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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 ) }+ d8 O* A) m. K$ ]  k
    https://caiyun.139.com/m/i?0F5CJAMhGgSJx* s% K6 h( N  \. ~3 B  o
    8 m' I& M. t9 X5 F. N3 X1 q
    2022
    ' \2 v$ f8 e* A9 X" fCertifificate Authority Cup International Mathematical Contest Modeling, o- Z9 b9 \" t. q% c0 I
    http://mcm.tzmcm.cn
    ; x! Y8 U8 o' |! [' N( s  eProblem A (MCM)1 w8 ^! ]/ A% d/ k' {# f* i! x; C
    How Pterosaurs Fly
    % t8 q& }. k/ N8 X3 Y# |8 f3 TPterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They
    " [7 Y! r, {+ l* I# Gexisted during most of the Mesozoic: from the Late Triassic to the end of+ I+ A" Y% N+ ~: }
    the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved
    7 d0 v8 M$ M4 i& _8 r/ b4 I, gpowered flflight. Their wings were formed by a membrane of skin, muscle, and
    2 W8 r! B; ]1 M- w+ p8 Kother tissues stretching from the ankles to a dramatically lengthened fourth  }4 o* h5 M+ F$ O, l! v3 J
    fifinger[1].
    0 }6 K0 ]& E4 T. j8 i9 r5 tThere were two major types of pterosaurs. Basal pterosaurs were smaller
    5 N; n1 z% _% f4 F% q: Wanimals with fully toothed jaws and long tails usually. Their wide wing mem, P. N( A7 ^( d1 \: J+ E& H$ g/ a7 y
    branes probably included and connected the hind legs. On the ground, they9 `# r. \% a5 I+ N5 R0 B( L8 W3 s
    would have had an awkward sprawling posture, but their joint anatomy and& ?# i9 L: O: S7 l! n
    strong claws would have made them effffective climbers, and they may have lived9 m8 |" x$ e: a  m
    in trees. Basal pterosaurs were insectivores or predators of small vertebrates.
    0 b( b; W5 y" p% H+ oLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles./ Q# _& z+ C7 J) m
    Pterodactyloids had narrower wings with free hind limbs, highly reduced tails,' _% G0 |1 ~* t. |* [
    and long necks with large heads. On the ground, pterodactyloids walked well on
      @! W+ O, z# @/ a4 [5 _$ pall four limbs with an upright posture, standing plantigrade on the hind feet and
    $ \* U4 h  T+ |  a7 Yfolding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil  Q+ q4 b# k/ u- D( Q" v' B+ ^6 B
    trackways show at least some species were able to run and wade or swim[2].0 @2 h) c( I( k; f$ `
    Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which, p/ \+ b1 `" W6 h$ t6 p' U' S- i
    covered their bodies and parts of their wings[3]. In life, pterosaurs would have( R  r9 C2 c( h4 u% G4 u$ Y; \
    had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug
    ! w4 P: \1 |8 ]  Y( `+ r" ygestions were that pterosaurs were largely cold-blooded gliding animals, de4 @0 J$ R3 Z# S' Y6 D2 w
    riving warmth from the environment like modern lizards, rather than burning
    1 `* \& y2 P$ G+ }7 r' E  g5 `- ?calories. However, later studies have shown that they may be warm-blooded: |- Q8 B2 P( f) {  [6 n/ L' B
    (endothermic), active animals. The respiratory system had effiffifficient unidirec7 E5 \  F% ?/ ]( w7 Q+ Q* E5 H
    tional “flflow-through” breathing using air sacs, which hollowed out their bones
      ~" z$ m9 @; F( vto an extreme extent. Pterosaurs spanned a wide range of adult sizes, from
    ! h% L" N0 R# F; K5 @2 [8 W9 u* c6 {the very small anurognathids to the largest known flflying creatures, including
    ( t6 e/ A4 U$ Z) d) EQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least) x% G2 u. h, b3 c
    nine metres. The combination of endothermy, a good oxygen supply and strong, E- x5 R5 @8 K0 @
    1muscles made pterosaurs powerful and capable flflyers.
    1 i3 [% r4 o' {The mechanics of pterosaur flflight are not completely understood or modeled
    # u% D5 D1 t& Q$ N2 `. j5 [6 u0 `at this time. Katsufumi Sato did calculations using modern birds and concluded
    & r* ^" l0 |8 o. v- G) _1 B+ j, q. hthat it was impossible for a pterosaur to stay aloft[6]. In the book Posture,! o: w8 ~1 o+ [5 u. J/ p
    Locomotion, and Paleoecology of Pterosaurs it is theorized that they were able; k1 a( B2 {" s
    to flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].* e( [4 Y3 L/ n) e; O, P+ j. O* ^
    However, both Sato and the authors of Posture, Locomotion, and Paleoecology0 b- s9 q# ^5 _  z: Q
    of Pterosaurs based their research on the now-outdated theories of pterosaurs
    : ~  J6 J+ `" N: S" c: ]$ pbeing seabird-like, and the size limit does not apply to terrestrial pterosaurs,- |8 q4 A6 ~# [1 L; n+ }
    such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that
    0 n2 F% x1 q( Hatmospheric difffferences between the present and the Mesozoic were not needed
    : ?' u% Q1 e; i4 j# ifor the giant size of pterosaurs[8].2 p0 w8 p# _8 P' c0 L
    Another issue that has been diffiffifficult to understand is how they took offff.4 N# \! P/ e: k/ ]
    If pterosaurs were cold-blooded animals, it was unclear how the larger ones
    1 [2 I' Z( S3 s9 E& G2 uof enormous size, with an ineffiffifficient cold-blooded metabolism, could manage
    2 z" R4 H) i0 M# H6 q0 Ya bird-like takeoffff strategy, using only the hind limbs to generate thrust for
    ( U: M' }6 X* w# m8 tgetting airborne. Later research shows them instead as being warm-blooded0 e( [1 [0 R7 g, \+ O" ]' w
    and having powerful flflight muscles, and using the flflight muscles for walking as
    6 @- n0 ^- s, f! Z& [/ {5 Tquadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of
    9 W9 R, @4 _3 L! D+ pJohns Hopkins University suggested that pterosaurs used a vaulting mechanism7 b' g. V9 Q8 a/ E8 g9 h; t0 [
    to obtain flflight[10]. The tremendous power of their winged forelimbs would
    8 [' O; _8 e3 \. `& a6 ~3 ~# j# renable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds. Y. `9 u. I2 g4 u1 @; u# f
    of up to 120 km/h and travel thousands of kilometres[10].
    7 Q& E9 H3 C0 rYour team are asked to develop a reasonable mathematical model of the
    1 S% W2 i( R  Zflflight process of at least one large pterosaur based on fossil measurements and
    1 E: M9 C- ~, M0 T+ Eto answer the following questions.
    : g' }* D1 p( f% X% G7 ?1 e1. For your selected pterosaur species, estimate its average speed during nor" d: c; U4 S+ n2 Q% O, N; k" i
    mal flflight.  P1 n  _3 H( {  ?
    2. For your selected pterosaur species, estimate its wing-flflap frequency during5 R. b2 I4 B' K! H: S7 w
    normal flflight.
    / `! F. J; V+ V& e! z3. Study how large pterosaurs take offff; is it possible for them to take offff like
    , ]6 T9 H& ~1 Ebirds on flflat ground or on water? Explain the reasons quantitatively.
    1 y( m9 Z) E  N+ oReferences6 f. N8 w7 Z7 a+ e# Q( P6 h9 ]
    [1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight( W: _) r' c5 q1 G# a% |+ O
    Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111.' _8 `# v* P; |
    2[2] Mark Witton. Terrestrial Locomotion.1 r! Y' H8 Y. K2 R1 @4 ]+ V
    https://pterosaur.net/terrestrial locomotion.php9 C, T' r* @- M1 g
    [3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs
    4 s$ j# m7 D4 K+ gWere Covered in Fluffffy Feathers. https://www.livescience.com/64324-
    5 q5 X# a4 H: P1 f8 Apterosaurs-had-feathers.html
    / v# ]/ m/ v  ^, o5 v7 Y9 ?) ?[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a5 x& K' Z: j3 e6 }
    rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)
    " k6 r# X/ e" \- A0 h" h9 r3 p5 Wfrom China. Proceedings of the National Academy of Sciences. 105 (6):
    * [: X' O: L6 \9 d6 G% P( Q+ R1 ~6 U1983-87.  X1 h) @5 w; A4 P" I: s
    [5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust
    9 L( Y8 @; T" }/ g9 R( O4 Hskull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4):. |7 o1 G6 ~9 h
    180-84.
    " ]  a! p; h" I9 u: |+ F[6] Devin Powell. Were pterosaurs too big to flfly?) ~0 Z# c( ~6 B1 o" R/ M; {) t# }5 d
    https://www.newscientist.com/article/mg20026763-800-were-pterosaurs' F- C& j! ^* n4 p; b5 V
    too-big-to-flfly/' D# I- ~3 G' @' b
    [7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology
    # Z/ J0 ?7 h5 Y3 _of pterosaurs. Boulder, Colo: Geological Society of America. p. 60.* }9 C* ]! ?+ |0 ]( f- C
    [8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable4 o6 J! O' V, V8 E# n
    air sacs in their wings.% t- P4 q- [! K
    https://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur
    7 {# L* {8 @: R7 i& g$ ubreathing-air-sacs& E- w2 k' Z5 ~/ ]4 a
    [9] Mark Witton. Why pterosaurs weren’t so scary after all.( N# a6 [; F5 g
    https://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils( b5 I5 `0 B0 F3 T) a
    research-mark-witton1 F$ E2 A% O. M
    [10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?$ c, f& S0 s. z
    https://www.newscientist.com/article/dn19724-did-giant-pterosaurs$ q* c4 w1 @2 Y, J
    vault-aloft-like-vampire-bats/
      a. Y& u" R$ ~3 |$ `1 C2 w* q0 `8 G% g; {' ?* ]6 V3 d
    20225 A+ }% S% {1 Z7 B0 A6 K; Q# X
    Certifificate Authority Cup International Mathematical Contest Modeling  N& `/ [1 a( A" ]
    http://mcm.tzmcm.cn
    / h& n3 a' L2 {/ Z+ SProblem B (MCM)
    # R  u, `% A/ s' f1 V: f4 C* MThe Genetic Process of Sequences
    $ W& v1 m8 r; g$ e4 A' s- ~Sequence homology is the biological homology between DNA, RNA, or protein
    + F- V2 Y* X: w5 msequences, defifined in terms of shared ancestry in the evolutionary history of
    9 ^! O; o8 e. P1 A: ^! c/ klife[1]. Homology among DNA, RNA, or proteins is typically inferred from their) N9 L+ ^! t4 A& r" T! v/ S
    nucleotide or amino acid sequence similarity. Signifificant similarity is strong& z, {9 w* c9 z- g7 j1 v6 h
    evidence that two sequences are related by evolutionary changes from a common
    - A1 p( W2 X3 Y* @8 Qancestral sequence[2].
    ! m/ K9 }+ A2 p7 q! W6 t+ ^Consider the genetic process of a RNA sequence, in which mutations in nu& H: l6 [5 k. K' _7 I  R
    cleotide bases occur by chance. For simplicity, we assume the sequence mutation: Y: z% K% l% t
    arise due to the presence of change (transition or transversion), insertion and
      w) p! R. P! v! Qdeletion of a single base. So we can measure the distance of two sequences by; ?+ s, ]# {0 T; I' ?0 j! S7 \
    the amount of mutation points. Multiple base sequences that are close together: @( j; u, ?; L9 D5 \; K: d
    can form a family, and they are considered homologous.
    $ \! l) J- r% t! s1 KYour team are asked to develop a reasonable mathematical model to com* s( G# s  U# S  L% g
    plete the following problems.
    ( {7 y7 h* P1 ?* {# w* J8 F1. Please design an algorithm that quickly measures the distance between
    # g% d+ n- f4 rtwo suffiffifficiently long(> 103 bases) base sequences.
    % k% Q/ x& k# z" }) i. q2. Please evaluate the complexity and accuracy of the algorithm reliably, and7 N+ T  f9 J) D% I  Y
    design suitable examples to illustrate it.+ \/ @+ i7 m8 M# l" K% M
    3. If multiple base sequences in a family have evolved from a common an4 f- }" V' F9 C1 i$ I
    cestral sequence, design an effiffifficient algorithm to determine the ancestral
    ' I! s7 U) d* R! P/ ~( @sequence, and map the genealogical tree.
    $ A5 N0 Q- v& U' r; zReferences
    8 M* T6 j- q& R! Q. v' `[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re
    * D  v/ k* v% N: K7 @2 O3 xview of Genetics. 39: 30938, 2005.
    8 o6 H' j. l; ~* R3 ^, ~[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE,& ^/ X" R9 Q. w5 q8 m" K% ~
    et al. “Homology” in proteins and nucleic acids: a terminology muddle and- @) n7 A4 s9 I( P! @! V+ \
    a way out of it. Cell. 50 (5): 667, 1987.# |8 Z; j6 j% M

    " D, ]+ y! s# d/ [' }2022+ C% y& D) u# g& O
    Certifificate Authority Cup International Mathematical Contest Modeling3 `6 l- J! j: m0 L
    http://mcm.tzmcm.cn
    # Q4 Z4 M+ z1 gProblem C (ICM)
    ) w8 G4 \+ ]2 W% z8 sClassify Human Activities- g# t) v1 H1 _2 X- ^
    One important aspect of human behavior understanding is the recognition and1 C! |# L' x( r( T% z+ g
    monitoring of daily activities. A wearable activity recognition system can im& N$ y$ y6 d( y( b8 j2 w1 _7 w$ x  x# r
    prove the quality of life in many critical areas, such as ambulatory monitor
    8 p* w- Y! }/ Q* _; J1 y4 Q6 oing, home-based rehabilitation, and fall detection. Inertial sensor based activ
    ' E+ c! N% Y2 r/ b4 a, ?ity recognition systems are used in monitoring and observation of the elderly
    # {$ V: @: J0 c: X2 A3 X) W7 l  Eremotely by personal alarm systems[1], detection and classifification of falls[2],  e, k- D) P  C4 v0 ^' x4 j' D6 k
    medical diagnosis and treatment[3], monitoring children remotely at home or in
    ) z+ o! Z8 ]+ o# vschool, rehabilitation and physical therapy , biomechanics research, ergonomics,
    # N* s/ T( k# K) @sports science, ballet and dance, animation, fifilm making, TV, live entertain" M, e! C) `3 L. j/ T
    ment, virtual reality, and computer games[4]. We try to use miniature inertial
    ( k4 X5 X2 f8 j) d$ k- n" C9 tsensors and magnetometers positioned on difffferent parts of the body to classify9 S  _; w( f- m9 E% X( E
    human activities, the following data were obtained.+ |, c/ W& g/ G7 |% o
    Each of the 19 activities is performed by eight subjects (4 female, 4 male,0 k& `) X; m# A2 k
    between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes5 D) @0 k9 |1 n$ v
    for each activity of each subject. The subjects are asked to perform the activ
    & |3 N! G( H" }8 _ities in their own style and were not restricted on how the activities should be
      f8 y" v0 \, H, Uperformed. For this reason, there are inter-subject variations in the speeds and
    5 }" x4 B  ?* g; B5 W9 v1 W+ G* v) gamplitudes of some activities.
    & P2 f# O" h# O5 J' eSensor units are calibrated to acquire data at 25 Hz sampling frequency.
    : w) r- q1 o  M7 v# @The 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal. }- v& n' m! B( e
    segments are obtained for each activity.
    - D: X' A0 U- m" ZThe 19 activities are:
    : b- |& p1 Q* e0 b1. Sitting (A1);: f( }7 f6 s- C
    2. Standing (A2);1 q. O5 N# [. `5 I/ c
    3. Lying on back (A3);: ]" d8 {# O) h5 y  {# w
    4. Lying on right side (A4);9 W% b1 i* T# B/ J- I" O7 P
    5. Ascending stairs (A5);& Z+ z4 ^" H- z+ F, L
    16. Descending stairs (A6);
    : g0 \; a) B8 i- J' g# V( T7. Standing in an elevator still (A7);0 J! V6 l4 J/ F1 M' {, I7 O% ~! w! N. X- S
    8. Moving around in an elevator (A8);
    / c7 I  d2 U( J1 C# q' m2 e9. Walking in a parking lot (A9);: k8 p' A! p( @/ \% l
    10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg: R* N# c" ]$ @$ q, g; O
    inclined positions (A10);$ E+ ~' I& `( T  [
    11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions
    3 K  C6 m4 ]5 U! I2 v  P2 R(A11);7 x! a; a; G' S3 g0 I
    12. Running on a treadmill with a speed of 8 km/h (A12);  w! E* `) x- v$ R" s9 H2 e1 W
    13. Exercising on a stepper (A13);
    ( P: |0 B) c) I2 `4 N14. Exercising on a cross trainer (A14);$ H! w+ ^7 W8 i( _* @) a' F+ ?
    15. Cycling on an exercise bike in horizontal position (A15);
    4 g; j  f) [. c9 L5 D4 D/ v16. Cycling on an exercise bike in vertical position (A16);" q* a* I' ?8 O% h
    17. Rowing (A17);
    . _. i5 w% g/ P, }3 Y. H. F8 L18. Jumping (A18);6 V% B! x; S# h9 p7 ^
    19. Playing basketball (A19).
    2 V- O* z$ U  L) y* e7 cYour team are asked to develop a reasonable mathematical model to solve5 I; W" V2 Q+ D. d6 P
    the following problems.
    0 M' K; F2 [. ~/ J8 p8 o( R# S1. Please design a set of features and an effiffifficient algorithm in order to classify
    9 `4 C) A( Z) z1 Athe 19 types of human actions from the data of these body-worn sensors.
    , Y, b, U/ u7 f2. Because of the high cost of the data, we need to make the model have% A! n9 ^  N2 c& p! X/ u& p* [
    a good generalization ability with a limited data set. We need to study6 [: {# q8 J  e' \- Y* a' n. E
    and evaluate this problem specififically. Please design a feasible method to1 |) d6 }/ [# o5 d& a
    evaluate the generalization ability of your model.) _5 M# N7 T9 L' Q$ r& w) R. g
    3. Please study and overcome the overfifitting problem so that your classififi-2 O0 w" _* W. y! ]/ {' Y% ?- p/ F; a) S/ y
    cation algorithm can be widely used on the problem of people’s action
    6 c% o; o4 f- Y. fclassifification.
    " D7 a+ @- t* {' c# eThe complete data can be downloaded through the following link:% T: x, k3 L4 v+ ?; l( F" c
    https://caiyun.139.com/m/i?0F5CJUOrpy8oq
    3 Y( T* H# X# l; {. I& `4 H2Appendix: File structure
    ! f* a9 W( Z+ U$ ]: u( [- ~• 19 activities (a)9 e5 u' W' F* t, l9 {! S0 R" i$ y
    • 8 subjects (p)% O9 z! N4 l& m
    • 60 segments (s)$ W1 K& }. j" ?+ }+ }! t3 I4 o. B  L
    • 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left: R5 M( ~0 ^0 u
    leg (LL); X4 b: j9 w1 Z
    • 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z( g* x0 q2 `( O
    magnetometers): N% N& y, m% a/ y& H3 Q
    Folders a01, a02, ..., a19 contain data recorded from the 19 activities.
    7 ?! K5 Q7 H7 h. |For each activity, the subfolders p1, p2, ..., p8 contain data from each of the+ T+ `4 U: z: ~: }
    8 subjects.
    ' u- Q* x; [. j! u! y4 vIn each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each
    + m" L1 Z1 u1 i8 R! e: }2 c" c+ g  p4 Tsegment.
    7 Z' u1 U- v: dIn each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25% [* u$ I3 N! v; E' e. Y
    Hz = 125 rows.2 C8 u/ v2 }5 m; }6 |6 d
    Each column contains the 125 samples of data acquired from one of the0 \7 b5 u. T; N& J" J9 i
    sensors of one of the units over a period of 5 sec.  H4 \0 y# U. {
    Each row contains data acquired from all of the 45 sensor axes at a particular6 e+ @2 I) B0 w1 v; t3 c3 \+ [
    sampling instant separated by commas.$ \1 K4 |" L0 o4 B# r
    Columns 1-45 correspond to:
    3 D2 n% T9 W9 ?. w4 k9 k• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,3 [% X; r) ]; N
    • RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag,% Q& w  f1 W" U4 ^3 S4 I1 t
    • LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag,
    8 o9 t- J4 O1 h# ]7 s. b4 G• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,
    / J( b( Y7 y/ g2 @• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag.
    " _  a4 D) U9 e! \( z$ w; TTherefore,
    , k4 H  N( [" c" y" r0 N: B# T  y: M; e• columns 1-9 correspond to the sensors in unit 1 (T),* a, A3 ?& O8 m' ^# R
    • columns 10-18 correspond to the sensors in unit 2 (RA),. u0 [5 i( [5 v1 Q+ \! G
    • columns 19-27 correspond to the sensors in unit 3 (LA),
    - O' }5 ?- {# b/ C! H3 G# V• columns 28-36 correspond to the sensors in unit 4 (RL),
      u6 r( T  L( v+ A" r1 c4 T• columns 37-45 correspond to the sensors in unit 5 (LL).
    1 F  h  B; M: U9 R3References
    + }" }$ K  t* {[1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic
    ) I' I0 y, I! Ydaily movements using a triaxial accelerometer. Med. Biol. Eng. Comput.
    : R0 d7 [6 C. i- t) U. W0 Z# n0 c42(5), 679-687, 2004
    7 ~$ k- ?7 |! K8 B[2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of
    0 [: F$ C: X& P) H8 ?# I. Wlow-complexity fall detection algorithms for body attached accelerometers./ g* z0 `2 O% H/ I; f' X
    Gait Posture 28(2), 285-291, 2008+ t5 x& F2 f5 ^5 G  S( k1 V: h- v
    [3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag' {# D, \' [3 w/ x1 V
    nosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.
    0 |% L$ {' U4 R6 EB. 11(5), 553-562, 2007- C. D4 D) ]9 ]7 W  `; L
    [4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con6 ~+ ?. D0 g1 L* ~/ F
    trol of a physically simulated character. ACM T. Graphic. 27(5), 2008
    , z  i! y$ b5 C3 U0 d8 z1 @( U2 T% J+ B/ {5 K. q! Z& T* W3 N
    2022
    6 Z6 g- K6 p* u0 |2 gCertifificate Authority Cup International Mathematical Contest Modeling# j, D# y) m/ y$ [2 g
    http://mcm.tzmcm.cn$ @& c& ^( ~' `6 _
    Problem D (ICM)
    " e# f2 _& Q1 j; |8 d0 {1 iWhether Wildlife Trade Should Be Banned for a Long
    ! G" e0 o# M4 ?- c  Z  `. R, B  P/ QTime- U/ T. B. r( Q. ]; |2 j+ g
    Wild-animal markets are the suspected origin of the current outbreak and the$ Y& T4 t' I6 b- E# t) j; N: A
    2002 SARS outbreak, And eating wild meat is thought to have been a source
    , a8 v' r% q1 d2 N7 r. [of the Ebola virus in Africa. Chinas top law-making body has permanently
    : t* ]0 h8 X/ A4 U  Btightened rules on trading wildlife in the wake of the coronavirus outbreak,6 `+ i# m" n$ x! ?  R  h8 a! F
    which is thought to have originated in a wild-animal market in Wuhan. Some
    $ ~9 X. N& O: b* Cscientists speculate that the emergency measure will be lifted once the outbreak+ N+ J( ~7 K5 I* e
    ends.
    6 x  u9 J+ y5 M3 x: I" r0 PHow the trade in wildlife products should be regulated in the long term?  _- l+ l" j8 A0 E+ ^7 Q
    Some researchers want a total ban on wildlife trade, without exceptions, whereas" ]. \4 a5 F# O! V1 o4 N
    others say sustainable trade of some animals is possible and benefificial for peo
    9 c: X4 E- r! n9 p% b" Tple who rely on it for their livelihoods. Banning wild meat consumption could
    . l1 w+ |8 v; dcost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil( ]& \1 |7 t2 [2 R
    lion people out of a job, according to estimates from the non-profifit Society of7 f: i1 w# k, L- D. y' A1 [$ I
    Entrepreneurs and Ecology in Beijing.
    1 ?, \. f* \) _! V& S& t. gA team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology
    % U; R+ \; T2 c9 @* r; g7 Zin China, chasing the origin of the deadly SARS virus, have fifinally found their
    : i+ Y& g5 [/ Lsmoking gun in 2017. In a remote cave in Yunnan province, virologists have
    6 v! ^( v1 z/ z* J7 s" oidentifified a single population of horseshoe bats that harbours virus strains with/ Y" g+ t+ }9 w$ J4 T
    all the genetic building blocks of the one that jumped to humans in 2002, killing& m  M. g$ T' m
    almost 800 people around the world. The killer strain could easily have arisen! u9 z" Q, S, h
    from such a bat population, the researchers report in PLoS Pathogens on 30
    4 L/ C  F5 m! h  a: p; n4 bNovember, 2017. Another outstanding question is how a virus from bats in* l) a( V) S* K" D0 ~' E$ Y
    Yunnan could travel to animals and humans around 1,000 kilometres away in, J/ ^: w; h/ |8 B& ~1 I# c
    Guangdong, without causing any suspected cases in Yunnan itself. Wildlife/ ]5 ^! \+ D3 [, J( M3 d
    trade is the answer. Although wild animals are cooked at high temperature+ w4 R0 |4 F$ {2 E, a: o( E0 J9 n
    when eating, some viruses are diffiffifficult to survive, humans may come into contact
    0 r" {$ A# I2 a1 F4 Ewith animal secretions in the wildlife market. They warn that the ingredients' D# Y5 Y9 a0 W( Z" }
    are in place for a similar disease to emerge again.! O& C" b1 ]) U# |2 b
    Wildlife trade has many negative effffects, with the most important ones being:
    / t( ]$ {1 j) h7 K/ A# q. j4 M1Figure 1: Masked palm civets sold in markets in China were linked to the SARS+ u2 h3 {2 }) f6 g
    outbreak in 2002.Credit: Matthew Maran/NPL6 g. Q0 \! l7 y! h% G
    • Decline and extinction of populations
    % I% o5 E2 f- x3 X• Introduction of invasive species
    4 S8 T9 Y5 W' |" v1 \% I* D. ^$ M• Spread of new diseases to humans; b2 r* \: B0 ^: g/ `# C7 e
    We use the CITES trade database as source for my data. This database8 E" [  X6 S" Z
    contains more than 20 million records of trade and is openly accessible. The
      k$ V9 B: L/ M8 q: u* D0 q. W  Pappendix is the data on mammal trade from 1990 to 2021, and the complete
    5 Z! |* l( \9 F" gdatabase can also be obtained through the following link:
    1 p5 m9 ?' E0 l/ xhttps://caiyun.139.com/m/i?0F5CKACoDDpEJ+ T  a/ J) \( p3 _
    Requirements Your team are asked to build reasonable mathematical mod
    $ ^2 C0 |6 e7 P; O0 rels, analyze the data, and solve the following problems:4 M5 F4 Z9 C3 R& {% ~. T; q
    1. Which wildlife groups and species are traded the most (in terms of live: C5 |8 ~; z7 M, _5 N( L7 ?3 a
    animals taken from the wild)?1 Q0 n2 E) q" k- |3 g. Q$ E: q
    2. What are the main purposes for trade of these animals?
    ' j$ y( A  M8 n; l4 z8 G/ ~3. How has the trade changed over the past two decades (2003-2022)?
    1 p$ n) C, b1 O9 e4. Whether the wildlife trade is related to the epidemic situation of major
    * `( L/ T( H4 l2 G8 h/ P8 z, [2 W5 q6 iinfectious diseases?
    5 F$ Z' s% [5 @3 L9 o. Z25. Do you agree with banning on wildlife trade for a long time? Whether it/ D, L7 m7 F2 a% \2 Z, Z. N
    will have a great impact on the economy and society, and why?
      J0 @/ z4 K' O- o( t6. Write a letter to the relevant departments of the US government to explain
    ; n; G' R5 p8 `- }# u. Fyour views and policy suggestions.
    ! G: W5 u7 @8 V! i# N2 E7 i- u3 @

    ; i" o& ^  `: \& z  A/ T. r/ F9 |4 j* ?8 h9 Q

    . i: N1 j: X1 F$ `1 U
    0 w% }/ k9 s, l8 d8 P  U( x! E5 K" o1 e. {5 a# z7 Y0 {
    2 P3 ?2 G6 v4 e* I

    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.rar

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