Y2 v4 c2 y& B/ u9 }
We use the CITES trade database as source for my data. This database- o# _0 S1 x$ E) T3 L2 S2 ]; n& e
contains more than 20 million records of trade and is openly accessible. The) }7 a& I. n! _
appendix is the data on mammal trade from 1990 to 2021, and the complete* K" `+ @% |/ _! H7 B% \! k
database can also be obtained through the following link: 6 O$ |; h9 d3 I9 O7 v" \https://caiyun.139.com/m/i?0F5CKACoDDpEJ# F. _) [+ P( }( g; k
Requirements Your team are asked to build reasonable mathematical mod 2 c/ @- y$ C5 R5 \" f; @8 ?els, analyze the data, and solve the following problems: 2 m4 V% _( F a4 o" q/ q# a1. Which wildlife groups and species are traded the most (in terms of live' L% |- T% o5 X6 Q0 K" [* c
animals taken from the wild)? ! }7 M1 j# ?8 M' g1 f% i2. What are the main purposes for trade of these animals?$ v! D2 K: d6 J8 u
3. How has the trade changed over the past two decades (2003-2022)? 3 ~% M( @* t% f3 S4 q# q4. Whether the wildlife trade is related to the epidemic situation of major . p; n8 M5 b: L+ F; A0 U: ninfectious diseases?7 M6 {8 x7 L7 |7 a
25. Do you agree with banning on wildlife trade for a long time? Whether it # F: e2 ~5 S! T Q& h' K$ }will have a great impact on the economy and society, and why?% p4 V& K% }$ M Y
6. Write a letter to the relevant departments of the US government to explain% w7 B) q- l- Z* C
your views and policy suggestions. 2 s- i- e O2 P0 V : A! u# [# s' r& J& F" w, s4 c/ d' s2 B. @4 T1 k
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