2022小美赛赛题的移动云盘下载地址 : c$ V0 j; G- B2 B& _
https://caiyun.139.com/m/i?0F5CJAMhGgSJx3 E8 b T- ? a2 Q$ T2 ?
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20220 K$ p8 ?# H5 w% t- \
Certifificate Authority Cup International Mathematical Contest Modeling/ n. m7 d; t7 K0 x3 W& R& R O
http://mcm.tzmcm.cn4 B F+ h: p% j8 }, m5 H
Problem A (MCM) 0 a1 ^) y+ g) _' S7 O% V$ ?% sHow Pterosaurs Fly# ?5 Z" z0 k6 m% A
Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They ) y9 w, { L, _! ~7 ]3 l& aexisted during most of the Mesozoic: from the Late Triassic to the end of1 L" y% m% }. B9 D* z& I
the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved( e0 H0 _6 h7 m: w+ v
powered flflight. Their wings were formed by a membrane of skin, muscle, and/ {* ?/ x6 }2 V7 o0 i6 `
other tissues stretching from the ankles to a dramatically lengthened fourth6 b2 X1 h& e. t1 ^
fifinger[1]. 5 u: U2 @8 y7 V u/ `1 O& Y- F. WThere were two major types of pterosaurs. Basal pterosaurs were smaller8 w6 A8 M1 t5 ?* n
animals with fully toothed jaws and long tails usually. Their wide wing mem + ?4 d7 |6 Q+ U/ \! {6 pbranes probably included and connected the hind legs. On the ground, they / T$ K D+ Z) H5 b; N0 nwould have had an awkward sprawling posture, but their joint anatomy and. G' C2 U9 K# G) s- X& ]3 e
strong claws would have made them effffective climbers, and they may have lived$ X1 O/ ^, H5 K A0 ~2 J, ~
in trees. Basal pterosaurs were insectivores or predators of small vertebrates.8 Y6 u* ~( S* Q
Later pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. . ~$ E9 _+ j$ c9 [& y6 X. [Pterodactyloids had narrower wings with free hind limbs, highly reduced tails,' `, T+ B( K, |9 U6 i
and long necks with large heads. On the ground, pterodactyloids walked well on , {+ ]& M* a1 {9 c' R3 t& Pall four limbs with an upright posture, standing plantigrade on the hind feet and 7 I5 m0 [$ I& O9 G+ f* }9 R6 kfolding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil: M* A8 h$ X% W! J9 r
trackways show at least some species were able to run and wade or swim[2].8 B3 T2 H1 C" r: C: m; L
Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which % L5 T9 i# T: A. O- K2 ?' ]4 R+ Pcovered their bodies and parts of their wings[3]. In life, pterosaurs would have3 h& }; t4 F% b- y1 Z. l, t. Q
had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug- d) \' C H, }7 _
gestions were that pterosaurs were largely cold-blooded gliding animals, de3 V( G* _" W+ [3 d% D( @7 X
riving warmth from the environment like modern lizards, rather than burning, O9 J2 \) i# w
calories. However, later studies have shown that they may be warm-blooded. V: P$ K, y% t- L+ t. W/ \! |/ U
(endothermic), active animals. The respiratory system had effiffifficient unidirec' I& \0 g6 o- w$ M
tional “flflow-through” breathing using air sacs, which hollowed out their bones6 ?; }5 J: e' |4 p3 j% N$ T
to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from 0 T' P/ l5 j6 [. Zthe very small anurognathids to the largest known flflying creatures, including6 T" ~+ M$ i+ l$ z$ X* @" P
Quetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least * i N, m3 A; U$ xnine metres. The combination of endothermy, a good oxygen supply and strong. K6 D: E( j3 H" S2 j; @+ a: I
1muscles made pterosaurs powerful and capable flflyers.* `7 }6 i; {4 p: o' v# r
The mechanics of pterosaur flflight are not completely understood or modeled' J2 h2 H% ]$ ~( L; j7 D# Q
at this time. Katsufumi Sato did calculations using modern birds and concluded' I2 c, V y/ A4 ?% i
that it was impossible for a pterosaur to stay aloft[6]. In the book Posture,) F- o& ?; Q' g% r
Locomotion, and Paleoecology of Pterosaurs it is theorized that they were able : p) J; i! O9 U0 R- ~& u+ Z$ {to flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7]. % q M2 I4 u9 a6 _ ~5 K- CHowever, both Sato and the authors of Posture, Locomotion, and Paleoecology! |% k& c' C; y3 e2 K, Q3 V
of Pterosaurs based their research on the now-outdated theories of pterosaurs4 s1 j }7 x x0 P* ?
being seabird-like, and the size limit does not apply to terrestrial pterosaurs,- g& U0 R) l6 o& I8 U, j
such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that 7 |: a4 J, M' V* x0 catmospheric difffferences between the present and the Mesozoic were not needed, D9 D* M# {6 {
for the giant size of pterosaurs[8]./ Z/ }/ ?+ `! v3 h
Another issue that has been diffiffifficult to understand is how they took offff. 2 j W9 ^3 r0 R4 g9 o& DIf pterosaurs were cold-blooded animals, it was unclear how the larger ones+ S' M7 x! Q \" F: E
of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage8 ~! X( e$ P$ D+ Q0 w c
a bird-like takeoffff strategy, using only the hind limbs to generate thrust for; @9 v* l" M( V9 u
getting airborne. Later research shows them instead as being warm-blooded) d( I I1 U& }3 ]$ u2 t
and having powerful flflight muscles, and using the flflight muscles for walking as/ ~9 s( [8 X6 k! W* o
quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of. x$ c! b6 T6 K( b- S% a4 V
Johns Hopkins University suggested that pterosaurs used a vaulting mechanism+ t* d6 U! y! B9 B2 M1 c; _
to obtain flflight[10]. The tremendous power of their winged forelimbs would 4 ?5 A' C+ [2 `3 i0 u+ renable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds* E, [! U% K e( Q
of up to 120 km/h and travel thousands of kilometres[10]. , O% U! f& {8 @4 UYour team are asked to develop a reasonable mathematical model of the 1 u1 q6 k1 ]7 A- ?) zflflight process of at least one large pterosaur based on fossil measurements and % y: P) ^& V) l& b# f1 @( A" R* ]to answer the following questions. , ?$ S. W) x u0 I1. For your selected pterosaur species, estimate its average speed during nor 2 l+ p9 Q9 C/ X* g- a1 `mal flflight.6 S" k1 s! y0 Z& B( V/ ]
2. For your selected pterosaur species, estimate its wing-flflap frequency during 4 v, u" l" Q' h/ lnormal flflight. 7 X( ^: ?: h- s0 x3. Study how large pterosaurs take offff; is it possible for them to take offff like; ^: |1 o$ z! `, f# g* a5 Q
birds on flflat ground or on water? Explain the reasons quantitatively.% o8 N. f8 M6 p! J0 J4 y4 o. e
References . R' S4 e; a" C8 o% I[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight1 z% Z7 \. Z% F5 ~8 x$ G4 `
Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111. % P( o; g3 }% M& F2[2] Mark Witton. Terrestrial Locomotion. 2 ~6 _$ M- L& V8 O5 rhttps://pterosaur.net/terrestrial locomotion.php. {0 V( |3 [. G3 e
[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs 2 j4 o( t" B( yWere Covered in Fluffffy Feathers. https://www.livescience.com/64324- ' b1 i* h4 F( C/ xpterosaurs-had-feathers.html! d5 @7 U( o e+ P7 {9 i
[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a2 c' r5 F1 d3 n% a- r
rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea) ; q4 t7 ?# [ Kfrom China. Proceedings of the National Academy of Sciences. 105 (6): $ P8 R9 {& R4 X6 R* v* ]$ [1983-87. 6 q- W& u( R) |6 j i[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust' K6 L: K8 d3 g0 y8 I
skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4): * [/ g# G5 E/ c- N5 G, S4 r0 g180-84.; s& {( D6 |' `
[6] Devin Powell. Were pterosaurs too big to flfly?& }! e" F! ]8 `" A: E7 I) Y6 h
https://www.newscientist.com/article/mg20026763-800-were-pterosaurs I' G2 O! _. L' `- V' h
too-big-to-flfly/# B( C0 M0 q6 q! h; m+ v0 \2 ^
[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology 7 z# a2 I$ X/ Z6 _7 Z; mof pterosaurs. Boulder, Colo: Geological Society of America. p. 60., G N. q) i( o. u* E! S- w3 T
[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable" G" d8 j) q* b$ [1 m; Y
air sacs in their wings. 7 t5 {& S7 {) J# g$ G7 Fhttps://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur / c- u. z8 h/ {' Ybreathing-air-sacs 7 e0 V+ g( {5 v" U' ^; c- M[9] Mark Witton. Why pterosaurs weren’t so scary after all. % ]( d6 j. t4 Jhttps://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils 6 }3 N2 X$ W& B9 `research-mark-witton ; k: S( s K7 t. L+ t[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats? ( S- r0 a/ V7 o* R; n% D, r8 |https://www.newscientist.com/article/dn19724-did-giant-pterosaurs9 A; c# \2 |% o
vault-aloft-like-vampire-bats/ ; Y9 @# V% V- h7 ~: j; C" [' ^ ) @. I& C0 P d) U" ~20226 F9 ^! Q" G! a7 s5 H* c- ]
Certifificate Authority Cup International Mathematical Contest Modeling+ W( b. N. L) e2 Q
http://mcm.tzmcm.cn ( [6 W* w$ r' T6 @% O( v. A7 WProblem B (MCM) 6 Z6 Z! ]; o# d8 C7 AThe Genetic Process of Sequences . ~. ~, h' g F6 uSequence homology is the biological homology between DNA, RNA, or protein + K7 E- }* n9 E. e/ o; q# t. U' Fsequences, defifined in terms of shared ancestry in the evolutionary history of : Q8 Z6 r/ _ \' e& i7 {: n- A( y# \life[1]. Homology among DNA, RNA, or proteins is typically inferred from their8 q" x5 f; ~9 l# ^1 r
nucleotide or amino acid sequence similarity. Signifificant similarity is strong% E8 h. @( Y7 w0 ~5 Z
evidence that two sequences are related by evolutionary changes from a common ; M1 H2 f' o; @ancestral sequence[2].; J0 [% x' m m" S. g$ m
Consider the genetic process of a RNA sequence, in which mutations in nu9 N8 H0 ~) j$ }
cleotide bases occur by chance. For simplicity, we assume the sequence mutation: z; T S" E5 V% T U, o
arise due to the presence of change (transition or transversion), insertion and - C. x& i+ T; g9 |) j! Ddeletion of a single base. So we can measure the distance of two sequences by2 v6 d2 v) ]! K, g: C! M* `
the amount of mutation points. Multiple base sequences that are close together# e. g2 j3 v2 Y8 |
can form a family, and they are considered homologous.) _ f, b; E! I' d' `* x# T- F
Your team are asked to develop a reasonable mathematical model to com. `" v# x' s# a0 b
plete the following problems. g1 t/ z" F1 ?, B& G
1. Please design an algorithm that quickly measures the distance between( ?: @7 P( C* c' l0 V
two suffiffifficiently long(> 103 bases) base sequences. ) R) E! c" i" m9 k1 `2. Please evaluate the complexity and accuracy of the algorithm reliably, and 1 S4 _& @$ N) ?$ R# |design suitable examples to illustrate it.& P; D' h8 T; V( z% Z1 B
3. If multiple base sequences in a family have evolved from a common an 3 ^* x2 `$ x' @9 S% q. m: pcestral sequence, design an effiffifficient algorithm to determine the ancestral: ?) c; q3 j: K4 M ?5 z
sequence, and map the genealogical tree.( d% y$ b- R* ^+ O
References * h6 n5 P8 Z! {4 C[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re 9 i# [8 v7 A) nview of Genetics. 39: 30938, 2005.- n. F, n9 _5 u% F# I
[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE,' o8 h% p. f f+ G# p' d% S; T
et al. “Homology” in proteins and nucleic acids: a terminology muddle and + F) u3 _9 v% S( M6 b" |a way out of it. Cell. 50 (5): 667, 1987. 8 [. T( e$ G' m/ O2 h2 p: b9 Y, ^7 h, X( z1 o
2022 / G% T( x B1 y7 y' MCertifificate Authority Cup International Mathematical Contest Modeling; S* D9 ?* K7 }5 m$ ^* T
http://mcm.tzmcm.cn. }& Q; q. j4 h; `2 j$ a
Problem C (ICM) : T' r7 v5 ~) ~/ F5 D5 V; EClassify Human Activities4 o* B0 {: e) @4 S6 c* r" t
One important aspect of human behavior understanding is the recognition and # e" O$ V! [4 E+ s% s7 D0 Kmonitoring of daily activities. A wearable activity recognition system can im2 l0 K$ e, W/ x& u1 {
prove the quality of life in many critical areas, such as ambulatory monitor . t- N" e. }2 v7 D( q I3 S7 Ting, home-based rehabilitation, and fall detection. Inertial sensor based activ) o& o& d+ e& ^0 Z5 ^% C0 ]7 e
ity recognition systems are used in monitoring and observation of the elderly . u4 ?( X% w7 j& L2 [remotely by personal alarm systems[1], detection and classifification of falls[2],2 u- `: r9 v. c- I; q" h
medical diagnosis and treatment[3], monitoring children remotely at home or in+ a9 S" X e; [, p* x
school, rehabilitation and physical therapy , biomechanics research, ergonomics, 0 e. {* R) }0 Qsports science, ballet and dance, animation, fifilm making, TV, live entertain) V# y& Z7 p7 N7 d' \
ment, virtual reality, and computer games[4]. We try to use miniature inertial ! `/ v( x+ O/ d7 M4 wsensors and magnetometers positioned on difffferent parts of the body to classify , F2 Z5 v$ `% H, [" P$ nhuman activities, the following data were obtained. 0 D0 R% f0 M# N# }. g- u" H- P% fEach of the 19 activities is performed by eight subjects (4 female, 4 male,+ f' q& X; e2 q, K9 k8 `! u3 ?6 q
between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes 2 K2 m# S2 T3 F3 U3 N L+ g" nfor each activity of each subject. The subjects are asked to perform the activ 8 u" [' [& }1 \! x% \ities in their own style and were not restricted on how the activities should be $ C* S; `- o8 K* Z% Tperformed. For this reason, there are inter-subject variations in the speeds and8 {7 m9 Z8 ]1 L# {8 l4 m* [
amplitudes of some activities.7 W' V0 c- l( _) m
Sensor units are calibrated to acquire data at 25 Hz sampling frequency.5 }. q2 b! D; b5 b0 ^" O: S( I
The 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal 6 k* Z) K2 I! C7 e) O( n1 } N4 Zsegments are obtained for each activity.7 N; q# Z$ E- K, D
The 19 activities are: 0 K* w' x7 ^4 `- B1. Sitting (A1); + H: e1 q4 ?9 R; K x) E& E2. Standing (A2);" G8 Y4 Z) j/ M
3. Lying on back (A3);. l4 R7 ^3 t/ y8 X p- U5 N; h
4. Lying on right side (A4);, ?4 V; N: a2 v4 Z C! L
5. Ascending stairs (A5); , @0 e8 U7 ~! F s6 @16. Descending stairs (A6); ! H6 d4 U! ~& r- J7. Standing in an elevator still (A7); : ^/ x) ?- ^6 Y/ Z- _% o; f8. Moving around in an elevator (A8); ' O" ]' ?9 \% d9 h4 G, @$ v( r3 G* z9. Walking in a parking lot (A9); 6 V9 e0 k8 _/ M% X, @ b+ e4 F10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg - B0 l- N- R% F! @% E1 J2 cinclined positions (A10);2 O- K! }* }% v4 i2 Y& k' h
11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions ; O2 f2 Y, {) G(A11); : [, B0 y9 n* T2 R" v4 w0 W12. Running on a treadmill with a speed of 8 km/h (A12); R9 i' m7 ]) T. x/ y* Q8 ?13. Exercising on a stepper (A13); " @# z: s2 e4 d' D) W8 n14. Exercising on a cross trainer (A14); " q, _$ D; H1 y* Q15. Cycling on an exercise bike in horizontal position (A15); 1 `+ R) m# _$ |* D; C- [16. Cycling on an exercise bike in vertical position (A16);0 x: R+ R: Z. W& N$ p u4 g
17. Rowing (A17); 5 ^* j' Z4 i( }$ q; Z* h% e18. Jumping (A18); 7 k: _$ s9 b3 N: o3 _+ C' J19. Playing basketball (A19). 1 o: s5 T! U5 i; jYour team are asked to develop a reasonable mathematical model to solve ) I' C2 S5 Z1 e6 y: g" ithe following problems. / h6 s7 }: u+ i- }2 x% Z8 c1. Please design a set of features and an effiffifficient algorithm in order to classify4 r* K, q# u( b: B7 M2 p
the 19 types of human actions from the data of these body-worn sensors.7 k) Z) Y" j8 W/ Q3 v7 z' L+ |' W2 [% H
2. Because of the high cost of the data, we need to make the model have ; V3 D- y3 A! x( @; z! Wa good generalization ability with a limited data set. We need to study 4 h4 h' @, {! r" w7 U/ F0 T; {and evaluate this problem specififically. Please design a feasible method to! j! s; y+ ^( p- V) [4 Y E$ S
evaluate the generalization ability of your model. & \8 S( I' ?* P3 Y9 [7 {3. Please study and overcome the overfifitting problem so that your classififi- 5 j. e ?2 l' ]( `cation algorithm can be widely used on the problem of people’s action 6 t* L" K1 i' y5 z; ]/ F/ Q/ Kclassifification.+ x; N1 T N7 I+ G3 c
The complete data can be downloaded through the following link: 4 v( c; o8 D3 Y% E Qhttps://caiyun.139.com/m/i?0F5CJUOrpy8oq . N. r0 ~$ D* M( r2Appendix: File structure$ k+ |, v `: t. h3 L$ V; g
• 19 activities (a)% e! h" ]$ t) E" ?4 c* J6 T
• 8 subjects (p) $ S8 Q) J& ^2 c" A• 60 segments (s)3 }. L$ |: a( E% a; N3 W
• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left* E: O. {/ |7 r$ x7 Y& H$ j
leg (LL) : X+ w* @' B0 i; o& l. o# S• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z9 i, @- I# H. A- q! D9 F
magnetometers)" I8 f0 j2 a& g4 [
Folders a01, a02, ..., a19 contain data recorded from the 19 activities. 3 `1 G5 ?+ A2 s: hFor each activity, the subfolders p1, p2, ..., p8 contain data from each of the 8 ]+ F8 k9 F; ?! _- r y8 subjects.$ } v: I, p) e* z3 Y+ C7 m/ S
In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each t9 p q& X, z
segment. / g7 E% f8 c; F7 f/ YIn each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25/ U w1 P1 T" {2 ]" X' a9 B
Hz = 125 rows. ! F- z! l; R8 K% v$ `7 S6 y; @$ sEach column contains the 125 samples of data acquired from one of the& e6 q, X( T# w1 d- |- C7 ~
sensors of one of the units over a period of 5 sec. & a5 t: [& p' D6 K! r% c1 w8 ?Each row contains data acquired from all of the 45 sensor axes at a particular 7 c! \; J: [8 w( B5 \! jsampling instant separated by commas. - G) d/ f) ^1 d. lColumns 1-45 correspond to: 8 V/ T$ B4 n. d3 N( m• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag," Z7 G* `* t" x+ X
• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag, 4 w; n" }+ f$ v• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag, ) q, [% C8 j; A/ r2 ^• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,4 D% k/ z1 J" `" P7 i6 S
• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag. 5 ]% ]# Y. E: h3 Y9 T! oTherefore,( V! `+ ?+ R3 ?3 F
• columns 1-9 correspond to the sensors in unit 1 (T),+ _5 w' O. U; A" K, h9 t2 H
• columns 10-18 correspond to the sensors in unit 2 (RA), ( H; G, t+ ?/ J• columns 19-27 correspond to the sensors in unit 3 (LA), / Z5 N6 g3 o' m6 Y6 w2 O• columns 28-36 correspond to the sensors in unit 4 (RL),8 E9 D0 S4 [ \) o# W$ `* O
• columns 37-45 correspond to the sensors in unit 5 (LL)." ^& x: W( m# ?: a2 [
3References # l/ F9 ]' M6 [ N: C$ d[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic m) G9 }' P" |, _- ?
daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. " \& X& Z' d7 K1 P/ K. y42(5), 679-687, 2004 1 t! S8 o: L0 r[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of z/ d' q9 _. B; ^6 @
low-complexity fall detection algorithms for body attached accelerometers. 4 a* a% d' V6 gGait Posture 28(2), 285-291, 2008 4 b. K! u( [* Q& U8 r$ O. ~. d[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag ' q! O R% e2 O% n5 c, snosis method for intelligent wearable sensor system. IEEE T. Inf. Technol. `* y% O i6 ~3 d3 rB. 11(5), 553-562, 2007 ; Y# |* T- ?+ i5 Y[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con / P1 q3 j8 p) k, ~trol of a physically simulated character. ACM T. Graphic. 27(5), 20087 T* n- k* v. ^; R$ G
: H* O. o" `: D6 _3 b2022 ' X* w1 M% a, B6 j% U! z4 lCertifificate Authority Cup International Mathematical Contest Modeling + a, r. B+ V7 t4 Z+ Bhttp://mcm.tzmcm.cn 4 x; _9 Q+ ]6 L0 GProblem D (ICM) I: S# B6 z) ~+ L; y- } z
Whether Wildlife Trade Should Be Banned for a Long5 @- y' }2 Y/ G* C. c
Time ' S4 z2 [0 f) D. _$ k% {/ ~Wild-animal markets are the suspected origin of the current outbreak and the 1 _7 J+ Q; z5 ^0 R4 x2002 SARS outbreak, And eating wild meat is thought to have been a source , R8 z# u1 p( h) p1 n) gof the Ebola virus in Africa. Chinas top law-making body has permanently: k W: O' U4 O. e+ h4 v
tightened rules on trading wildlife in the wake of the coronavirus outbreak,, J# r" l8 A- e$ |0 g, \
which is thought to have originated in a wild-animal market in Wuhan. Some ' }! t/ f ^8 @" j' u* Iscientists speculate that the emergency measure will be lifted once the outbreak+ J, e# S. }) M& h/ n# T
ends.% N7 A9 B) D+ G6 p
How the trade in wildlife products should be regulated in the long term?2 p4 |; m8 U8 H3 `" d0 |6 l: j
Some researchers want a total ban on wildlife trade, without exceptions, whereas 0 `3 K* N, z9 w0 E2 }others say sustainable trade of some animals is possible and benefificial for peo / t+ X0 G% w; y% J' u/ aple who rely on it for their livelihoods. Banning wild meat consumption could2 Q/ Q5 M5 U8 e! }( L' N
cost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil 4 I- l: D6 ?4 P; e: _lion people out of a job, according to estimates from the non-profifit Society of8 q+ H# D3 V( ~# c- B/ _
Entrepreneurs and Ecology in Beijing. ! v( K& B# B3 X$ T# S8 D& i# [$ ?A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology 2 Q2 o5 x- s2 K C& T8 iin China, chasing the origin of the deadly SARS virus, have fifinally found their / n& p4 g% V5 @ Ysmoking gun in 2017. In a remote cave in Yunnan province, virologists have O$ Z$ {! u* e4 a+ Bidentifified a single population of horseshoe bats that harbours virus strains with9 e* @/ ~+ w" Q' v2 j
all the genetic building blocks of the one that jumped to humans in 2002, killing 2 j; F! w {9 `4 Ualmost 800 people around the world. The killer strain could easily have arisen$ M3 P$ q6 L4 U% w, l$ v" l
from such a bat population, the researchers report in PLoS Pathogens on 300 R+ ^) T3 j& \8 {
November, 2017. Another outstanding question is how a virus from bats in$ a" w0 u! W4 D1 C
Yunnan could travel to animals and humans around 1,000 kilometres away in 6 l M! C7 p D! PGuangdong, without causing any suspected cases in Yunnan itself. Wildlife( f2 h& z5 l$ }, j
trade is the answer. Although wild animals are cooked at high temperature 6 F" J5 W* u' E) Dwhen eating, some viruses are diffiffifficult to survive, humans may come into contact / T* B0 \6 d& j+ @0 z4 swith animal secretions in the wildlife market. They warn that the ingredients; H$ o( S1 X: n4 W) }
are in place for a similar disease to emerge again. 0 v( U* D8 `& ~5 BWildlife trade has many negative effffects, with the most important ones being: 2 O7 j1 ^) A8 {2 l2 J8 `0 `- z& }1Figure 1: Masked palm civets sold in markets in China were linked to the SARS: U! h( S/ p- s0 `
outbreak in 2002.Credit: Matthew Maran/NPL + G1 e' C5 M6 L1 I1 }+ M+ F5 `• Decline and extinction of populations% w2 }: G4 o2 ~* ~% S
• Introduction of invasive species) T9 f$ a B O1 A3 R0 y9 b0 C
• Spread of new diseases to humans1 Q( b7 J+ \- k, n' U. U
We use the CITES trade database as source for my data. This database9 C2 B7 A" e% e0 A; e
contains more than 20 million records of trade and is openly accessible. The- J6 O; M9 }- E( o D i
appendix is the data on mammal trade from 1990 to 2021, and the complete( a, C4 V# X" ` E! t
database can also be obtained through the following link:; V1 W! X G6 d Q! P
https://caiyun.139.com/m/i?0F5CKACoDDpEJ 5 t$ {. \/ _$ J9 t' WRequirements Your team are asked to build reasonable mathematical mod A4 R8 U) p! K1 B m& \
els, analyze the data, and solve the following problems: - i0 k8 k! i: I, X$ {5 M1. Which wildlife groups and species are traded the most (in terms of live* P) @* q# ^) Y7 q4 B, O
animals taken from the wild)? 3 O+ O3 _5 w% W# D4 j2. What are the main purposes for trade of these animals? 1 E) F9 A5 w; c- g6 ^+ C; L1 T0 W3. How has the trade changed over the past two decades (2003-2022)?! p) l( _$ q& G# s+ `0 g" O( V& \
4. Whether the wildlife trade is related to the epidemic situation of major ( N9 b& V* g/ F6 a( Ginfectious diseases? ! C$ f: r _7 w* b25. Do you agree with banning on wildlife trade for a long time? Whether it 2 c( f( B. |1 y5 B( P7 J3 u" Ewill have a great impact on the economy and society, and why? ; `# q# q' I4 \& K6. Write a letter to the relevant departments of the US government to explain8 m+ \, b7 c/ t( d' g" N7 N
your views and policy suggestions.5 w, f$ i/ T; f
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