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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

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    发表于 2022-12-2 08:01 |只看该作者 |倒序浏览
    |招呼Ta 关注Ta
    2022小美赛赛题的移动云盘下载地址 ) t* ]8 z+ n: O
    https://caiyun.139.com/m/i?0F5CJAMhGgSJx
    . @* l! a: `0 B5 `  i$ G" z. h, z1 A9 x% C8 z! `9 W8 I% z
    2022
    $ H! ~  P& D& S- W. vCertifificate Authority Cup International Mathematical Contest Modeling
    - I! s8 l; `" a  H7 g0 rhttp://mcm.tzmcm.cn: s/ y: X+ m) q* R! A- A4 ?1 P
    Problem A (MCM)/ ?6 T' J/ R6 ~; b; K- f6 k' l
    How Pterosaurs Fly
    % v6 o6 W/ ^; y+ u% e" z1 S8 W+ hPterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They
    ( U* D* K5 j# i+ n+ [) Z; dexisted during most of the Mesozoic: from the Late Triassic to the end of, W' |4 t# s, A5 q; X* I& W
    the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved' |- W2 d( I) p$ }' e" h3 B
    powered flflight. Their wings were formed by a membrane of skin, muscle, and3 b% ~) `1 y& O9 f8 I& O
    other tissues stretching from the ankles to a dramatically lengthened fourth' j, w8 P) o2 o1 Z6 q* S9 y
    fifinger[1].8 w+ }9 T9 I' w3 X* d/ I
    There were two major types of pterosaurs. Basal pterosaurs were smaller* \( b1 m4 e; S( T# |
    animals with fully toothed jaws and long tails usually. Their wide wing mem
    ) V4 V- a) B( V8 Q- ^+ {3 ]* Ebranes probably included and connected the hind legs. On the ground, they
    / d+ ?, p& l; V  z5 C8 Q/ ^would have had an awkward sprawling posture, but their joint anatomy and
    3 a8 q3 G- ?+ pstrong claws would have made them effffective climbers, and they may have lived, _  h% h& I  R4 k/ A8 ]. r
    in trees. Basal pterosaurs were insectivores or predators of small vertebrates.8 f4 W9 m& r# Q( c7 [
    Later pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles.# A5 D. M% p. R: R  _/ q+ T3 t% q, J
    Pterodactyloids had narrower wings with free hind limbs, highly reduced tails,
    $ a" X8 x" m5 W* F* ^6 ^  w- K# jand long necks with large heads. On the ground, pterodactyloids walked well on
    ( R' J7 `1 s* `3 h: @) J6 {: Jall four limbs with an upright posture, standing plantigrade on the hind feet and5 m, O6 s; _/ B; U  y$ b/ T' ?- `
    folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil  F) z& v: o5 M1 N$ J& M
    trackways show at least some species were able to run and wade or swim[2].
    ) n5 c) Y' Y3 D6 yPterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which! B8 \+ Q; u9 ]7 A$ G
    covered their bodies and parts of their wings[3]. In life, pterosaurs would have
    % \' l* C! \- h+ L, b- `& b) Q( w4 ]had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug
    + d$ F& M7 g' G5 @gestions were that pterosaurs were largely cold-blooded gliding animals, de6 ^4 v0 U# W  e7 c* y2 G
    riving warmth from the environment like modern lizards, rather than burning
    - d% M: w5 {- Bcalories. However, later studies have shown that they may be warm-blooded* S9 r( R% F+ i8 h' ~$ Z
    (endothermic), active animals. The respiratory system had effiffifficient unidirec
    & e2 W, G6 P, Utional “flflow-through” breathing using air sacs, which hollowed out their bones
    9 Z* j7 v3 y% {" W5 Tto an extreme extent. Pterosaurs spanned a wide range of adult sizes, from7 s' K' D$ r, V7 F
    the very small anurognathids to the largest known flflying creatures, including
    ' X% b- E) t. C3 yQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least% l( Y+ t1 v4 w0 D- r3 B, i
    nine metres. The combination of endothermy, a good oxygen supply and strong% P. x+ S- k  e! t; r. B2 l
    1muscles made pterosaurs powerful and capable flflyers.0 V3 {% ^( m5 w
    The mechanics of pterosaur flflight are not completely understood or modeled
    0 |, t1 X3 B% u3 {! B/ Fat this time. Katsufumi Sato did calculations using modern birds and concluded
    3 N0 K& j7 e6 O. a$ o% S$ gthat it was impossible for a pterosaur to stay aloft[6]. In the book Posture,
    6 ~. o% ]. Z' ILocomotion, and Paleoecology of Pterosaurs it is theorized that they were able
    , D. G6 v  F. T$ qto flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].* p: }1 H/ Y$ |) N* [# }
    However, both Sato and the authors of Posture, Locomotion, and Paleoecology
    6 Q$ O  w+ Q8 x8 dof Pterosaurs based their research on the now-outdated theories of pterosaurs# ^: V2 X  O, f/ l- i, R0 P
    being seabird-like, and the size limit does not apply to terrestrial pterosaurs,
    ! F& S8 |. l6 @such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that
    * u/ i: ?. Q/ {! Eatmospheric difffferences between the present and the Mesozoic were not needed
    0 [4 ^9 h2 B8 r* [5 Lfor the giant size of pterosaurs[8].1 j  ?" ~2 t. n+ r  M9 _- H
    Another issue that has been diffiffifficult to understand is how they took offff.' X0 O! f1 E5 I# t2 {+ t
    If pterosaurs were cold-blooded animals, it was unclear how the larger ones
    ' f$ D2 l" y1 {# K; L; _of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage! ]0 B! M3 l/ q
    a bird-like takeoffff strategy, using only the hind limbs to generate thrust for4 L, y* u# p& y9 B: E3 B/ B
    getting airborne. Later research shows them instead as being warm-blooded
    2 W9 b4 p3 c% ]& |" eand having powerful flflight muscles, and using the flflight muscles for walking as/ D  V3 ^( v5 }: M6 ?: ]
    quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of
      W; b- n; ^: r) X& X% U, EJohns Hopkins University suggested that pterosaurs used a vaulting mechanism$ A3 n: T3 F9 ~6 Y
    to obtain flflight[10]. The tremendous power of their winged forelimbs would
    0 V! L- A' |' j* eenable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds0 i0 ^$ r& k* F  u1 k* q
    of up to 120 km/h and travel thousands of kilometres[10].
    ) N5 d1 e% u( v8 jYour team are asked to develop a reasonable mathematical model of the
    . F; W5 B6 t! a8 i) p+ J  m# r, uflflight process of at least one large pterosaur based on fossil measurements and. l1 X- F+ h( Z  {4 ~1 U* K0 O
    to answer the following questions.: b; z7 K7 p2 P; @# V
    1. For your selected pterosaur species, estimate its average speed during nor, q: h) c8 K5 @: P6 W2 g
    mal flflight.. T/ C7 q+ [) y
    2. For your selected pterosaur species, estimate its wing-flflap frequency during3 S+ V$ b% k* n: i
    normal flflight.7 }) S( p& T5 _/ f3 Q2 k
    3. Study how large pterosaurs take offff; is it possible for them to take offff like  A/ Q# S: V: b4 B/ p7 r
    birds on flflat ground or on water? Explain the reasons quantitatively.1 N' r$ `* R1 j1 B' B) D6 O# B
    References# A" Y; [' ]% h' x+ x" [8 L
    [1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight2 W: c7 |, g! G
    Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111.
    7 g. `' N0 d$ Z6 Q0 d! y$ d  a0 \8 e4 U2[2] Mark Witton. Terrestrial Locomotion.5 ?! g. r' j3 y8 e2 j  ]- u
    https://pterosaur.net/terrestrial locomotion.php
    # Q/ p5 R) ~) |% S- @[3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs
    . {: w8 Q1 V0 ^6 U4 h. u- gWere Covered in Fluffffy Feathers. https://www.livescience.com/64324-7 i9 X0 Y- V$ q: G" H, a) s9 `
    pterosaurs-had-feathers.html
    4 n) ?% G$ i( t. Y' ][4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a3 h0 {( \% B6 `7 E+ w+ o9 d9 \
    rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)" h3 y7 B' e: j! [* i5 e  U
    from China. Proceedings of the National Academy of Sciences. 105 (6):
    : c1 M' M' y2 [7 }0 a1983-87.0 n6 k9 H7 Z$ E$ v5 l0 ~, x
    [5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust. A8 g2 D* U: U
    skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4):
    / |/ _, H# ^5 {, _% {180-84.2 n* b- {5 L4 f. u* f. y3 g/ C
    [6] Devin Powell. Were pterosaurs too big to flfly?
    9 P( |: g* _8 b0 y, ?8 ehttps://www.newscientist.com/article/mg20026763-800-were-pterosaurs  {3 [' J! n3 X' h- r
    too-big-to-flfly/
    % a  I, A  z: f7 a" r8 _) _" L[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology
    # E- c" e5 O' c* Hof pterosaurs. Boulder, Colo: Geological Society of America. p. 60.  F% d& T! f6 }6 W
    [8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable
    ; I) e8 w( n6 r. s7 \1 ^air sacs in their wings.: n/ w% }) }! @3 E5 P2 ]
    https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur8 E+ x  P. A" S, T" g  i( _1 F0 I
    breathing-air-sacs
    ( O* u  s4 c9 S! ~6 V[9] Mark Witton. Why pterosaurs weren’t so scary after all.' r2 m5 v: O5 t$ X
    https://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils
    , o* t+ |. [* ^8 b) c/ C2 L3 Kresearch-mark-witton
    ) G; F2 F  @4 q& Q9 g# U[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?' T6 X8 I& @2 X1 [# `* B" `1 B) x
    https://www.newscientist.com/article/dn19724-did-giant-pterosaurs
    5 A# V. P% W# Y7 v: U  R! Q) W: Fvault-aloft-like-vampire-bats/4 z7 m# o% ^4 R) J+ B& R/ r1 n  }

    / x2 p* ]( ^. W0 u2022
      ]1 e& C& M7 JCertifificate Authority Cup International Mathematical Contest Modeling
    . D; h- \  g% P1 E. f8 E4 zhttp://mcm.tzmcm.cn6 s; B$ Q5 G$ N
    Problem B (MCM)
    + l8 y* R3 m8 N8 IThe Genetic Process of Sequences% i$ D8 A! Z, X5 B; \* ?
    Sequence homology is the biological homology between DNA, RNA, or protein) m3 N% {5 Y  {# L8 \
    sequences, defifined in terms of shared ancestry in the evolutionary history of
    # f$ q) {" e7 w; X" C# _life[1]. Homology among DNA, RNA, or proteins is typically inferred from their
    8 X+ s. }. G5 d! a- N  \2 o) }! Wnucleotide or amino acid sequence similarity. Signifificant similarity is strong
    ' P: ^5 f2 w4 Y# T! e" Nevidence that two sequences are related by evolutionary changes from a common4 M2 [0 |( n0 G" k2 a3 u# R) h
    ancestral sequence[2].# @  Y6 c% B4 G- B+ X! P
    Consider the genetic process of a RNA sequence, in which mutations in nu5 z  r9 A  Q. C  e% v- c) z+ R
    cleotide bases occur by chance. For simplicity, we assume the sequence mutation
    ) m4 S0 M( ~" l# j. x2 b' J- Darise due to the presence of change (transition or transversion), insertion and
    % w9 `4 N$ [  |* G/ F: N  Pdeletion of a single base. So we can measure the distance of two sequences by
    ! z. y. a. l3 j: e" |the amount of mutation points. Multiple base sequences that are close together7 _: z3 Z8 ~  h$ p9 c- I
    can form a family, and they are considered homologous.
      N5 ^1 F7 S8 `1 A+ G" ^" [Your team are asked to develop a reasonable mathematical model to com
    ) G; r) |5 z1 h6 o1 |plete the following problems.
    ) V# l, _& P  j0 P2 Q* f1. Please design an algorithm that quickly measures the distance between
    1 v! w! t2 k8 O' J( f3 c1 atwo suffiffifficiently long(> 103 bases) base sequences.
    3 m" ~: q1 K* L9 S1 H' o2. Please evaluate the complexity and accuracy of the algorithm reliably, and
    7 `" |, q1 \5 }. ~  P, }/ mdesign suitable examples to illustrate it.
    + J7 i) ]+ V5 Z3. If multiple base sequences in a family have evolved from a common an, m; `% z# Q5 P1 a4 m
    cestral sequence, design an effiffifficient algorithm to determine the ancestral, D2 Z! |4 z' i# j# l" Q
    sequence, and map the genealogical tree.) [+ D+ \  W" f6 ^0 j2 ]! R$ `
    References
    . f) n2 t; I' F9 U' e. A9 B' n$ t[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re
    , p* ~$ l1 M3 z1 }view of Genetics. 39: 30938, 2005.
    . \  V5 s  |5 _/ y- V  L3 N[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE,' f, M0 Q  M: I0 c% C' [9 n
    et al. “Homology” in proteins and nucleic acids: a terminology muddle and
    ' n6 D9 X- R: w5 m) N6 n( M4 \# F0 e' s0 ba way out of it. Cell. 50 (5): 667, 1987.( ]7 a3 e5 [6 B# I" ^- g6 k
    5 I' r/ J& H' _) F& v
    2022
    ( Z, a! x* G+ ZCertifificate Authority Cup International Mathematical Contest Modeling
    5 y! R0 n1 Q$ L5 O' f& Ghttp://mcm.tzmcm.cn
    * U2 W# U$ ~7 w0 `" J7 U4 QProblem C (ICM)) {" B4 o! I% F' c% |
    Classify Human Activities3 q- b, V# e/ ^8 C( T
    One important aspect of human behavior understanding is the recognition and& u- r& Y- V% t/ o
    monitoring of daily activities. A wearable activity recognition system can im
    1 w& U) e( q; Z3 _prove the quality of life in many critical areas, such as ambulatory monitor
    4 p; O' Q/ m3 z2 h6 ^ing, home-based rehabilitation, and fall detection. Inertial sensor based activ
    * Z! G; S+ K. u* X" N" Oity recognition systems are used in monitoring and observation of the elderly+ a, |7 j: j/ [" ^; n
    remotely by personal alarm systems[1], detection and classifification of falls[2],
    6 w3 H) u0 f' ~. p' Y4 cmedical diagnosis and treatment[3], monitoring children remotely at home or in
    : ^5 u: n" Q% r3 _9 Cschool, rehabilitation and physical therapy , biomechanics research, ergonomics,
    ) @% b6 k4 j; k1 P1 i; Xsports science, ballet and dance, animation, fifilm making, TV, live entertain
    ( G0 v) J% Q0 ^% kment, virtual reality, and computer games[4]. We try to use miniature inertial
    " E+ I/ V( s- B+ j  C& z3 x" Dsensors and magnetometers positioned on difffferent parts of the body to classify' f$ K3 r" g7 Z" \- u6 y
    human activities, the following data were obtained." G0 t  u+ H) c  U; s- j- Z; b
    Each of the 19 activities is performed by eight subjects (4 female, 4 male,6 \3 i* E" F5 @% b( C! q9 {
    between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes$ Q# s: u& P/ o; H: n7 V
    for each activity of each subject. The subjects are asked to perform the activ
    . s" U3 c1 {3 a3 M+ Zities in their own style and were not restricted on how the activities should be
    ; q+ v% ^2 M6 D& hperformed. For this reason, there are inter-subject variations in the speeds and- e  z% e2 k& J8 A: _) n* c
    amplitudes of some activities.
    7 t, D9 B8 |& c5 N; oSensor units are calibrated to acquire data at 25 Hz sampling frequency.) ?, C( I7 k, ]+ N
    The 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal/ Q$ U% f6 R# f: ^' _
    segments are obtained for each activity.  s% h, K5 l. j- }! Y
    The 19 activities are:( q8 }4 |* c$ B4 b! i& g
    1. Sitting (A1);7 D; @' I2 y  r$ e: I9 C0 M! A
    2. Standing (A2);
    6 n2 L7 t. V  H1 U# }0 i3. Lying on back (A3);
    1 u; S  s3 `3 F  t  X6 F4. Lying on right side (A4);
    9 m6 \- x2 e9 Y: K; R, f5. Ascending stairs (A5);0 O$ u8 B. g2 N. g& {
    16. Descending stairs (A6);
    7 W- w, b/ z+ _9 B7. Standing in an elevator still (A7);" c: r* x5 ]& W1 Y' p
    8. Moving around in an elevator (A8);
    . v6 B& A: h+ q/ p( E' G. v9. Walking in a parking lot (A9);
    & [( z3 t1 p! d0 F/ e% t0 \10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg+ a3 C$ t7 @2 b# x' `$ T' r! t4 r
    inclined positions (A10);
    9 `* U% i/ r( _, M11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions* v3 d  n# K$ Z* D* R) ^
    (A11);/ Y. \& z+ I; q0 w3 f+ U, k" N
    12. Running on a treadmill with a speed of 8 km/h (A12);
    # T( \: U) N/ u$ @# k0 F13. Exercising on a stepper (A13);
    7 X4 Y9 |  |0 l& T14. Exercising on a cross trainer (A14);* G+ B% e4 {9 e, g" Z7 c
    15. Cycling on an exercise bike in horizontal position (A15);8 {8 x* L, i& _/ [
    16. Cycling on an exercise bike in vertical position (A16);& }$ y, j3 I! x. J# N0 w- M& H* V3 ^
    17. Rowing (A17);% E+ {1 g3 N8 c. j) j
    18. Jumping (A18);
    0 O  Y1 B+ e6 d7 n# h& ?19. Playing basketball (A19).
      B/ U2 A5 E& E/ }5 j" _Your team are asked to develop a reasonable mathematical model to solve8 j/ |6 L) ]) ?6 ]& @& d/ e0 `
    the following problems.
    0 j; W. K; V% i& L1. Please design a set of features and an effiffifficient algorithm in order to classify
    . d* S2 n- C% H  rthe 19 types of human actions from the data of these body-worn sensors., ?+ k; F! p8 k- I! t6 [
    2. Because of the high cost of the data, we need to make the model have0 i" z/ z6 a- r
    a good generalization ability with a limited data set. We need to study4 N7 d2 Z2 C% h
    and evaluate this problem specififically. Please design a feasible method to
    ( ~" h- f7 w6 z. G& cevaluate the generalization ability of your model.
    + v3 }" `* W+ C+ n/ j7 v! L3. Please study and overcome the overfifitting problem so that your classififi-) Z! k' e4 N% s0 P+ T: R
    cation algorithm can be widely used on the problem of people’s action
    % L( ~# B0 }$ E7 m4 T0 X6 iclassifification.
    ! p) G& _/ r% G3 i8 D3 m1 g, X/ dThe complete data can be downloaded through the following link:
    ) i  m  g2 k! h. Mhttps://caiyun.139.com/m/i?0F5CJUOrpy8oq, D4 C* Z2 ~) }& {  t3 f7 A* U0 O# o4 ?
    2Appendix: File structure
    $ s/ |' }# h( v• 19 activities (a)
    $ r' z2 |- {/ T' s5 v) I• 8 subjects (p)9 T+ }# z: ^( J6 Y. \) t
    • 60 segments (s)- B) @  b9 z0 _0 P* x1 q% i- d
    • 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left
    : _" q# d% m9 a% x, j: D7 y3 kleg (LL)
    - k" p" w( k1 X. S% j7 ]• 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z# W% ~' d! X' O$ c% i
    magnetometers)
    1 x- _  A) r( n1 X7 t; W. y/ A8 oFolders a01, a02, ..., a19 contain data recorded from the 19 activities.
    & m( u) z/ k: YFor each activity, the subfolders p1, p2, ..., p8 contain data from each of the: S" u* M. ?4 g  C/ q
    8 subjects.
    ' j; S3 \9 g3 C  H- B2 _3 EIn each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each2 @# N* h0 \8 C
    segment.5 m5 q  P8 q* M3 G: C: Z
    In each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25
    " ~# ~; O# q4 M% }9 u& DHz = 125 rows.
    3 p+ A" {  P9 c$ DEach column contains the 125 samples of data acquired from one of the
    # }& R1 K9 o5 m3 ?. o& a, [: M$ }sensors of one of the units over a period of 5 sec.
    $ U% d9 c  T. X6 QEach row contains data acquired from all of the 45 sensor axes at a particular/ C/ u) N* A8 C! K( {+ t9 W
    sampling instant separated by commas.
    1 L; M4 M. w0 N, y; ?0 @Columns 1-45 correspond to:& o' F' o6 E1 _& @9 R
    • T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,5 b: h7 E  e8 ], ~
    • RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag,
    5 W1 _7 N9 G( O: u) A• LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag,3 R) ~! |- U; `- \3 E: g! B
    • RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,
    . X, e4 @! E% e$ J• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag.
    : d) D: y( c3 s5 ~, JTherefore,9 Z! [1 V( c) n* o4 c1 z
    • columns 1-9 correspond to the sensors in unit 1 (T),
    # U2 B- W- o. U/ z8 d" u2 u9 G• columns 10-18 correspond to the sensors in unit 2 (RA),0 v$ F% R6 C1 Q5 f1 j
    • columns 19-27 correspond to the sensors in unit 3 (LA),
    + Q) V+ s& p( Q: V" t) z$ ], M5 Q( N! q• columns 28-36 correspond to the sensors in unit 4 (RL),
    , H6 P- ?0 E% y, U, q; {0 ^• columns 37-45 correspond to the sensors in unit 5 (LL).- H! U, L+ J: ]* G- l* ?( K
    3References
    , Q. B9 G8 ?! \[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic
    9 b8 f8 _' l$ k, {daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput.
      \- d" ?/ T& [' H42(5), 679-687, 2004$ i6 _2 e" k; r3 o  Z+ u
    [2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of
    ! P) ?8 ]3 a0 blow-complexity fall detection algorithms for body attached accelerometers.
    # R2 W$ L0 R& p* u2 b5 o9 sGait Posture 28(2), 285-291, 2008+ g: n9 j  Q2 _- M
    [3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag" k& v+ H2 a8 L, h: Z% d: u
    nosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.4 K3 [: a7 a6 q7 L: N
    B. 11(5), 553-562, 2007; u8 ^6 N  \# Z6 O$ {6 J
    [4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con* x/ E7 {% I& ^, P( G6 }! C
    trol of a physically simulated character. ACM T. Graphic. 27(5), 2008
    2 \5 {1 L9 x6 M* [/ o1 K4 o% C
    + _+ Z  K2 h6 U- p2 l$ t1 S, N' Y! r2022
    5 T* G: H9 z1 I- @Certifificate Authority Cup International Mathematical Contest Modeling
    2 O$ V3 B# A4 b8 b9 T( V4 x; }http://mcm.tzmcm.cn1 @9 ?- F/ U3 ?0 ~# @' w5 j
    Problem D (ICM); r6 o$ u( l. H( M& X3 {4 `
    Whether Wildlife Trade Should Be Banned for a Long9 @- x6 T6 d: I! Q* Y
    Time
    - x4 A! j5 t0 [5 P9 K: F% rWild-animal markets are the suspected origin of the current outbreak and the
    8 c* t; w1 J8 v: X2002 SARS outbreak, And eating wild meat is thought to have been a source1 u/ c9 L% m6 P7 _# O- ?1 L
    of the Ebola virus in Africa. Chinas top law-making body has permanently
    ) n2 b( N" y1 g2 F5 ?0 `6 B6 V" ]tightened rules on trading wildlife in the wake of the coronavirus outbreak,' X/ y% N- T+ E0 _
    which is thought to have originated in a wild-animal market in Wuhan. Some0 a/ R1 @/ g7 B9 j
    scientists speculate that the emergency measure will be lifted once the outbreak
    & ^7 o2 p8 u$ f5 A3 I3 W. Y% I) fends.7 o3 g3 ~" ]6 V4 A
    How the trade in wildlife products should be regulated in the long term?- o2 c% c7 i( ^0 t6 l: c
    Some researchers want a total ban on wildlife trade, without exceptions, whereas* H- q% A$ E, E" V1 C/ R
    others say sustainable trade of some animals is possible and benefificial for peo/ Q& c8 E7 M; |! d) I
    ple who rely on it for their livelihoods. Banning wild meat consumption could
    ( F) ^4 @) s/ c# m* Q1 v9 L' gcost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil( B/ h2 e& _! l, ]( g
    lion people out of a job, according to estimates from the non-profifit Society of
    # M+ a# q3 m; T4 WEntrepreneurs and Ecology in Beijing.
    2 C7 V$ ?3 |. `+ W& j- X: KA team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology7 ^: g8 [0 w& N* N/ D/ z3 j
    in China, chasing the origin of the deadly SARS virus, have fifinally found their
    . |: s# d3 C, l- Q1 u7 F# jsmoking gun in 2017. In a remote cave in Yunnan province, virologists have
    / _% o& E4 ?5 x8 t7 w6 \8 m# Fidentifified a single population of horseshoe bats that harbours virus strains with' m: A2 w+ Z. Y/ \. \9 ]3 @
    all the genetic building blocks of the one that jumped to humans in 2002, killing
    ' a4 [* z8 e. s. Z( \& X, Kalmost 800 people around the world. The killer strain could easily have arisen& y! @4 z; }" ^  n
    from such a bat population, the researchers report in PLoS Pathogens on 30/ u; P- T- V  c& o9 L( P
    November, 2017. Another outstanding question is how a virus from bats in
    ; A  f' r# ?5 `9 e) ^Yunnan could travel to animals and humans around 1,000 kilometres away in/ W9 e- }0 B& W, W) \2 Z1 Y
    Guangdong, without causing any suspected cases in Yunnan itself. Wildlife9 r% k" u& e0 y2 ]" S# ~) p" u; {) P
    trade is the answer. Although wild animals are cooked at high temperature
    6 C8 W7 q+ t* B' ^% R% u* |5 Owhen eating, some viruses are diffiffifficult to survive, humans may come into contact) m% f  V! |* p+ e0 i- _7 z7 g
    with animal secretions in the wildlife market. They warn that the ingredients5 Z8 m0 @! i* i5 n) `1 W: i8 V7 S
    are in place for a similar disease to emerge again.9 Q* \. P4 m& k1 J5 X" j4 U  n
    Wildlife trade has many negative effffects, with the most important ones being:4 n, L. y3 ]4 c- ~; i
    1Figure 1: Masked palm civets sold in markets in China were linked to the SARS6 V* _; s+ b* c- ^
    outbreak in 2002.Credit: Matthew Maran/NPL
    : o: `# X+ t$ O+ @# ]+ r' |1 G• Decline and extinction of populations
    + [* S: }/ L% _8 l! {7 L+ h• Introduction of invasive species0 q, F! x7 j: y! G- b0 v2 j
    • Spread of new diseases to humans
    9 b' B4 _+ A3 P* i/ h' }6 aWe use the CITES trade database as source for my data. This database
    % G) h7 e0 W$ i, m! x' lcontains more than 20 million records of trade and is openly accessible. The6 `5 u  I  M  D3 f; \
    appendix is the data on mammal trade from 1990 to 2021, and the complete8 f: L5 D/ {9 m$ p* U4 d+ R
    database can also be obtained through the following link:
    ! o' k: F+ }2 i' {! g- _) @7 ahttps://caiyun.139.com/m/i?0F5CKACoDDpEJ
    , o) T9 r6 Q" {: YRequirements Your team are asked to build reasonable mathematical mod) \! Y+ u1 g- F
    els, analyze the data, and solve the following problems:
    % f  F( [& ~8 B1 o0 x3 `: m, o+ }& }1. Which wildlife groups and species are traded the most (in terms of live
    1 }$ c3 r0 _3 Janimals taken from the wild)?
    7 w4 K/ }4 a3 }) i" s# @% d3 \2. What are the main purposes for trade of these animals?0 n9 F+ |4 {+ J7 u
    3. How has the trade changed over the past two decades (2003-2022)?$ B/ P( g8 j6 j" c
    4. Whether the wildlife trade is related to the epidemic situation of major
    " q1 o& U! i. V  r. S* finfectious diseases?
    ( K: v- Z# p' T25. Do you agree with banning on wildlife trade for a long time? Whether it
    ; b* @" n1 @0 A" Fwill have a great impact on the economy and society, and why?
    ' d  p; `3 ~7 E8 |3 P6 g6. Write a letter to the relevant departments of the US government to explain4 e( `, ]& Q8 a: e
    your views and policy suggestions.1 r/ a& R: e# F8 M; Q. @9 [
    9 ?! a/ q" _7 y5 j4 }
      M! i3 }; k% M' T9 a# @$ Z

    $ w& M0 q! D( H/ w5 F7 [; O' c+ z9 @& U/ E6 x+ l5 J( w2 K* G

    / t' _- q% f  t% o) w, M! h% N6 T
    4 {  |7 E( O; M: o9 {! N* ^. d
    & J/ G; Z! E% @  }$ Y/ }

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