2022小美赛赛题的移动云盘下载地址 " R& A" X0 @' S" l/ i1 chttps://caiyun.139.com/m/i?0F5CJAMhGgSJx 9 ~, N0 [3 c6 s+ Q$ x: R8 P& u6 ]! h& \
20228 u1 V: w; p0 d" r( m4 |
Certifificate Authority Cup International Mathematical Contest Modeling7 {( h: } w2 @; b$ b7 W
http://mcm.tzmcm.cn X, Y5 q5 K9 d rProblem A (MCM)) T7 N6 t' `, T& K1 I& w* `4 K7 G3 k% S
How Pterosaurs Fly , j) D8 ~/ E$ Y1 _Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They; K# k! ^: V$ {. ~1 B) ~
existed during most of the Mesozoic: from the Late Triassic to the end of ; ^5 X& E3 s7 a( ?the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved $ U# g( }& l% epowered flflight. Their wings were formed by a membrane of skin, muscle, and 6 @# y+ u& @' y) n2 d; @ a" j' ]other tissues stretching from the ankles to a dramatically lengthened fourth. ^# V9 z* x3 G% S" |5 X( q
fifinger[1].5 q3 L# ~+ m4 o1 |, Z& R" @
There were two major types of pterosaurs. Basal pterosaurs were smaller- Q7 `3 w7 V8 z- f7 C. H
animals with fully toothed jaws and long tails usually. Their wide wing mem * \( s2 k' c7 jbranes probably included and connected the hind legs. On the ground, they ; v, X8 G9 M" z: \* z2 `would have had an awkward sprawling posture, but their joint anatomy and 8 X; c# m( O' R: v) v2 b4 kstrong claws would have made them effffective climbers, and they may have lived3 l# ?4 u/ X$ C$ f; ]: z4 F
in trees. Basal pterosaurs were insectivores or predators of small vertebrates. 0 a6 p: p1 m7 W7 OLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles.$ s8 v) U6 h! b* H. b# Z& V
Pterodactyloids had narrower wings with free hind limbs, highly reduced tails,6 d: W. f0 a! ], I7 a( H
and long necks with large heads. On the ground, pterodactyloids walked well on, r! _* T6 b) }$ }# E
all four limbs with an upright posture, standing plantigrade on the hind feet and # u5 q* |0 O3 F Cfolding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil - b m% o+ }% T2 j5 ltrackways show at least some species were able to run and wade or swim[2]. - G6 Q& c% l( }/ y7 ]" tPterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which1 M% D! e6 U% l: { H5 x* h0 D
covered their bodies and parts of their wings[3]. In life, pterosaurs would have" ?# v8 n8 F0 I C: `
had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug $ L( T# P6 u4 k3 N9 M+ Cgestions were that pterosaurs were largely cold-blooded gliding animals, de + z t% T- N. p( |/ w% C# Criving warmth from the environment like modern lizards, rather than burning% N- l$ O/ }7 A) h
calories. However, later studies have shown that they may be warm-blooded * }+ ~; D: L! e- V3 s(endothermic), active animals. The respiratory system had effiffifficient unidirec5 R! e0 ?* _+ C! |$ i1 _
tional “flflow-through” breathing using air sacs, which hollowed out their bones ' T; H: p0 F: cto an extreme extent. Pterosaurs spanned a wide range of adult sizes, from 5 I/ v' t9 q: Zthe very small anurognathids to the largest known flflying creatures, including0 |0 R1 B1 e8 F
Quetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least% m9 b& W' g+ z9 ]* A s8 ^
nine metres. The combination of endothermy, a good oxygen supply and strong# K6 J4 [6 r. y, c0 w2 e) M
1muscles made pterosaurs powerful and capable flflyers.- u! f- l0 ?/ i8 o" m
The mechanics of pterosaur flflight are not completely understood or modeled% {' X9 `! r9 S0 p$ {4 Q: D- _
at this time. Katsufumi Sato did calculations using modern birds and concluded0 J ~4 R3 w. K) Q$ h( _
that it was impossible for a pterosaur to stay aloft[6]. In the book Posture, ! s$ b/ V7 U; H( t8 y) p tLocomotion, and Paleoecology of Pterosaurs it is theorized that they were able # o6 X. V! r1 h. N: x1 Qto flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].$ u4 V% W7 o& g6 b% h% ]# X3 C
However, both Sato and the authors of Posture, Locomotion, and Paleoecology9 v& V9 w( H: }- u8 `
of Pterosaurs based their research on the now-outdated theories of pterosaurs q3 m t9 m4 k+ y1 G% _being seabird-like, and the size limit does not apply to terrestrial pterosaurs, 3 T% ^* s2 A8 _. ]1 ^such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that; a- V. k& m3 l. Z
atmospheric difffferences between the present and the Mesozoic were not needed% O: f7 S# m" Z& J4 g2 g) R% z6 k- w
for the giant size of pterosaurs[8]./ E) W1 _( n) P1 I7 x4 v+ [
Another issue that has been diffiffifficult to understand is how they took offff.+ g6 {$ ~ \; J8 i$ k4 {
If pterosaurs were cold-blooded animals, it was unclear how the larger ones' y- J+ ]3 O$ ?( y, D
of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage5 |& T0 o- K/ G8 l: Q& f* Y {
a bird-like takeoffff strategy, using only the hind limbs to generate thrust for9 q; ] e* \5 ?/ N
getting airborne. Later research shows them instead as being warm-blooded# I/ P/ z+ C) K
and having powerful flflight muscles, and using the flflight muscles for walking as4 _$ _- ^7 @4 x$ [2 S3 F* ^: G
quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of7 m! ~: M3 {4 o9 w" u6 l( F, O
Johns Hopkins University suggested that pterosaurs used a vaulting mechanism3 f& m' A) ]& e$ f# h3 j- \
to obtain flflight[10]. The tremendous power of their winged forelimbs would/ r/ n6 i$ I7 D9 |9 }" H+ E. a
enable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds2 n, c- S( f4 r! L6 |1 ?7 @1 ?
of up to 120 km/h and travel thousands of kilometres[10]. 8 [+ c3 c. R7 ]5 H; D" lYour team are asked to develop a reasonable mathematical model of the# d D1 H& a F; N: \& E$ `+ n
flflight process of at least one large pterosaur based on fossil measurements and % [1 r3 I3 v% w u) G S0 U* Qto answer the following questions.' Y3 }* d; K! h2 ^
1. For your selected pterosaur species, estimate its average speed during nor* D# O; Z! ^0 `* w
mal flflight. * [7 L4 N* m5 G4 S; {4 `2. For your selected pterosaur species, estimate its wing-flflap frequency during 3 v H% T% x1 Z- hnormal flflight. 2 e/ j& |) F3 G3. Study how large pterosaurs take offff; is it possible for them to take offff like 5 ?$ V; P1 y0 |2 j4 t8 b. W3 I" \birds on flflat ground or on water? Explain the reasons quantitatively. , ]! ]0 [+ p2 \0 s+ w& PReferences n9 b [% w, J$ q) A[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight - I% T5 }) ]8 g4 Y$ A$ ~Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111. : N" i/ H6 f: D# C7 \2[2] Mark Witton. Terrestrial Locomotion. 4 v7 i8 h1 M5 i! Nhttps://pterosaur.net/terrestrial locomotion.php2 I4 B0 q$ ?# D8 }) p
[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs) w* h& U b0 o1 t. Z
Were Covered in Fluffffy Feathers. https://www.livescience.com/64324- / h, _9 x) ?0 G# T- Tpterosaurs-had-feathers.html 2 H! b5 k! H* V) S6 W& w4 X[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a $ i$ W* x J8 F; q/ \% {9 D1 K S2 Wrare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)& ^; W7 j; d- y: z# _- P+ R
from China. Proceedings of the National Academy of Sciences. 105 (6):. j2 ^' ^' Z! s ~
1983-87.$ J" `1 E: s0 c+ X
[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust 5 \( e, a8 K& X; ~ D5 m9 }( D; t9 n9 |skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4):9 K: F. v; p7 d8 E
180-84.: H, Q3 b5 k* ~6 O
[6] Devin Powell. Were pterosaurs too big to flfly? 6 z; p! u Z. Q3 [* n" b* rhttps://www.newscientist.com/article/mg20026763-800-were-pterosaurs2 e) ^6 ?8 [* A0 Q1 i. ~3 O
too-big-to-flfly/ 3 l2 W5 a" e1 t9 R[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology0 R* W2 u/ {- @' U, Z! M
of pterosaurs. Boulder, Colo: Geological Society of America. p. 60. % K" G. J( x: c[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable9 [" k9 ^8 ^, u+ V& r
air sacs in their wings.' d) a) s0 Y1 V: G
https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur + M; M9 R+ ]& \breathing-air-sacs 1 |$ j0 Q; t: O$ n[9] Mark Witton. Why pterosaurs weren’t so scary after all. ! q# x! h7 N1 i! M F9 i" H; Dhttps://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils # G% [' z2 e2 X ?' kresearch-mark-witton0 {; c u% i4 n4 s; b
[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats? , I- F, C) h% H. R+ M* |% o0 zhttps://www.newscientist.com/article/dn19724-did-giant-pterosaurs ; y7 n& a; H# W& ?, ?vault-aloft-like-vampire-bats/1 a- L; b5 u/ N; b H. x
7 l9 q- @9 ?, L2022 * s0 K. L0 Q- G- I' v2 eCertifificate Authority Cup International Mathematical Contest Modeling' t: P* M& z+ @) r* s2 o
http://mcm.tzmcm.cn , b5 Z/ i# x8 n6 u. uProblem B (MCM)5 K# ~ t( y& n3 j5 a8 R3 e0 `. [
The Genetic Process of Sequences4 A9 a! L) d% L% W# e
Sequence homology is the biological homology between DNA, RNA, or protein9 _1 Q! T* n& H* x
sequences, defifined in terms of shared ancestry in the evolutionary history of ) q# K! x8 X( z z% J4 Q- wlife[1]. Homology among DNA, RNA, or proteins is typically inferred from their2 ~/ _4 V* z* O" y2 q4 V% d
nucleotide or amino acid sequence similarity. Signifificant similarity is strong 8 l2 M9 v3 G( S, v# v5 I( D2 ~evidence that two sequences are related by evolutionary changes from a common 0 E0 A* _& ~" q* ~4 _5 |% aancestral sequence[2]. ; h3 ^$ E) R% Y I# `3 BConsider the genetic process of a RNA sequence, in which mutations in nu x0 G3 ?* |: m7 J( ^2 Zcleotide bases occur by chance. For simplicity, we assume the sequence mutation% L+ f/ o w7 R5 S
arise due to the presence of change (transition or transversion), insertion and% ] Z4 Z% N+ U4 \2 _0 s+ n& e
deletion of a single base. So we can measure the distance of two sequences by - B( R/ J0 t9 { ~1 ?the amount of mutation points. Multiple base sequences that are close together H" ^) y# Y7 o0 Q6 j& {
can form a family, and they are considered homologous." T8 X1 }) I$ t9 \* e, p
Your team are asked to develop a reasonable mathematical model to com6 q5 L: Y- O* U8 U1 b- c- H
plete the following problems.; A+ k r9 L9 W: V1 J9 T/ u
1. Please design an algorithm that quickly measures the distance between # I# b, @- F9 n7 |0 c) B% Y2 Xtwo suffiffifficiently long(> 103 bases) base sequences. 4 l& `) k% e7 p2 K$ j0 J2. Please evaluate the complexity and accuracy of the algorithm reliably, and( q7 B8 Z# I, e. z3 e
design suitable examples to illustrate it. . v5 M1 ?5 _1 p8 K3. If multiple base sequences in a family have evolved from a common an5 h/ \+ j. R* `! M
cestral sequence, design an effiffifficient algorithm to determine the ancestral: o1 ? B$ D8 d% t! F
sequence, and map the genealogical tree.7 ]$ M0 x. d; U
References g& F) z& g. C5 T# }* u0 B[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re& W/ f( p: F; s) |$ E a( f7 v0 b
view of Genetics. 39: 30938, 2005. 0 k9 L+ d, n8 A[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE,0 }( H$ |$ {3 [3 t; J
et al. “Homology” in proteins and nucleic acids: a terminology muddle and: y* |' X$ ]9 R8 M/ P
a way out of it. Cell. 50 (5): 667, 1987. " `4 r5 A( ^5 {- n: f3 f0 z( o8 f$ l9 I
2022# |# a* u4 ~; `8 |* J
Certifificate Authority Cup International Mathematical Contest Modeling2 x( g6 H7 i! {7 K A
http://mcm.tzmcm.cn 3 J2 p3 O6 u. ZProblem C (ICM) $ i0 o% Z! k9 e! u" a/ UClassify Human Activities 1 w9 ?& f3 I! m! d# i# Y, o9 lOne important aspect of human behavior understanding is the recognition and 7 ]3 f& w/ G% T& t7 a0 o4 T. [6 Fmonitoring of daily activities. A wearable activity recognition system can im + P$ t0 [' O( z% t: Oprove the quality of life in many critical areas, such as ambulatory monitor3 V; Q" i7 N$ b; V5 O+ I
ing, home-based rehabilitation, and fall detection. Inertial sensor based activ ) F* p& H+ `+ R; mity recognition systems are used in monitoring and observation of the elderly ( L/ a- d* }% Q5 cremotely by personal alarm systems[1], detection and classifification of falls[2],1 f# _8 f. p& P, @5 [. z
medical diagnosis and treatment[3], monitoring children remotely at home or in& ]( u8 Y3 x' A0 }
school, rehabilitation and physical therapy , biomechanics research, ergonomics, " w, @& w' p' @) hsports science, ballet and dance, animation, fifilm making, TV, live entertain" {" D5 J7 z3 ^- G! x8 W3 q
ment, virtual reality, and computer games[4]. We try to use miniature inertial% {) n* D, [* \ u# _8 C$ h
sensors and magnetometers positioned on difffferent parts of the body to classify t% V% A5 J. X h3 Y7 @6 o
human activities, the following data were obtained. ' }2 X7 G$ b5 g/ u0 m9 nEach of the 19 activities is performed by eight subjects (4 female, 4 male, 8 k( D3 z$ H& M9 c4 M; w$ p( c: d6 }between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes/ A: H. Q! h2 t
for each activity of each subject. The subjects are asked to perform the activ . T5 E4 F2 f6 v& j1 K7 Gities in their own style and were not restricted on how the activities should be % D4 k9 I0 B m( @* j' }7 Mperformed. For this reason, there are inter-subject variations in the speeds and" e+ w; o8 t1 Q( b, k, k
amplitudes of some activities. , [5 Q9 a2 ^+ r7 J6 E$ S q& RSensor units are calibrated to acquire data at 25 Hz sampling frequency. % [/ a$ O. ]# r$ w/ u9 pThe 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal ) f1 D' u0 j: ?segments are obtained for each activity., `: ~, h, @/ b5 y0 a: o3 a! r2 x
The 19 activities are: o0 R9 K1 {1 Y; k# v8 I( c
1. Sitting (A1);& K3 W) H, w7 W; _) B- V- i
2. Standing (A2);( S, E0 j# ?3 w/ g7 G P/ t* y# M
3. Lying on back (A3); ' c8 g* T. A$ x1 p0 G" J( i4. Lying on right side (A4);; K$ y4 Z- o& f; V5 Z( {
5. Ascending stairs (A5);* h& x+ q9 X! D
16. Descending stairs (A6);- X! \% x; P% E% [1 {; Z
7. Standing in an elevator still (A7); / B& Q# M$ ^4 D" A2 X9 v8. Moving around in an elevator (A8); 2 t" A3 f6 }7 p! J; E' M& v4 E9. Walking in a parking lot (A9);5 ? [" H- @5 B2 e* `. i4 e$ K
10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg 9 K9 B% L4 ], d V) |# N4 a0 tinclined positions (A10); 5 z5 E$ E. j' g% a( H11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions4 I" P1 O |) k4 |+ | S6 s- B
(A11); S+ e h6 Y0 Z9 {+ B
12. Running on a treadmill with a speed of 8 km/h (A12); ) t2 }* r& G! p5 L13. Exercising on a stepper (A13);7 ?+ w0 A; c) @( c! x+ I D' W
14. Exercising on a cross trainer (A14); * J/ T! D* y L0 C15. Cycling on an exercise bike in horizontal position (A15); & [; X- ~% k4 |0 r- v* |16. Cycling on an exercise bike in vertical position (A16);4 z) ~ k. ^% z# {" c* {
17. Rowing (A17);+ G0 H2 T& C; P- I0 E- H9 u7 R
18. Jumping (A18); 7 ?0 i( h2 E# C( ?4 k19. Playing basketball (A19).8 V0 m( U8 D. n8 n7 x6 [
Your team are asked to develop a reasonable mathematical model to solve + B" Y4 W* @# ?( P u! A) ithe following problems. . a9 ~0 K% B/ W1. Please design a set of features and an effiffifficient algorithm in order to classify' S8 M& ^/ A( n' O, Q/ |/ U5 p/ M
the 19 types of human actions from the data of these body-worn sensors. 0 f, h5 S0 n% y; p; `) s2. Because of the high cost of the data, we need to make the model have 9 {5 l- w+ R Ca good generalization ability with a limited data set. We need to study6 G: Z6 g4 ]# M+ s
and evaluate this problem specififically. Please design a feasible method to |# a5 r0 @* vevaluate the generalization ability of your model. ( f( w3 S; w3 s+ x6 Q- d" ~3. Please study and overcome the overfifitting problem so that your classififi- ; S0 t) {' L% N8 K4 Ccation algorithm can be widely used on the problem of people’s action # J: r2 k0 S5 ^9 R, q ^ P- xclassifification. 8 D2 C9 o3 R5 s. R0 A8 c" {& RThe complete data can be downloaded through the following link: 0 U- U; x2 J4 V7 ]6 T/ fhttps://caiyun.139.com/m/i?0F5CJUOrpy8oq 3 `& B" g5 i8 R4 ^6 _ Z2Appendix: File structure, e v3 H# i0 t1 g
• 19 activities (a)4 i* d7 d' z, Z$ ~4 [: f
• 8 subjects (p) A) }2 Y! t+ N* _6 I7 D3 Y" ^& j- k• 60 segments (s) J; I; x+ [ [) Q/ F• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left 3 a. p' [& U4 }: Dleg (LL) ! r. N; o" X" o; F' m• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z 5 [/ B. N& k( d' Ymagnetometers) - C3 @9 r3 G3 e/ fFolders a01, a02, ..., a19 contain data recorded from the 19 activities.' V0 |3 _) i% [0 H# s
For each activity, the subfolders p1, p2, ..., p8 contain data from each of the) O5 N0 E [+ ~* ?9 U1 `) T1 T3 |
8 subjects. 8 M# M4 ~& e6 G: H d) ~In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each2 ^8 \: }8 w2 }4 U6 G' x
segment. # Z- d& q1 P# ?8 j! u g7 {In each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25) Y5 F4 `1 P% s
Hz = 125 rows. . b5 P7 A' U" } P' S& O' yEach column contains the 125 samples of data acquired from one of the . A1 F; d+ }: w- Osensors of one of the units over a period of 5 sec. 4 w" V4 b9 ~% O) B& ]Each row contains data acquired from all of the 45 sensor axes at a particular , T5 I! @& H2 M, N* ]5 Z0 R4 _) b0 Y% @sampling instant separated by commas. K5 G' U" ]5 T$ V. JColumns 1-45 correspond to:( s7 Z: O) X3 B2 Z# @
• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,% ]0 A- k! P/ a" P% b( C
• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag,1 G# O% ^/ C! Z& `# o
• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag, 5 p# b* n3 J2 H0 r+ m) U4 _• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag, % |1 }* k' \/ n" U• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag. : o: b# C( ?, q1 zTherefore,, c* x; T6 E, a. i( ~2 S/ M% w
• columns 1-9 correspond to the sensors in unit 1 (T),5 m' W! ]6 z- t! |5 i
• columns 10-18 correspond to the sensors in unit 2 (RA), ( H; v9 Y2 |" |" u* p, e i& g• columns 19-27 correspond to the sensors in unit 3 (LA), 3 C" i0 {, _% A O- q8 Q• columns 28-36 correspond to the sensors in unit 4 (RL), 3 U# W% h+ Z% u6 s, D• columns 37-45 correspond to the sensors in unit 5 (LL). 0 Z5 K/ n4 x& N8 p% a8 [& w! S3References 1 u1 q7 t* j7 N4 x& d[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic $ c- x7 v! r6 L l( Odaily movements using a triaxial accelerometer. Med. Biol. Eng. Comput./ {! z, y/ X. R5 y
42(5), 679-687, 2004; D b- j+ r8 _. c. X- b" x$ Y- r P
[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of 1 i3 l* V/ e7 z9 I8 G5 Xlow-complexity fall detection algorithms for body attached accelerometers. % G0 R2 Y" z% Y6 VGait Posture 28(2), 285-291, 2008! F7 t0 I1 y6 k2 W/ g& ~
[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag , a" K r6 L' j) t! \/ B0 X/ znosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.4 W8 T9 r# {2 r$ Z7 ^
B. 11(5), 553-562, 2007 ( H! Y! c* r" w) ~2 b[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con 7 {5 \0 i* x3 Ntrol of a physically simulated character. ACM T. Graphic. 27(5), 2008 7 ~5 Y5 X4 L0 Y) W5 s+ M: D' m& N2 }: _
2022% z! F' x. o. P2 ^3 U" D9 @* a& C2 r: x
Certifificate Authority Cup International Mathematical Contest Modeling : I4 R, Y% m; Shttp://mcm.tzmcm.cn 9 P" p& d8 ?3 I/ L1 Y1 \9 w5 OProblem D (ICM) L9 \6 d, W7 `
Whether Wildlife Trade Should Be Banned for a Long ! [, z. i u, U8 U6 _Time , O' _$ ~6 \% ~# mWild-animal markets are the suspected origin of the current outbreak and the* m& a5 s- l; r, k& S- H; n
2002 SARS outbreak, And eating wild meat is thought to have been a source* ]1 y3 J. y8 W, _; S. \: V1 @
of the Ebola virus in Africa. Chinas top law-making body has permanently% d; `0 b7 U! }
tightened rules on trading wildlife in the wake of the coronavirus outbreak,3 j7 b- r0 C; l0 [5 S
which is thought to have originated in a wild-animal market in Wuhan. Some3 L) D8 ]" ~* R! `+ | ?7 K
scientists speculate that the emergency measure will be lifted once the outbreak 8 z" K+ a6 q/ M, P2 Pends. $ A* P! m# Y) y( E9 _: PHow the trade in wildlife products should be regulated in the long term?0 o `. I y& d, L
Some researchers want a total ban on wildlife trade, without exceptions, whereas , O' I/ R0 Q+ o: X, t: u. C) eothers say sustainable trade of some animals is possible and benefificial for peo ' @5 d: @( R w2 }* b: dple who rely on it for their livelihoods. Banning wild meat consumption could . T8 c& G, u7 f5 dcost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil 3 C/ }& d% {' O0 rlion people out of a job, according to estimates from the non-profifit Society of7 g, y8 P x6 a. g' e
Entrepreneurs and Ecology in Beijing.8 H" U9 i' B. b) v- y1 u
A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology2 H, C+ F( z( e2 H& T
in China, chasing the origin of the deadly SARS virus, have fifinally found their * c' m9 D. s; m: ^9 Hsmoking gun in 2017. In a remote cave in Yunnan province, virologists have 3 e5 U3 y; I9 p' u/ }+ Cidentifified a single population of horseshoe bats that harbours virus strains with$ l( ?/ u6 H! [9 o. d/ V
all the genetic building blocks of the one that jumped to humans in 2002, killing ^7 ?, c. W' L; Zalmost 800 people around the world. The killer strain could easily have arisen9 N$ i. `8 V) S: X4 M# B
from such a bat population, the researchers report in PLoS Pathogens on 30 ; L, o1 D3 ?1 ~/ V+ hNovember, 2017. Another outstanding question is how a virus from bats in( }/ U8 t! ~' X
Yunnan could travel to animals and humans around 1,000 kilometres away in: H; \5 Y. ^) P
Guangdong, without causing any suspected cases in Yunnan itself. Wildlife4 E" }- F/ ?: A0 E& K! F2 Z. B
trade is the answer. Although wild animals are cooked at high temperature0 |3 c( ?, n6 X1 s
when eating, some viruses are diffiffifficult to survive, humans may come into contact; E0 ^9 D' {4 g' b7 f+ ~
with animal secretions in the wildlife market. They warn that the ingredients # \& q! N8 ?+ j X7 B! W7 Care in place for a similar disease to emerge again.' x3 G1 c5 ^! Z: p
Wildlife trade has many negative effffects, with the most important ones being:' r6 j# ^ E- q6 |3 C4 f& Y
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS $ l+ F- |* l1 {9 ?) F" T: ~outbreak in 2002.Credit: Matthew Maran/NPL: B" w. D% Y- j6 u$ z4 l
• Decline and extinction of populations* R) E! g/ I. o- q& P. H g
• Introduction of invasive species( L' O7 t; [/ I2 z& M7 J# v/ F
• Spread of new diseases to humans) Z+ { ]. e3 X J" Q7 Z5 s! C7 F
We use the CITES trade database as source for my data. This database, u1 ]% L, h `2 q6 {
contains more than 20 million records of trade and is openly accessible. The- m5 V1 Z- r3 i2 M1 u
appendix is the data on mammal trade from 1990 to 2021, and the complete 2 n2 E+ Q0 x+ Z, x& f& d8 udatabase can also be obtained through the following link: ! d" K8 M( i. dhttps://caiyun.139.com/m/i?0F5CKACoDDpEJ # Q( z5 G# J6 e' G3 jRequirements Your team are asked to build reasonable mathematical mod6 F% ~( W3 K( q4 N2 v. J) e" {8 ^5 `
els, analyze the data, and solve the following problems:! v1 b: D& o& ^- [, D% X; U' ~2 }
1. Which wildlife groups and species are traded the most (in terms of live & K. X9 }3 Q/ a5 R' f6 G% Sanimals taken from the wild)? # f5 a! ~3 o' s2. What are the main purposes for trade of these animals?; o1 B( `; g$ j6 g( o& C
3. How has the trade changed over the past two decades (2003-2022)? ( j3 u8 I3 j o& O. E/ K3 O4. Whether the wildlife trade is related to the epidemic situation of major4 {8 d) P6 L) F8 h2 f4 U" [
infectious diseases? 2 y5 z8 O8 A% d25. Do you agree with banning on wildlife trade for a long time? Whether it 5 \3 N6 `7 i5 ?9 Swill have a great impact on the economy and society, and why? 3 T ^) ] b' d8 ~6 P0 W6. Write a letter to the relevant departments of the US government to explain! Q, D5 e+ I) Y! f0 _
your views and policy suggestions. 6 ?. w* Q& v) r7 P" J : A2 s+ v' m& n7 G2 i9 h. ~ 7 s, |* g" Y7 c. x3 ?- b0 _; r9 O- O+ e2 ]9 O
C' @% _! B2 B6 `+ o7 z8 ~9 H ! w0 _1 w% Z X& S' ^ 6 ]) }" J" I1 R- p1 t2 y9 T% }$ ~) S1 h