2022小美赛赛题的移动云盘下载地址 0 q# w; L& F, uhttps://caiyun.139.com/m/i?0F5CJAMhGgSJx $ q# D6 @) Q/ i. }6 Z6 d9 C6 R) ]# V; F2 F- S
20226 a2 M; G& f6 I5 q8 e6 Q
Certifificate Authority Cup International Mathematical Contest Modeling( a/ ~" l4 F& T9 S- W e: o9 }
http://mcm.tzmcm.cn 0 q5 t' c, T2 v- V% O9 d2 S+ ZProblem A (MCM)9 j: W4 Q, B* K; M
How Pterosaurs Fly: h/ Y, w. I# f: b1 ^
Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They$ _2 @2 u- o9 R$ B$ i" n5 o
existed during most of the Mesozoic: from the Late Triassic to the end of ( ]$ w/ v% Y$ \4 u z$ |the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved # y$ H6 x# l# c7 k$ l" Ipowered flflight. Their wings were formed by a membrane of skin, muscle, and 3 M8 C" c6 j: r2 Tother tissues stretching from the ankles to a dramatically lengthened fourth 6 K: @2 R6 `% b+ jfifinger[1]. * d0 `8 j8 t* N% h* yThere were two major types of pterosaurs. Basal pterosaurs were smaller ; M8 ^: g! q# W8 D6 _, V) a. Xanimals with fully toothed jaws and long tails usually. Their wide wing mem9 n* L; I) u; K$ H( N
branes probably included and connected the hind legs. On the ground, they * G( F0 I0 x, a( c/ W) F% C8 Fwould have had an awkward sprawling posture, but their joint anatomy and+ A! C4 k1 n! Y% K6 _
strong claws would have made them effffective climbers, and they may have lived + r4 J$ O, \' E# j6 Nin trees. Basal pterosaurs were insectivores or predators of small vertebrates. & e; Z2 x( i% @' g3 E3 aLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles., L0 `; d5 P D3 ~% v, _4 U8 V
Pterodactyloids had narrower wings with free hind limbs, highly reduced tails,6 }2 _6 E9 A) d5 q7 c# ?- E
and long necks with large heads. On the ground, pterodactyloids walked well on) ]* v+ `& N0 B' r1 M
all four limbs with an upright posture, standing plantigrade on the hind feet and 9 N& e# d$ v9 b$ j$ K9 afolding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil 1 y7 i6 u$ L7 b9 @! u+ Atrackways show at least some species were able to run and wade or swim[2].4 J2 V- | b( ^( K$ q
Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which * m/ Q" \8 z' ^+ P. pcovered their bodies and parts of their wings[3]. In life, pterosaurs would have: d9 x7 p5 H( y8 y7 c, z
had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug8 r7 }. k8 J: |
gestions were that pterosaurs were largely cold-blooded gliding animals, de# m' X& c+ t* m% I0 K7 _! ]8 ~: H
riving warmth from the environment like modern lizards, rather than burning4 Y. l1 n9 t9 M, _3 O2 b) k
calories. However, later studies have shown that they may be warm-blooded) w" u, Z, p, M$ _
(endothermic), active animals. The respiratory system had effiffifficient unidirec; Z x& A' K7 m, j! T7 U
tional “flflow-through” breathing using air sacs, which hollowed out their bones% _' b) @4 x! u; I
to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from % T0 a6 b" x2 Y2 `8 F2 Dthe very small anurognathids to the largest known flflying creatures, including 4 i- ]- @: _3 w% w- O U# NQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least 4 }- w8 S4 M7 A& ~( \% Cnine metres. The combination of endothermy, a good oxygen supply and strong- h8 @: g$ l" g" [ C5 o" {# G
1muscles made pterosaurs powerful and capable flflyers.5 s* z# T; L& k5 z
The mechanics of pterosaur flflight are not completely understood or modeled& O. F! N9 w# b! e0 n5 }- N/ J
at this time. Katsufumi Sato did calculations using modern birds and concluded 4 h+ T/ p, A+ ?* othat it was impossible for a pterosaur to stay aloft[6]. In the book Posture, `( `) U: w6 JLocomotion, and Paleoecology of Pterosaurs it is theorized that they were able % B$ I1 v) s5 J6 _" Q# M1 S3 {3 hto flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7]. 6 i" F l( h4 ~1 g* iHowever, both Sato and the authors of Posture, Locomotion, and Paleoecology6 r; g6 [; A/ o3 t; A
of Pterosaurs based their research on the now-outdated theories of pterosaurs8 ]" V2 w3 b% R- n3 J) p( y
being seabird-like, and the size limit does not apply to terrestrial pterosaurs,( }3 \& L, W6 u6 m1 ~6 ~/ p
such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that ) I" Z e3 C' S3 E% }# f: T1 Vatmospheric difffferences between the present and the Mesozoic were not needed # }/ J, s0 Y3 W1 V3 Y% lfor the giant size of pterosaurs[8].; }8 V/ Z+ y+ O' p# V( B6 N h* W5 @
Another issue that has been diffiffifficult to understand is how they took offff./ B2 L) [+ c# H! l
If pterosaurs were cold-blooded animals, it was unclear how the larger ones ( E; L' p# o. D- v* B0 b; v$ dof enormous size, with an ineffiffifficient cold-blooded metabolism, could manage . y1 q6 T* U! w! u9 O! va bird-like takeoffff strategy, using only the hind limbs to generate thrust for " Q9 D; A" E- C. B3 N: p" zgetting airborne. Later research shows them instead as being warm-blooded 7 u* H) Y7 l$ K/ i) qand having powerful flflight muscles, and using the flflight muscles for walking as 0 A; l, T& F5 Iquadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of 5 Q# ]# U" A9 k0 n3 F' n" K$ `; IJohns Hopkins University suggested that pterosaurs used a vaulting mechanism 2 Z0 s& @- S& p0 ]9 k4 f" V8 x1 ?to obtain flflight[10]. The tremendous power of their winged forelimbs would d- l2 V' a. Denable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds % d" U# |8 |& t. a$ s+ ?1 @of up to 120 km/h and travel thousands of kilometres[10]. / W- l3 Z' t6 DYour team are asked to develop a reasonable mathematical model of the8 i; i$ n6 p2 N6 l" \3 @. F, Y
flflight process of at least one large pterosaur based on fossil measurements and' ]" n+ o# q/ I; V" Y( E8 z
to answer the following questions.) Z( ^3 x1 w$ _! L0 g% k: J% r
1. For your selected pterosaur species, estimate its average speed during nor0 I5 c5 [$ Q; y! ]
mal flflight. ; x: z: J+ Y: Z1 q1 s2 ^$ B2. For your selected pterosaur species, estimate its wing-flflap frequency during " }" E. u+ {5 X. n# K( ~+ D) s) {normal flflight.8 o9 D$ F8 f% W5 l, m
3. Study how large pterosaurs take offff; is it possible for them to take offff like& _, m9 ]( ?! J+ ~
birds on flflat ground or on water? Explain the reasons quantitatively. - L/ T1 k) g9 Y# `* wReferences" x X! |1 F8 i G1 l6 P9 c* J! c
[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight- Q( b3 M4 V; I# Q
Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111. $ [ t3 f% m$ h8 [$ @1 D2[2] Mark Witton. Terrestrial Locomotion.+ V% t, W0 K1 C0 Z5 a
https://pterosaur.net/terrestrial locomotion.php4 s, A, n W* {9 _
[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs ) D; P# L) u( [2 c* S% ~Were Covered in Fluffffy Feathers. https://www.livescience.com/64324-; m$ x! ]3 |1 s4 M# h+ n# \
pterosaurs-had-feathers.html0 Q! K- f# g# g4 }- \3 N
[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a: U1 ^. a& b" S8 Y4 q" N
rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea) 6 W( ` p- @, N& yfrom China. Proceedings of the National Academy of Sciences. 105 (6):6 T) J$ n% b9 ~: x) l3 X8 S' G) ^# ^
1983-87. 2 ]/ q$ q* B& D2 ]/ [7 W# t[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust( L$ V* R2 T* T8 ^9 X2 |
skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4):. j j! r( Y- B" Q
180-84.1 Y9 E% @" X5 \7 ]3 F1 P( f
[6] Devin Powell. Were pterosaurs too big to flfly?" Y- r% w" C. c% x9 B
https://www.newscientist.com/article/mg20026763-800-were-pterosaurs( f2 x% J$ I- R5 Y* x$ a( O, M
too-big-to-flfly/ 7 W2 o2 `2 B. l) [' G+ Q. {[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology & G8 Q- R, r& sof pterosaurs. Boulder, Colo: Geological Society of America. p. 60.% U/ F, g/ G4 V! T5 ~
[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable * _( M. F' H$ {# T9 jair sacs in their wings. 5 p8 u" I' y# g) z- ?https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur) N0 k& }% I+ a! u, `
breathing-air-sacs% ^& `- Z) \; v; C% n
[9] Mark Witton. Why pterosaurs weren’t so scary after all. * q2 T" X/ H' ^, A, H1 Qhttps://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils - F* {7 s- {& @3 I) \research-mark-witton ) S% M8 d& l* \5 f. `. ?[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats? # c7 b- _& Z, v/ u; B( Uhttps://www.newscientist.com/article/dn19724-did-giant-pterosaurs8 [7 ]; |3 W8 I) B }
vault-aloft-like-vampire-bats/ w$ @) F# v( O: j: ~# }7 s 9 y }) d# M. D4 w: w# L# x' A2022* ^, x! V8 n3 Y+ X) b. `
Certifificate Authority Cup International Mathematical Contest Modeling' N5 @/ P! q- R+ @+ g( _ h% _
http://mcm.tzmcm.cn ! j g- g/ Z7 M9 U% ^# CProblem B (MCM)0 v& K! v- j* ?4 ?/ e" Z4 a
The Genetic Process of Sequences c. C! d$ l, ]4 V
Sequence homology is the biological homology between DNA, RNA, or protein1 x1 Q$ I& e# ~2 e
sequences, defifined in terms of shared ancestry in the evolutionary history of$ e0 x; b; z4 k4 o2 U
life[1]. Homology among DNA, RNA, or proteins is typically inferred from their6 `9 |8 C o2 M) `6 z
nucleotide or amino acid sequence similarity. Signifificant similarity is strong& Q) b/ K) e. k/ n% ^; V2 g
evidence that two sequences are related by evolutionary changes from a common 4 U0 r* q3 q% X) Mancestral sequence[2].- P# o+ Z# G# {' ^6 f+ x
Consider the genetic process of a RNA sequence, in which mutations in nu E. [7 `, r, N# l
cleotide bases occur by chance. For simplicity, we assume the sequence mutation' D! g) ]! M |( r; s* G
arise due to the presence of change (transition or transversion), insertion and % u7 P# T x0 ?1 G" A& x3 odeletion of a single base. So we can measure the distance of two sequences by 3 \) E1 T5 p n2 ^. sthe amount of mutation points. Multiple base sequences that are close together3 d" p; R1 U( A. Q/ _0 I
can form a family, and they are considered homologous. 7 x: e7 R2 c; _; t$ \! @Your team are asked to develop a reasonable mathematical model to com) `. j2 {* A8 J1 k
plete the following problems.% ?# s a0 t% c- r
1. Please design an algorithm that quickly measures the distance between 4 C, S7 h% c7 i6 D$ S, _two suffiffifficiently long(> 103 bases) base sequences.1 R9 @: ]& m: i: {
2. Please evaluate the complexity and accuracy of the algorithm reliably, and # |" }% |. i8 E4 R4 b; jdesign suitable examples to illustrate it.4 L% z0 b9 x) n3 ?: W" l6 P( y
3. If multiple base sequences in a family have evolved from a common an" K, l7 b5 @$ ?0 s/ t
cestral sequence, design an effiffifficient algorithm to determine the ancestral , W. S3 ^, w1 Nsequence, and map the genealogical tree. " i/ u: [$ z$ T' BReferences , M! ^& K# n5 g3 R/ g0 A; C8 J[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re 0 _* e ?: p& n9 d6 w- q* p7 Iview of Genetics. 39: 30938, 2005. % J* i$ V6 G+ ~% R[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, + t3 c4 o, \4 e _5 C$ o( }et al. “Homology” in proteins and nucleic acids: a terminology muddle and1 \: @5 m2 f" o& Z' D3 Q0 M
a way out of it. Cell. 50 (5): 667, 1987. 5 [- ~ S2 F, D( _& O/ ]7 q 1 h7 x0 W7 Z4 ~2022 3 x) p2 R! t M4 j! ?7 k" wCertifificate Authority Cup International Mathematical Contest Modeling * R% V, j, V) p. g. }3 Y- t$ Zhttp://mcm.tzmcm.cn! @# z( ~* t- \3 O: _* x% B
Problem C (ICM) / V- ]7 U7 K& ]6 n/ ]# JClassify Human Activities. t, |6 n6 x$ x, _6 O" T8 g: s, `7 ^9 k
One important aspect of human behavior understanding is the recognition and- \' S' Q: @4 ]) R9 h
monitoring of daily activities. A wearable activity recognition system can im & h3 O+ f/ \) P% [! N `prove the quality of life in many critical areas, such as ambulatory monitor6 @6 J* _: B N$ q6 H3 c
ing, home-based rehabilitation, and fall detection. Inertial sensor based activ' g- L" |$ s3 Z8 r: R
ity recognition systems are used in monitoring and observation of the elderly4 z" ]" S6 t0 D l0 a# A6 x
remotely by personal alarm systems[1], detection and classifification of falls[2], & Q& j% Q4 O, Z: A$ Jmedical diagnosis and treatment[3], monitoring children remotely at home or in2 F: g/ A9 Q1 F8 _4 q) l6 g& g
school, rehabilitation and physical therapy , biomechanics research, ergonomics, 6 T+ n) M. u8 [2 Qsports science, ballet and dance, animation, fifilm making, TV, live entertain ) I# R5 P) g2 f' Q. dment, virtual reality, and computer games[4]. We try to use miniature inertial7 M1 W* x/ B9 s) o
sensors and magnetometers positioned on difffferent parts of the body to classify0 s$ B) J0 t; s) k; S6 e
human activities, the following data were obtained. 3 @3 A0 V; }# e: H5 N6 g( SEach of the 19 activities is performed by eight subjects (4 female, 4 male,3 P" Z P$ O2 r9 ?
between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes ( O1 j9 s0 }2 I% Z0 Q& J3 afor each activity of each subject. The subjects are asked to perform the activ + D2 \9 S& y/ a, Z# h/ r- T' M1 Dities in their own style and were not restricted on how the activities should be i O5 k& W. w+ L) g$ I% p* V
performed. For this reason, there are inter-subject variations in the speeds and 9 j: K5 [) f. P* damplitudes of some activities.8 y9 i; q8 J6 V4 z/ T5 H
Sensor units are calibrated to acquire data at 25 Hz sampling frequency. & t( |" F; n3 m) QThe 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal 9 N. }# }2 j2 y8 w6 Q0 D7 Esegments are obtained for each activity. 2 A9 d: p8 V0 }The 19 activities are:# L, u7 C0 t$ h5 f5 D! z; [7 f
1. Sitting (A1);7 j% }5 k4 `' W; N7 @. V% E
2. Standing (A2); $ D1 e1 s$ D L" E% X3. Lying on back (A3); $ j# Q( f# L' c. c6 J4. Lying on right side (A4); 0 \$ B0 `2 x/ ?. \" V' \5. Ascending stairs (A5); 0 K" S) R; G0 `9 }+ q5 G16. Descending stairs (A6); ' n8 S0 `$ _' Y R+ i7 J1 S7. Standing in an elevator still (A7);5 u3 E& N6 \/ _" U/ ~
8. Moving around in an elevator (A8); ; q0 J* p8 t* O0 J$ ?, Z9. Walking in a parking lot (A9);% P! R, T, s; X% m' E, {
10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg+ Z1 b, U$ b# E( Z& K# b$ ^' G. v5 P$ R
inclined positions (A10); ' d% M+ n) d- }11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions# y$ q6 }9 y7 Q$ ^' P
(A11); ) G" {" @& X9 @/ i; I4 ]' L2 @3 S12. Running on a treadmill with a speed of 8 km/h (A12); . _, P' ^: N1 N8 }3 L" w: r13. Exercising on a stepper (A13);) s* |0 O; F9 x. |* |7 c" l
14. Exercising on a cross trainer (A14); " ?7 I6 W6 {, S' X4 z7 ~15. Cycling on an exercise bike in horizontal position (A15);' o0 h1 Z5 u; G, b* W x
16. Cycling on an exercise bike in vertical position (A16); 3 v0 U9 E0 P5 L17. Rowing (A17);2 d4 q0 b7 [) c) h. M/ g# ?. Z5 u
18. Jumping (A18); * _' @/ z- b( `. M19. Playing basketball (A19). 0 T# I' y& S- D: b: YYour team are asked to develop a reasonable mathematical model to solve , Q5 s8 u* y9 Ethe following problems.3 c# {6 i/ ^9 l
1. Please design a set of features and an effiffifficient algorithm in order to classify ( j9 E; q# f4 h4 e$ Ythe 19 types of human actions from the data of these body-worn sensors. ; o: k- e# E2 Z% R* Q2. Because of the high cost of the data, we need to make the model have0 s: E% g7 s! H( Q4 `
a good generalization ability with a limited data set. We need to study H5 o+ K. a0 `/ K% Q2 U rand evaluate this problem specififically. Please design a feasible method to$ x* _+ D# i% `: Y7 R
evaluate the generalization ability of your model. 2 w5 N# O9 o1 U- |% H3. Please study and overcome the overfifitting problem so that your classififi-* _$ z% B' H, `# {
cation algorithm can be widely used on the problem of people’s action' {# |, s4 f: {, w1 `
classifification.; N- R7 o4 X' M" H
The complete data can be downloaded through the following link:8 H+ w: f0 k/ N( G
https://caiyun.139.com/m/i?0F5CJUOrpy8oq# j+ P: \$ `/ H5 H0 b
2Appendix: File structure8 L, j- j# m, H4 I
• 19 activities (a) 7 o' Q; r0 z E2 b# g• 8 subjects (p) 9 O8 B' G+ t3 |• 60 segments (s)+ }4 I% ]9 C* z( G! a; o. V
• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left! I9 P/ E, n' ?! b* e1 o0 j! e
leg (LL)4 ~, M U) a1 N [0 m
• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z ( r9 Y! j5 s0 P( Gmagnetometers)( g6 j7 B4 l0 T& Q
Folders a01, a02, ..., a19 contain data recorded from the 19 activities. - w6 X R' H4 M, }+ O0 l" qFor each activity, the subfolders p1, p2, ..., p8 contain data from each of the: y* F0 Q( X; c9 U0 i+ J
8 subjects. $ F' j7 i6 t; ], P1 ]1 LIn each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each + F9 w, y6 m: g/ t4 l9 ~% esegment.4 ?: T$ {9 e: s& o
In each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25 G) Z% E9 ^+ W. T9 FHz = 125 rows.# I( m0 s0 r. U1 I3 K& F( t
Each column contains the 125 samples of data acquired from one of the5 y9 s! Q$ ~8 q' e e6 x* R
sensors of one of the units over a period of 5 sec. : h" `, N" v; W" L* N6 q3 w3 eEach row contains data acquired from all of the 45 sensor axes at a particular* `- N5 h$ B/ O$ h8 S
sampling instant separated by commas., U% a" D% [4 ]" n# { {
Columns 1-45 correspond to: 8 [1 n1 i7 n' C4 l# z• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,' b6 Y/ v' m% e# S
• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag, @+ c& f9 j" o6 {7 c
• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag, 5 T/ N: z. |% a5 m" @ }2 f) A• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag, 2 ?1 Y/ p3 f: ~. C; y+ F• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag. 1 ~7 x. V8 W: D1 v* G- dTherefore, , e$ E' j+ p( V• columns 1-9 correspond to the sensors in unit 1 (T), * M3 k7 Y/ C* Y: G1 V! K5 f• columns 10-18 correspond to the sensors in unit 2 (RA), 8 u2 E, U/ Q: ?: B8 Z: [• columns 19-27 correspond to the sensors in unit 3 (LA),9 V4 `7 ?0 [8 \7 g7 B$ `* ^( y
• columns 28-36 correspond to the sensors in unit 4 (RL), k4 B* ?: X0 Y) T! N% H/ F• columns 37-45 correspond to the sensors in unit 5 (LL). 5 Y5 M: T# T( v7 x# u; a3References ( z+ O( L( { \- h7 L[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic \3 e [" ~4 L9 r+ t) F6 bdaily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. ! o, r1 I. l7 R, h42(5), 679-687, 2004 1 |7 L5 O; L% O7 j) O' n[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of' k# ~1 [& X3 K! S# }8 D
low-complexity fall detection algorithms for body attached accelerometers. ; U& ?0 `1 q4 ?6 j8 cGait Posture 28(2), 285-291, 2008( A/ L" e- {5 |9 ^/ o6 N
[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag : J* R7 D' S3 X5 {7 ~) knosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.9 u' L- S6 g" B |& ^8 L
B. 11(5), 553-562, 2007- l% y, f! | @$ p/ L/ \
[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con: S7 D, ^- z* N6 \! c8 M
trol of a physically simulated character. ACM T. Graphic. 27(5), 2008 / ]* G5 [0 O* ^ r Y' p' a8 }/ C( r
2022 y* j8 o! B* {' L- Z, u
Certifificate Authority Cup International Mathematical Contest Modeling 7 `5 ^( R9 a" Ghttp://mcm.tzmcm.cn$ l2 }) S- h' i
Problem D (ICM)8 W7 H, u7 Y& @+ \
Whether Wildlife Trade Should Be Banned for a Long: @4 F, v9 J9 L1 u9 y
Time: R( }( N7 r, J! m" R2 N, E
Wild-animal markets are the suspected origin of the current outbreak and the # T. R0 ~6 {( Y) M( E l6 ?! C2002 SARS outbreak, And eating wild meat is thought to have been a source - I+ u7 m1 d7 }- l/ Q6 v$ ~5 s, R) eof the Ebola virus in Africa. Chinas top law-making body has permanently B2 r1 t" z5 h* {tightened rules on trading wildlife in the wake of the coronavirus outbreak, 6 y5 f. Z5 w4 C& {8 cwhich is thought to have originated in a wild-animal market in Wuhan. Some ( h6 @; m' j% Z) Bscientists speculate that the emergency measure will be lifted once the outbreak 6 K: O: d- y* m9 ?3 V9 C. {ends.. ~4 C0 L5 _' \, [
How the trade in wildlife products should be regulated in the long term? ; e3 j' p- j1 g7 [. bSome researchers want a total ban on wildlife trade, without exceptions, whereas. Z, |# m- g8 |
others say sustainable trade of some animals is possible and benefificial for peo7 x+ h T' R& Y1 W4 d, F: C* j
ple who rely on it for their livelihoods. Banning wild meat consumption could9 T1 x. m" N% _% R1 A1 Z
cost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil & I$ s. U6 m ulion people out of a job, according to estimates from the non-profifit Society of & V2 V, n! m* O# E9 }! C2 OEntrepreneurs and Ecology in Beijing.8 }& @8 M7 N4 D0 W6 f8 A3 {0 ^! A" M- w
A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology. G) _8 w; s% \; `+ V+ m) W
in China, chasing the origin of the deadly SARS virus, have fifinally found their; B1 Z1 d& V( p
smoking gun in 2017. In a remote cave in Yunnan province, virologists have + M$ X. Z. G& N; Q x$ H; C$ yidentifified a single population of horseshoe bats that harbours virus strains with - Q* v, m$ T) Yall the genetic building blocks of the one that jumped to humans in 2002, killing7 q+ h5 ]3 J' b' P4 e* B0 q
almost 800 people around the world. The killer strain could easily have arisen& o7 k7 G& h9 n" V9 A- M
from such a bat population, the researchers report in PLoS Pathogens on 30# r( M( U x- p. f$ L
November, 2017. Another outstanding question is how a virus from bats in 6 `( S) j2 x$ }- Z% VYunnan could travel to animals and humans around 1,000 kilometres away in* c! M) u* J! ^" M# E8 X7 }
Guangdong, without causing any suspected cases in Yunnan itself. Wildlife1 M- a- [1 z# L
trade is the answer. Although wild animals are cooked at high temperature 4 [! D1 N$ [' K: f4 B) Y" bwhen eating, some viruses are diffiffifficult to survive, humans may come into contact ) d2 R# R2 E% z5 o' ]* ewith animal secretions in the wildlife market. They warn that the ingredients O# n0 q# e6 D4 C4 X dare in place for a similar disease to emerge again./ f( R! Y% k- W
Wildlife trade has many negative effffects, with the most important ones being:2 N. t+ M$ k+ M& n0 P6 K
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS5 o% ?5 A% O% q) w
outbreak in 2002.Credit: Matthew Maran/NPL$ B' c9 Q4 m. E. B2 L; F
• Decline and extinction of populations+ y* |5 p) Q1 N
• Introduction of invasive species8 b* H; Z" p7 Z% }/ H9 Q1 I1 G
• Spread of new diseases to humans5 u8 O C4 j! n" E1 L: X
We use the CITES trade database as source for my data. This database6 C6 _1 `$ M4 s) b* y0 k# m
contains more than 20 million records of trade and is openly accessible. The 2 R9 t; N, |% S$ G- z) happendix is the data on mammal trade from 1990 to 2021, and the complete 2 L( ^: p5 L7 C- O9 O& a$ ?database can also be obtained through the following link: 8 S( T! l0 _3 ?0 |2 x% ^https://caiyun.139.com/m/i?0F5CKACoDDpEJ 8 o# Z, ?5 Y5 Q1 F. yRequirements Your team are asked to build reasonable mathematical mod% b$ j+ ]" I8 n/ h
els, analyze the data, and solve the following problems: ) q) }+ }7 s! e- }1. Which wildlife groups and species are traded the most (in terms of live 0 I) p: }2 K5 i% W! A# D$ Fanimals taken from the wild)? ( X4 ?/ ~9 F* S- H0 A2. What are the main purposes for trade of these animals? j" G0 J1 V7 V$ \& |- p& T5 R2 s. g
3. How has the trade changed over the past two decades (2003-2022)?. `9 a, B7 `6 o$ z& y+ n
4. Whether the wildlife trade is related to the epidemic situation of major2 `0 W$ \1 G$ L# v6 H/ z M
infectious diseases?* z: B" F. a g& t- g5 P2 e5 v, [
25. Do you agree with banning on wildlife trade for a long time? Whether it s* q* B- x# U) ~, ^
will have a great impact on the economy and society, and why?4 d2 a* s* X, Y9 A4 z
6. Write a letter to the relevant departments of the US government to explain " z) E% ~& L$ ^your views and policy suggestions.+ ~. @( m* z/ Z' b* M
8 i6 b: L/ v, N. P" U
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8 z. D3 d' x6 o