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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 ( {5 p! K9 u4 m8 |5 v0 D& y. j1 y6 C
    https://caiyun.139.com/m/i?0F5CJAMhGgSJx
    ( X% D% J4 g+ T" I1 E# e) y
    # }3 M8 U( `' ]' [; M, \  M; l2022
    8 @9 w, ]/ Z  P7 z0 R2 z8 f0 nCertifificate Authority Cup International Mathematical Contest Modeling- T' F2 J% Z' l
    http://mcm.tzmcm.cn
    - W8 I6 b0 y! _1 D/ X- m2 fProblem A (MCM)
    ' b0 T( j* E  Q7 ?9 hHow Pterosaurs Fly2 k& l5 f9 [& B/ [; P  @
    Pterosaurs is an extinct clade of flflying reptiles in the order, Pterosauria. They
    & B1 a  L* }4 o( G* g2 Cexisted during most of the Mesozoic: from the Late Triassic to the end of+ S+ R$ A* B( G
    the Cretaceous. Pterosaurs are the earliest vertebrates known to have evolved
    9 O! _: E) ~  t( B: f" Rpowered flflight. Their wings were formed by a membrane of skin, muscle, and
    : M& \; s. N6 L4 W3 k& gother tissues stretching from the ankles to a dramatically lengthened fourth
    ( M2 Q) D% R# e1 O$ Z5 Hfifinger[1].7 u6 i1 u+ b; |' U: {! B
    There were two major types of pterosaurs. Basal pterosaurs were smaller
    / B; m0 M. R" W% J! R4 Q8 ~  ]animals with fully toothed jaws and long tails usually. Their wide wing mem( Q9 O' e" S* h9 u1 G
    branes probably included and connected the hind legs. On the ground, they
    4 }; M- ?; V# A2 E$ i" i6 Mwould have had an awkward sprawling posture, but their joint anatomy and
    ; M! n8 J9 n) D. u& o: Rstrong claws would have made them effffective climbers, and they may have lived
    " Q$ U& ?1 g, x1 |1 yin trees. Basal pterosaurs were insectivores or predators of small vertebrates.! c  ^) o/ z- B) D! `, X
    Later pterosaurs (pterodactyloids) evolved many sizes, shapes, and lifestyles.
    0 P' Q; v" E' ^- ^# z' |- M" t9 vPterodactyloids had narrower wings with free hind limbs, highly reduced tails,0 c$ ]) K9 A6 X7 m$ Z9 Z% ?) h6 K# r
    and long necks with large heads. On the ground, pterodactyloids walked well on
    ' q# E+ J: f8 F4 Fall four limbs with an upright posture, standing plantigrade on the hind feet and  w+ ?  H6 A2 g2 h# q+ Z- W" P7 ?* H& L
    folding the wing fifinger upward to walk on the three-fifingered “hand”. The fossil
    2 Y- |. \5 E5 @0 @6 j  |trackways show at least some species were able to run and wade or swim[2].3 Y' M' ~7 P: u" T' e# {
    Pterosaurs sported coats of hair-like fifilaments known as pycnofifibers, which" w: K* F2 s, w! D( u9 v
    covered their bodies and parts of their wings[3]. In life, pterosaurs would have2 m8 ~( V2 {* B+ \" ^4 D/ U3 r; `: C
    had smooth or flfluffffy coats that did not resemble bird feathers. Earlier sug0 k" X# C9 ~$ |. W
    gestions were that pterosaurs were largely cold-blooded gliding animals, de
    - K/ R# o  v/ lriving warmth from the environment like modern lizards, rather than burning
    ) O! n" R  ~0 Z4 |4 L# a, v. Rcalories. However, later studies have shown that they may be warm-blooded2 \7 k" e  Z: @: k
    (endothermic), active animals. The respiratory system had effiffifficient unidirec
    3 p; `/ ?# f/ m. E7 u9 Xtional “flflow-through” breathing using air sacs, which hollowed out their bones
    7 T5 ]+ t% Q5 j( C# }2 u: Mto an extreme extent. Pterosaurs spanned a wide range of adult sizes, from6 Z" U1 A3 |) ^: V3 C
    the very small anurognathids to the largest known flflying creatures, including6 Q' b6 ^% N; @: d8 T+ k
    Quetzalcoatlus and Hatzegopteryx[4][5], which reached wingspans of at least1 M, v8 q  \. I; @) O2 h7 S5 Y
    nine metres. The combination of endothermy, a good oxygen supply and strong
    9 y( A0 e7 f+ w; E/ W1muscles made pterosaurs powerful and capable flflyers.
    4 E7 O" ]2 B% F  a1 AThe mechanics of pterosaur flflight are not completely understood or modeled
    # c) K4 T  o9 \" H4 ]* g8 L5 Sat this time. Katsufumi Sato did calculations using modern birds and concluded
    $ R! V5 t8 r- n* O" Tthat it was impossible for a pterosaur to stay aloft[6]. In the book Posture,8 F- I$ ^+ o  e, @/ B
    Locomotion, and Paleoecology of Pterosaurs it is theorized that they were able
    4 n- K: Q' G4 _0 Z9 |to flfly due to the oxygen-rich, dense atmosphere of the Late Cretaceous period[7].# q" F4 q; W% M+ u- M5 S
    However, both Sato and the authors of Posture, Locomotion, and Paleoecology7 ?6 u8 E" R7 g& ~4 n
    of Pterosaurs based their research on the now-outdated theories of pterosaurs: [! j8 D; |  c$ A7 F, F, @/ D
    being seabird-like, and the size limit does not apply to terrestrial pterosaurs,0 R! ^2 {  W- f) B9 p. Y. l  ]
    such as azhdarchids and tapejarids. Furthermore, Darren Naish concluded that
    ( j; Y7 S" O) Q  l9 n$ Matmospheric difffferences between the present and the Mesozoic were not needed3 ^' i* Q7 Z% w! I/ b& o
    for the giant size of pterosaurs[8].7 U/ F6 ^& c; E6 x5 X$ M
    Another issue that has been diffiffifficult to understand is how they took offff.
    2 d6 A$ Y1 q% P/ K, EIf pterosaurs were cold-blooded animals, it was unclear how the larger ones
    3 P6 j# ^  H1 u0 wof enormous size, with an ineffiffifficient cold-blooded metabolism, could manage0 ^$ n$ M2 n' K8 L2 D/ j2 x9 h
    a bird-like takeoffff strategy, using only the hind limbs to generate thrust for/ Y; v6 I- J( n* r
    getting airborne. Later research shows them instead as being warm-blooded8 k3 t6 e7 @7 F% t
    and having powerful flflight muscles, and using the flflight muscles for walking as
    9 \1 ^* I0 J4 h/ |1 ?0 [quadrupeds[9]. Mark Witton of the University of Portsmouth and Mike Habib of
    / [6 S5 G- A( L* ]Johns Hopkins University suggested that pterosaurs used a vaulting mechanism* e8 N; [1 m. ?5 ~( R3 q" |
    to obtain flflight[10]. The tremendous power of their winged forelimbs would
    7 N* V" i' h! ienable them to take offff with ease[9]. Once aloft, pterosaurs could reach speeds
    - @: p: s- K5 _4 Q" k1 yof up to 120 km/h and travel thousands of kilometres[10].
    5 g5 U3 @; |! _. D6 J5 ZYour team are asked to develop a reasonable mathematical model of the
    9 C! r6 g6 k8 z3 @- A. |# ]flflight process of at least one large pterosaur based on fossil measurements and+ S: s) u4 w8 {& D, [+ t: c, k% M; {
    to answer the following questions.
      W" a0 Z  G; L: q7 T8 X  {1. For your selected pterosaur species, estimate its average speed during nor% b3 g/ K7 b! h1 `
    mal flflight.
    $ U8 W6 u9 U5 G5 h$ P8 L2. For your selected pterosaur species, estimate its wing-flflap frequency during" n! d  e& [1 c4 ^$ y) R
    normal flflight.
    / k/ _) _4 J+ \$ K/ c4 p! C3. Study how large pterosaurs take offff; is it possible for them to take offff like, K# x6 E% h- Z6 K  ?% }
    birds on flflat ground or on water? Explain the reasons quantitatively./ X4 v# C) t& j. \8 d# d2 U
    References: n2 ?0 }  o' z2 U: H
    [1] Elgin RA, Hone DW, Frey E (2011). The Extent of the Pterosaur Flight% E, e% e  m/ o5 ]0 g) |; Q7 F) [* v
    Membrane. Acta Palaeontologica Polonica. 56 (1): 99-111.# \. c# D- W. v- z, w
    2[2] Mark Witton. Terrestrial Locomotion.
    $ U) V+ W3 [8 J7 B0 r- w9 qhttps://pterosaur.net/terrestrial locomotion.php1 J" J6 g7 S$ \8 {! t+ ^/ L
    [3] Laura Geggel. It’s Offiffifficial: Those Flying Reptiles Called Pterosaurs
    4 g% D$ a7 T+ F; fWere Covered in Fluffffy Feathers. https://www.livescience.com/64324-- C* \: ]. `. z2 i. Q6 v7 Y: \5 M+ M
    pterosaurs-had-feathers.html
    ! r- P" u( i6 e/ n  O6 U5 u[4] Wang, X.; Kellner, A.W.A.; Zhou, Z.; Campos, D.A. (2008). Discovery of a
    , W4 a# W9 p, o% @* K4 ^rare arboreal forest-dwelling flflying reptile (Pterosauria, Pterodactyloidea)) V5 ?% `) j& W% t) ?2 W
    from China. Proceedings of the National Academy of Sciences. 105 (6):
    8 L0 o1 M5 P7 t1983-87.
    / ]1 C4 ?1 l  |) R1 m; {[5] Buffffetaut E, Grigorescu D, Csiki Z. A new giant pterosaur with a robust/ b9 T5 j7 z4 Z4 e2 Z2 P! H
    skull from the latest cretaceous of Romania. Naturwissenschaften. 89 (4):
    " ^' T- t1 \* _180-84.
    8 t' |6 `' v' o3 L[6] Devin Powell. Were pterosaurs too big to flfly?
    6 q# V1 J2 M8 D: o: [https://www.newscientist.com/article/mg20026763-800-were-pterosaurs' l. d$ j: n$ S
    too-big-to-flfly/
    : J1 H+ U' o; h1 C[7] Templin, R. J.; Chatterjee, Sankar. Posture, locomotion, and paleoecology( g/ X- w* Z) }9 ^& o3 E5 F" C1 j
    of pterosaurs. Boulder, Colo: Geological Society of America. p. 60.
    1 A( X7 f' M  C  E- q[8] Naish, Darren. Pterosaurs breathed in bird-like fashion and had inflflatable6 D( {& n0 k1 g, r
    air sacs in their wings.
    + [, t- o8 F8 U3 W$ F4 bhttps://scienceblogs.com/tetrapodzoology/2009/02/18/pterosaur6 T- S% n' B% [7 L
    breathing-air-sacs6 v- F. U$ q( Y7 y2 j6 S
    [9] Mark Witton. Why pterosaurs weren’t so scary after all.
    % t7 \5 r: P- W  Y9 P  C: u( Jhttps://www.theguardian.com/science/2013/aug/11/pterosaurs-fossils5 Y9 ^: P0 Z: Q' v9 m! }1 ~+ u% J
    research-mark-witton
    + `# I+ `. T" ?! G4 X. h[10] Jeffff Hecht. Did giant pterosaurs vault aloft like vampire bats?
    $ }: J, v$ u6 ]  ^- Nhttps://www.newscientist.com/article/dn19724-did-giant-pterosaurs
    " Q0 t" c! {& T0 t' F# }9 b4 {) lvault-aloft-like-vampire-bats/1 F8 p- _: L  }' f' B+ Z! k' X

    . ]' z5 g( h% |  R% r0 F20220 ?6 a- V* f9 t) z$ k0 ^; q
    Certifificate Authority Cup International Mathematical Contest Modeling
    . y. d' i; R0 B# s3 zhttp://mcm.tzmcm.cn
    2 n- ]  O2 M; D1 B$ x$ VProblem B (MCM). @& J0 q, C: d( a& Q* ]5 m
    The Genetic Process of Sequences
    / O6 l: O+ e+ V- @Sequence homology is the biological homology between DNA, RNA, or protein' V% k3 |1 D9 {6 P0 E' p
    sequences, defifined in terms of shared ancestry in the evolutionary history of# c# w$ Z; D' J8 n  ^
    life[1]. Homology among DNA, RNA, or proteins is typically inferred from their
      }) L6 _: V$ ?3 N! s' b8 V$ Anucleotide or amino acid sequence similarity. Signifificant similarity is strong7 d" y  a0 M0 _% d, M+ ?% T
    evidence that two sequences are related by evolutionary changes from a common9 T. ^0 ?' }7 q* r$ C
    ancestral sequence[2].
    3 i6 V/ i& \( h8 i: w: qConsider the genetic process of a RNA sequence, in which mutations in nu
    ; D! U- z# z$ z- s8 k5 |: Tcleotide bases occur by chance. For simplicity, we assume the sequence mutation
    % Q$ W# u7 E5 R1 H' {) oarise due to the presence of change (transition or transversion), insertion and9 A2 h6 \4 T0 {
    deletion of a single base. So we can measure the distance of two sequences by/ X& n7 |* W! S: N) e  {, ]' Q
    the amount of mutation points. Multiple base sequences that are close together. Q4 c" E6 Z+ k( m$ j1 h
    can form a family, and they are considered homologous.
    " f% ]$ i' \5 z  g) D8 A8 xYour team are asked to develop a reasonable mathematical model to com
    / g- \, O6 A. v8 R. Tplete the following problems.8 M! r1 A$ A7 F% z' @! K
    1. Please design an algorithm that quickly measures the distance between" V+ b# b. d& f3 }( ?% P0 M' ?
    two suffiffifficiently long(> 103 bases) base sequences.5 o' n4 X0 h( Z8 l' w. w- s
    2. Please evaluate the complexity and accuracy of the algorithm reliably, and
    ; ?1 j2 L$ r* D/ R7 }( pdesign suitable examples to illustrate it.
    ' Y9 w: }% ?9 ^* B" G6 o3. If multiple base sequences in a family have evolved from a common an" X8 L, s  u: H5 y; i- N
    cestral sequence, design an effiffifficient algorithm to determine the ancestral  Q0 W; `# f8 t$ W7 q+ E
    sequence, and map the genealogical tree.' Y+ m6 _7 a, [* L! S( C
    References
    ! y+ v: i" v) O[1] Koonin EV. “Orthologs, paralogs, and evolutionary genomics”. Annual Re7 J& T& v% x! F7 V0 |$ s8 c) l; d% j
    view of Genetics. 39: 30938, 2005.
    # n1 r& v% y% _  d) |: q7 |[2] Reeck GR, de Han C, Teller DC, Doolittle RF, Fitch WM, Dickerson RE,% I  o( b& v4 n/ k( n3 h
    et al. “Homology” in proteins and nucleic acids: a terminology muddle and
    $ s; K7 Z, u2 d4 \8 a; Ma way out of it. Cell. 50 (5): 667, 1987.
    & W' z1 o8 r" l7 w, M. q9 t" ?, ]0 @  H
    2022
    " j( O) B7 L( a. l7 I2 LCertifificate Authority Cup International Mathematical Contest Modeling2 C- w9 v/ b$ M+ r! {/ p
    http://mcm.tzmcm.cn! F; V: K" z0 g8 A+ F
    Problem C (ICM)
    : _5 M1 @5 K0 i/ n1 YClassify Human Activities
    ; g: k9 |' b, I6 o% XOne important aspect of human behavior understanding is the recognition and
    - m0 z+ O% V! w# o- J, y9 Rmonitoring of daily activities. A wearable activity recognition system can im
    ) A1 \* r1 W& b9 B' F6 x4 f2 ~prove the quality of life in many critical areas, such as ambulatory monitor; U: ?- m" j! k- B! F' W0 i
    ing, home-based rehabilitation, and fall detection. Inertial sensor based activ
    8 w. ^# Y, h7 ~8 city recognition systems are used in monitoring and observation of the elderly
    ! v4 ?+ ~# p) X8 G% g' xremotely by personal alarm systems[1], detection and classifification of falls[2],0 Q) Z% H) Q* K+ M3 `- D
    medical diagnosis and treatment[3], monitoring children remotely at home or in0 h! P# y, D# t4 `
    school, rehabilitation and physical therapy , biomechanics research, ergonomics,
    ) ~$ ^8 _' ]" g5 E" ]& Z/ {+ Qsports science, ballet and dance, animation, fifilm making, TV, live entertain1 P' N' _% \3 Y% x# M9 k
    ment, virtual reality, and computer games[4]. We try to use miniature inertial
    - t1 v) u4 a# P3 J4 i: P8 i$ `6 csensors and magnetometers positioned on difffferent parts of the body to classify& t1 h2 ^( g- x4 w: I; p9 l
    human activities, the following data were obtained.9 Z1 T7 }! H! U1 i- x. V5 C
    Each of the 19 activities is performed by eight subjects (4 female, 4 male,
    : x% Z- K7 z/ U: ~5 fbetween the ages 20 and 30) for 5 minutes. Total signal duration is 5 minutes0 ?" K5 _5 O1 ^7 |3 O
    for each activity of each subject. The subjects are asked to perform the activ4 B4 J, r2 `! b
    ities in their own style and were not restricted on how the activities should be1 Z  U2 O4 q; |
    performed. For this reason, there are inter-subject variations in the speeds and
    " s. d% I- \4 ]* Iamplitudes of some activities.
    7 ]  W+ Z! d6 ESensor units are calibrated to acquire data at 25 Hz sampling frequency.: N+ D+ j. w5 F1 R; Z) Q
    The 5-min signals are divided into 5-sec segments so that 480(= 60 × 8) signal
    2 v* n/ v* v* M6 Ysegments are obtained for each activity.
    5 N* E: v, Z1 J' M% t- U/ u5 AThe 19 activities are:- O3 [- v3 V' j: a5 V7 M
    1. Sitting (A1);
    6 Q7 w+ H4 ?* g" e2. Standing (A2);2 E' W! f6 c/ P. z  K. B  ^8 T& y
    3. Lying on back (A3);; H$ d; f5 p$ I( O
    4. Lying on right side (A4);5 m) F+ e  C- B6 G* D: r+ r9 K
    5. Ascending stairs (A5);! Z, P9 n" u0 Z. Z: Z$ x7 x
    16. Descending stairs (A6);
    8 n1 y+ U! U' U2 `; B% j7 @7. Standing in an elevator still (A7);
    7 q0 R2 K& y, ]/ J8. Moving around in an elevator (A8);5 o5 a6 a  A: o7 C, w  ~8 f# s5 k
    9. Walking in a parking lot (A9);
    $ p( [% Y) a- i( r# w. p' j10. Walking on a treadmill with a speed of 4 km/h in flflat position and 15 deg- p$ I6 Z" k# i* ]
    inclined positions (A10);
    0 W& `; W% K5 [, O: E11. Walking on a treadmill with a speed of 4 km/h in 15 deg inclined positions
    , K7 \+ c% @2 N! A5 b* Q0 K(A11);
    $ L9 D/ m: l0 F; f12. Running on a treadmill with a speed of 8 km/h (A12);
    / T* g5 W7 @- W' m( p13. Exercising on a stepper (A13);. a+ v6 ~8 ]2 o
    14. Exercising on a cross trainer (A14);
    * `+ X9 s" m' W15. Cycling on an exercise bike in horizontal position (A15);6 |, b6 M1 r1 X7 A! l. r7 O* f
    16. Cycling on an exercise bike in vertical position (A16);
      Z  R+ k' z0 _6 s4 T3 N* C( t/ B17. Rowing (A17);
    3 W6 r5 _+ K5 n/ @( _5 j18. Jumping (A18);
    * Q& o/ G! _- M+ E/ \3 x5 z19. Playing basketball (A19).$ w% |7 a) X& l" h) N
    Your team are asked to develop a reasonable mathematical model to solve
    2 |4 w4 s! }! q! ~$ Kthe following problems.( b# G$ t3 z; V2 f' }- d
    1. Please design a set of features and an effiffifficient algorithm in order to classify
    8 i5 j' [' z& p. i; pthe 19 types of human actions from the data of these body-worn sensors.
    0 x( D1 ?  G5 a3 }( v1 x2. Because of the high cost of the data, we need to make the model have0 o& f9 e# W+ V3 G
    a good generalization ability with a limited data set. We need to study
    . n) \" G* F! q& C3 Z& T& l9 L% sand evaluate this problem specififically. Please design a feasible method to; V: @  |- W! `
    evaluate the generalization ability of your model.9 `* A0 j6 e8 |9 H  a* }/ u$ U7 U
    3. Please study and overcome the overfifitting problem so that your classififi-
    ( {1 @: A3 h$ G5 C$ k! L8 T) {cation algorithm can be widely used on the problem of people’s action2 T  |% c& k/ K  i# G% m
    classifification.* I7 _1 a3 x& e9 V! e( g
    The complete data can be downloaded through the following link:
    # y/ u0 O( X7 x( _) chttps://caiyun.139.com/m/i?0F5CJUOrpy8oq8 L- w' i0 O  F2 @( k
    2Appendix: File structure
    5 h8 G, C' c4 m$ c5 U( V/ m1 A! a• 19 activities (a)
    ( b5 `( p1 W: U/ i- s! @• 8 subjects (p)7 P* V1 |2 k3 p5 h
    • 60 segments (s)
    7 a  A) j% j2 u0 J$ A( g• 5 units on torso (T), right arm (RA), left arm (LA), right leg (RL), left
    6 x4 s8 b4 G. u2 I( F* Zleg (LL); t" Y; V# Q5 S! h! N6 K
    • 9 sensors on each unit (x, y, z accelerometers, x, y, z gyroscopes, x, y, z
    8 t8 _) o, V0 H; W: Fmagnetometers)
    % b7 V  v* _5 ^3 ]  ~% o1 U) ?) Z; FFolders a01, a02, ..., a19 contain data recorded from the 19 activities.
    ( j. ~: m4 d* K  m! OFor each activity, the subfolders p1, p2, ..., p8 contain data from each of the
    $ b! t; f4 ~6 C" R3 B8 subjects.
    - @/ A6 U) V, l8 o4 `7 VIn each subfolder, there are 60 text fifiles s01, s02, ..., s60, one for each. h9 z7 g2 A% l' h- W* b8 i
    segment.
    & R& c1 E! z- q& w0 q, t/ CIn each text fifile, there are 5 units × 9 sensors = 45 columns and 5 sec × 258 c% C6 f' C1 ?3 ?1 g  v  Q& a- w
    Hz = 125 rows.
    . ^7 Y; S/ q3 o: VEach column contains the 125 samples of data acquired from one of the, ]6 R# P1 F: C1 n9 |
    sensors of one of the units over a period of 5 sec.; F; z; U: |( J4 ]# t' W3 A
    Each row contains data acquired from all of the 45 sensor axes at a particular
    $ r* o1 O( n0 S& jsampling instant separated by commas.
    $ I1 J" t: p$ t- V9 r4 S5 N/ r1 gColumns 1-45 correspond to:6 ~! T# A: Z; x' X0 ]
    • T_xacc, T_yacc, T_zacc, T_xgyro, ..., T_ymag, T_zmag,
      a/ g( F- S( R( T$ N; t* g. u• RA_xacc, RA_yacc, RA_zacc, RA_xgyro, ..., RA_ymag, RA_zmag,3 q% e. h" h- Y- S: n! Y) L. Z
    • LA_xacc, LA_yacc, LA_zacc, LA_xgyro, ..., LA_ymag, LA_zmag,
    7 w- d' }" b5 {7 d) Z5 a" P  ~• RL_xacc, RL_yacc, RL_zacc, RL_xgyro, ..., RL_ymag, RL_zmag,
      u+ z! ]3 s$ n& t) V• LL_xacc, LL_yacc, LL_zacc, LL_xgyro, ..., LL_ymag, LL_zmag.
    7 g$ k+ z2 F( O" V, d4 Q9 Z" _: ~Therefore,
    9 S" f4 y+ d. `* Z6 T• columns 1-9 correspond to the sensors in unit 1 (T),
    / o. l% Z2 e; |+ W* q! q1 |• columns 10-18 correspond to the sensors in unit 2 (RA),/ g6 P: X; S! T: |! O) Z
    • columns 19-27 correspond to the sensors in unit 3 (LA)," `) ^( s, R. ?" f
    • columns 28-36 correspond to the sensors in unit 4 (RL),
      t, @! _- M, @$ n• columns 37-45 correspond to the sensors in unit 5 (LL).
    4 W$ ?: Z" ?% R: u3References- [4 s! v" `% ]7 G
    [1] Mathie M.J., Celler B.G., Lovell N.H., Coster A.C.F. Classifification of basic4 v! L, }7 }# l# r- J
    daily movements using a triaxial accelerometer. Med. Biol. Eng. Comput.
    * |- `4 H( H' X) ~42(5), 679-687, 2004& G4 U) ~3 ?  {' C" R
    [2] Kangas M., Konttila A., Lindgren P., Winblad I., Ja¨msa¨ T. Comparison of3 X  ]" k( i5 M$ l
    low-complexity fall detection algorithms for body attached accelerometers.
    / U7 Q3 e1 m$ C6 B- O0 L5 NGait Posture 28(2), 285-291, 2008" {2 S: R& S6 v
    [3] Wu W.H., Bui A.A.T., Batalin M.A., Liu D., Kaiser W.J. Incremental diag
    . ?. ^, o2 P/ T/ anosis method for intelligent wearable sensor system. IEEE T. Inf. Technol.* S2 b- n! L& s$ Q
    B. 11(5), 553-562, 2007+ j+ m- h1 z  y6 j& i
    [4] Shiratori T., Hodgins J.K. Accelerometer-based user interfaces for the con7 f3 w: {  F& C# W
    trol of a physically simulated character. ACM T. Graphic. 27(5), 2008
    & X  @: \% q  O2 H% o
    " A! [( h- X# ?9 ^! Z0 I1 d) R0 V2022
    ( N" r8 j: J0 f6 Q2 yCertifificate Authority Cup International Mathematical Contest Modeling% o2 H8 y+ u% Z6 |
    http://mcm.tzmcm.cn
    3 m; T% g& E' i2 w/ W" W! @) `Problem D (ICM)' E1 [1 N, C& V+ s6 b1 W. n8 f
    Whether Wildlife Trade Should Be Banned for a Long
    ( D1 n, L! b& A  ~' \Time
    / C# D0 ?4 F% j; v3 jWild-animal markets are the suspected origin of the current outbreak and the/ o2 t) Y, t  w: ~; R( U) q5 S
    2002 SARS outbreak, And eating wild meat is thought to have been a source8 w- b& ~, k- q
    of the Ebola virus in Africa. Chinas top law-making body has permanently
    # g- ?' f% |9 M3 {3 Utightened rules on trading wildlife in the wake of the coronavirus outbreak,
    " E, k$ B, B+ N$ m* Y3 vwhich is thought to have originated in a wild-animal market in Wuhan. Some! C2 x0 _+ o7 E3 u
    scientists speculate that the emergency measure will be lifted once the outbreak- Z+ ?, T. e8 b9 X
    ends.9 J" I5 u& F6 {- n# z
    How the trade in wildlife products should be regulated in the long term?0 o+ k- C3 r- N- S
    Some researchers want a total ban on wildlife trade, without exceptions, whereas
    4 U! x$ E. R' @others say sustainable trade of some animals is possible and benefificial for peo
    ( |9 _' S" K' d1 m# l- ~3 xple who rely on it for their livelihoods. Banning wild meat consumption could
    8 G& J* E, P: `8 Q6 G5 Scost the Chinese economy 50 billion yuan (US $ 7.1 billion) and put one mil
    ' ~) T0 x3 B: m0 ^6 t8 g$ Dlion people out of a job, according to estimates from the non-profifit Society of
    % ]9 B9 g+ V9 U4 vEntrepreneurs and Ecology in Beijing.$ L$ F/ ^7 l7 S# h1 c0 f4 R4 P
    A team led by Shi Zheng-Li and Cui Jie of the Wuhan Institute of Virology
    , T- m: m; E. ein China, chasing the origin of the deadly SARS virus, have fifinally found their
    ( N$ \1 @3 i8 d  ?3 I5 M! [smoking gun in 2017. In a remote cave in Yunnan province, virologists have
    4 B* Z1 M7 x  Z! I; hidentifified a single population of horseshoe bats that harbours virus strains with3 d9 e. K) S/ l* ]
    all the genetic building blocks of the one that jumped to humans in 2002, killing
    , k) D& n: G2 a2 I8 halmost 800 people around the world. The killer strain could easily have arisen2 f- C3 Q# u- Z% w
    from such a bat population, the researchers report in PLoS Pathogens on 308 P5 y9 o# Z  W5 ^
    November, 2017. Another outstanding question is how a virus from bats in& j6 n% S+ u/ q
    Yunnan could travel to animals and humans around 1,000 kilometres away in
    ; x) t- X8 e  }7 ^  E2 w; eGuangdong, without causing any suspected cases in Yunnan itself. Wildlife# k# b; {5 W" f+ G1 A
    trade is the answer. Although wild animals are cooked at high temperature$ ~! n% H9 c5 o. K: [
    when eating, some viruses are diffiffifficult to survive, humans may come into contact$ _0 t3 n6 ]/ r! o4 `0 T
    with animal secretions in the wildlife market. They warn that the ingredients
    ( a4 Y4 n) Z. k% ?/ h) _# [are in place for a similar disease to emerge again.6 A$ q( j" f) F
    Wildlife trade has many negative effffects, with the most important ones being:- e3 Q# |8 J9 M. x! x
    1Figure 1: Masked palm civets sold in markets in China were linked to the SARS
    5 G) M8 v1 z$ V* U! poutbreak in 2002.Credit: Matthew Maran/NPL7 I4 T& }- r# s# k  z
    • Decline and extinction of populations5 z1 L" E+ C* v) o" u% G
    • Introduction of invasive species8 p% C  u$ \8 M
    • Spread of new diseases to humans! ]/ i# o5 X0 H
    We use the CITES trade database as source for my data. This database
    + [3 ?0 d( X7 s) Ccontains more than 20 million records of trade and is openly accessible. The/ U0 y. u' O, t0 M- _
    appendix is the data on mammal trade from 1990 to 2021, and the complete4 l2 V0 V- `. U7 w" f
    database can also be obtained through the following link:
    # A" _1 d- [, ?% k& {2 a+ g" Lhttps://caiyun.139.com/m/i?0F5CKACoDDpEJ
    2 o, Z' L2 X% M" J; z: O  NRequirements Your team are asked to build reasonable mathematical mod$ M3 s6 l2 {/ T. B5 w% ?
    els, analyze the data, and solve the following problems:2 d' T6 P' Q1 O
    1. Which wildlife groups and species are traded the most (in terms of live
    4 `$ W% h# G# L  e6 Y6 oanimals taken from the wild)?
    " Z# X$ W% c' b0 `  X2 R2. What are the main purposes for trade of these animals?8 X' _& B$ q  F8 K5 S/ p% W
    3. How has the trade changed over the past two decades (2003-2022)?: L% S) \1 g3 b* s- X' o
    4. Whether the wildlife trade is related to the epidemic situation of major$ ^3 I1 |4 c1 f- d5 ], N' W
    infectious diseases?
    1 }& o* \2 h/ ]  K; P$ v25. Do you agree with banning on wildlife trade for a long time? Whether it
    4 l+ M2 e& ^+ t$ a3 f$ fwill have a great impact on the economy and society, and why?+ F" Q( Q4 h9 m4 m' \
    6. Write a letter to the relevant departments of the US government to explain8 }3 {  T9 U, x( |0 c3 ]' Y, J
    your views and policy suggestions.
    " u+ c+ I- O" p
    " r2 k! x1 U# ?; ?' Q+ e1 x, x* B, f  ?
    1 `9 T( [. Z& x1 J, b" [# y# i6 G

    $ X1 e4 G* W, D& _0 p3 S2 |% S/ L9 l& Z) j' L
    . k1 z$ t& i6 k5 ?8 n+ W
    6 G/ h9 E4 ^) ^& _* v% k( w% k

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