2022小美赛赛题的移动云盘下载地址 0 U; T: c, {9 ]. u. A$ {1 R$ e; w
https://caiyun.139.com/m/i?0F5CJAMhGgSJx5 H% K4 \3 L: V$ y+ P7 {) d
6 A0 K+ ]% p8 j! `) g H' ^) s
2022 ; o9 _- y$ i+ Y5 X5 LCertifificate Authority Cup International Mathematical Contest Modeling9 \0 i7 a! K. Z: Z
http://mcm.tzmcm.cn8 I; b6 X: X! w R+ P& C; l
Problem A (MCM) ! O; ^ R3 i& ?5 M0 q- E1 iHow Pterosaurs Fly# ^: S/ O3 A# ~
Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They5 M* X& H3 a0 ?- o2 w4 e
existed during most of the Mesozoic: from the Late Triassic to the end of # N5 ^, W4 s& D hthe Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved% {0 X$ `+ @$ c" {+ {
powered flflight. Their wings were formed by a membrane of skin, muscle, and, W, ^$ \, |* |, J9 v
other tissues stretching from the ankles to a dramatically lengthened fourth9 L# T u! K2 a
fifinger[1].: E' P+ S% T7 d) f$ p: u4 K( `
There were two major types of pterosaurs. Basal pterosaurs were smaller& @# g: D' w3 Q4 z! e* i
animals with fully toothed jaws and long tails usually. Their wide wing mem ) W7 l/ ]+ E7 u0 y) }9 x/ dbranes probably included and connected the hind legs. On the ground, they& Y" T) |' Y0 ~! ^6 o0 ]. y9 I
would have had an awkward sprawling posture, but their joint anatomy and / u1 n2 V1 | f9 M% Lstrong claws would have made them effffective climbers, and they may have lived1 D5 H" e' f1 ?7 L8 J, O6 C
in trees. Basal pterosaurs were insectivores or predators of small vertebrates. + g# B' T2 D' @ u% d' M, kLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles.2 `4 e. ?+ b. y, }3 e! F$ W% f/ N
Pterodactyloids had narrower wings with free hind limbs, highly reduced tails, 4 o( r0 j9 M8 n# ?( tand long necks with large heads. On the ground, pterodactyloids walked well on ' f! h6 Z7 g/ z$ \ Aall four limbs with an upright posture, standing plantigrade on the hind feet and( t' B0 K7 i' U/ {8 C5 b& O3 x2 C6 P
folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil 1 m: ~0 n b4 h* C( s$ }trackways show at least some species were able to run and wade or swim[2].- V# i( P% o6 A+ B, I
Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which 8 h' t* h% e& q9 W2 z, O* ~5 i) Scovered their bodies and parts of their wings[3]. In life, pterosaurs would have $ r N: P7 |- Uhad smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug7 N8 ~: g. G7 v* y2 h
gestions were that pterosaurs were largely cold-blooded gliding animals, de4 L ]) N q/ E9 P* [) J
riving warmth from the environment like modern lizards, rather than burning: q3 N$ {' U. k
calories. However, later studies have shown that they may be warm-blooded 4 k/ k6 Q0 H8 _% p- K5 D(endothermic), active animals. The respiratory system had effiffifficient unidirec # T7 C2 Q: j, q7 {& w- C, utional “flflow-through” breathing using air sacs, which hollowed out their bones9 O% M# A8 `' H; k/ A4 V9 s. b
to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from 6 e; e5 e3 m# y+ H' Y% xthe very small anurognathids to the largest known flflying creatures, including, u$ f( R* f2 u
Quetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least a- M8 {1 g# [6 u# F5 ]6 y, d7 j% K
nine metres. The combination of endothermy, a good oxygen supply and strong 2 Q9 k- V7 @- b& y9 p2 i) a, x1muscles made pterosaurs powerful and capable flflyers. & k" h- H: O6 kThe mechanics of pterosaur flflight are not completely understood or modeled & F8 q! z2 u5 V# _" R7 Hat this time. Katsufumi Sato did calculations using modern birds and concluded * r/ W5 i" y% n9 Q( othat it was impossible for a pterosaur to stay aloft[6]. In the book Posture,* X( f# u$ U T
Locomotion, and Paleoecology of Pterosaurs it is theorized that they were able 5 [# b& `2 X( v% y \to flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].2 n$ R+ F3 V+ S; g: M) y
However, both Sato and the authors of Posture, Locomotion, and Paleoecology0 D6 B( J- t( @+ a+ c* T* d
of Pterosaurs based their research on the now-outdated theories of pterosaurs/ L7 G* ^% i/ M1 ^' S. F
being seabird-like, and the size limit does not apply to terrestrial pterosaurs,# u5 {' \3 Q0 b% T! t1 u$ F& \
such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that , Q! Q1 b7 X0 F* p& c0 z. Xatmospheric difffferences between the present and the Mesozoic were not needed ?4 A, J, n* q# b1 o! K
for the giant size of pterosaurs[8].) R: J5 ^( o1 L) t
Another issue that has been diffiffifficult to understand is how they took offff.0 z, v1 V0 D/ \% J6 `( j4 \$ d
If pterosaurs were cold-blooded animals, it was unclear how the larger ones0 i0 q1 k: a$ k6 b, w- a" q3 s @
of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage " w3 I5 E. w5 h0 u7 `a bird-like takeoffff strategy, using only the hind limbs to generate thrust for/ e, u' Y0 m* i% P' }
getting airborne. Later research shows them instead as being warm-blooded, B" [. Q( B5 F% j: C% `) X
and having powerful flflight muscles, and using the flflight muscles for walking as3 o9 ~5 O: x2 R& G8 l
quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of $ d) a/ N( v0 FJohns Hopkins University suggested that pterosaurs used a vaulting mechanism : _8 R8 ]$ S# Gto obtain flflight[10]. The tremendous power of their winged forelimbs would , k% L! I$ v2 o! q# p# {) r% qenable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds, Z" b: ^8 a7 x2 p9 @
of up to 120 km/h and travel thousands of kilometres[10].: P' U0 Y$ B% L; J3 r: Y
Your team are asked to develop a reasonable mathematical model of the 4 C' v8 R* R* f& m! ^, x1 mflflight process of at least one large pterosaur based on fossil measurements and ! }, b: u7 P, z h! Y. B6 _5 oto answer the following questions. ; n$ {2 r9 H4 V& s. B) D5 s1. For your selected pterosaur species, estimate its average speed during nor% D% J, W* n8 |6 P8 r# ~* |! g
mal flflight.( @/ v8 @# [; W+ |, w
2. For your selected pterosaur species, estimate its wing-flflap frequency during 7 ^& H# p/ Y- H! _9 [normal flflight.$ t* ^: Y/ V2 K: Y$ p* O
3. Study how large pterosaurs take offff; is it possible for them to take offff like5 o4 \; ^ I: u7 G5 g
birds on flflat ground or on water? Explain the reasons quantitatively. 7 V( _0 N/ V' a: S. nReferences 8 l* s8 P7 v2 g% e5 G& v[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight) m n, @) F$ [$ T+ g s$ F
Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111. % N6 o* l9 P3 T- B" S2[2] Mark Witton. Terrestrial Locomotion. + f) D3 r9 O0 N3 i# I- x+ f. xhttps://pterosaur.net/terrestrial locomotion.php 2 c2 C- k4 Y3 S% p* w& e' U2 y8 y[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs; ]* G. L6 T, Q% V' c& ?' e, H( b$ a$ W
Were Covered in Fluffffy Feathers. https://www.livescience.com/64324- , e$ t& w8 F9 j% G, dpterosaurs-had-feathers.html , W; q; \4 i# U" p" j) f# f[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a) M# g6 |* t, N
rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea) 7 E$ p' D( D2 u; ?1 I- hfrom China. Proceedings of the National Academy of Sciences. 105 (6):, T0 [0 a) ?7 d0 R# _
1983-87.; z7 g) P% Z# k3 W
[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust ' K6 h& `6 R- @$ y" h0 ]; U4 tskull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4): : z6 W4 M2 h$ y6 {7 A& W/ s/ h: W180-84./ z4 @% y; T4 t0 j. B
[6] Devin Powell. Were pterosaurs too big to flfly? % x# `+ |( q% A5 W" m: n& @https://www.newscientist.com/article/mg20026763-800-were-pterosaurs ! W4 I4 x+ Y; F' f u- B: Y6 _too-big-to-flfly/ : n! F. _, u( s- j1 Y$ j[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology ) B2 H" Y. _9 \7 ]of pterosaurs. Boulder, Colo: Geological Society of America. p. 60. 6 B0 ^1 s" |' }+ g[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable 5 T" t) [! T2 f9 k0 {/ t* ?air sacs in their wings.3 N0 `7 e, C: [) ^
https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur; Q1 {( w% H4 L8 w6 `5 V& u
breathing-air-sacs : a0 v2 w2 [- @; r[9] Mark Witton. Why pterosaurs weren’t so scary after all. 2 ^# [6 s2 ~/ ~$ c4 ^4 \8 mhttps://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils$ `9 b5 g9 x" s( @6 K: C" S1 e
research-mark-witton0 g# C4 m( e6 h6 f/ A# Y3 E
[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats? + [& D% S2 f+ lhttps://www.newscientist.com/article/dn19724-did-giant-pterosaurs ) _# x$ B d+ L6 U: Y& Pvault-aloft-like-vampire-bats/7 b2 K" B( ?5 G c L3 p# j% w' v1 \
# ~5 R3 h; y; [6 I2 M& J2022 8 @8 M9 S/ {# l }9 J+ ACertifificate Authority Cup International Mathematical Contest Modeling , D9 G+ F# v$ @1 lhttp://mcm.tzmcm.cn . e$ M4 }( P" ^/ e# xProblem B (MCM) . c9 ?7 |3 G( C* O6 fThe Genetic Process of Sequences ) w! |9 C" Z$ P( r( O: B2 Z) kSequence homology is the biological homology between DNA, RNA, or protein * u$ i& m7 R5 L( m: Z4 X- asequences, defifined in terms of shared ancestry in the evolutionary history of ( o6 K2 _9 Z; k4 |4 E* xlife[1]. Homology among DNA, RNA, or proteins is typically inferred from their; o) H+ K- J8 t8 T1 t: U' ?, \- u
nucleotide or amino acid sequence similarity. Signifificant similarity is strong ' {! M, G7 Z4 pevidence that two sequences are related by evolutionary changes from a common2 Y* P) c4 s7 B$ f/ f3 e* w
ancestral sequence[2]. _$ u# ]9 h8 i3 p) PConsider the genetic process of a RNA sequence, in which mutations in nu" k+ i e% P6 c6 [ l# ?) y
cleotide bases occur by chance. For simplicity, we assume the sequence mutation4 z1 w- t1 N' M
arise due to the presence of change (transition or transversion), insertion and) e+ r4 H$ U7 w9 F( O: M; w, p
deletion of a single base. So we can measure the distance of two sequences by ) c, [2 V4 }, g1 tthe amount of mutation points. Multiple base sequences that are close together & Y; a" L1 [, U4 wcan form a family, and they are considered homologous. + F- Z5 c: H, b1 e* zYour team are asked to develop a reasonable mathematical model to com. v/ y4 g/ @; q" |7 H
plete the following problems. ( z- k# D5 X* _; k1. Please design an algorithm that quickly measures the distance between% C3 f; W$ d, A, W t4 U
two suffiffifficiently long(> 103 bases) base sequences." O4 m) M/ F4 U; U: C3 b3 g6 N
2. Please evaluate the complexity and accuracy of the algorithm reliably, and: n/ l+ D# L; B3 `; \5 o
design suitable examples to illustrate it. 6 ~. b, A; _/ w9 V0 c3. If multiple base sequences in a family have evolved from a common an 2 a+ D2 i4 Z9 o/ l: y7 l' Icestral sequence, design an effiffifficient algorithm to determine the ancestral& j1 g* d8 d! a2 `; w4 k7 x
sequence, and map the genealogical tree. 5 b; R0 V# u' E( L3 H! _References& |( n1 A+ {1 O9 S
[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re# L. v0 ~6 G6 D/ [" i
view of Genetics. 39: 30938, 2005.# \3 S2 T4 j/ W0 U- l4 j$ G
[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, . G3 ?3 \# Y: v' N+ x. U. Z0 get al. “Homology” in proteins and nucleic acids: a terminology muddle and + Z* }) F9 ~" T6 Fa way out of it. Cell. 50 (5): 667, 1987. / _2 O6 [8 {6 r8 C+ p7 E: i5 [, D6 f) ?4 }# B. Y# g
2022 3 a5 }' Z$ f" dCertifificate Authority Cup International Mathematical Contest Modeling: T0 I8 d( V) I: D" n& W
http://mcm.tzmcm.cn% G. O. u0 u) Y0 H5 R: U6 k
Problem C (ICM)" G6 w, I, ^% F+ T
Classify Human Activities5 O( i# f- n+ t% U- Z
One important aspect of human behavior understanding is the recognition and7 c( b+ u8 G: o( `4 Z2 T3 W, l4 v
monitoring of daily activities. A wearable activity recognition system can im- a j6 @. `' X1 ]9 \
prove the quality of life in many critical areas, such as ambulatory monitor # \# q4 E( D8 s2 wing, home-based rehabilitation, and fall detection. Inertial sensor based activ; k" K3 W& B( J( F$ Z8 Y( s) |
ity recognition systems are used in monitoring and observation of the elderly3 l9 x( Y$ |0 i% B; x
remotely by personal alarm systems[1], detection and classifification of falls[2],& _/ R3 \ o4 p% t2 e1 |
medical diagnosis and treatment[3], monitoring children remotely at home or in $ [6 p! B0 y& k: Gschool, rehabilitation and physical therapy , biomechanics research, ergonomics,& `1 {% O3 n$ |$ a3 J
sports science, ballet and dance, animation, fifilm making, TV, live entertain0 q4 f2 V. y' O% e
ment, virtual reality, and computer games[4]. We try to use miniature inertial ( m8 U- ^3 O8 W) ssensors and magnetometers positioned on difffferent parts of the body to classify) \7 w+ F: Z+ ]' V% E
human activities, the following data were obtained., X; m/ i3 U9 {, ^( J( ^# W
Each of the 19 activities is performed by eight subjects (4 female, 4 male, 3 d: W% A3 X3 U8 ?between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes5 S+ `# l2 v6 C* r( q9 r$ R
for each activity of each subject. The subjects are asked to perform the activ 1 N7 y# P: S+ ]ities in their own style and were not restricted on how the activities should be, `, k1 i) t8 D1 ~8 C/ ~ G
performed. For this reason, there are inter-subject variations in the speeds and ! X3 ^) o6 F$ p9 iamplitudes of some activities.' t5 T! I/ G( r3 @. T1 }
Sensor units are calibrated to acquire data at 25 Hz sampling frequency. 4 c8 h" K8 G9 D5 d6 PThe 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal$ Q `! B" ]. g
segments are obtained for each activity.* O$ X' y5 |% ^' ]" f' b
The 19 activities are: ) w1 w+ k& z, z, s( X8 D1. Sitting (A1); a( w7 e% C9 o* m7 e6 y, K
2. Standing (A2); 5 ]9 N" g1 P! \7 O3. Lying on back (A3);/ ?4 e: Q# q f5 W2 w8 H! W
4. Lying on right side (A4);, J; E2 r$ h& R, k0 w! h
5. Ascending stairs (A5);0 W3 x# X5 W; s
16. Descending stairs (A6);" h3 U: B6 m9 Z
7. Standing in an elevator still (A7);) l+ N9 a8 t# Y: C/ f; Y- {
8. Moving around in an elevator (A8); , q& E2 l' B# ?' |, z$ T8 }4 p9 y9. Walking in a parking lot (A9); % q; e# M! @9 g5 z1 R$ j10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg ) i0 v" |) H; Y4 Tinclined positions (A10); 0 k( }* X3 p9 c11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions ! W0 a0 Z- V" E(A11);/ m/ [6 i( j4 w ^
12. Running on a treadmill with a speed of 8 km/h (A12); ' w8 u! B0 Z3 |3 I j- F7 D13. Exercising on a stepper (A13);. a$ r& |1 o. V! e- l+ U; w
14. Exercising on a cross trainer (A14);% c q+ N7 s6 J0 U) O
15. Cycling on an exercise bike in horizontal position (A15);( I6 v4 N* Q/ v" l& M
16. Cycling on an exercise bike in vertical position (A16); / ]5 k% h8 L& E17. Rowing (A17);4 ^1 W# v; G; u3 t2 T1 h Z6 z
18. Jumping (A18); $ j% T [! \' t; u. O7 [19. Playing basketball (A19).0 m, W% m: d- K- G7 V& k
Your team are asked to develop a reasonable mathematical model to solve0 O) J$ a3 n7 {8 E* Z
the following problems. * I, x, U7 K2 ~1. Please design a set of features and an effiffifficient algorithm in order to classify 8 Y2 m% j; T' \2 o3 q/ G0 Fthe 19 types of human actions from the data of these body-worn sensors. P2 _7 @7 d* p8 j' `2 a1 x+ v8 b( N
2. Because of the high cost of the data, we need to make the model have ! e! f- B- L7 |% A+ P3 ?a good generalization ability with a limited data set. We need to study% {8 ?* d* E: f/ i8 n# o
and evaluate this problem specififically. Please design a feasible method to# ?! s$ t `2 E: d" T7 ?
evaluate the generalization ability of your model.4 S; t# F# a9 _3 c& V) p& n
3. Please study and overcome the overfifitting problem so that your classififi- & r# {; I4 z' J6 w% J) Lcation algorithm can be widely used on the problem of people’s action2 @) {" K8 p8 d/ U& Y/ \
classifification.6 \2 _: l; ^3 b7 d
The complete data can be downloaded through the following link: 5 B/ ?1 a) i( \( H7 D: Mhttps://caiyun.139.com/m/i?0F5CJUOrpy8oq/ r* ]5 i' C; l
2Appendix: File structure + _3 ^+ r! S$ V2 a( {( |2 C• 19 activities (a)" y7 i8 v! O; ^) m6 I* m
• 8 subjects (p)* [1 \5 W U9 I0 V( [0 K% c& y+ e O
• 60 segments (s) $ I" r, h e2 o* e% u• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left9 r6 o% r! S% i' H4 E" d' F
leg (LL) 5 \8 q9 y3 M# u! h• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z 3 J: e) W5 x% Z+ g2 tmagnetometers)( O( B" o( @& ? t; u7 f' a6 ]
Folders a01, a02, ..., a19 contain data recorded from the 19 activities.* _* V" r0 f$ @+ G
For each activity, the subfolders p1, p2, ..., p8 contain data from each of the+ X* H" Q' j# O' l$ z$ B
8 subjects.+ |1 L, A6 j: ~; S& `* C6 _: C
In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each- h* W9 Q8 a: k4 i% `
segment. ) S8 o3 T7 B( T# pIn each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25 ~. @$ y, e, ?- U D5 s- M& WHz = 125 rows. 1 C+ e) D5 k0 m* J% EEach column contains the 125 samples of data acquired from one of the4 O# a+ V% h4 b- D
sensors of one of the units over a period of 5 sec. : Y- A* ^" V8 F) L+ Z* z+ vEach row contains data acquired from all of the 45 sensor axes at a particular 6 {! G( Z) N9 Fsampling instant separated by commas.% {7 p& |. n k$ }1 D+ ]$ B
Columns 1-45 correspond to:$ q$ z/ e3 S. E& ?
• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag, 1 d& t: |" D- F) @# }8 Z• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag,3 F- E7 B2 E2 L/ N V2 y5 E) S1 h
• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag, 6 w0 g: o& H0 Y9 }6 Y# c. b• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag, & _' T' X3 n# z• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag.0 Q: v9 r# o& t
Therefore,5 e# @* l! @- _5 M
• columns 1-9 correspond to the sensors in unit 1 (T), ! V$ @& V+ H7 z/ j# l' y# p1 V- M• columns 10-18 correspond to the sensors in unit 2 (RA), ( n; ~: S* D0 J# y5 k• columns 19-27 correspond to the sensors in unit 3 (LA),# j1 c# @1 y' h! P
• columns 28-36 correspond to the sensors in unit 4 (RL),5 c& `6 o, k/ F3 H. Z. F
• columns 37-45 correspond to the sensors in unit 5 (LL).- Q7 ^. M$ l5 R/ l/ A
3References$ W" p1 o1 n" S+ }0 n/ `
[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic& Z4 `. ^8 z2 i6 ^, b8 u+ I
daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. ) U) ?6 a/ a0 i/ K42(5), 679-687, 20042 }7 u; G8 Z2 l: s
[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of ; n2 S" _% e& ?& }8 E9 Dlow-complexity fall detection algorithms for body attached accelerometers.- g% r3 E3 R- F l k8 n9 H
Gait Posture 28(2), 285-291, 2008 ; H; X. q- C6 i% F8 \[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag & o. ~- `! u# o0 z( A/ B8 Z Hnosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.& h4 ~# p3 f) d: G* @) K
B. 11(5), 553-562, 20078 l. E* h& |8 p }! v+ E
[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con ) _& e. \7 v, h- Z: D* ltrol of a physically simulated character. ACM T. Graphic. 27(5), 2008. l4 D6 ? m8 ?2 m( o1 l. P
+ Q0 e9 k A3 X" V0 j4 m6 m5 _4 \
2022( `7 T" S7 K- L* l i N( |
Certifificate Authority Cup International Mathematical Contest Modeling 4 P& G& J) m9 qhttp://mcm.tzmcm.cn- g0 n- O; S7 ]8 O
Problem D (ICM)0 X1 s1 j, ]+ W: S$ o: g
Whether Wildlife Trade Should Be Banned for a Long # Y% F$ `$ G2 \Time . R( g) |3 d2 l+ O7 r( LWild-animal markets are the suspected origin of the current outbreak and the! J$ {0 O2 X; V+ O0 Z3 {' c
2002 SARS outbreak, And eating wild meat is thought to have been a source * ?- Z( H. j7 ^& H0 ~/ @, F$ Cof the Ebola virus in Africa. Chinas top law-making body has permanently/ q) }' c4 W, B$ K' ?
tightened rules on trading wildlife in the wake of the coronavirus outbreak,& S' Z a/ m$ F/ ^, f5 E% D
which is thought to have originated in a wild-animal market in Wuhan. Some1 A, x& f4 r- C
scientists speculate that the emergency measure will be lifted once the outbreak o' V. J* O& h% W9 N$ ]
ends.9 C- [, X5 u% x" s5 a0 N
How the trade in wildlife products should be regulated in the long term?* Z2 C- G8 K3 O/ X' F' u3 `
Some researchers want a total ban on wildlife trade, without exceptions, whereas 5 X0 p* D: b1 Y* U! z, s, uothers say sustainable trade of some animals is possible and benefificial for peo , ~" X: M9 H! W8 ?8 U" }8 Nple who rely on it for their livelihoods. Banning wild meat consumption could 9 N- _% q3 h m/ n Z, icost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil9 B* q' o* k4 t6 i1 x- y5 |
lion people out of a job, according to estimates from the non-profifit Society of . C6 }% d, s* `( N O4 g8 e4 w; CEntrepreneurs and Ecology in Beijing. / _6 w( y1 W( `. d! \A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology 0 X* K$ b( j6 gin China, chasing the origin of the deadly SARS virus, have fifinally found their2 {2 \0 B" ?7 H8 ?
smoking gun in 2017. In a remote cave in Yunnan province, virologists have' g: y! [: B. d- s \
identifified a single population of horseshoe bats that harbours virus strains with , d& ~# v0 r$ K" Iall the genetic building blocks of the one that jumped to humans in 2002, killing 5 D4 P+ B/ G" r- n" c4 ralmost 800 people around the world. The killer strain could easily have arisen0 R. d' ]* y0 r$ t! s6 l
from such a bat population, the researchers report in PLoS Pathogens on 30) q% B' `9 m2 R( R3 U1 ]$ ]1 M
November, 2017. Another outstanding question is how a virus from bats in% Y* X" U6 ?7 ?
Yunnan could travel to animals and humans around 1,000 kilometres away in 9 X3 L6 r4 u; h/ W" vGuangdong, without causing any suspected cases in Yunnan itself. Wildlife * O: D4 H6 {3 Q! u+ k; s8 Ntrade is the answer. Although wild animals are cooked at high temperature( i H u z5 J' Y
when eating, some viruses are diffiffifficult to survive, humans may come into contact . n- M. D& v( t u! ~7 K1 vwith animal secretions in the wildlife market. They warn that the ingredients ; b% x+ e) u H# mare in place for a similar disease to emerge again. 1 l3 M8 t0 N! w7 kWildlife trade has many negative effffects, with the most important ones being:6 @9 p9 ~3 H: o- f
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS ; d" }% V5 x5 E; `# x, Zoutbreak in 2002.Credit: Matthew Maran/NPL ( O1 s k) E0 B! J+ n$ g• Decline and extinction of populations- C# P9 S4 z: V6 u, E! @+ C8 s5 R
• Introduction of invasive species3 X# A- @4 y# ]
• Spread of new diseases to humans - a" H: }( J, k G2 [2 i4 q4 rWe use the CITES trade database as source for my data. This database; t/ V ]( g% V
contains more than 20 million records of trade and is openly accessible. The+ q! G% m$ A8 P' U5 m0 ^
appendix is the data on mammal trade from 1990 to 2021, and the complete$ x; H: G+ p0 L+ x
database can also be obtained through the following link: 3 H4 F. D0 I$ |3 B/ _1 [https://caiyun.139.com/m/i?0F5CKACoDDpEJ, n& G1 T0 I5 ?; x: b
Requirements Your team are asked to build reasonable mathematical mod7 Q# f& Y, e( _1 Y* X& s( G
els, analyze the data, and solve the following problems:& L' _' q: O2 h$ a5 G
1. Which wildlife groups and species are traded the most (in terms of live/ t4 e6 G i# Z" D s. H
animals taken from the wild)?0 {+ ^, c% M0 H: h6 V0 R
2. What are the main purposes for trade of these animals?" o2 V1 c2 [7 K
3. How has the trade changed over the past two decades (2003-2022)? ; Q8 w! J* b; G! D4. Whether the wildlife trade is related to the epidemic situation of major* ]3 S9 E9 M' a
infectious diseases? / A3 v; [3 n# ~4 s& [8 Z25. Do you agree with banning on wildlife trade for a long time? Whether it5 n# E; j, I- e: y1 h
will have a great impact on the economy and society, and why? $ c3 X( d' W1 ^/ V& ~; j3 u6. Write a letter to the relevant departments of the US government to explain 1 O M( ?$ ^- B0 V6 k lyour views and policy suggestions. 4 t! u, J/ m% F- h, I 6 u& t4 t: \) A1 Z! e 1 |. P% U5 c6 A- d+ Y ; B; }. x' Z- d( Y0 ]* _3 b- v; W! w" f/ ]( |
6 W" v0 Q4 R+ C1 r0 y ( M, P$ y- w/ p$ r, P' m) V3 m$ R* f- g