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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
    4 P) E' P8 T8 C4 v' D* u, B* Z9 Rhttps://caiyun.139.com/m/i?0F5CJAMhGgSJx
    ' j  P# ?/ o) k9 Z- L2 b3 W
    8 G9 r# \! e$ T9 `8 ?* X: @20224 [3 g% ?$ d) @" W
    Certifificate Authority Cup International Mathematical Contest Modeling3 L- \7 x( p0 F
    http://mcm.tzmcm.cn
    7 L% r' P" i. c4 b# ?9 xProblem A (MCM)4 x' L- k7 G' H" R8 B0 g
    How Pterosaurs Fly5 B, d; O7 i* Y" w
    Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They( f% R  h% ?( I) D
    existed during most of the Mesozoic: from the Late Triassic to the end of
    8 U; g0 ]6 @' P! n0 w$ ?the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved- h8 C  j9 N" x% J0 Q2 S
    powered flflight. Their wings were formed by a membrane of skin, muscle, and
    5 U- Q# Y4 A5 ~# J7 f7 U" rother tissues stretching from the ankles to a dramatically lengthened fourth& {" M0 j: r/ s" g! j4 n0 g. i
    fifinger[1].) \: X  V/ X( C2 i7 {
    There were two major types of pterosaurs. Basal pterosaurs were smaller
    ) i9 W- M1 R3 e8 danimals with fully toothed jaws and long tails usually. Their wide wing mem5 i8 G4 t; I5 l. H. [1 Y
    branes probably included and connected the hind legs. On the ground, they
    % @1 I# ^2 c* H+ g  Owould have had an awkward sprawling posture, but their joint anatomy and8 f- q, z3 O; ?) B  }, u# ~5 G' R
    strong claws would have made them effffective climbers, and they may have lived" A: r/ p- p8 J; [
    in trees. Basal pterosaurs were insectivores or predators of small vertebrates.
    4 f) {' D8 F) d' HLater pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles.
    1 K' K( h, J5 o; Q1 W& p( _% DPterodactyloids had narrower wings with free hind limbs, highly reduced tails,; P. i" f4 M" F$ P+ ]8 c
    and long necks with large heads. On the ground, pterodactyloids walked well on8 c$ F5 N4 Y, d% _1 t+ Z! I
    all four limbs with an upright posture, standing plantigrade on the hind feet and- i) E, W1 }4 R' {
    folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil9 P6 g7 D. L4 x% U
    trackways show at least some species were able to run and wade or swim[2].
    , Z' C# U3 c$ C6 m: Y' PPterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which
    . J  s2 B+ B3 Ycovered their bodies and parts of their wings[3]. In life, pterosaurs would have3 |4 P% J% w# p1 Q% Z* |- U
    had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug& S, j' w& z4 }% f% ]* n
    gestions were that pterosaurs were largely cold-blooded gliding animals, de5 [& G1 E9 {8 F9 J! i( _! U
    riving warmth from the environment like modern lizards, rather than burning
    7 G* ^  _6 n0 qcalories. However, later studies have shown that they may be warm-blooded) f' G+ g5 Y% H. D: k8 {' P0 v3 O
    (endothermic), active animals. The respiratory system had effiffifficient unidirec
    5 [  f- g1 W( _2 A2 k. A% O, m2 b2 ctional “flflow-through” breathing using air sacs, which hollowed out their bones0 o5 p. W! r' f9 K% h7 E! f) _
    to an extreme extent. Pterosaurs spanned a wide range of adult sizes, from
    - Y( u: s% T5 l( @: e. \/ Ithe very small anurognathids to the largest known flflying creatures, including
    * c  }% q5 ^8 HQuetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least) K  h, `% n& e  _
    nine metres. The combination of endothermy, a good oxygen supply and strong
    . J) M$ R( y: e1muscles made pterosaurs powerful and capable flflyers.1 q: y$ C, s: U% L
    The mechanics of pterosaur flflight are not completely understood or modeled
    & K5 {5 `1 G/ Qat this time. Katsufumi Sato did calculations using modern birds and concluded
    / n6 \$ p+ h  ?# @* ~that it was impossible for a pterosaur to stay aloft[6]. In the book Posture,% e- T8 M2 h2 h* o( S- N
    Locomotion, and Paleoecology of Pterosaurs it is theorized that they were able
    & u5 ]- I( B8 R6 ?& U, Cto flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].
    6 N- H( D. a0 S7 s" ~However, both Sato and the authors of Posture, Locomotion, and Paleoecology5 a' `) C3 r( I* n; d
    of Pterosaurs based their research on the now-outdated theories of pterosaurs% Q  F# f9 o, _; H
    being seabird-like, and the size limit does not apply to terrestrial pterosaurs,9 E1 N  d) ]6 `& H: r
    such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that
    0 x; M) \. ^. u3 Vatmospheric difffferences between the present and the Mesozoic were not needed
    % \& x5 P0 i+ q* Q+ c1 \for the giant size of pterosaurs[8].
      n* i, f3 F& d  w1 t1 xAnother issue that has been diffiffifficult to understand is how they took offff.
      S' ^: ]4 M' l3 n, p$ J3 a% jIf pterosaurs were cold-blooded animals, it was unclear how the larger ones0 ^- u1 Z  o6 ~" `* \1 `, \
    of enormous size, with an ineffiffifficient cold-blooded metabolism, could manage8 x% j; ^6 d* I
    a bird-like takeoffff strategy, using only the hind limbs to generate thrust for
    " i* R1 i/ n4 h; l, ~2 S% w$ A; n0 `getting airborne. Later research shows them instead as being warm-blooded1 x& D" C+ j9 ]$ ~2 i) ?
    and having powerful flflight muscles, and using the flflight muscles for walking as
    ; A$ [2 g2 }2 t8 `quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of
    ' \# y- K8 ^) G2 s5 D$ ~Johns Hopkins University suggested that pterosaurs used a vaulting mechanism
    8 i5 Z& E% I; S9 dto obtain flflight[10]. The tremendous power of their winged forelimbs would
    6 R8 f' R, [/ G3 }9 v) N3 l2 `enable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds, c, y: x4 [/ U) E1 N
    of up to 120 km/h and travel thousands of kilometres[10]., Z/ q9 _0 o% f5 h7 c; O1 N' q
    Your team are asked to develop a reasonable mathematical model of the
    9 b9 n5 z4 j! x) l' m# N& lflflight process of at least one large pterosaur based on fossil measurements and; |" j. P& X* w( s7 f0 X+ G
    to answer the following questions.
    * C% x/ F% u4 j  f( \, b0 E1. For your selected pterosaur species, estimate its average speed during nor. P& c$ y: v9 _0 e0 d2 k% I) o
    mal flflight.
    . m. l5 f! G, i) |: a2. For your selected pterosaur species, estimate its wing-flflap frequency during" s* G; k6 O5 {4 Y5 c+ q2 o
    normal flflight.. T: V' }; m" \9 P- ~
    3. Study how large pterosaurs take offff; is it possible for them to take offff like: ]: I0 x$ w8 W# n8 K7 j. p/ e. N3 T
    birds on flflat ground or on water? Explain the reasons quantitatively.) m+ w1 j# t5 ]
    References
    ; }# `( ^: o% d( c' \3 K[1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight
    1 P% Y& z+ A! P0 B2 v; y" D. IMembrane. Acta Palaeontologica Polonica. 56 (1): 99-111.7 {% d, K9 H8 r! s* O+ C3 K* ^# q
    2[2] Mark Witton. Terrestrial Locomotion.
    6 `6 u1 {. g2 x& `8 Bhttps://pterosaur.net/terrestrial locomotion.php2 P* B6 ~" i2 j1 m
    [3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs
    # M) I  @( P9 X7 M# m4 OWere Covered in Fluffffy Feathers. https://www.livescience.com/64324-
    4 r# r1 @- Q8 j+ Ppterosaurs-had-feathers.html& d  E; u( Y& k0 L' F1 e5 x
    [4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a
    " w) c- u8 V/ w" grare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)& v- J: Z2 `; b/ m6 @1 N; f. c2 O
    from China. Proceedings of the National Academy of Sciences. 105 (6):
    & Q# G8 k1 X$ ^' G- q( g+ ?1983-87., y1 {7 I9 Q2 Q' R+ J
    [5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust6 E4 I* Y8 y8 ~. C
    skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4):
    0 k3 o/ N9 i- A# O/ A% y180-84.
    0 `* X4 G# Q$ |; p[6] Devin Powell. Were pterosaurs too big to flfly?5 f: X, D+ M& ]8 c4 t! z$ c5 z
    https://www.newscientist.com/article/mg20026763-800-were-pterosaurs
    8 v, b2 u" [# ^7 R$ q& Rtoo-big-to-flfly/
    % \  `* [4 G: Y# _[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology+ y9 |. s2 V6 |% z* E0 ?0 A
    of pterosaurs. Boulder, Colo: Geological Society of America. p. 60.
    . A3 P* F/ l3 i" {[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable
    ' W0 e" {" W, V! i# T/ yair sacs in their wings.
    , G9 c6 ?4 ?* S: Ohttps://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur8 B2 Q3 v# g/ }/ f9 W% Y- Y
    breathing-air-sacs# y, \! z$ j7 L" ?1 t- J: k
    [9] Mark Witton. Why pterosaurs weren’t so scary after all.6 L/ J" K4 {) P, L8 S" ?4 T
    https://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils
    + ~3 \) ^' f, Y4 f& Wresearch-mark-witton
    " n1 }% @' M3 q& c5 @8 E: Q) m* q[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?
    + ^/ [7 w6 E: P( E  ^; Q' Shttps://www.newscientist.com/article/dn19724-did-giant-pterosaurs
      h  S; P4 i& z2 S4 H, X: cvault-aloft-like-vampire-bats/
    0 q1 R  e* U" C7 n; b. v  ~. F/ |* x
    2022
    2 o) v: P3 U! f4 b: S0 _Certifificate Authority Cup International Mathematical Contest Modeling
    8 j+ k% J0 f" i; K/ |/ l8 Y0 |http://mcm.tzmcm.cn) p$ s& W) }, p
    Problem B (MCM)0 F( C( u: U- }' D5 b: r. u. H: _3 q7 h
    The Genetic Process of Sequences
    # R9 ^. }  A0 D+ bSequence homology is the biological homology between DNA, RNA, or protein( p2 E8 _! `: z' e( G: U8 g7 A
    sequences, defifined in terms of shared ancestry in the evolutionary history of# m* L1 v! C& V& ~+ Y: ]* x5 |: D
    life[1]. Homology among DNA, RNA, or proteins is typically inferred from their- L: w4 D; Z- Z2 L" s. ^
    nucleotide or amino acid sequence similarity. Signifificant similarity is strong' ^6 z& F8 l4 M; w- c
    evidence that two sequences are related by evolutionary changes from a common
    4 @5 C6 [' S5 R2 p4 qancestral sequence[2]./ a  V/ r* a* k1 Q
    Consider the genetic process of a RNA sequence, in which mutations in nu
    / X+ y  k3 C' ~, x8 Q8 u' d; vcleotide bases occur by chance. For simplicity, we assume the sequence mutation
    6 X9 }$ U2 R' S2 M. g, j, j5 zarise due to the presence of change (transition or transversion), insertion and
    ( k. K, R* L* k. a0 pdeletion of a single base. So we can measure the distance of two sequences by
    ; k& m5 m& c8 i# }4 u$ Z0 Vthe amount of mutation points. Multiple base sequences that are close together- D% M: c! o- ^$ d6 z- B# z
    can form a family, and they are considered homologous.* k+ e7 Q2 p3 \9 F
    Your team are asked to develop a reasonable mathematical model to com  w0 ]1 \* O& L- v' ^6 U" u
    plete the following problems.) D3 v7 x8 b* k1 y% O
    1. Please design an algorithm that quickly measures the distance between
    9 `9 }2 H3 }. R5 @, Atwo suffiffifficiently long(> 103 bases) base sequences.
    ' E' N; u3 w- M. _, S/ y, \2. Please evaluate the complexity and accuracy of the algorithm reliably, and
    6 {6 q; z0 f; E$ ]design suitable examples to illustrate it.# q8 g& P7 S5 s% k& A, Y4 d
    3. If multiple base sequences in a family have evolved from a common an
    ; d1 H; L4 H" bcestral sequence, design an effiffifficient algorithm to determine the ancestral  }: p% n  Z, e1 N
    sequence, and map the genealogical tree.
    8 H. k0 d4 J6 `+ x3 }1 i6 w4 _* h: q% rReferences
    * q- U3 y9 b0 D[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re
    - T. b& F- t: U4 i& W4 l* Aview of Genetics. 39: 30938, 2005.
    ( I/ c' [7 v* c  }$ r4 w5 [9 l/ C. U+ T1 P[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE,
    8 D, n. L( y; D/ z# Pet al. “Homology” in proteins and nucleic acids: a terminology muddle and8 s6 u, a7 D& j$ S, `. A3 x
    a way out of it. Cell. 50 (5): 667, 1987.
    % ~( W/ l( B( L! b: C& R  I* P: _6 [+ x0 {5 }" i' P! R/ s, B
    2022
    * B0 z1 n! H1 HCertifificate Authority Cup International Mathematical Contest Modeling" t5 s4 a4 \$ J4 A4 E  k1 ^. H
    http://mcm.tzmcm.cn9 H; q* C0 q4 _8 r+ C- O+ c. k
    Problem C (ICM)* ]0 M9 V8 ^& }/ r" c' p
    Classify Human Activities
    3 k0 c) u5 M0 r9 e% QOne important aspect of human behavior understanding is the recognition and
    + Y) E7 \. l& L/ gmonitoring of daily activities. A wearable activity recognition system can im" i4 H$ k/ v8 x( ^& Q# Q4 {
    prove the quality of life in many critical areas, such as ambulatory monitor
    & l3 u$ c* L5 v! Y& p7 B6 Zing, home-based rehabilitation, and fall detection. Inertial sensor based activ
    5 z  a  T" g$ j5 g: |  \& tity recognition systems are used in monitoring and observation of the elderly) H, q4 {1 L; C. |" ]
    remotely by personal alarm systems[1], detection and classifification of falls[2],
    3 ~# s2 T' n/ _: Gmedical diagnosis and treatment[3], monitoring children remotely at home or in! X7 S" B/ L) O7 b. B
    school, rehabilitation and physical therapy , biomechanics research, ergonomics,
    0 \- e+ ]3 R( Lsports science, ballet and dance, animation, fifilm making, TV, live entertain5 _# f' r- a! W7 C7 t
    ment, virtual reality, and computer games[4]. We try to use miniature inertial- S# j, W5 D1 @) g+ x/ C' s
    sensors and magnetometers positioned on difffferent parts of the body to classify
    & @, P& J% |% a, }# }human activities, the following data were obtained.
    ( B+ n8 j7 ^% M) w$ u- E% eEach of the 19 activities is performed by eight subjects (4 female, 4 male,0 E4 @7 x  {7 ]# f& @6 j/ B
    between the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes. p3 U% ^* E+ [3 \" b9 ~
    for each activity of each subject. The subjects are asked to perform the activ8 r4 X. s. ]/ |: y/ G& z5 B. y
    ities in their own style and were not restricted on how the activities should be* j6 v. B0 ]+ R5 d+ }+ Q9 s
    performed. For this reason, there are inter-subject variations in the speeds and
    , o. z: z8 b0 Uamplitudes of some activities.- u" ?2 G2 M) \2 y; H! \9 C
    Sensor units are calibrated to acquire data at 25 Hz sampling frequency.
      W% x( j, ^7 O" eThe 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal
    / v) [' M& v0 w2 }: Bsegments are obtained for each activity.* k- p6 m1 W: v( @( Y/ c
    The 19 activities are:
    . }. r; `* w$ L; f, ?2 i1. Sitting (A1);/ q+ n- ]7 q, E% R* ]
    2. Standing (A2);' n5 f: F5 l5 P% D: W  s
    3. Lying on back (A3);
    : S# P) q( X/ l& E" O4. Lying on right side (A4);
    - t, [- {! |$ b& E5. Ascending stairs (A5);' x4 d/ R- q! q5 d
    16. Descending stairs (A6);3 t, }& @, c* ~7 @* ~6 b! b
    7. Standing in an elevator still (A7);
    + c+ M3 j9 \% R& I8. Moving around in an elevator (A8);" U1 @/ y4 R8 h& E5 b; p
    9. Walking in a parking lot (A9);: z8 H4 R1 f4 R/ z, f; X# O
    10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg# ]3 o; }7 N8 l
    inclined positions (A10);% o* f8 J' ~# U/ X2 @; r+ @1 E
    11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions
    , {/ ]& R9 G$ R(A11);
    / y& r4 V' M0 y; R12. Running on a treadmill with a speed of 8 km/h (A12);
    & g( T! @+ L* V9 T4 a13. Exercising on a stepper (A13);; G0 i6 Y, Z$ o
    14. Exercising on a cross trainer (A14);
    % z: f* \( o9 U& e. |+ o. U$ H" |15. Cycling on an exercise bike in horizontal position (A15);& Y4 v/ Z4 u$ a* c6 o
    16. Cycling on an exercise bike in vertical position (A16);
    * ~/ \3 H. T4 a* F9 f6 X17. Rowing (A17);# m! H/ M/ i; F' E; o
    18. Jumping (A18);
    6 x$ }/ t4 f! b. n/ p  q. a) O8 ?7 f7 H19. Playing basketball (A19).
    9 u4 ~8 |# J, E3 V9 dYour team are asked to develop a reasonable mathematical model to solve) s5 f8 H8 t* \
    the following problems.; S; s' ~- N6 y/ _6 e+ }& f
    1. Please design a set of features and an effiffifficient algorithm in order to classify
    0 a+ @& D7 b/ r2 X* Rthe 19 types of human actions from the data of these body-worn sensors.( ~$ p) b) E2 b8 T( K$ F6 c
    2. Because of the high cost of the data, we need to make the model have
    & I) A- Y( o2 ]5 X0 g+ H" [/ t$ E& Ga good generalization ability with a limited data set. We need to study
    " s8 G5 \" K2 d  a7 s& Gand evaluate this problem specififically. Please design a feasible method to) _8 k& ^- @$ e% M, O' u
    evaluate the generalization ability of your model.
    ' G9 C) @. V/ D& p+ ]8 I3. Please study and overcome the overfifitting problem so that your classififi-
    / u: g4 s5 h* G2 |cation algorithm can be widely used on the problem of people’s action2 {. A+ h) J! S3 @8 K
    classifification.
    * S: c/ \3 Z4 _  L. ?& qThe complete data can be downloaded through the following link:9 e$ c! F5 a) B! c+ D
    https://caiyun.139.com/m/i?0F5CJUOrpy8oq7 z% B8 O% m. F
    2Appendix: File structure' p9 P3 {* V% P( F
    • 19 activities (a)2 E- X* H9 {7 K
    • 8 subjects (p)$ B- a" L- T, W4 o& c4 D! ^% m
    • 60 segments (s)& p6 {, j( G- H; c# K& m
    • 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left
    5 z! F  b- C' Eleg (LL)+ `1 Y  J& X# p& V
    • 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z
    4 O4 n# {; t- o. B4 b5 amagnetometers)" e+ l) c' w- [# E
    Folders a01, a02, ..., a19 contain data recorded from the 19 activities.
    / F4 O5 G  m) h" L9 \For each activity, the subfolders p1, p2, ..., p8 contain data from each of the
    5 g% F3 \8 U  X: h+ C" k8 subjects.- d, K1 f: K6 V
    In each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each
    9 b7 N! \. }: w' ]$ Rsegment.
      Y7 i+ a$ N( u! {0 `& k6 xIn each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 25; p8 q' ?2 j1 J/ {8 P1 ?- m
    Hz = 125 rows.
    8 x/ {. z5 T) n% IEach column contains the 125 samples of data acquired from one of the( X/ D9 a1 z5 i) J6 V) A  H) ^1 Y
    sensors of one of the units over a period of 5 sec.
    ' Z+ K8 d8 n3 \- |7 ?Each row contains data acquired from all of the 45 sensor axes at a particular
    8 p) E6 H2 b+ m2 Q2 e0 msampling instant separated by commas.: Q& t# o* b( k" I& A
    Columns 1-45 correspond to:
      a8 T; @% M2 _6 x! \* L( X• T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,; o- f1 f9 f" a3 X, P
    • RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag," D+ ~5 f: ]( g/ s$ W6 q/ c
    • LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag,
    & H8 v0 e  c' R5 q9 O1 Y" \• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,! o3 e) G# V# {7 A; v3 D: P( p
    • LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag.
    & y' b4 |. W" c! y7 t# {; qTherefore," n) M8 M, K/ |. N3 ^2 X
    • columns 1-9 correspond to the sensors in unit 1 (T),$ K. v* {! F' o) F6 R% R" [7 K
    • columns 10-18 correspond to the sensors in unit 2 (RA),2 p) J6 f( Y$ q) _: D% m! I8 Y3 Y
    • columns 19-27 correspond to the sensors in unit 3 (LA),
    6 g6 [; B$ [1 U" u7 z  ]5 g• columns 28-36 correspond to the sensors in unit 4 (RL),5 S2 [# g6 H' [) e/ E% G
    • columns 37-45 correspond to the sensors in unit 5 (LL).
    / j) C) S. I# G7 `6 w; v0 i) g3References- o" L# S+ S+ j- D* I
    [1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic; @7 R& W& E2 l
    daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput.
    / @" v$ e  W) g# U0 ~- ]) N6 C42(5), 679-687, 2004- ?, U6 ]7 n& x6 d% A& |
    [2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of
    $ L( {1 u2 |" l2 u( zlow-complexity fall detection algorithms for body attached accelerometers.) c8 n% N. g& N$ y0 R
    Gait Posture 28(2), 285-291, 2008
    0 J* e( x0 ]' K: y" D2 @' ?[3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag
    ; J* q  z" R% O$ U/ S* E& [0 ynosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.; R0 k& ~( O1 l; K5 c
    B. 11(5), 553-562, 2007
    ) z/ V' u$ A) z9 _/ U- k/ Y) d/ y2 p% r[4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con
    * _; i( S+ q+ m$ k9 D$ A0 b8 W$ C. Otrol of a physically simulated character. ACM T. Graphic. 27(5), 2008
    0 ~8 s! _$ t; y% S$ P, Z/ h7 q8 o$ H$ k7 _
    20229 N& l' d, p( A( _! m! ]
    Certifificate Authority Cup International Mathematical Contest Modeling
    3 a6 _5 m" \; w0 shttp://mcm.tzmcm.cn
    6 O4 d+ }" l( Y( A1 |* {/ D9 a$ [Problem D (ICM)
    ' k, O5 }( X9 T6 d& y: ~9 V3 `! d+ WWhether Wildlife Trade Should Be Banned for a Long5 g  b8 \+ }( @  ?; Y6 H: z
    Time3 O4 [9 r+ B% s$ l9 U8 A7 s
    Wild-animal markets are the suspected origin of the current outbreak and the
    4 o; S* r# `7 @. b9 w, g2002 SARS outbreak, And eating wild meat is thought to have been a source
    8 h8 Z0 A& S" ]" r. Qof the Ebola virus in Africa. Chinas top law-making body has permanently* v: M. @/ h( s7 k. T( B
    tightened rules on trading wildlife in the wake of the coronavirus outbreak,; R8 Y# ]- A+ }' C1 O. b
    which is thought to have originated in a wild-animal market in Wuhan. Some6 t& I  v; ]% p* ]8 {1 B) B/ o
    scientists speculate that the emergency measure will be lifted once the outbreak
    5 l+ P2 P/ s( b! h' Uends.4 e& i+ L: a8 V5 e: c, e. p5 ]' a
    How the trade in wildlife products should be regulated in the long term?
    1 C& ]- Q& b, H/ `$ a8 A" QSome researchers want a total ban on wildlife trade, without exceptions, whereas7 s/ ?% G5 l# i# u1 I; G( o: f
    others say sustainable trade of some animals is possible and benefificial for peo1 @6 d* O$ U3 ^
    ple who rely on it for their livelihoods. Banning wild meat consumption could
    * e9 l2 x7 h7 Tcost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil
    ! n2 G, w% u/ v4 ?- jlion people out of a job, according to estimates from the non-profifit Society of. R. d0 d" s4 X
    Entrepreneurs and Ecology in Beijing.
      M, t9 D9 R. `5 z3 x" H6 YA team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology
    $ k1 U5 b, X8 U7 W2 E6 kin China, chasing the origin of the deadly SARS virus, have fifinally found their8 Q5 S. N1 W; U, _4 S' w
    smoking gun in 2017. In a remote cave in Yunnan province, virologists have' L5 y& S$ Z0 J
    identifified a single population of horseshoe bats that harbours virus strains with9 _  v- a" U1 @. i/ C' U
    all the genetic building blocks of the one that jumped to humans in 2002, killing: A: p2 U$ e+ o; R" d# `' B
    almost 800 people around the world. The killer strain could easily have arisen
    8 S  C! M, N+ |2 pfrom such a bat population, the researchers report in PLoS Pathogens on 30
    # w- I2 g+ Q, jNovember, 2017. Another outstanding question is how a virus from bats in
    5 |6 F1 f* q! @/ h5 \4 uYunnan could travel to animals and humans around 1,000 kilometres away in' [: b+ U. T) a$ ?
    Guangdong, without causing any suspected cases in Yunnan itself. Wildlife7 F' g) i7 |+ i1 H% J* N/ J
    trade is the answer. Although wild animals are cooked at high temperature
    ' U( j9 _- I& g4 ~! p% a9 z/ k, Pwhen eating, some viruses are diffiffifficult to survive, humans may come into contact
    - @8 a. |3 W4 P4 @. Bwith animal secretions in the wildlife market. They warn that the ingredients
    / F! S& T2 b; |0 C8 Aare in place for a similar disease to emerge again.; I" h2 |( K5 ]1 D* e! C9 E' \: z
    Wildlife trade has many negative effffects, with the most important ones being:8 [( p9 M" ^$ L  U, L
    1Figure 1: Masked palm civets sold in markets in China were linked to the SARS! y- Q8 O6 W* @( m6 ?
    outbreak in 2002.Credit: Matthew Maran/NPL0 ^& y; D7 }; j
    • Decline and extinction of populations
    3 H: W( M; `* B7 `1 V, b9 [• Introduction of invasive species! ?% E( n/ |0 |6 }, R
    • Spread of new diseases to humans2 ^0 p7 @1 V) ]& ]/ B" \% d* e
    We use the CITES trade database as source for my data. This database
    + z; k4 q7 ?4 r$ p0 pcontains more than 20 million records of trade and is openly accessible. The& P* {3 G0 P  \2 K) ^% |
    appendix is the data on mammal trade from 1990 to 2021, and the complete
    " F$ q  S$ v1 N5 v% ~; idatabase can also be obtained through the following link:% z' J. p5 b! t1 x# {! B
    https://caiyun.139.com/m/i?0F5CKACoDDpEJ
    3 E4 j. `. Y5 W9 ^6 p% \% A2 eRequirements Your team are asked to build reasonable mathematical mod4 F, o, ]4 S3 J- D! b
    els, analyze the data, and solve the following problems:$ X$ G2 M$ I! ]( O! c: W, f
    1. Which wildlife groups and species are traded the most (in terms of live7 `: [% |8 P8 \
    animals taken from the wild)?
    " a" S6 a% J4 v4 T* J  ?! y1 A$ E2. What are the main purposes for trade of these animals?8 r, K* u/ |& f* u
    3. How has the trade changed over the past two decades (2003-2022)?
    6 _4 X- c% G! e  C- ?( j! q4. Whether the wildlife trade is related to the epidemic situation of major2 o, f; m) S0 D. j
    infectious diseases?; r6 Z  d& G: w& n9 t+ V
    25. Do you agree with banning on wildlife trade for a long time? Whether it
    , e# g. _$ b2 f. e! mwill have a great impact on the economy and society, and why?2 q) r$ {# x4 t9 @2 ^9 q  w4 f
    6. Write a letter to the relevant departments of the US government to explain
    - ]" U: `1 h1 N' ^( zyour views and policy suggestions.2 A+ n1 ?( Y5 y. ]
    5 _: U* j  Y8 R" |! R; _

    % k! M" i) F+ i
    4 q5 R' B( c3 H+ H- }# H7 {( `9 e& r  ^  [8 v4 m- {
    1 c6 W# g9 a8 n8 w, G

    3 c0 M: M$ |4 D; V6 W/ y1 z+ R& U. A
    / q5 \) O' H4 b* x* o5 E  g

    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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