2022小美赛赛题的移动云盘下载地址 $ B. h! z- F/ C8 Y# I# l" H' \3 |
https://caiyun.139.com/m/i?0F5CJAMhGgSJx; q9 r" V$ u- B
8 q4 }/ A `! E6 O. y2022 9 K" P& B7 m8 X) F, {& e2 H2 _% ?Certifificate Authority Cup International Mathematical Contest Modeling" Q, C" i; s1 A% t
http://mcm.tzmcm.cn" {4 }' Y5 J# Q; P( n; @3 s( p
Problem A (MCM)9 d6 I/ q0 F1 f8 X' g9 h# d5 j
How Pterosaurs Fly # p6 G4 {2 e/ g# UPterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They& ^% e8 t* j- F* b$ ~& S
existed during most of the Mesozoic: from the Late Triassic to the end of ! P$ S' y! Q2 ]9 G2 m3 W0 ~+ vthe Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved 6 @$ m& t! c6 g0 `$ {) Ipowered flflight. Their wings were formed by a membrane of skin, muscle, and 6 G$ F! [% o9 O! x1 I$ T1 bother tissues stretching from the ankles to a dramatically lengthened fourth Z1 B6 a: d1 p1 }; |9 Dfifinger[1].9 M0 _7 U+ j6 g5 o
There were two major types of pterosaurs. Basal pterosaurs were smaller ! d' Y" |# i' aanimals with fully toothed jaws and long tails usually. Their wide wing mem 1 a# [- B1 ~) C9 k0 Lbranes probably included and connected the hind legs. On the ground, they' m, P# V9 ~( B7 _5 \7 W0 e
would have had an awkward sprawling posture, but their joint anatomy and8 Y& ], V8 H Q P7 O M _
strong claws would have made them effffective climbers, and they may have lived 8 r7 H8 e# ` Xin trees. Basal pterosaurs were insectivores or predators of small vertebrates. 9 D, Z1 z4 y- YLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. 7 U; P' h3 |$ r: w. xPterodactyloids had narrower wings with free hind limbs, highly reduced tails, : u7 l. i' |& g. S, F4 J( v( @' t; h) ~and long necks with large heads. On the ground, pterodactyloids walked well on+ P. l2 L, \, _5 N* _" B8 |" z
all four limbs with an upright posture, standing plantigrade on the hind feet and3 Y* y0 |3 ~2 p8 `# R( m3 o5 C* I) a
folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil: @& j7 C& F2 Y# p7 i
trackways show at least some species were able to run and wade or swim[2].( L" ]' X8 s. N" E
Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which 1 `: H/ X6 a$ b% @) o% X- i% I; P: Acovered their bodies and parts of their wings[3]. In life, pterosaurs would have + {& A. z1 P+ G( @2 P: `had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug7 t) S# n0 u( l S
gestions were that pterosaurs were largely cold-blooded gliding animals, de0 Q, E- Y0 C& _4 P
riving warmth from the environment like modern lizards, rather than burning/ m" A# N6 i- p
calories. However, later studies have shown that they may be warm-blooded7 q/ M/ G4 }' o% H
(endothermic), active animals. The respiratory system had effiffifficient unidirec4 F' {% s9 u# C
tional “flflow-through” breathing using air sacs, which hollowed out their bones 1 y# Y/ o7 ~! l& Cto an extreme extent. Pterosaurs spanned a wide range of adult sizes, from z+ T1 }5 @6 x+ e$ q
the very small anurognathids to the largest known flflying creatures, including + i0 C1 n5 a7 }3 i3 A' x1 {Quetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least # f" w& ?9 R7 [" ~# I& d4 ~2 fnine metres. The combination of endothermy, a good oxygen supply and strong 5 c' h8 j3 j7 L8 v7 C( F3 ~1muscles made pterosaurs powerful and capable flflyers. . U. g. O- e: _3 |5 k, @% XThe mechanics of pterosaur flflight are not completely understood or modeled( g" d1 c) g3 d! g8 ?/ \9 t+ k8 R
at this time. Katsufumi Sato did calculations using modern birds and concluded) e4 {5 r% Z3 d
that it was impossible for a pterosaur to stay aloft[6]. In the book Posture, % l( ]+ A* t3 TLocomotion, and Paleoecology of Pterosaurs it is theorized that they were able2 t {2 M, b8 u8 i
to flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7]. 7 `# v! C/ u7 |3 \3 _# i9 i2 S( GHowever, both Sato and the authors of Posture, Locomotion, and Paleoecology0 w% F; |: ^3 R) e8 h
of Pterosaurs based their research on the now-outdated theories of pterosaurs5 r$ V' }% @8 }/ G! C/ a- S
being seabird-like, and the size limit does not apply to terrestrial pterosaurs,5 w4 ^- L; ~4 C
such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that+ O6 ?' U" w9 O8 f
atmospheric difffferences between the present and the Mesozoic were not needed, g& p6 e; @$ p1 v1 K$ R
for the giant size of pterosaurs[8]. & Q0 {* j6 a$ \. M: S( ~ f& mAnother issue that has been diffiffifficult to understand is how they took offff. " E, e1 V2 \3 K% e( ], ]$ P H) CIf pterosaurs were cold-blooded animals, it was unclear how the larger ones4 c1 t8 E: x8 U6 j8 E9 A
of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage0 H) X& r2 y1 h! E8 ^
a bird-like takeoffff strategy, using only the hind limbs to generate thrust for$ ~5 D! i+ a/ }# T8 X1 w
getting airborne. Later research shows them instead as being warm-blooded$ A0 H) i% q8 _% T# ]+ Z( a! {
and having powerful flflight muscles, and using the flflight muscles for walking as9 T- U) Q- ~, @; w
quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of+ f! h" H: T) M
Johns Hopkins University suggested that pterosaurs used a vaulting mechanism/ T) m+ b' B/ z" x; r
to obtain flflight[10]. The tremendous power of their winged forelimbs would : k" v) k/ _; ^# @) p* Yenable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds {! Q. p V; k# Y
of up to 120 km/h and travel thousands of kilometres[10]. 8 \5 G! C5 x/ |- cYour team are asked to develop a reasonable mathematical model of the& n) B$ a* m1 n$ a4 a [
flflight process of at least one large pterosaur based on fossil measurements and& F4 ~% p0 `9 U$ B; G1 c
to answer the following questions.3 F3 D9 W: p& @- q) @' Y$ `
1. For your selected pterosaur species, estimate its average speed during nor 4 ]2 j( W2 r* C( Smal flflight. 2 p* v4 |) P+ N O2. For your selected pterosaur species, estimate its wing-flflap frequency during& ?# F0 j0 m5 j* {) s! x
normal flflight.# ^7 s9 f* y: s3 a$ f1 u
3. Study how large pterosaurs take offff; is it possible for them to take offff like 9 b9 O# P' J; q6 g+ U2 Zbirds on flflat ground or on water? Explain the reasons quantitatively.7 K8 ?1 ?/ X6 I7 R x' s9 D# @
References+ T, G3 P! P. V! {+ _3 }1 }
[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight ) u( u% N% P3 `$ U M {( a7 v5 g) z) OMembrane. Acta Palaeontologica Polonica. 56 (1): 99-111.2 T& B% C. d* ]3 Z9 T
2[2] Mark Witton. Terrestrial Locomotion. " X& C1 N" q/ d( _) k9 lhttps://pterosaur.net/terrestrial locomotion.php 7 t8 R. m. j) N' W2 M3 {, |[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs ; X/ t5 {8 t; Q. y* f9 W8 [Were Covered in Fluffffy Feathers. https://www.livescience.com/64324- 8 {$ D7 F; Q- C- _8 ^" opterosaurs-had-feathers.html 9 v* |% z. d7 ]2 e& r' Z/ i[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a 9 L7 |0 r; n& J/ c5 ^rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea) " _# M9 c; d4 X: a& T# a5 tfrom China. Proceedings of the National Academy of Sciences. 105 (6): " v0 E0 I2 d+ p1983-87. t9 O' z7 |: q; o
[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust " e' N3 j9 @0 Z( `skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4): 3 u$ f7 U; A0 n9 s& H180-84. 2 q$ [2 b! h" z& A I/ f) X* z[6] Devin Powell. Were pterosaurs too big to flfly? & q P6 a7 |+ f- Y2 A s( O! _+ shttps://www.newscientist.com/article/mg20026763-800-were-pterosaurs - N$ G$ x3 D2 D3 ^: H1 u5 dtoo-big-to-flfly/' Z" ?7 h9 j) Y3 r8 x
[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology' @1 P( j$ l6 ~; i6 I
of pterosaurs. Boulder, Colo: Geological Society of America. p. 60.( c. o1 b( h) p2 J, a; b. Z
[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable: R/ Y7 D/ P0 O1 R; T. Z3 L" L: Z
air sacs in their wings.6 U: ]$ X) e5 `: |6 \0 m- x0 u
https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur & _7 V# x2 @1 l( |breathing-air-sacs0 W. ?8 [ ]2 [& V
[9] Mark Witton. Why pterosaurs weren’t so scary after all.$ _) ]+ k( G2 L# v; l0 d% p5 e
https://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils1 G/ w8 y$ Y4 Y1 B! X+ J
research-mark-witton . r8 Z8 q' X5 ^) g# U) y[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats? - |" M8 Z$ I8 e7 phttps://www.newscientist.com/article/dn19724-did-giant-pterosaurs & h: h. y; ?" lvault-aloft-like-vampire-bats/0 z! I, P6 ?+ V7 `* x m) R6 ^
: V5 I/ {+ B8 `0 ~9 e+ W3 j
2022 ( j$ U" b7 E9 P3 {! l3 U( dCertifificate Authority Cup International Mathematical Contest Modeling8 `( A, O2 n2 I: i1 r! B, o
http://mcm.tzmcm.cn % b( m* h9 ]7 K. RProblem B (MCM) : }" o3 H+ p! J) I/ D/ e' WThe Genetic Process of Sequences. L9 U8 {4 K' ^% A3 v
Sequence homology is the biological homology between DNA, RNA, or protein 9 Y+ }! U( s) Z" z. z( Csequences, defifined in terms of shared ancestry in the evolutionary history of, ]2 @+ }% O8 ]2 Y3 A# E* w
life[1]. Homology among DNA, RNA, or proteins is typically inferred from their & V4 w& T s% C1 ]* S8 {nucleotide or amino acid sequence similarity. Signifificant similarity is strong ; k# q( n+ j( g8 \/ Uevidence that two sequences are related by evolutionary changes from a common2 ?0 N9 a: {0 v9 ^. B* h. r- F
ancestral sequence[2].% t. h% h% g9 `" |# ~
Consider the genetic process of a RNA sequence, in which mutations in nu D/ Q: y; Y/ `% B' X* j
cleotide bases occur by chance. For simplicity, we assume the sequence mutation6 t q; y0 Z* F/ a
arise due to the presence of change (transition or transversion), insertion and; p8 [" C( d; S1 T( o
deletion of a single base. So we can measure the distance of two sequences by ! d5 a# t* ~$ r: Qthe amount of mutation points. Multiple base sequences that are close together& W5 b' H. U8 P, t4 G
can form a family, and they are considered homologous.0 x# ]. z5 j/ A$ ]+ x- N/ _
Your team are asked to develop a reasonable mathematical model to com+ C0 B+ B" J$ I3 |& v
plete the following problems.( ~2 B, e/ }. K; B9 S" K
1. Please design an algorithm that quickly measures the distance between5 }$ N$ t$ @0 X. P6 q: Z4 P' b5 X
two suffiffifficiently long(> 103 bases) base sequences.2 _& C F; R: T, m
2. Please evaluate the complexity and accuracy of the algorithm reliably, and 1 A1 n( {- j, y" m8 c6 k8 e9 Zdesign suitable examples to illustrate it.7 N, r+ @2 S+ t; G( A
3. If multiple base sequences in a family have evolved from a common an * D4 b* b9 O! w/ z/ ^! H+ Dcestral sequence, design an effiffifficient algorithm to determine the ancestral5 i$ S" ~2 @0 G3 w6 I& g
sequence, and map the genealogical tree. / P) x6 g- V% Q6 D9 IReferences$ [1 m9 v( p0 g* N* i o
[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re / |) r4 e. E- Kview of Genetics. 39: 30938, 2005." k! ]$ `) Y' m7 l# p( X2 u7 }- y
[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, $ q( I% }/ r) f. S: het al. “Homology” in proteins and nucleic acids: a terminology muddle and & @: Q3 m& }2 c$ f1 I2 [a way out of it. Cell. 50 (5): 667, 1987. 8 o5 \$ \7 O/ i3 B5 [) f' t$ W7 ]4 r. q$ `% b9 a2 a2 E J
2022& j1 l" P1 S* |4 `! n' F
Certifificate Authority Cup International Mathematical Contest Modeling; P1 ^ I5 a5 z( m, l
http://mcm.tzmcm.cn : g9 B) P. f, y" u+ K& F; S' BProblem C (ICM) , a$ K# R- X5 uClassify Human Activities : b9 R X! V/ T; q9 I8 XOne important aspect of human behavior understanding is the recognition and+ A I' F% T+ G$ {
monitoring of daily activities. A wearable activity recognition system can im* Q( f2 b0 H- X5 ]3 {6 D' H
prove the quality of life in many critical areas, such as ambulatory monitor6 { j9 b5 g( c8 h# w% C+ |
ing, home-based rehabilitation, and fall detection. Inertial sensor based activ7 F) p+ o9 o1 X6 ^) f
ity recognition systems are used in monitoring and observation of the elderly+ V2 ?% } p$ N! j
remotely by personal alarm systems[1], detection and classifification of falls[2],# O# {# q. f' ?) `# C# [; f
medical diagnosis and treatment[3], monitoring children remotely at home or in, i0 o' j3 P; w* h/ C
school, rehabilitation and physical therapy , biomechanics research, ergonomics,! K$ z6 d& s7 T
sports science, ballet and dance, animation, fifilm making, TV, live entertain ' t1 C7 J7 I9 Fment, virtual reality, and computer games[4]. We try to use miniature inertial7 G6 S# g& [# y! f/ r: A5 H& z% o
sensors and magnetometers positioned on difffferent parts of the body to classify + {- r% c' s7 {5 M) d7 [human activities, the following data were obtained./ A2 K! ~2 N1 g+ e) {
Each of the 19 activities is performed by eight subjects (4 female, 4 male, 7 C; z! f7 ?. t/ z" Z# ^1 xbetween the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes! T4 L7 T6 X/ B( J
for each activity of each subject. The subjects are asked to perform the activ4 ?8 a4 @5 E; D" P! d# q. U; Q% [
ities in their own style and were not restricted on how the activities should be/ \* t1 l2 ^! V2 H/ Z
performed. For this reason, there are inter-subject variations in the speeds and' ~4 i3 I; a3 ?& q5 p
amplitudes of some activities. 0 e8 N$ t+ U! zSensor units are calibrated to acquire data at 25 Hz sampling frequency., k8 K4 u1 }( i% j
The 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal' _8 @# y7 V4 ~; K9 s
segments are obtained for each activity. 9 F8 h8 f" r. _The 19 activities are:" h9 b* ^! ]7 Y1 q& H0 P: o6 y5 G# n
1. Sitting (A1); 8 e: Y$ G; }6 S: R# {- G2. Standing (A2); " E4 v$ Y% U4 I! h3. Lying on back (A3);) b# D% M6 B2 d) E; e
4. Lying on right side (A4); r) ^" E$ O4 {5. Ascending stairs (A5);: {; B* R, J3 e" R& r/ V
16. Descending stairs (A6); . _ w. y8 N# \& i- ]- t% a7. Standing in an elevator still (A7);; \& [) x4 E: K6 u! m
8. Moving around in an elevator (A8);4 g* b% |7 [; l7 t2 P0 {
9. Walking in a parking lot (A9);" R. Y2 z6 p5 c
10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg ! A/ `. J. X O' hinclined positions (A10); , u& T: p3 B' M7 f11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions3 ^0 L- Y( r7 s" X. p
(A11);' G& B7 p: F8 P, G7 }+ j
12. Running on a treadmill with a speed of 8 km/h (A12); ; P% m# x9 N& R13. Exercising on a stepper (A13); " w: A, G6 S7 F z5 z d14. Exercising on a cross trainer (A14); + O1 U3 B H# x& }% \4 b15. Cycling on an exercise bike in horizontal position (A15);7 w: W; k* X; L. j$ V! d5 n; [8 @
16. Cycling on an exercise bike in vertical position (A16);# l4 X) B) g% |9 ]( P
17. Rowing (A17);6 j/ Z8 U3 P4 M$ M% L# y
18. Jumping (A18);( U! v' i5 J( L# A; U4 C" t
19. Playing basketball (A19).: O9 e1 C- O, i8 S. p0 o2 B
Your team are asked to develop a reasonable mathematical model to solve |: y7 q, l; R
the following problems.' w& X9 G: j, n2 o7 D% T( Y- W; l
1. Please design a set of features and an effiffifficient algorithm in order to classify $ X( h, C) g }the 19 types of human actions from the data of these body-worn sensors. * Q5 Y* E; g9 X( d2. Because of the high cost of the data, we need to make the model have. U2 x+ O5 u3 {
a good generalization ability with a limited data set. We need to study ! j& p! K7 d. [and evaluate this problem specififically. Please design a feasible method to, J7 f0 e& i+ w; d
evaluate the generalization ability of your model.& Q& v. T( a5 n
3. Please study and overcome the overfifitting problem so that your classififi- , X7 B4 P8 }6 Y/ bcation algorithm can be widely used on the problem of people’s action / e( R4 j+ T; ]1 Xclassifification.: C8 X9 z& {1 U2 |3 \8 ~- Q
The complete data can be downloaded through the following link: 9 ~9 Z6 ~2 t0 dhttps://caiyun.139.com/m/i?0F5CJUOrpy8oq & i$ G6 g M& q+ s! ^2Appendix: File structure 1 n, L$ Q0 D6 j) L1 i/ @( p. v% E• 19 activities (a)) w; v7 r w7 Y; |. {& C
• 8 subjects (p) 8 Y& p# a* d$ ^* |$ j, J5 N• 60 segments (s) " k7 o4 E* L& S2 U% z t# G• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left" v* q( X( X! b3 w
leg (LL)0 [( t+ |) L4 f+ d+ }, t. W; y
• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z & s' u; h" {$ N& p* X c4 tmagnetometers) ; ]6 Y# c7 l( c0 K* R9 [; s' i+ sFolders a01, a02, ..., a19 contain data recorded from the 19 activities./ J' x0 y6 S3 ^$ M; E$ j+ h
For each activity, the subfolders p1, p2, ..., p8 contain data from each of the . S. R' H4 a, K3 j! G8 subjects. 5 ~& F3 z! z1 R C- @In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each& f0 F1 @8 k" e: D1 o& a
segment., i1 _( x9 z! P$ T
In each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25+ e" w0 U* |; O. O: V
Hz = 125 rows.5 H4 y. p" v, S Z! _4 A$ o
Each column contains the 125 samples of data acquired from one of the6 l. H9 u3 m8 E6 H1 e
sensors of one of the units over a period of 5 sec.# R4 E1 C( p* i# r
Each row contains data acquired from all of the 45 sensor axes at a particular 6 u+ Y+ g/ o" [. a: Psampling instant separated by commas. . C( e) R F- U; ^Columns 1-45 correspond to: + x# Z9 b; ~, \3 O1 x) p• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,3 u) T& M2 P8 v7 F% w2 ?
• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag,0 E1 S6 [0 ~+ I Z% V
• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag, v6 u; e7 E( }& ~( j• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag, 8 W7 x5 H0 \$ N& @) R• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag.$ M8 c- b, X3 C9 i8 B& N) G
Therefore,. \: H* W: {$ C% Z% L
• columns 1-9 correspond to the sensors in unit 1 (T), 9 S$ M, l$ p! Q: G: I• columns 10-18 correspond to the sensors in unit 2 (RA), 0 Z# f, e7 W0 _) U) ?% M6 i1 A• columns 19-27 correspond to the sensors in unit 3 (LA),0 y( ]; X; L% X$ H/ X# D
• columns 28-36 correspond to the sensors in unit 4 (RL), . d( B% d2 I8 Y. {( s* S! B/ ?• columns 37-45 correspond to the sensors in unit 5 (LL).. u/ q* i* w3 X2 A
3References4 J" I' d, s4 R5 Z r6 t
[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic- A' g: [. u% u2 O/ }- l
daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. % v9 K6 v) c& v1 k4 A! I42(5), 679-687, 2004 9 y# K X# S! q9 N' w1 ?6 m[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of2 S4 p: G5 f- y) x* q
low-complexity fall detection algorithms for body attached accelerometers. * ?6 J, [/ Z# Q0 G$ UGait Posture 28(2), 285-291, 2008 & z0 s& w! [0 a. i8 m1 Q, Z[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag2 ~9 c8 j2 D6 {6 y. N
nosis method for intelligent wearable sensor system. IEEE T. Inf. Technol. . n5 c6 G% Z0 f2 @6 p; uB. 11(5), 553-562, 2007& Y3 ^$ K; c/ e/ \8 S# C; D
[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con " f7 D" n1 Y8 f% E+ ]1 J/ |% h5 C( ctrol of a physically simulated character. ACM T. Graphic. 27(5), 2008 & i7 T5 d3 b+ D2 D- n/ {' M! ?! O+ H# Q8 Q8 ~2 E3 N
2022 ) E' p! x; G/ U, I. c. zCertifificate Authority Cup International Mathematical Contest Modeling. k. \$ ?) s& F" a/ y% H
http://mcm.tzmcm.cn ' T5 u" a9 K: u7 R nProblem D (ICM)7 H, o; k$ [ K3 g3 o" y
Whether Wildlife Trade Should Be Banned for a Long8 _, ~% G6 p- s* n( m& c1 _5 ]
Time ' g3 E& n# s2 l& y* |5 jWild-animal markets are the suspected origin of the current outbreak and the Z( Z: {& L3 w/ A2002 SARS outbreak, And eating wild meat is thought to have been a source + t8 `% l3 f9 }1 L, F8 pof the Ebola virus in Africa. Chinas top law-making body has permanently3 _8 F9 a8 c2 q3 K
tightened rules on trading wildlife in the wake of the coronavirus outbreak, c5 s) q; Z! K% J2 C4 g8 @which is thought to have originated in a wild-animal market in Wuhan. Some 7 |' X7 c9 \3 E5 z4 C Xscientists speculate that the emergency measure will be lifted once the outbreak( e; @. [- F6 k# X6 a
ends. ; b6 g% v D, VHow the trade in wildlife products should be regulated in the long term? 2 \6 x% F, t7 T; YSome researchers want a total ban on wildlife trade, without exceptions, whereas8 J9 ^) a9 a6 m2 l, ]4 C. |, [6 x
others say sustainable trade of some animals is possible and benefificial for peo: X% c/ Z& k1 k. T1 D% H- @
ple who rely on it for their livelihoods. Banning wild meat consumption could 7 ?3 w% |. ^8 r7 x% `- Ccost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil0 `; ?% _; c# S
lion people out of a job, according to estimates from the non-profifit Society of3 x3 x: }0 J& g% u9 P$ H( e# {
Entrepreneurs and Ecology in Beijing.1 v7 M2 p( f* }$ ?8 L
A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology ) x, a3 {4 S7 Xin China, chasing the origin of the deadly SARS virus, have fifinally found their! K( t: ~& c3 N% Q( T0 P
smoking gun in 2017. In a remote cave in Yunnan province, virologists have# k# [& F3 H; \; L$ g1 K4 j5 \# i
identifified a single population of horseshoe bats that harbours virus strains with K% |! }8 W$ l9 W+ ]# wall the genetic building blocks of the one that jumped to humans in 2002, killing 6 ~1 N/ R$ K# t! a% walmost 800 people around the world. The killer strain could easily have arisen) U6 A: S6 P) D$ R1 Y* N Z
from such a bat population, the researchers report in PLoS Pathogens on 302 E5 p& U. h* B" h& \
November, 2017. Another outstanding question is how a virus from bats in0 \7 s/ W1 M( }& Z; Q6 h
Yunnan could travel to animals and humans around 1,000 kilometres away in 2 s( t( a' n. T4 [+ pGuangdong, without causing any suspected cases in Yunnan itself. Wildlife 4 w. r. S4 Y0 ?trade is the answer. Although wild animals are cooked at high temperature ; \ R# O7 U' S# ]: u7 {5 N9 o! wwhen eating, some viruses are diffiffifficult to survive, humans may come into contact 2 G7 T7 c4 ^+ V+ b( C$ ewith animal secretions in the wildlife market. They warn that the ingredients! l! [! ?* X! o% U$ e2 ~- l( V3 A
are in place for a similar disease to emerge again.4 x- k: J0 {4 G, ~" {9 O
Wildlife trade has many negative effffects, with the most important ones being: 6 F. E" A% y+ y- q1Figure 1: Masked palm civets sold in markets in China were linked to the SARS; y9 H7 a. ], W5 A$ n% v
outbreak in 2002.Credit: Matthew Maran/NPL & z) X' w( M0 X6 J1 e; \1 [• Decline and extinction of populations 9 S1 N0 c/ ]+ x, u• Introduction of invasive species3 u4 j: r3 T! q; j
• Spread of new diseases to humans 5 Y/ j' z4 x1 |/ Z$ \0 a% XWe use the CITES trade database as source for my data. This database5 \; ^: |% P9 r6 U
contains more than 20 million records of trade and is openly accessible. The# L8 q; J: U+ y* M. M+ O$ S
appendix is the data on mammal trade from 1990 to 2021, and the complete 0 C1 }( C+ T4 ]0 U) _( sdatabase can also be obtained through the following link:- x6 b2 H8 t& ]
https://caiyun.139.com/m/i?0F5CKACoDDpEJ( X t! `! C W6 F; ]& `; V* N
Requirements Your team are asked to build reasonable mathematical mod # E* M) P2 p5 h) i& w' Gels, analyze the data, and solve the following problems: * m/ l" B2 _' E u; q1. Which wildlife groups and species are traded the most (in terms of live ! P: J% k s+ c# ~5 eanimals taken from the wild)? + b! x; y; J- d0 w2. What are the main purposes for trade of these animals? ; ]: O0 X* Z0 b# T8 w3 B1 E) x5 [3. How has the trade changed over the past two decades (2003-2022)? - m+ N% o8 m9 P; K" q/ {. w! q4. Whether the wildlife trade is related to the epidemic situation of major4 F% u8 D2 H' d* W. \
infectious diseases? 2 O# G+ r3 k- I" c$ U- Y4 H7 x. ]25. Do you agree with banning on wildlife trade for a long time? Whether it " h# c9 ^ j: ^1 {( f* H4 d* t& mwill have a great impact on the economy and society, and why? ; ]- y" n1 m |0 o9 c6. Write a letter to the relevant departments of the US government to explain& U) K7 m0 H$ k6 t# V& o i. D
your views and policy suggestions.5 f/ Z ~# l0 v6 ?. a9 m5 m
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