标题: 2022年第十一届认证杯数学中国数学建模国际赛（小美赛）赛题发布 [打印本页] 作者: ilikenba 时间: 2022-12-2 08:01 标题: 2022年第十一届认证杯数学中国数学建模国际赛（小美赛）赛题发布 2022小美赛赛题的移动云盘下载地址 ( ~7 V+ b2 p! {9 |5 I2 N, Yhttps://caiyun.139.com/m/i?0F5CJAMhGgSJx- N! W" R0 [. N8 R! h
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2022 7 M! z# x* P- ?: P$ E2 y @Certifificate Authority Cup International Mathematical Contest Modeling. d: Q/ j5 f5 k. x# H! L- h
http://mcm.tzmcm.cn 5 W) X. A3 b( G J3 J0 t4 f0 {4 b% ~Problem A (MCM)) v! `$ {* s; j- n( P" q
How Pterosaurs Fly % F- c2 \3 p m& kPterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They " X- u: @/ H( p# z3 fexisted during most of the Mesozoic: from the Late Triassic to the end of( k! K X1 j. W! Z* L
the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved" j Z o; A. \( j. x$ _ |
powered flflight. Their wings were formed by a membrane of skin, muscle, and - F$ u$ z. b/ bother tissues stretching from the ankles to a dramatically lengthened fourth : d* a& I8 B' V- pfifinger[1]. : M% A; ~! S' j5 k* I. a" K6 d) AThere were two major types of pterosaurs. Basal pterosaurs were smaller9 i! B% L$ t* g
animals with fully toothed jaws and long tails usually. Their wide wing mem4 x6 J' ?2 E, [1 V7 q O
branes probably included and connected the hind legs. On the ground, they# x! Z8 h& n) U8 ?( t1 k, ~$ X
would have had an awkward sprawling posture, but their joint anatomy and4 D$ h$ |, \& f& v% [
strong claws would have made them effffective climbers, and they may have lived `8 }+ l' A2 E4 s. L: U& c5 fin trees. Basal pterosaurs were insectivores or predators of small vertebrates. 8 u4 X! Z/ x! p5 L& S) TLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles. 7 y( m5 L" t7 T# ]$ x+ }4 l Y' MPterodactyloids had narrower wings with free hind limbs, highly reduced tails,4 C/ ?9 S: |# _& B ]8 ~
and long necks with large heads. On the ground, pterodactyloids walked well on# _; Q& {7 }" E7 b! w
all four limbs with an upright posture, standing plantigrade on the hind feet and" j6 H' e) V( Z/ P% q( y4 F) @
folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil % b `! R2 {1 Q: jtrackways show at least some species were able to run and wade or swim[2]. + `- P# _, F% Q0 UPterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which% v: z. y1 ]4 Y `* m
covered their bodies and parts of their wings[3]. In life, pterosaurs would have9 V1 Y7 O+ g$ m0 d- m+ R
had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug. T2 X# J. ^3 C8 g) G
gestions were that pterosaurs were largely cold-blooded gliding animals, de - p( W! u( j% e" T; z# d5 V1 uriving warmth from the environment like modern lizards, rather than burning 0 l a7 G0 f+ N3 A# h) o/ S- ucalories. However, later studies have shown that they may be warm-blooded/ x* B; Q9 \" P2 [4 A$ J' w
(endothermic), active animals. The respiratory system had effiffifficient unidirec 5 z3 A8 P) v5 e1 j" c$ }tional “flflow-through” breathing using air sacs, which hollowed out their bones9 f1 l7 |, t- b: d$ ^& u d' R
to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from + b0 |; |" `: n% n; athe very small anurognathids to the largest known flflying creatures, including ) [( [+ j- _1 cQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least i8 z) v8 t2 c' c1 K unine metres. The combination of endothermy, a good oxygen supply and strong 9 e4 l. ]+ ?% P' Y1muscles made pterosaurs powerful and capable flflyers.9 s* B; w; a: y
The mechanics of pterosaur flflight are not completely understood or modeled! a5 O9 S) s, Q0 ~/ R
at this time. Katsufumi Sato did calculations using modern birds and concluded $ Y0 ]0 c) ^$ A- ^: @9 dthat it was impossible for a pterosaur to stay aloft[6]. In the book Posture, 4 ?# T9 ^5 A- G" L$ RLocomotion, and Paleoecology of Pterosaurs it is theorized that they were able5 B" m8 w" F1 K1 S, M
to flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].6 ^1 f" k0 U( J( B5 L+ h1 ^$ j
However, both Sato and the authors of Posture, Locomotion, and Paleoecology. [* P# K) V/ v* [3 {( b
of Pterosaurs based their research on the now-outdated theories of pterosaurs' ]- a8 W4 c: X( t8 M# V
being seabird-like, and the size limit does not apply to terrestrial pterosaurs, % w" K4 C+ I4 S. E7 v+ Y4 Gsuch as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that " y& j& W5 E a G* [' V, K: V/ Hatmospheric difffferences between the present and the Mesozoic were not needed1 Y, l6 i" O5 p( m- l4 z
for the giant size of pterosaurs[8].3 w, [5 x7 F" |! J; e; x' k
Another issue that has been diffiffifficult to understand is how they took offff.& ~! b, d p+ h- I9 J, h0 M! |' {
If pterosaurs were cold-blooded animals, it was unclear how the larger ones & a1 `! [+ m# z Rof enormous size, with an ineffiffifficient cold-blooded metabolism, could manage: E) w' I7 Y0 @
a bird-like takeoffff strategy, using only the hind limbs to generate thrust for 9 V8 B0 F! C# H1 U. }7 Jgetting airborne. Later research shows them instead as being warm-blooded ; {# P" f& L, f- | B$ ^1 Zand having powerful flflight muscles, and using the flflight muscles for walking as1 _9 c# ^7 o+ z# Y- ?/ P* ?! h
quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of& T p+ e* |/ i3 p9 ~( o- m6 a
Johns Hopkins University suggested that pterosaurs used a vaulting mechanism, Z. |; i% `0 P) T( G
to obtain flflight[10]. The tremendous power of their winged forelimbs would7 a* c$ S9 [( Y9 Y& X# `2 h7 x
enable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds . e. V! j j P P W8 U' wof up to 120 km/h and travel thousands of kilometres[10].4 n9 |0 t; j. _. Z" V9 S
Your team are asked to develop a reasonable mathematical model of the . o& V. O3 O! T& I8 s8 G3 gflflight process of at least one large pterosaur based on fossil measurements and( ]& T# D/ J9 i( @
to answer the following questions./ c2 X: t I0 i/ _" g6 o! i. j6 Q Z
1. For your selected pterosaur species, estimate its average speed during nor * d0 l' }9 ~" f& u) n$ ^' Vmal flflight.& l/ {4 f" l6 g' ?- Y
2. For your selected pterosaur species, estimate its wing-flflap frequency during" I K$ v& {. R; \: D$ I+ ?; H" m
normal flflight. @( {. s/ I7 e9 Z- f3 D0 v3. Study how large pterosaurs take offff; is it possible for them to take offff like 8 Y6 N# c% K: ~* _8 U* wbirds on flflat ground or on water? Explain the reasons quantitatively.3 R1 U4 ^, b, ? f- b
References 1 f7 x: m L5 f7 ]" J9 r% ?[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight , I9 Q5 t" ^5 ?0 RMembrane. Acta Palaeontologica Polonica. 56 (1): 99-111.* v0 }) P7 s" p) l
2[2] Mark Witton. Terrestrial Locomotion.# Y# P7 Q$ N t) b: [+ F: F
https://pterosaur.net/terrestrial locomotion.php9 ~; w$ f: {; c) }+ ^; N
[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs* v% H; q0 B/ Z6 j b$ M# C
Were Covered in Fluffffy Feathers. https://www.livescience.com/64324- 5 c9 l: H _ Jpterosaurs-had-feathers.html 8 L. M4 ?- b& N* S[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a ( C) P/ w6 I* F) vrare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)* I5 G/ P6 L& T& d' q( J% C/ E
from China. Proceedings of the National Academy of Sciences. 105 (6):5 P# W' @9 }( {7 p
1983-87.( X! U8 V; w9 L5 c
[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust# C* g7 {) c( H+ J1 O+ m# M
skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4):6 {+ N" ^3 U0 j6 n) e
180-84. ' ^( J' x+ H0 D' a- C[6] Devin Powell. Were pterosaurs too big to flfly? 1 t& k0 W0 i) u0 x5 B$ r% A& l) a6 Chttps://www.newscientist.com/article/mg20026763-800-were-pterosaurs * ?+ [, w+ {% ltoo-big-to-flfly/ 9 Z2 l$ G- C. n4 E. J; K' K/ @4 p[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology: K0 p6 x, y d5 O5 p1 L' l9 t& D
of pterosaurs. Boulder, Colo: Geological Society of America. p. 60.; X/ U7 Y9 v* f/ D
[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable# L9 ~) }! _0 H8 _
air sacs in their wings.: T7 {2 D( \, L
https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur 6 W/ Y+ y% R/ a+ i, p5 D+ ^3 N. dbreathing-air-sacs; c9 ]& n& e9 I [
[9] Mark Witton. Why pterosaurs weren’t so scary after all.8 c. C } K0 M* I
https://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils 2 b6 Z3 T# `0 S3 a: b8 k9 n. |research-mark-witton6 s4 [1 \# d, e y
[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?$ F+ \5 ]2 ~0 [+ C# t9 F
https://www.newscientist.com/article/dn19724-did-giant-pterosaurs 9 P9 Z, ]) ?; e' f! d9 b" Yvault-aloft-like-vampire-bats/2 v+ w) k; m$ A# {% v% q+ Q& I0 U
# W4 h; q% O% T6 n Q5 H2022( ]" C5 o3 W/ q3 ]: s z
Certifificate Authority Cup International Mathematical Contest Modeling1 k2 a5 Z0 S# o4 X
http://mcm.tzmcm.cn # _0 S8 g9 r4 JProblem B (MCM) ' b9 D7 M; I8 E- b7 X& x! \The Genetic Process of Sequences p0 x( C+ {7 P3 s) P. M6 N0 s% |* hSequence homology is the biological homology between DNA, RNA, or protein0 R# } X9 n0 V' y, g* M0 K' A- ]5 _
sequences, defifined in terms of shared ancestry in the evolutionary history of( o" m- R( T) j) c8 H- H, v
life[1]. Homology among DNA, RNA, or proteins is typically inferred from their 7 M; Z# H7 t5 r+ {7 Enucleotide or amino acid sequence similarity. Signifificant similarity is strong( u' g- j% V8 Y+ J# a
evidence that two sequences are related by evolutionary changes from a common' S' ]! ~) }9 N( i
ancestral sequence[2].4 H9 F2 _: K4 H* \" i* B
Consider the genetic process of a RNA sequence, in which mutations in nu, U1 L# l3 b- D
cleotide bases occur by chance. For simplicity, we assume the sequence mutation ~3 k7 c9 m g
arise due to the presence of change (transition or transversion), insertion and T9 T. C+ x7 bdeletion of a single base. So we can measure the distance of two sequences by: X! m \+ b/ X% f, P4 H
the amount of mutation points. Multiple base sequences that are close together! j9 w8 R# O* e3 n
can form a family, and they are considered homologous.9 `+ C. i! _' K+ O0 R
Your team are asked to develop a reasonable mathematical model to com0 o: _; F5 X. a6 @; X
plete the following problems.8 s# \0 o' m3 N1 h2 c
1. Please design an algorithm that quickly measures the distance between$ U( v+ {' H& |& ]3 {1 v$ t
two suffiffifficiently long(> 103 bases) base sequences. # s: D8 ~5 l+ j7 I; @. A2. Please evaluate the complexity and accuracy of the algorithm reliably, and " a3 {' N5 D; Z9 Z" q5 odesign suitable examples to illustrate it. ) W& }; e+ w3 |4 F% A2 K6 J3. If multiple base sequences in a family have evolved from a common an 2 D3 p1 y( w* H8 s6 X; ucestral sequence, design an effiffifficient algorithm to determine the ancestral ( ?$ B- a8 j$ e0 Msequence, and map the genealogical tree. $ ~# s9 j8 M/ q9 lReferences # W! o. v- c- P; e' Q$ m5 E[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re ; E% o. z `# K7 k; e9 G& Hview of Genetics. 39: 30938, 2005.! ` G2 `% n+ f/ e
[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE, 4 W+ ?6 `8 }1 Iet al. “Homology” in proteins and nucleic acids: a terminology muddle and( o3 C$ K, [6 Q1 w3 i
a way out of it. Cell. 50 (5): 667, 1987.& R! V4 H/ n% J
" ? T' o: u; X0 w
2022# p* ^1 ^3 l' y/ t# Q
Certifificate Authority Cup International Mathematical Contest Modeling 1 B+ }, `- z( O0 X$ u. D$ thttp://mcm.tzmcm.cn0 w; L6 A7 x/ [: l: L
Problem C (ICM) 0 k1 E: P: L7 a9 a" U; jClassify Human Activities) }7 j% T& }/ E) W. y9 g
One important aspect of human behavior understanding is the recognition and+ h! x$ T1 T: F8 _- o' q# ^
monitoring of daily activities. A wearable activity recognition system can im K6 m8 X( ]4 P! R |1 T+ N% aprove the quality of life in many critical areas, such as ambulatory monitor1 L6 a/ \+ Y/ {9 c* t3 K
ing, home-based rehabilitation, and fall detection. Inertial sensor based activ 9 \, T" R) p0 q: Wity recognition systems are used in monitoring and observation of the elderly $ [3 t! c2 T/ l' Fremotely by personal alarm systems[1], detection and classifification of falls[2],4 Y. E; p3 c4 _& _' x
medical diagnosis and treatment[3], monitoring children remotely at home or in) l( T9 D" @# b3 T' g: v
school, rehabilitation and physical therapy , biomechanics research, ergonomics,) b; E9 H0 G9 c; m$ r4 ~
sports science, ballet and dance, animation, fifilm making, TV, live entertain& K( B' W7 T5 V) X0 H! J
ment, virtual reality, and computer games[4]. We try to use miniature inertial + K0 G' n+ X5 G4 @( F" c, V/ }8 `$ nsensors and magnetometers positioned on difffferent parts of the body to classify * E& ]8 {1 w A% Y, n; ]/ Ghuman activities, the following data were obtained.+ O/ e* T7 Z" x+ e9 F8 U7 G
Each of the 19 activities is performed by eight subjects (4 female, 4 male,; H0 r* R; D* o8 V; X4 u
between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes ( K" I# E9 w6 L/ ^for each activity of each subject. The subjects are asked to perform the activ5 k; ?. X$ x; {6 ^$ f
ities in their own style and were not restricted on how the activities should be/ d7 |% e3 }' J7 Z7 a% ?
performed. For this reason, there are inter-subject variations in the speeds and' [: n7 ]8 B9 L: s, l
amplitudes of some activities.& b% V! ~) x4 m8 n) a
Sensor units are calibrated to acquire data at 25 Hz sampling frequency. # S' |3 V5 @8 c' _; EThe 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal , T5 P; C: ?8 @) I% k' Y4 ^segments are obtained for each activity., [% U" }7 O1 C& h
The 19 activities are:5 K* P* L# z* s
1. Sitting (A1); ) Y% b1 A; m, ?1 A; U2. Standing (A2); 8 x* N( F/ D5 g# p' x5 G1 k3. Lying on back (A3); $ E( F2 S- w1 _0 Z8 _5 ?4. Lying on right side (A4);4 `9 E3 a2 ?: }. B# y9 o u
5. Ascending stairs (A5);8 c$ j* Y% c" r" ^% J
16. Descending stairs (A6);$ Q# J' S0 V2 R8 p! b! x
7. Standing in an elevator still (A7); $ k5 Z6 s, F3 Y6 S0 `4 L* K# D" v. M8. Moving around in an elevator (A8);8 A% W* n; w4 g& H
9. Walking in a parking lot (A9); $ I6 ~7 X5 o6 T! D10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg + K' r3 Y6 N( F: K+ J5 xinclined positions (A10); 9 W8 {# I o ~1 U' U! m) n( M11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions& w' y. e' R" M7 _( n6 r. @( g& ?9 w
(A11); $ z% A7 E- E2 d# `$ M12. Running on a treadmill with a speed of 8 km/h (A12); ; J* A& d/ S% H! M13. Exercising on a stepper (A13);: X7 @0 L$ W, |/ J& j
14. Exercising on a cross trainer (A14); ; o& R4 f' O y/ P- N: K15. Cycling on an exercise bike in horizontal position (A15);8 M5 S7 \( t7 E' ] l1 X; ?
16. Cycling on an exercise bike in vertical position (A16);5 K5 Q: C# g O, U% d4 \5 J
17. Rowing (A17); 0 u( c( c. y' D' M/ i; M1 k9 n6 U18. Jumping (A18); A/ q. F8 Z* ]. I19. Playing basketball (A19)./ P, ?& {0 C7 Y- `' [
Your team are asked to develop a reasonable mathematical model to solve# i0 E% a. u9 w9 v/ h" f
the following problems.& D; O5 E6 m& [- f
1. Please design a set of features and an effiffifficient algorithm in order to classify7 r e2 S3 f- _: z5 c9 M B
the 19 types of human actions from the data of these body-worn sensors. & ~* _; ]1 ]0 c% x$ X; [4 o2. Because of the high cost of the data, we need to make the model have2 f. N/ z0 D4 H* ]) t' h8 B
a good generalization ability with a limited data set. We need to study8 @- Y" w" {6 v& s) u7 O
and evaluate this problem specififically. Please design a feasible method to+ @. c0 q* r: Q f: f5 b9 C
evaluate the generalization ability of your model. p( p. m) J3 Y
3. Please study and overcome the overfifitting problem so that your classififi- - j+ ~" s5 P, E) @: i: V- Qcation algorithm can be widely used on the problem of people’s action , M! Q) g1 X/ P. P# Bclassifification.$ J/ O+ Y+ P; E- e
The complete data can be downloaded through the following link: 3 ^8 K9 R5 g; \9 }https://caiyun.139.com/m/i?0F5CJUOrpy8oq% B5 R( v5 N- @
2Appendix: File structure 9 ]% g9 B4 V6 z8 ] L9 i• 19 activities (a)* O; u' w' C3 j2 Q) \1 ^/ O R4 D
• 8 subjects (p) ) M5 Q5 p e9 t' h2 r4 O! s# U$ r# u• 60 segments (s) 4 e/ Y: _' Q% T" z• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left& Q @5 v7 E! D6 s+ {8 B
leg (LL) `+ h! q) ?9 [4 z5 ~
• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z a2 i/ b; [* Omagnetometers). R) ]; p+ j2 Q- P
Folders a01, a02, ..., a19 contain data recorded from the 19 activities.& o7 x3 V+ S) `: Z
For each activity, the subfolders p1, p2, ..., p8 contain data from each of the 6 K- A; A* _* Z5 E* X, K" ?3 a8 subjects.% `9 ]; o. u4 o6 B) f
In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each: L* c0 ]* E) p. x% w3 J
segment.9 [8 a4 E4 J) P1 F g+ q, n
In each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25 ! L* n7 ]% d8 E. cHz = 125 rows.% K, F6 v' f3 E0 d% d- e3 o
Each column contains the 125 samples of data acquired from one of the + ^! `9 E+ H$ d# q3 H# ysensors of one of the units over a period of 5 sec.% ^% O: ~) {; Y- t. u, }4 Q
Each row contains data acquired from all of the 45 sensor axes at a particular - f; p. i* H2 G1 x7 s' K* Rsampling instant separated by commas. 6 Z$ z5 w9 b6 ^% p& w/ mColumns 1-45 correspond to: + N3 K% W; f# L( {5 X• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,( w T0 s$ ~7 X; h' b" w
• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag,3 A- k5 N& p: e* `$ }" @6 l
• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag,! Y* _1 Y% p: T8 ?& K
• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag, 8 _2 T$ f0 \, t) U; U, ~8 A' L• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag. ) L; I6 U: T' |- S$ f' ~Therefore, . a: q8 V! H" ?4 I k$ o9 `• columns 1-9 correspond to the sensors in unit 1 (T), 8 y* B0 L7 z7 O. }9 L- T• columns 10-18 correspond to the sensors in unit 2 (RA), 5 S# g# ~5 x1 I$ b; _! G6 h• columns 19-27 correspond to the sensors in unit 3 (LA),' n2 `, e, S; G {3 w' W. @+ H
• columns 28-36 correspond to the sensors in unit 4 (RL),- i$ K9 B( _7 u8 p) E# ~
• columns 37-45 correspond to the sensors in unit 5 (LL).! t% `& @& O$ L; ] z: y
3References2 W F4 K$ ]5 ]& u0 U
[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic 1 r/ W4 m1 b. G" |# {1 [: tdaily movements using a triaxial accelerometer. Med. Biol. Eng. Comput. % @# t: T3 k3 j42(5), 679-687, 20043 r; @1 i. O& u3 n1 ~1 {' i
[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of7 e& I# [! L0 z1 a( \& o+ Q! v. l
low-complexity fall detection algorithms for body attached accelerometers. % {. D# W3 a1 ^Gait Posture 28(2), 285-291, 2008 3 W" O# Z: i, M6 l[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag7 c- J! O5 f* V8 z
nosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.# H3 b/ a8 x4 u1 B( j2 Z% U4 _. k
B. 11(5), 553-562, 2007 1 Z3 l# i" X0 M$ f. H5 k[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con - h0 Z, D" t6 l/ {% y" Htrol of a physically simulated character. ACM T. Graphic. 27(5), 2008. ]! C h5 N# U4 P
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2022) q, e3 n( O! k. H
Certifificate Authority Cup International Mathematical Contest Modeling 3 p% a; t8 F8 V T/ P' @$ Y4 g/ Ihttp://mcm.tzmcm.cn' F; Q5 f* ^$ H, M/ b/ T* b# P2 ]/ U
Problem D (ICM) & g. p$ y+ C6 A# [ Q' l( k qWhether Wildlife Trade Should Be Banned for a Long9 I& k, ~6 D3 G+ ]/ `
Time 6 V0 r, u& _' bWild-animal markets are the suspected origin of the current outbreak and the $ b6 E4 M M0 a, C0 a2002 SARS outbreak, And eating wild meat is thought to have been a source 0 T3 A) K; K+ [% Uof the Ebola virus in Africa. Chinas top law-making body has permanently ( a; w9 H0 ^& d+ S' ytightened rules on trading wildlife in the wake of the coronavirus outbreak,0 L0 x- }5 K/ m v4 n3 q+ K
which is thought to have originated in a wild-animal market in Wuhan. Some 0 L! M, |! C7 M. z; gscientists speculate that the emergency measure will be lifted once the outbreak. S. Z, Y4 D; f# N; L/ v1 m
ends." d ]+ t* G3 x0 g& E- g9 j
How the trade in wildlife products should be regulated in the long term?) h# c% `9 D' w0 B, B1 H3 |
Some researchers want a total ban on wildlife trade, without exceptions, whereas # o% i8 g u c* s4 A! sothers say sustainable trade of some animals is possible and benefificial for peo% F0 S0 f: m- p3 F& ?) D# J
ple who rely on it for their livelihoods. Banning wild meat consumption could, a% P+ z- {" A |/ c
cost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil / i" E! E Z( ]: j/ dlion people out of a job, according to estimates from the non-profifit Society of " w3 ?3 y5 l3 t6 e. F9 hEntrepreneurs and Ecology in Beijing. 6 W3 A$ d8 k3 r! Z7 `+ C9 V8 _# mA team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology8 l( b# ^2 ^2 p- D# J6 H
in China, chasing the origin of the deadly SARS virus, have fifinally found their $ D* }7 y1 i3 S4 |7 z, o& Nsmoking gun in 2017. In a remote cave in Yunnan province, virologists have b! E; B4 q- Jidentifified a single population of horseshoe bats that harbours virus strains with" R1 D6 ~3 X% C3 K8 W' m& x( V6 G9 U& r
all the genetic building blocks of the one that jumped to humans in 2002, killing" j4 V/ ]% E; m9 r
almost 800 people around the world. The killer strain could easily have arisen$ F+ ]; D3 [+ z( h, v) e, W
from such a bat population, the researchers report in PLoS Pathogens on 304 K. I9 ]' y" U
November, 2017. Another outstanding question is how a virus from bats in$ k4 ]' g+ y0 V& i( W+ e! l Z
Yunnan could travel to animals and humans around 1,000 kilometres away in" z6 ?9 A/ K& c( o( X# k6 ?
Guangdong, without causing any suspected cases in Yunnan itself. Wildlife ) {6 d- l5 P9 ^3 dtrade is the answer. Although wild animals are cooked at high temperature Z0 \) |+ q# \$ l3 o/ h* J6 v2 zwhen eating, some viruses are diffiffifficult to survive, humans may come into contact8 c/ d/ c7 @- l7 n& I* K
with animal secretions in the wildlife market. They warn that the ingredients" q/ S7 d. m/ L# u1 s
are in place for a similar disease to emerge again.9 H3 H( f9 h; w$ p" b+ l
Wildlife trade has many negative effffects, with the most important ones being:1 ]* }# x$ }' ^
1Figure 1: Masked palm civets sold in markets in China were linked to the SARS ( l* P; ^( F- p* ?- Voutbreak in 2002.Credit: Matthew Maran/NPL : |( O" X& x* ~% x$ }' Y+ h# N• Decline and extinction of populations ! p$ g" S5 R7 V• Introduction of invasive species 0 K/ R5 N7 G9 a }" \: F" y• Spread of new diseases to humans) C! v" o+ d$ g6 E
We use the CITES trade database as source for my data. This database! O3 [4 O" L% T6 g7 P) O8 i! i
contains more than 20 million records of trade and is openly accessible. The * }9 b: V/ V: Fappendix is the data on mammal trade from 1990 to 2021, and the complete 3 x0 q5 x6 y; }5 W% Mdatabase can also be obtained through the following link:3 O! Y' m7 [+ t
https://caiyun.139.com/m/i?0F5CKACoDDpEJ ' L8 ]5 ^: Q# g5 JRequirements Your team are asked to build reasonable mathematical mod ) }# E# Q- ^" p1 \els, analyze the data, and solve the following problems: ' z6 |2 R+ ]- C; R0 M1. Which wildlife groups and species are traded the most (in terms of live * ?; t# O/ v! X0 banimals taken from the wild)? 4 Q- Y, R; M. q, j2. What are the main purposes for trade of these animals?3 i# }" ]0 N. p+ C! u4 _
3. How has the trade changed over the past two decades (2003-2022)? % l. M& R5 k6 f& ~( B. N& Z4. Whether the wildlife trade is related to the epidemic situation of major* R8 V1 Y. J3 t- ~8 ]; ^
infectious diseases?0 b- e* R& _+ A7 d$ @( e1 r2 |
25. Do you agree with banning on wildlife trade for a long time? Whether it; ?$ \& }; S' ~% }
will have a great impact on the economy and society, and why? - b) `7 o3 m$ X B% f6. Write a letter to the relevant departments of the US government to explain. Z$ f t2 f& Q/ M! h) W, Z4 J
your views and policy suggestions. 6 O- {# B" y& G& ^1 S R ; ~9 z& ]" `" H- w" l. m4 d T2 r4 G( r2 r9 j' C0 x
