2022小美赛赛题的移动云盘下载地址 : j& }7 Q0 q: s0 o+ T1 h4 rhttps://caiyun.139.com/m/i?0F5CJAMhGgSJx $ F" I5 `% Q7 C9 ]7 a, Q9 T( F8 T$ q
2022 4 A) }/ X; O$ Y7 v3 Y& R2 QCertifificate Authority Cup International Mathematical Contest Modeling & e1 p6 l1 P! h8 T8 D ^. v- jhttp://mcm.tzmcm.cn9 r8 n2 j# e) o
Problem A (MCM) : l( i2 m! e2 XHow Pterosaurs Fly6 \! d3 @. E! [$ B) c& a- f6 w: |5 ]5 [
Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They 0 h i5 h6 X( M, l+ ?9 x! texisted during most of the Mesozoic: from the Late Triassic to the end of4 Y* r% R; |2 ~; m, z/ I& G0 p
the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved3 s' @- ?2 O% M3 x
powered flflight. Their wings were formed by a membrane of skin, muscle, and ( ^ G6 u4 d1 m. b2 I5 m# [# ]other tissues stretching from the ankles to a dramatically lengthened fourth + R! l6 c8 ^ x4 Q6 }; efifinger[1]. ( k d" L2 d q7 S0 v* sThere were two major types of pterosaurs. Basal pterosaurs were smaller4 g* d6 n# j! d2 s. x
animals with fully toothed jaws and long tails usually. Their wide wing mem 7 i2 O7 o" o1 J4 Hbranes probably included and connected the hind legs. On the ground, they / @, {% r! v" p$ U6 Wwould have had an awkward sprawling posture, but their joint anatomy and) z4 c& i `- J5 j' r' f
strong claws would have made them effffective climbers, and they may have lived 1 X4 |# x9 Y3 P- tin trees. Basal pterosaurs were insectivores or predators of small vertebrates. 0 U7 y( q: }8 W5 _) b D* A; TLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. 7 `% v- v# a' k9 X/ }Pterodactyloids had narrower wings with free hind limbs, highly reduced tails,0 V3 m1 \, h7 a7 {6 x' o; A
and long necks with large heads. On the ground, pterodactyloids walked well on & c6 d! d n* f6 I h. Ball four limbs with an upright posture, standing plantigrade on the hind feet and8 T% Q. m+ P6 W- n
folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil$ a2 d, [0 Z' ~8 e- M$ e: _, D
trackways show at least some species were able to run and wade or swim[2].8 P( f! C: g2 s
Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which# K5 p3 I3 [7 j0 }4 {1 H& U3 b
covered their bodies and parts of their wings[3]. In life, pterosaurs would have ! U$ ^4 u0 n# u- m5 z* Ghad smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug/ C2 R; v* ^' w, y d1 }& u
gestions were that pterosaurs were largely cold-blooded gliding animals, de* k7 x9 h- p8 s# ~- q5 S
riving warmth from the environment like modern lizards, rather than burning8 g& M4 G, P3 E. c' B
calories. However, later studies have shown that they may be warm-blooded # ]1 I( k! j! y N(endothermic), active animals. The respiratory system had effiffifficient unidirec 1 S" n* R* T: A/ Y y" [tional “flflow-through” breathing using air sacs, which hollowed out their bones - j% d4 C% D s5 A, @) oto an extreme extent. Pterosaurs spanned a wide range of adult sizes, from. C; O3 z a# u) I8 C* Z
the very small anurognathids to the largest known flflying creatures, including 7 w6 I. c1 l" C, }, CQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least) t3 x" a4 b8 a, F6 n- f
nine metres. The combination of endothermy, a good oxygen supply and strong3 X' g, Q) n' d: D8 p; X
1muscles made pterosaurs powerful and capable flflyers.6 @ X4 r5 d" s+ ?4 r
The mechanics of pterosaur flflight are not completely understood or modeled : H2 R- M! O+ r3 _3 ~at this time. Katsufumi Sato did calculations using modern birds and concluded 7 T) k! V* D( W& Z" ] d- V- A7 cthat it was impossible for a pterosaur to stay aloft[6]. In the book Posture, $ Y& M* q5 p: g! ] q, yLocomotion, and Paleoecology of Pterosaurs it is theorized that they were able ) J6 k0 Z3 V0 _ Bto flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7]. 5 Q( ]7 B" g6 S, K: j7 u+ @However, both Sato and the authors of Posture, Locomotion, and Paleoecology . E2 ^3 L2 p2 c0 kof Pterosaurs based their research on the now-outdated theories of pterosaurs! F5 Z4 K; f! ~5 d( k
being seabird-like, and the size limit does not apply to terrestrial pterosaurs, ( B* D6 I, o% c/ u! D/ g7 w% B1 [. j* Hsuch as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that 9 @0 n; n* g$ O+ X- x# ^3 ratmospheric difffferences between the present and the Mesozoic were not needed % g. G. ~ Q( n3 Wfor the giant size of pterosaurs[8]. $ P- c) o; k8 n+ V2 f+ cAnother issue that has been diffiffifficult to understand is how they took offff.7 [& m4 _( ]1 N8 c# D1 ~' U
If pterosaurs were cold-blooded animals, it was unclear how the larger ones& }! s: |, N: p0 |( H' C
of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage& P! t8 b6 u3 |" N( c. K$ p
a bird-like takeoffff strategy, using only the hind limbs to generate thrust for3 O) @4 U7 Y$ O6 n% |& _
getting airborne. Later research shows them instead as being warm-blooded5 I8 o' Z7 o: c+ c8 t& u
and having powerful flflight muscles, and using the flflight muscles for walking as ) a) P; ^6 W$ Z& ^! Iquadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of0 M7 ]2 ^. j. M/ B
Johns Hopkins University suggested that pterosaurs used a vaulting mechanism 7 ]. f/ d, t8 U" i Kto obtain flflight[10]. The tremendous power of their winged forelimbs would6 p6 o5 M7 [- _4 a4 ~% Y
enable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds ' B+ Y2 |9 n: t) e4 q3 ]of up to 120 km/h and travel thousands of kilometres[10].3 a+ `0 n8 K: A$ w' T
Your team are asked to develop a reasonable mathematical model of the ) l9 H4 t9 ~& \1 P1 Z4 `flflight process of at least one large pterosaur based on fossil measurements and ) J3 R/ c. A0 J1 F- M% pto answer the following questions.+ F2 ^( u$ C, u4 N' z/ n0 \! c9 L- j6 w
1. For your selected pterosaur species, estimate its average speed during nor / a" G# A% g5 Z6 G7 P/ ~% b8 Jmal flflight. 3 U8 Y% j" _' |" b `, v: D7 S! r2. For your selected pterosaur species, estimate its wing-flflap frequency during % x/ N6 v9 S1 Z* J! \/ {normal flflight.' B% Y9 C7 [4 r9 i1 v
3. Study how large pterosaurs take offff; is it possible for them to take offff like1 W6 g. S Y8 V. f* s1 s( L
birds on flflat ground or on water? Explain the reasons quantitatively. 9 a5 P, e, _; g7 |! b! F) aReferences$ m9 C4 M4 ^9 F9 b) F
[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight7 H+ f3 A* i' a7 W& _0 e
Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111.* V4 P, d) f' J4 Q" ?4 Z0 d( F1 C
2[2] Mark Witton. Terrestrial Locomotion. " N; D% m( ? x1 p: S% {$ Z4 X2 ^+ yhttps://pterosaur.net/terrestrial locomotion.php ' X) F0 G( M7 ]! v# |[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs . j6 u+ ~! c/ M6 ~- tWere Covered in Fluffffy Feathers. https://www.livescience.com/64324-) |# N' n# D$ L2 y4 }5 _2 a8 ?# ^
pterosaurs-had-feathers.html# @0 L N/ l/ E: T! p
[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a- C9 L h2 I, l7 j/ }, H' `: q) I
rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea) 5 m; ^# j+ G; R' S3 r/ C2 Ufrom China. Proceedings of the National Academy of Sciences. 105 (6):- F! s; p6 B+ U. ?
1983-87. 5 L3 H" {" ~0 {7 \9 {" d[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust/ G" J7 F+ k' _
skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4):$ d* N2 B4 y; x# X, v1 T
180-84. / n! @0 [# ^; o7 W$ M[6] Devin Powell. Were pterosaurs too big to flfly? V( T+ m- g2 z* M F) p
https://www.newscientist.com/article/mg20026763-800-were-pterosaurs 2 Z$ @; ^: t) L% N# Ktoo-big-to-flfly/ ( }2 E) z9 q$ m7 U$ b$ d[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology $ m# c" E- H' G2 B) m% y' ]+ Vof pterosaurs. Boulder, Colo: Geological Society of America. p. 60. 4 X E* x) I0 y- [[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable) G- V- Y3 w6 P* O1 o; v! t* X
air sacs in their wings. " L8 g6 i6 j0 rhttps://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur- S2 x1 i( ~. E+ @
breathing-air-sacs + [1 b0 r7 h# [0 `2 l! Y[9] Mark Witton. Why pterosaurs weren’t so scary after all. 5 [0 l! }( G2 ]6 f Nhttps://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils + J9 S% h/ Q2 H! @0 p& aresearch-mark-witton0 m/ z; ~1 F& Q& a8 S: _# H
[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?1 a0 t" \( @" s P# o
https://www.newscientist.com/article/dn19724-did-giant-pterosaurs; o" H6 n) g# b2 P: U/ e; g
vault-aloft-like-vampire-bats/: k! a4 o }5 H1 F8 h- o: U+ V
) F; f0 e/ E- E h2 S! c2022 % h' P/ j: P; Z8 _( h+ dCertifificate Authority Cup International Mathematical Contest Modeling/ V8 ^% _( O- U5 ~* S& p! z% j
http://mcm.tzmcm.cn4 F; y x1 U& o! h* G0 c6 n0 Q
Problem B (MCM) 9 k! m- G7 L8 l. U1 l' I. yThe Genetic Process of Sequences 2 v0 @: B# R! t' _* ^& f- WSequence homology is the biological homology between DNA, RNA, or protein7 G& h- ` q0 {3 W9 y
sequences, defifined in terms of shared ancestry in the evolutionary history of1 ?, f( g0 l) V: r: `6 z* k K
life[1]. Homology among DNA, RNA, or proteins is typically inferred from their0 z: b' a) y* f# g/ J, E0 L
nucleotide or amino acid sequence similarity. Signifificant similarity is strong# a9 P2 G9 J1 t& K, M) r9 k
evidence that two sequences are related by evolutionary changes from a common 8 x1 x5 W8 I$ c2 V$ Wancestral sequence[2].! k. r7 [$ Q# F' L9 q" d
Consider the genetic process of a RNA sequence, in which mutations in nu/ ^0 q" F" ~ C6 d8 `5 u$ p) Q, l
cleotide bases occur by chance. For simplicity, we assume the sequence mutation( C0 v+ s9 E4 Q
arise due to the presence of change (transition or transversion), insertion and) B3 F& z3 v* f% ~
deletion of a single base. So we can measure the distance of two sequences by % n- m1 k, F0 {8 G3 L! t) R0 cthe amount of mutation points. Multiple base sequences that are close together/ j- D! y/ [+ ^- s( k2 d
can form a family, and they are considered homologous. ?- D. O- e2 S( {2 x4 X+ aYour team are asked to develop a reasonable mathematical model to com 3 O! `- X/ c, O7 O+ k vplete the following problems. 5 U: U0 e- q1 t8 {: l$ v [1. Please design an algorithm that quickly measures the distance between; i* Y1 h- N) C! i$ s" }
two suffiffifficiently long(> 103 bases) base sequences.; ^1 N% T2 x" a" A$ ^. u) o0 X: N
2. Please evaluate the complexity and accuracy of the algorithm reliably, and9 O N, B- p$ O' @- o! [4 k
design suitable examples to illustrate it. . F8 o+ c7 w6 ?- j3. If multiple base sequences in a family have evolved from a common an5 H, A4 A& \6 p$ a. n( h7 |4 b
cestral sequence, design an effiffifficient algorithm to determine the ancestral, c0 H( v) m7 b8 O6 r' W5 ~6 X7 o
sequence, and map the genealogical tree. r! Q, s: Y0 q! S% a/ T+ [& {References 7 b# p6 T5 q- I* N( l[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re7 z; T3 K* \& b# e
view of Genetics. 39: 30938, 2005.- n( L% c* J d: t
[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, 6 J" r. I& }* Cet al. “Homology” in proteins and nucleic acids: a terminology muddle and t" o: S0 ?$ E- Oa way out of it. Cell. 50 (5): 667, 1987.8 N2 F$ a) j( r/ K; @- V% `
- P- C5 d; ]* ]1 P& ?( j2022 + r# r* ^2 y2 a" }Certifificate Authority Cup International Mathematical Contest Modeling 6 X( A* _4 C* Z4 b7 M* V) j( nhttp://mcm.tzmcm.cn 8 G" }* y" D5 o/ F9 p7 x$ GProblem C (ICM) b V+ h5 ~+ X4 K4 bClassify Human Activities * h/ P5 }# }4 ]$ lOne important aspect of human behavior understanding is the recognition and ! e# \: B' ]" q4 Y2 J1 jmonitoring of daily activities. A wearable activity recognition system can im8 v% x" Z8 b8 T
prove the quality of life in many critical areas, such as ambulatory monitor " c0 g! ]; D0 D9 m" L$ n! wing, home-based rehabilitation, and fall detection. Inertial sensor based activ4 ?9 V. r- S1 U. T; J$ k5 D
ity recognition systems are used in monitoring and observation of the elderly & S0 X& K5 D- j7 ~4 premotely by personal alarm systems[1], detection and classifification of falls[2],8 d! |3 T9 B7 x- N5 Y
medical diagnosis and treatment[3], monitoring children remotely at home or in 3 |0 G0 O4 t" _; p- Dschool, rehabilitation and physical therapy , biomechanics research, ergonomics,9 c; \8 D' ~* X! Y8 o! V1 o
sports science, ballet and dance, animation, fifilm making, TV, live entertain& C r) b4 c1 G- i" v
ment, virtual reality, and computer games[4]. We try to use miniature inertial; _) r: y, s; ?* F
sensors and magnetometers positioned on difffferent parts of the body to classify! ^. D7 a- i! N/ `- z
human activities, the following data were obtained. 9 ~1 G2 i$ E; QEach of the 19 activities is performed by eight subjects (4 female, 4 male,) G: w: p8 @- x
between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes3 R+ Q4 F( x4 ?6 f/ U9 n
for each activity of each subject. The subjects are asked to perform the activ ( b2 B0 Y" f0 zities in their own style and were not restricted on how the activities should be " N% o/ q" T/ l: cperformed. For this reason, there are inter-subject variations in the speeds and $ F5 [0 o/ e$ o! S' Vamplitudes of some activities. 9 c: r: i0 _4 u# P( Y' jSensor units are calibrated to acquire data at 25 Hz sampling frequency. & c) }2 a1 K. }/ L8 z& IThe 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal; ]2 u% c# @& b' x% X4 F
segments are obtained for each activity. , `/ l; J7 ~+ t& N% A8 v$ z" sThe 19 activities are:2 I, Z& q) ^9 G8 `+ E! T
1. Sitting (A1); 2 D, `. F) _$ ] E# i2. Standing (A2); 8 Z. Z1 F) Z( J; d" x. n+ ~- y3. Lying on back (A3);3 [! F* d* T" H7 a4 _# C- u
4. Lying on right side (A4);( j5 K& c! _+ o4 E
5. Ascending stairs (A5);& F: L7 f6 L3 w, d( N
16. Descending stairs (A6); / \( T, ]6 P* p" d7. Standing in an elevator still (A7);6 [' ^5 V; N2 C, y' b; `( e( b9 R! M
8. Moving around in an elevator (A8);; o1 g5 h& U- `2 Y, J
9. Walking in a parking lot (A9); . c$ H2 v4 S9 m! N10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg 3 a1 t, w9 q3 Q. Xinclined positions (A10); " ?! z2 I: n* \& Q8 P/ k% v U; p11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions # L! T; S6 [6 J P% C) t(A11); 2 n" i+ v9 I3 O12. Running on a treadmill with a speed of 8 km/h (A12);' |8 I6 m: l9 Q4 o4 E: H; d
13. Exercising on a stepper (A13);( ]7 y9 R! E2 v( l
14. Exercising on a cross trainer (A14); : T, F# [$ x6 N3 e; V" l/ ~& b; U+ B! U0 e15. Cycling on an exercise bike in horizontal position (A15);# n3 I) j- E2 h! D" m d4 u. ^
16. Cycling on an exercise bike in vertical position (A16); ; d& G7 X+ v' W2 W. _1 H9 x17. Rowing (A17);3 X- u O7 L# H$ d' X( y
18. Jumping (A18);& Y9 i+ d9 U( S0 ]/ E- q
19. Playing basketball (A19).+ |- |" M8 v" J/ e" z' }
Your team are asked to develop a reasonable mathematical model to solve. g/ d3 x2 b& G& ~5 z+ Z6 K: L
the following problems. ( ]' `1 q% @! ^0 Q1. Please design a set of features and an effiffifficient algorithm in order to classify* d+ ~# g t2 Y9 {7 y6 @
the 19 types of human actions from the data of these body-worn sensors./ b! s& o9 o, A
2. Because of the high cost of the data, we need to make the model have% l q9 N4 W; a# J5 W2 ?2 Y: R8 D' f
a good generalization ability with a limited data set. We need to study 4 v: _% s+ H) O3 Q; _$ r. `0 w9 Iand evaluate this problem specififically. Please design a feasible method to' }# `. R% I# c% q# K f# _
evaluate the generalization ability of your model.! ?& e0 F: P6 k1 n/ z5 _
3. Please study and overcome the overfifitting problem so that your classififi-7 ?* Y/ L* t; l* h# @5 r& |2 |
cation algorithm can be widely used on the problem of people’s action3 x: {& w, B' b( u2 _
classifification. " Z- K0 d/ {1 N; V9 w; ~The complete data can be downloaded through the following link:( M" S4 h$ o5 U5 c- B/ p
https://caiyun.139.com/m/i?0F5CJUOrpy8oq 6 r3 A" e4 }/ ~9 K7 f& z2Appendix: File structure & O6 _ X7 J4 I* Z) k• 19 activities (a)% X; a0 V% f: ^9 n9 _4 H6 f
• 8 subjects (p)) b$ y: `7 e+ l- d. V0 O% h
• 60 segments (s)" R# D# u" f' i; b
• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left ; [* o+ ?, Q: H' p3 N1 ?leg (LL); x& T/ W- |) D( @0 r
• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z * x0 m# X8 \& u1 jmagnetometers) 0 f. o" P" _6 K7 NFolders a01, a02, ..., a19 contain data recorded from the 19 activities.. |; @( t9 l1 x
For each activity, the subfolders p1, p2, ..., p8 contain data from each of the; t- U2 h3 G" x, A+ {
8 subjects. * r: ^2 \, x& @" H) w8 F7 A& w$ `In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each. q+ h& y F0 A
segment. 8 z% h8 M- q& ]8 z3 d# }. J, ^In each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25- v# s" d H/ k* `: k
Hz = 125 rows. 9 S) c3 i3 r! A }Each column contains the 125 samples of data acquired from one of the U* c/ m: J9 U/ j0 M
sensors of one of the units over a period of 5 sec. - p+ ]! }3 _: x" lEach row contains data acquired from all of the 45 sensor axes at a particular 8 o* d! @; Q: ], {% j+ ]% ?sampling instant separated by commas. ) z) t4 K8 C: s/ v2 @Columns 1-45 correspond to:$ K7 R' I$ }, D
• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag, 6 E* q! H$ a9 `* {2 m• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag, ( e0 i- ]4 X& }+ h• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag, + D& V- m8 i+ Q* G• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag, ) H& Q c& R+ ^! h: c8 o• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag. / L% s1 m" k) \$ ETherefore,. h) @4 J/ i2 ]
• columns 1-9 correspond to the sensors in unit 1 (T),8 |1 O- J8 r' v0 _4 |; K
• columns 10-18 correspond to the sensors in unit 2 (RA), + C3 z N- d0 z4 E• columns 19-27 correspond to the sensors in unit 3 (LA), + Y; w- J" f' O• columns 28-36 correspond to the sensors in unit 4 (RL),/ K+ K$ l$ y! X7 E) i
• columns 37-45 correspond to the sensors in unit 5 (LL)." D5 R6 x+ }- Y4 J
3References2 w1 H! q5 w! n' g h
[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic' d, v9 ], S8 l
daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput.( _1 e, @, g3 Z+ ]
42(5), 679-687, 2004 6 X; T0 N2 o2 x9 E0 A[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of 4 H: C" |: y! T- Rlow-complexity fall detection algorithms for body attached accelerometers.- v% R/ u+ S. F& o7 a+ I
Gait Posture 28(2), 285-291, 2008 " p- l% m7 n' @' \" ?& D[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag , n0 t3 i! s1 z% f9 g5 A; ^nosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.8 o* O0 O# q* y6 `: I' K0 w9 O
B. 11(5), 553-562, 2007( c" O( o W9 v. H3 A$ X% G
[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con1 C% N4 ~8 b r3 P) e" D& h& D
trol of a physically simulated character. ACM T. Graphic. 27(5), 2008: ]/ \, P- e0 n( g" {7 A% Y0 L* H
& r- e7 e _8 _ M1 Y' Z2022: Q% X) W2 p; o9 D4 @
Certifificate Authority Cup International Mathematical Contest Modeling , `9 q7 b" ?/ A( M4 ~; Uhttp://mcm.tzmcm.cn" J. H+ o# e/ ?
Problem D (ICM) / t9 `8 C! o* g$ @% n% V$ _6 WWhether Wildlife Trade Should Be Banned for a Long " w3 b% L6 `, k+ Y) Y* I" t( D" F) zTime9 ?: E* a, E0 y: x
Wild-animal markets are the suspected origin of the current outbreak and the 3 V" x* Z) O1 F- t5 T0 ?; x( h0 j' n% a2002 SARS outbreak, And eating wild meat is thought to have been a source ; y- t; W4 n3 G c5 bof the Ebola virus in Africa. Chinas top law-making body has permanently 1 ]# y( K. k1 T9 ktightened rules on trading wildlife in the wake of the coronavirus outbreak,, G5 n' Y/ X- F8 c/ ?5 V
which is thought to have originated in a wild-animal market in Wuhan. Some9 p- |6 u8 l8 p- N
scientists speculate that the emergency measure will be lifted once the outbreak ! }# W( T) a) _* Lends.& F( o5 M4 b4 ~0 D
How the trade in wildlife products should be regulated in the long term? 9 @* @1 w C0 ISome researchers want a total ban on wildlife trade, without exceptions, whereas ; z3 c' g) h7 x+ dothers say sustainable trade of some animals is possible and benefificial for peo " a4 j( G& t( E5 lple who rely on it for their livelihoods. Banning wild meat consumption could4 |! b; s# i7 Y6 u/ f8 b* Z
cost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil& b2 ~. |+ b. X# I4 V3 I9 w8 w2 J
lion people out of a job, according to estimates from the non-profifit Society of2 ^) a4 ?- v: l* m' H* @/ A$ ^
Entrepreneurs and Ecology in Beijing. 5 J, W2 u8 V; YA team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology% w) b) n6 i0 T
in China, chasing the origin of the deadly SARS virus, have fifinally found their5 f0 N! ?# ~1 c/ S" Z- x/ c
smoking gun in 2017. In a remote cave in Yunnan province, virologists have 3 ~/ h7 N8 w uidentifified a single population of horseshoe bats that harbours virus strains with ) O1 E/ s4 Y4 n lall the genetic building blocks of the one that jumped to humans in 2002, killing 0 } `, v7 e+ ]. @- @$ w: C Jalmost 800 people around the world. The killer strain could easily have arisen' k3 `4 g U6 C
from such a bat population, the researchers report in PLoS Pathogens on 30! ?, ]- v6 V) m: c! ]
November, 2017. Another outstanding question is how a virus from bats in7 Z$ i3 r" }' J2 B1 o5 W
Yunnan could travel to animals and humans around 1,000 kilometres away in N/ K& k6 N# n0 L* o2 fGuangdong, without causing any suspected cases in Yunnan itself. Wildlife , d% s! n5 Y* f# Mtrade is the answer. Although wild animals are cooked at high temperature 0 N( o! k9 G/ Xwhen eating, some viruses are diffiffifficult to survive, humans may come into contact 1 }( P% ]1 e5 Z O1 `with animal secretions in the wildlife market. They warn that the ingredients6 u8 F/ s. U( L/ O8 @" e
are in place for a similar disease to emerge again., e- k) l! c/ e& \5 Y/ ]2 o
Wildlife trade has many negative effffects, with the most important ones being:5 S. ~/ B: t% k# v Z' x/ h
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS6 X1 b8 @ w- N6 h2 d% I( I4 O
outbreak in 2002.Credit: Matthew Maran/NPL% E0 G+ |4 p. d& ^3 Q
• Decline and extinction of populations; a l; M- y( f8 i# h( Z C
• Introduction of invasive species4 H- |; B' e% Q0 k" C
• Spread of new diseases to humans * T1 t/ k6 F* f) g5 n: S2 ^We use the CITES trade database as source for my data. This database , F$ z# v! L& C# [contains more than 20 million records of trade and is openly accessible. The . G. D/ M, d4 v2 d& Oappendix is the data on mammal trade from 1990 to 2021, and the complete : B- r1 C" q& f$ l A2 pdatabase can also be obtained through the following link:2 A5 a3 E3 G' O& R; O
https://caiyun.139.com/m/i?0F5CKACoDDpEJ - S( a% o, |! h/ t2 A. j& ERequirements Your team are asked to build reasonable mathematical mod9 }' i+ S0 I6 Z+ { V% P
els, analyze the data, and solve the following problems: ) v$ z/ }- Y$ B1. Which wildlife groups and species are traded the most (in terms of live" S7 i8 m1 g: T8 N
animals taken from the wild)?; s. N0 H, r, S& n& u; u$ N: \. B
2. What are the main purposes for trade of these animals?) ] t! c: \" _; w9 Z
3. How has the trade changed over the past two decades (2003-2022)? ( z) X! `7 @5 G/ Q4. Whether the wildlife trade is related to the epidemic situation of major . Q5 f( Y1 c+ A8 j' ?! f! ^infectious diseases?5 q# f1 ?4 |" V) I) F
25. Do you agree with banning on wildlife trade for a long time? Whether it % e. n1 \. M$ |2 P |will have a great impact on the economy and society, and why? ! B% P- N& d9 f" }- \: {6. Write a letter to the relevant departments of the US government to explain & u1 O9 ?& `- c# ~your views and policy suggestions. 4 U. W4 J0 e: X3 ^ : I. B2 ]& J, X/ ]2 y; h1 h4 U. P, L( `) y8 f. ?' X1 J, f
9 a: U6 V/ @* _+ @3 z# \5 C! @
1 U0 d% q' C. w9 P @9 u; z$ h 7 ]8 x" O2 V% C, J. X0 F , a4 c; C. c+ F& ^) B3 T+ x7 u : \- R: l% z4 ?2 T Q7 s$ C+ ]