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太阳集团游戏2138网站

《志趣追寻—太阳集团游戏2138网站国际部初高中生研究论文选编》附录和参考文献


CONTENTS

Physics

1.Investigating the Effect of the Backlight Angle on the Aerodynamic Drag Experienced by Notchbacks in the Presence of Flow Separation

2.Application of Twin Type Ia supernovae to reduce Hubble diagram dispersion

3.Long-Range Wireless Energy Transmission through Stimulated Emission in Atmospheric Molecular Oxygen


Math and Computer Science

1.The Effect of Topological Parameters on the Predictive Performance of Long Short-Term Memory for Financial Forecasting

2.FRACTIONAL LINEAR FUNCTIONS

3.Modelling The Best Hospital

A Mathematical Approach with Calculus and Analytic Geometry to the Projecting Process of a Planar Image onto a Sphere

Class 12(1A), Hongyuan Yan, 2018/1, High School

4.How will a figure distort

5.An Asymptotic Approach to the Analysis of Swings


Engineering

1.Multi-purpose Foldable Crutch

2.An Integrated Intelligent Medical Infusion Device Based on PLC

3.The Design and Construction of an Intelligent Selfie Robot

4.Household water-saving system

5.SEATING POSITION ADJUSTER

6.MONERE THE CALENDAR


Biology and Environmental Science

1.Pathogen Identification and Environmental Effects Analysis of Black Rot Disease of Imported Vietnamese ‘Red’ Pitaya

2.The Effect of Oral Administered Grape-Seed Extract on Cerebral Hypoperfusion Dementia in Mice

3.Bacterium Improves the Efficiency of Root Regeneration in A. thaliana

4.Molecular Tool to Improve the Root System for Stress Resistance

5.EEG Results Indicate Quality of Sleep and Mood in the Elderly

6.The Effect of Excretory Factor EREG Released by Stromal Cells during Chemotherapy on the Malignant Phenotype of Prostate Cancer

7.An Organoid Culture Based Investigation: The Prevention Mechanism of Tea Polyphenols on Prostate Cancer

8.Decay of Urban Rail Transit-induced Ground-borne Vibration and Rapid Prediction Methods

9.Evaluation of Avian Species Diversity at Microforests of Nanhui Dongtan Wetlands in Terms of Human Disturbance and Edge Effect

10.Effects of Sleep Intervention and Herb Medication Ganwei on Behavioral and Biochemical Responses in Drosophila Alzheimer’s Disease Model

11.The Effect of Vitamin C on SW480 Colon Cancer Cells In Vitro

12.The Inhibitory Effect of Chemical and Biological Food Preservatives on growth of Escherichia coli and Rhizopus stolonifer

13.EGFR Mutation Testing

14.Mitigating Biodiversity Loss

15.Investigate the effect of e-liquid, high temperature stress and UV-C radiation exposure on the growth of Saccharomyces cerevisiae (yeast).

16.Surface Electromyogram Analysis of Muscle Reactivity During Tennis Top-spin Serve


Investigating the Effect of the Backlight Angle on the Aerodynamic Drag Experienced by Notchbacks in the Presence of Flow Separation

Class 12(1B), Tsz Shun Cheung, 2019/1, High School

References

1. “What is Aerodynamics?”. NASA. NASA. Web. 3 July 2018. 

2. Hucho, W. H., & Sovran, G. (1993). “Aerodynamics of road vehicles. Annual review of fluid mechanics”. 25(1), 485-537.

3. Mayer, W., & Wickern, G. (2011). “The New Audi A6/A7 Family-Aerodynamic Development of Different Body Types on One Platform”. SAE International Journal of Passenger Cars-Mechanical Systems, 4(1), 197-206.

4. Cooper, K. R. (1993). “Bluff body aerodynamics as applied to vehicles”. Journal of Wind Engineering and Industrial Aerodynamics, 49, pp. 1-22.

5. “Aerodynamic Drag Reduction of a Square-Back Car Model Using Linear Genetic Programming and Physic-Based Control” - Scientific Figure on ResearchGate. [accessed 15 Feb, 2019]

6. S. R. Ahmed, G. Ramm, and G. Faltin. (1984). “Some salient features of the time averaged ground vehicle wake”. SAE Paper 840300.

7. “Drag and lift reduction of a 3D bluff-body using active vortex generators” - Scientific Figure on ResearchGate. [accessed 14 Feb, 2019]

8. R. Gilhome, Brendan & W. Saunders, Jeffrey & Sheridan, John. (2001). “Time Averaged and Unsteady Near-Wake Analysis of Cars”. 10.4271/2001-01-1040.

9. M. K. A. B. Salleh, “Simulation and analsys drag and lift coefficent between sedan and hatchback car”. Bachelor thesis, PAHANG: University Malaysia Pahang, 2009

10. Buresti, Guido. (2000) “Bluff-Body Aerodynamics Lecture Notes”. Department of Aerospace Engineering University of Pisa, Italy.

11. Hucho, Wolf-Heinrich (1981). Aerodynamics of Road Vehicles: From Fluid Mechanics  Vehicle Engineering. English Edition.

12. Butterworth-Heinemann Ltd, 1987.Anagnost, A., Alajbegovic, A., Chen, H., Hill, D. et al., "DIGITAL PHYSICS™ Analysis of the Morel Body in Ground Proximity," SAE Technical Paper 970139, 1997

13. Wickern, G., Wagner, A., and Zoerner, C., "Induced Drag of Ground Vehicles and Its Interaction with Ground Simulation," SAE Technical Paper 2005-01-0872, 2005

14. Marklund, J. and Chalmers tekniska högskola and Chalmers tekniska högskola. Institutionen för tillämpad mekanik. “Under-body and Diffuser Flows of Passenger Vehicles”. Chalmers University of Technology, 2013.

15. Cockrell DJ,Markland E. “Diffuser behavior, a review of past experimental work, relevant today”. Aircr Engng.1974;46:16.

16. R. Ramkissoon and K. Manohar. “Design and Calibration of a Low Speed Wind Tunnel”. British Journal of Applied Science & Technology. SCIENCEDOMAIN international. 4 (20): 2878-2890, 2014. Retrieved 7 July 2018.

17. Mehta RD, Bradshaw P. “Design Rules for Small Low Speed Wind Tunnels”. Aeronautical Journal.1979;443-449.

18. “Air - Density, Specific Weight and Thermal Expansion Coefficient at Varying Temperature and Constant Pressures Online calculator”. The Engineering ToolBox. 

19. Carr, G. W., Influence of Rear Body Shape on the Aerodynamic Characteristics of Saloon Cars, MIRA Report 1974/2, 1974.

20. Nouzawa, T., Hiasa, K., Nakamura, T., Kawamoto, A., and Sato, H., Unsteady-Wake Analysis of the Aerodynamic Drag of a Notchback Model with Critical Afterbody, “Vehicle Aerodynamics: Wake Flows, Computational Fluid Dynamics, and Aerodynamic Testing”, SP-908, SAE, Pennsylvania, pp. 1-12.

21. Hucho, W. H., Aerodynamic Drag of Passenger Cars, Aerodynamics of Road Vehicles, edited by W. H. Hucho, SAE, Pennsylvania, 1989, pp. 131-238.



Application of Twin Type Ia supernovae to reduce Hubble diagram dispersion

Class 11(3), Matthew Rui Zhang, 2016/1, High School

References

[1]  HUBBLE SITE., 2004. The progenitor of a type Ia supernova. Available at: http://hubblesite.org/newscenter/archive/releases/star/supernova/2004/34/image/d/format/large_web/.

[2] Paolo A. Mazzali., Friedrich K. Röpke., Stefano Benetti., & Wolfgang Hillebrandt. (2007). A Common Explosion Mechanism For Type Ia Supernovae. Science, 315 (5813), 825-828. [doi: 10.1126/science.1136259].

[3]  Riess AG et al. (1998) Observational Evidence from Supernovae for An Accelerating Universe And A Cosmological Constant. The Astronomical Journal, 116: 1009-1038.

[4]  A. G. Kim et al. Standardizing Type Ia Supernova Absolute Magnitudes Using Gaussian Process Data Regression. The Astrophysical Journal, 766:84 (22pp) (2013)

[5]  McCully, C., Jha, S. W., Foley, R. J., Bildsten, L., Fong, W. F., Kirshner, R. P., & Stritzinger, M. D. (2014). A luminous, blue progenitor system for the type Iax supernova 2012Z. Nature, 512(7512), 54-56.

[6]  Fakhouri, H. K., Boone, K., Aldering, G., Antilogus, P., Aragon, C., Bailey, S., & Buton, C. (2015). Improving Cosmological Distance Measurements Using Twin Type Ia Supernovae. The Astrophysical Journal,815(1), 58.

[7]  Stephane Blondin, 2016. Supernovae Identification. Stephane Blondin. Available at: https://people.lam.fr/blondin.stephane/software/snid/. Accessed on 7-18-2016.

[8]  Tim Pearson, 2002. PGPLOT Graphic Subroutine Library. California Institute of Technology. Available at: http://www.astro.caltech.edu/~tjp/pgplot/. Accessed on 7-19-2016.

[9]  Graham, M. L., Foley, R. J., Zheng, W., Kelly, P. L., Shivvers, I., Silverman, J. M., & Ganeshalingam, M. (2015). Twins for life? A comparative analysis of the Type Ia supernovae 2011fe and 2011by.Monthly Notices of the Royal Astronomical Society, 446(2), 2073-2088.

[10] Stephane Blondin, 2016. Homepage of Stephane Blondin. Available at: https://people.lam.fr/blondin.stephane/index.html. Accessed on 7-18-2016.

[11] Samantha L. Hoffmann et al. (2015) Optical Identification of Ceipheid in 19 Host Galaxies of Type Ia Supernovae and NGC 4258 with the HUBBLE SPACE TELESCOPE. Astrophysics-SR, arXiv:1607.08658v2.

[12] Riess et al. (2016) A 2.4% DETERMINATION OF THE LOCAL VALUE OF THE HUBBLE CONSTANT. The Astrophysical Journal, 826:56 (31pp).


Long-Range Wireless Energy Transmission through Stimulated Emission in Atmospheric Molecular Oxygen

Class 12(1A), Victor Shichen Yu, 2017/1, High School

References

[1] N. Tesla, Apparatus for transmitting electrical energy, US patent number 1,1 19,732, Dec 1914

[2] J.M. Fernandez, and J.A. Borras, Contactless battery charger with wireless control link, US patent number 6,184,651, Feb 2001

[3] A. Esser, H.-C. Skudelny, IEEE Trans. Industry Appl. 27 (1991) 872.

[4] J. Hirai, T.-W. Kim, A. Kawamura, IEEE Trans. Power Electron. 15 (2000) 21.

[5] Karalis, Aristeidis, J. D. Joannopoulos, and Marin Soljacic. "Efficient Wireless Non-radiative Mid-range Energy Transfer." Annals of Physics (2007): n. pag. Print.

[6]"Dryden Flight Research Center, Beamed Laser Power For UAVs". Nasa.gov. 7 May 2008.

[7] Dickinson, Richard M. (1976). "Performance of a high-power 2.388 GHz receiving array in wireless power transmission over 1.54 km."(PDF). MTT-S Int'l Microwave Symposium Digest:139–141.doi:10.1109/mwsym.1976.1123672.

[8] Strandberg, M. W. P.; Meng, C. Y.; Ingersoll, J. G. (1949). "The Microwave Absorption Spectrum of Oxygen". Phys.Rev. 75 (10)

[9] Cosmovici, C. B.; Montebugnoli, S.; Pogrebenko, S.; Colom, P. Water MASER Detection at 22 GHz after the SL-9/Jupiter Collision,Bulletin of the American Astronomical Society

[10] Chen, Szu-yuan; Maksimchuk, Anatoly; Umstadter, Donald (December 17, 1998). "Experimental observation of relativistic nonlinear Thomson scattering"

[11] National Institute of Standards and Technology

[12] Einstein, A (1916). "Strahlungs-emission und -absorption nach der Quantentheorie". Verhandlungen der Deutschen Physikalischen Gesellschaft.

[13] Wikimedia, User: Borb

[14] A. E. Siegman (1986). Lasers. University Science Books.

[15] Lide, David R. Handbook of Chemistry and Physics. Boca Raton, FL: CRC, 1996: 14-7

[16] “Solar Arrays” Nasa.gov. NASA, n.d. Web.

[17] "Field Army ISTAR Handbook (Restricted)"


The Effect of Topological Parameters on the Predictive Performance of Long Short-Term Memory for Financial Forecasting


Class 12(1A), Angle Qian, 2018/11, High School


References

[1] S. Siami-Namini and A. Saimi Namin, "Forecasting economics and financial time series: Arima vs. lstm," Jul. 2017. doi: 10.1371/journal.pone.0180944.

[2] M. Hansson, "On stock return prediction with LSTM networks," 2017. [Online]. Avail-able: https://lup.lub.lu.se/student-papers/search/publication/8911069.

[3] W. Bao, J. Yue, and Y. L. Rao, "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," Jul. 2017. doi: 10.1371/ journal.pone.0180944.

[4] S. Selvin, R. Vinayakumar, E. A. Gopalakrishnan, V. K. Menon, and K. P. Soman, "Stock price prediction using LSTM, RNN and CNN-sliding window model," pp. 1643{ 1647, Sep. 2017. doi: 10.1109/ICACCI.2017.8126078.

[5] H. Y. Kim and C. H. Won, "Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models," Expert Systems with Applications, vol. 103, pp. 25{37, 2018, issn: 0957-4174. doi: https://doi.org/10. 1016/j.eswa.2018.03.002. [Online]. Available: http://www.sciencedirect.com/ science/article/pii/S0957417418301416.

[6] W. S. McCulloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity," The bulletin of mathematical biophysics, vol. 5, no. 4, pp. 115{133, Dec. 1943, issn: 1522-9602. doi: 10.1007/BF02478259. [Online]. Available: https://doi. org/10.1007/BF02478259.

[7] F. Rosenblat, "The perceptron: A probabilistic model for information storage and organization in the brain," Psychological Review, pp. 65{386, 1958.

[8] K. Hornik, "Approximation capabilities of multilayer feedforward networks," Neural Networks, vol. 4, no. 2, pp. 251{257, 1991, issn: 0893-6080. doi: https://doi.org/ 10.1016/0893-6080(91)90009-T. [Online]. Available: http://www.sciencedirect. com/science/article/pii/089360809190009T.

[9] B. C. Csaji, "Approximation with arti cial neural networks," 2001.

[10] C. Szegedy, A. Toshev, and D. Erhan, "Deep Neural Networks for object detection," C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger, Eds., pp. 2553{2561, 2013. [Online]. Available: http://papers.nips.cc/paper/5207-deep-neural-networks-for-object-detection.pdf.

[11] Y. Bengio, "Learning deep architectures for AI," Foundations and Trends in Ma-chine Learning, vol. 2, no. 1, pp. 1{127, Jan. 2009, issn: 1935-8237. doi: 10.1561/ 2200000006. [Online]. Available: http://dx.doi.org/10.1561/2200000006.

[12] Y. Bengio, P. Frasconi, and P. Simard, "The problem of learning long-term dependen-cies in recurrent networks," 1183{1188 vol.3, 1993. doi: 10.1109/ICNN.1993.298725.

[13] S. Hochreiter, "The vanishing gradient problem during learning recurrent neural nets and problem solutions," Int. J. Uncertain. Fuzziness Knowl.-Based Syst., vol. 6, no. 2, pp. 107{116, Apr. 1998, issn: 0218-4885. doi: 10.1142/S0218488598000094. [Online]. Available: http://dx.doi.org/10.1142/S0218488598000094.

[14] S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Comput., vol. 9, no. 8, pp. 1735{1780, Nov. 1997, issn: 0899-7667. doi: 10.1162/neco.1997. 9.8.1735. [Online]. Available: http://dx.doi.org/10.1162/neco.1997.9.8.1735.

[15] F. Gers, J. Schmidhuber, and F. Cummins, "Learning to forget: Continual prediction with LSTM," Neural Computation, vol. 12, pp. 2451{2471, 1999.

[16] F. Gers, "Long Short-Term Memory in Recurrent Neural Networks," 2001.

[17] Yahoo Finance, Historical data for the S&P 500. [Online]. Available: https : / / finance.yahoo.com/quote/%5EGSPC/history/.

[18] J. Chou and T. Nguyen, "Forward forecast of stock price using sliding-window metaheuristic-optimized machine-learning regression," IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3132{3142, Jul. 2018, issn: 1551-3203. doi: 10.1109/TII.2018. 2794389.


Modelling the Best Hospital

Class 11(1A), James Tyler Liu, Class 11(7), Yuxuan Wang, Class 11(1B), John Xiaoshu Zhou, and Class 11(6), Lizhi Li, 2019/5, High School

Reference

[1] Health Forum LLC. "Fast Facts on U.S. Hospitals, 2018 | AHA." American Hospital Association, Feb. 2018, www.aha.org/statistics/fast-facts-us-hospitals. Accessed 18 Mar. 2018.

[2]Ableau. "China: Number of Hospitals in 2017 | Statistic." Statista, 2018, www.statista.com/statistics/279322/number-of-hospitals-in-china/. Accessed 18 Mar. 2018.

[3](equotemd, 2018) "Different Types of Hospitals and Hospital Medical Malpractice Insurance." EQuoteMD,18 Oct. 2011,www.equotemd.com/blog/different-types-of-hospitals-and-hospital-medical-malpractice-insurance/. Accessed 18 Mar. 2018.

[4] 秩名. NCI:不同年段的癌症风险数据[N]. 生物资讯, 2015-08-06(1).

[5] "Cancer Mortality by Age." Cancer Research UK, 27 Sept. 2017, www.cancerresearchuk.org/health-professional/cancer-statistics/mortality/age#collapseZero. Accessed 16 Mar. 2018.

[6] Hall, Margaret, et al. "Trends in Inpatient Hospital Deaths: National Hospital Discharge Survey, 2000–2010." Centers for Disease Control and Prevention, National Center for Health Statistics, 24 May 2017, www.cdc.gov/nchs/products/databriefs/db118.htm. Accessed 16 Mar. 2018.

[7] Bernstein AB, Hing E, Moss AJ, Allen KF, Siller AB, Tiggle RB. Health care in America: Trends in utilization. Hyattsville, Maryland: National Center for Health Statistics. 2003.

[8]"Population Pyramids of the World: 2018." PopulationPyramid.net, 2018, www.populationpyramid.net/world/2018/. Accessed 17 Mar. 2018.

[9]"Cancer Incidence by Age." Cancer Research UK, 13 Feb. 2018, www.cancerresearchuk.org/healthprofessional/cancer-statistics/incidence/age#collapseZero.Accessed 16 Mar. 2018. [10] Niska, Richard, et al. "National Hospital Ambulatory Medical Care Survey: 2007 Emergency Department Summary." Centers for Disease Control and Prevention, 6 Aug. 2010, www.cdc.gov/nchs/data/nhsr/nhsr026.pdf. Accessed 16 Mar. 2018.

[11] Greenwood, Beth. "The Average Length of Doctors' Careers." Work - Chron.com, 19 Nov. 2012, work.chron.com/average-length-doctors-careers-13376.html. Accessed 18 Mar. 2018.

[12] "Physicians and Surgeons: Occupational Outlook Handbook." U.S. Bureau of Labor Statistics, 30 Jan. 2018, www.bls.gov/ooh/healthcare/physicians-and-surgeons.htm#tab-4. Accessed 18 Mar. 2018.

[13] Ryan, Camille L., and Kurt Bauman. "Educational Attainment in the United States: 2015." Census.gov, Mar. 2016,www.census.gov/content/dam/Census/library/publications/2016/demo/p20-578.pdf. Accessed 18 Mar. 2018.

[14] Buchmueller, Thomas, et al. "How far to the hospital? The effect of hospital closures on access to care." The National Bureau of Economic Research, 13 Dec. 2005, users.nber.org/~jacobson/Buchmuelleretal2006.pdf. Accessed 19 Mar. 2018.

[15] “Compare Hospitals.” Centers for Medicare & Medicaid Services, 31 Dec. 2016, www.medicare.gov/hospitalcompare. 20 Mar. 2018.

Coding Results

R-1:

General model Exp1:

f(x) = a*exp(b*x)

Coefficients (with 95% confidence bounds):

a = 0.4022 (0.03357, 0.7709)

b = 0.05434 (0.04035, 0.06834)

Goodness of fit:

SSE: 7.304

R-square: 0.9751

Adjusted R-square: 0.9709

RMSE: 1.103

C-2.2:

R-2.1:

Result:

Linear model Poly4:

f(x) = p1*x^4 + p2*x^3 + p3*x^2 + p4*x + p5

Coefficients (with 95% confidence bounds):

p1 = 0.00924 (-0.002183, 0.02066)

p2 = -1.743 (-3.695, 0.2092)

p3 = 94.22 (-12.19, 200.6)

p4 = -1054 (-3059, 951.4)

p5 = 1.356e+04 (2962, 2.416e+04)

Goodness of fit:

SSE: 6.131e+07

R-square: 0.9218

Adjusted R-square: 0.8437

RMSE: 3915

Part 3: Coding

C-1.1:

age=(0:10:80)

probability_of_incurable_desease=[0.18,0.18,0.46,1.08,2.70,6.35,12.47,17.16,16.15]

plot(x,probability_of_incurable_desease)

C-1.2

function [ y ] =probability_of_incurable_desease( x )

y = 2.695*exp(1.331*x)

end

incurable_disease=(1:5)

syms x

incurable_disease(1)=int(probability_of_incurable_disease(x),0,3)/3

incurable_disease(2)=int(probability_of_incurable_disease(x),3,18)/15

incurable_disease(3)=int(probability_of_incurable_disease(x),18,30)/12

incurable_disease(4)=int(probability_of_incurable_disease(x),30,60)/30

incurable_disease(5)=int(probability_of_incurable_disease(x),60,90)/30

C-2.1:

age=[85,70,60,50,40,30,20,10,0]

percentage=[17208,23485,34691,42994,28864,25785,19385,11302,13106]

plot(x,y)

cftool

C-2.2:

function [ y ] = percentage( x )

y=(231*x^4)/25000 - (1743*x^3)/1000 + (4711*x^2)/50 - 1054*x + 13560

end

age_percentage=(1:5)

syms x

t=int(percentage(x),0,90)

age_percentage(1)=int(percentage(x),0,3)/t

age_percentage(2)=int(percentage(x),3,18)/t

age_percentage(3)=int(percentage(x),18,30)/t

age_percentage(4)=int(percentage(x),30,60)/t

age_percentage(5)=int(percentage(x),60,90)/t

C-3 (Visual Basic Code of the Hospital Evaluation Program):

Public Class frmMain

Const integralPrecision = 0.01

Dim compMortalityMatrix = {0.0097, 0.0451, 0.0627, 0.2345, 0.148}

Private Sub frmMain_Load(sender As Object, e As EventArgs) Handles MyBase.Load

picBG.Load("hospital1.png")

End Sub

Private Sub rdbNetMortality_CheckedChanged(sender As Object, e As EventArgs) Handles rdbNetMortality.CheckedChanged

If rdbNetMortality.Checked Then

txtNetMortality.Enabled = True

txtMortality0_3.Enabled = False

txtMortality3_18.Enabled = False

txtMortality18_30.Enabled = False

txtMortality30_60.Enabled = False

 txtMortality60.Enabled = False

Else

txtNetMortality.Enabled = False

txtMortality0_3.Enabled = True

txtMortality3_18.Enabled = True

txtMortality18_30.Enabled = True

txtMortality30_60.Enabled = True

txtMortality60.Enabled = True

End If

End Sub

Private Sub btnCalScore_Click(sender As Object, e As EventArgs) Handles btnCalScore.Click

Dim dist As Single = txtDist.Text

Dim mortalityScore As Single

If rdbNetMortality.Checked Then

mortalityScore = txtNetMortality.Text * 0.01

Else

Dim compMortalities = {Val(txtMortality0_3.Text), Val(txtMortality3_18.Text), Val(txtMortality18_30.Text),

Val(txtMortality30_60.Text), Val(txtMortality60.Text)}

For i As Byte = 0 To 4

compMortalities(i) *= compMortalityMatrix(i) * 0.01

mortalityScore += compMortalities(i) ^ 2

Next

mortalityScore ^= 0.5

End If

mortalityScore = 2.5 * (1 - mortalityScore)

Dim doctorAgeOffset As Byte = txtDoctorAvgAge.Text - 28

Dim doctorExperienceScore As Single = calTotalDoctorExp(doctorAgeOffset) * txtPatientPerDay.Text / txtDoctorNum.Text

doctorExperienceScore = 2.5 - 1 / doctorExperienceScore

Dim doctorCareScore As Single = calTotalDoctorExp(0.2 * doctorAgeOffset) - calTotalDoctorExp(0.2 * doctorAgeOffset - 8.5)

doctorCareScore *= (2.5 / 1.5) ^ 2

Dim distScore As Single

If txtDist.Text <= 3.78 Then

distScore = 2.5

ElseIf txtDist.Text <= 6.68 Then

distScore = 2.5 - 0.86 * (txtDist.Text - 3.78)

End If

lblScore.Text = distScore + mortalityScore + doctorCareScore + doctorExperienceScore

End Sub

Function calTotalDoctorExp(ByVal doctorAgeOffset As Single)

Dim a As Single

For i As Single = 0 To 0.7 * doctorAgeOffset Step integralPrecision

a += integralPrecision * Math.E ^ (-i ^ 2)

Next

Return a

End Function

End Class


An Asymptotic Approach to the Analysis of Swings

Class 12(1B), John Xiaoshu Zhou, 2019/10, High School

Bibliography

King, A., Billingham, J., & Otto, S. (2003). Asymptotic Methods: Differential Equations. In Differential Equations: Linear, Nonlinear, Ordinary, Partial (pp. 303-371). Cambridge: Cambridge University Press. doi:10.1017/CBO9780511755293.013


Multi-purpose Foldable Crutch

Class 8(5), Guo HaoHui, 2017/12, Middle School

References

胡海滔,李志忠,肖惠,严京滨,王晓芳,郑力.北京地区老年人人体尺寸测量[J].人类工效学,2006(01):39-42.

胡湛,彭希哲.应对中国人口老龄化的治理选择[J].中国社会科学,2018(12):134-155+202.

黄文静.社会学视角下老年人的医疗和照顾需要研究[J].中国全科医学,2017,20(07):842-851.

景婷婷,陆小左,傅琳洁.老年人多功能智能拐杖的设计与实现[J].电子产品世界,2015,22(07):40-42.



An Integrated Intelligent Medical Infusion Device Based on PLC

Class 7(1), Raymond Zheng Tang, Max Liu, Yuxiang Wu, 2017/12, Middle School

References

郭雯,王海涛.智能输液系统的发展与应用[J].医疗卫生装备,2012,33(11):95-97.

孔雪卉,张慧芬,焦婷婷. 一种智能输液控制系统的设计[J]. 国外电子测量技术,2014,33(06):73-77.

李和太,赵新,李新,夏加宽.智能输液监控系统的研制[J],沈阳工业大学学报.2006(03):318-322+326.

王鸿彬.一种无线智能输液监护系统的设计[J].电子技术与软件工程,2016(17):162-163.

肖达勇,冯子建,李勤,王豫林.流感大流行对医疗机构的威胁及应对策略[J].现代预防医学,2008(16):3185-3186+3188.

徐光宪,郭琳,陆伟. 智能输液监控系统的设计与实现[J].激光杂志.2014,35(09):119-121.

杨光伟. 一种新型智能输液监护系统的研制[D].南京航空航天大学,2012.

袁侨英,李学军,徐强,肖利,司良毅.一种全新的自动输液辅助装置的研制[J].医疗卫生装备,2011,32(10):33-34.

周正阳,王聪聪,王一钦,李可,郑琦琪.智慧医疗——智能输液系统[J].物联网技术,2017,7(06):51-53.


The Design and Construction of an Intelligent Selfie Robot

Class 7(8), Leo Lu2018/12, Middle School

Reference

曾令远,吴东.基于移动机器人的智能摄影软件设计及实现[J].现代计算机(专业版),2016(28):53-57

张博,王南,王泽仁.基于SolidWorks的摄影机器人虚拟设计与运动分析[J].河北工业科技,2014,31(01):24-26.

OpenCV-Python Tutorials, https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_tutorials.html

Supplementary materials

Codes in Python:

l Controlling the Servo:

import RPi.GPIO as GPIO

from time import sleep

def rotate_to(angle):

GPIO.setmode(GPIO.BOARD)

GPIO. setup(3, GPIO.OUT)

pwm = GPIO. PWM(3, 50) #Set input to pin 3

pwm.start(0) # Set movement to zero


duty = angle / 18 + 2.5 #Turn degrees (0-180) into signals (2.5~12.5)

pwm.ChangeDutyCycle(duty) # Set the Duty Cycle (Rotation)

GPIO.output(3, True)

sleep(1)

GPIO.output(3, False)

pwm.ChangeDutyCycle(0)

pwm.stop()

GPIO.cleanup() # End

l Controlling the Vehicle:

import RPi.GPIO as g

import time

import sys

dirm = 4

dirnm = 25

powerm = 10

powernm = 17

#m is the labeled side, nm is the unlabeled side

cc = 1/100 #Convert Constant, convert degrees into turning time

g.setmode(g.BCM)

g.setwarnings(False)

g.setup([dirm,dirnm,powerm,powernm],g.OUT)

#set basic parameters

def forward(t):

g.output(dirm,g.LOW)

g.output(dirnm,g.HIGH) #control “direction”

g.output([powerm,powernm],g.HIGH) #control “enabled”

time.sleep(t)

g.output([powerm,powernm],g.LOW) #stop the rotation


def backward(t):

g.output(dirm,g.HIGH)

g.output(dirnm,g.LOW)

g.output([powerm,powernm],g.HIGH)

time.sleep(t)

g.output([powerm,powernm],g.LOW)


def turnright(d):

t = d * cc

g.output([dirnm,dirm],g.LOW)

g.output([powerm,powernm],g.HIGH)

time.sleep(t)

g.output([powerm,powernm],g.LOW)


def turnleft(d):

t = d * cc

g.output([dirnm,dirm],g.HIGH)

g.output([powerm,powernm],g.HIGH)

time.sleep(t)

g.output([powerm,powernm],g.LOW)

l Code for Face Detection:

import time

import io  #”Input and Output” Module

import picamera

import numpy as np

import cv2

if __name__ == '__main__': #Determine whether the program is running individually


#Use the preset face detection model

face_cascade = cv2.CascadeClassifier  ('/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml')

# Use the preset eye detection model

eye_cascade = cv2.CascadeClassifier  ('/usr/local/share/OpenCV/haarcascades/haarcascade_eye.xml')

# Use the preset mouth detection model

eye_cascade = cv2.CascadeClassifier  ('/usr/local/share/OpenCV/haarcascades/haarcascade_mouth.xml')

with picamera.PiCamera() as camera:

 camera.rotation = 180

 camera.resolution = (640, 480)

 camera.framerate = 90

 camera.video_stabilization = True #Set basic parameters

 time.sleep(2)

stream = io.BytesIO() #Store “stream” into a buffer

for foo in camera.capture_continuous(stream,format='jpeg',use_video_port=True):

data = np.fromstring(stream.getvalue(), dtype=np.uint8) #Retrieve data

# Process the image so that it can be analyzed

image = cv2.imdecode(data, cv2.IMREAD_COLOR)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #Save power with grayscale

 faces = face_cascade.detectMultiScale(gray, 1.3, 5) #Detect Faces

 cap = bool(len(faces))

for (x,y,w,h) in faces:

cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,0),2)

roi_gray = gray[y:y+h, x:x+w]

 roi_color = image[y:y+h, x:x+w]

eyes = eye_cascade.detectMultiScale(roi_gray) #Detect Eyes

 mouths = mouth_cascade.detectMultiscale(roi_gray) #Detect Mouths

if len(eyes) != 2 or len(mouths) != 1:

 cap = False

for (ex,ey,ew,eh) in eyes:

cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

 for (mx,my,mw,mh) in eyes:

cv2.rectangle(roi_color,(mx,my),(mx+mw,my+mh),(255,0,0),2)

 if cap:

#If there’re any faces, with two eyes and a mouth per face, save the picture

 facesize = faces[0][2] * faces[0][3]

 _name = str(len(faces)) + “*” + str(facesize) #Name picture by its quality

 cv2.imwrite(“/home/pi/Desktop/” + _name + “. jpg”)  

cv2.imshow("img", image) #Show the picture

if cv2.waitKey(1) & 0xFF == ord('q'):

break #break when Ctrl+Q is pressed

stream.truncate()

stream.seek(0)

cv2.destroyAllWindows() #Clear the program

l Filtering the Pictures:

def takefirst(elem):

return elem[0] #Order only according to the quality of the picture

def update():

import os, os.path

DIR = '/home/pi/Desktop/Pictures'

listofnames = []

q = []

for name in os.listdir(DIR):

if os.path.isfile(os.path.join(DIR,name)):

listofnames.append(name) #List all the names

if len(listofnames) > 10: #If there are too many pictures:

i = 0

for name in listofnames:

nq = name.split("*")

nq[1] = int(nq[1].replace(".jpg",""))

qe = int(int(nq[0]) * nq[1]) #Process name into (integer) quality

q.append((qe,i)) #Package data in the form of (Quality, Index)

i += 1

q = sorted(q,key = takefirst) #Sort the pictures from lowest to highest quality

last = q[0][1]

os.remove(os.path.join(DIR,listofnames[last])) #Remove the first picture


Pathogen Identification and Environmental Effects Analysis of Black Rot Disease of Imported Vietnamese ‘Red’ Pitaya

Class 7(11) Ashley Fan, Class 7(3) Yolanda Xing, Class 7(6) Ho Alicia, 2014/12, Middle School

References

陈杰, 庞江琳, 李尚德, 陈嘉曦, 莫丽儿, 揭新明(2004). 火龙果的微量元素含量分析. 广东微量元素科学, 11 (5): 56~57.

李敏, 胡美姣, 薛丁榕, 杨冬平, 杨波, 张正科, 赵超, 高兆银(2013). 火龙果黑斑病菌 Bipolaris cactivora (Petrak) Alcorn 生物学特性研究. 热带作物学报, 34 (9): 1770~1775.

刘少华, 陆金萍, 朱瑞良, 戴富明(2006). 一种快速简便的植物病原真菌基因组 DNA 提取方法. 植物病理学报, 35 (4): 362~365.

刘月廉, 周娟, 赵志慧, 习平根, 姜子德(2011). 广东省火龙果腐烂病病原鉴定. 华中农业大学学报, 30 (5): 585~588.

章四平(2010) 效益看好, 火龙果尚有扩种空间. 南方农村报, 8~17.

郑良永(2004). 海南岛火龙果丰产栽培技术. 热带农业科学, 24 (4): 36~41.

周真, 杜妍娴, 李希清(2011). 黑色素与常见病原真菌致病性的关系. 中国真菌学杂志, (6): 23.

Alcorn JL (1983). Generic concepts in drechslera, bipolaris and exserohilum. Mycotaxon, 17: 1~86.

Anderson EF, Brown R (2001). The Cactus Family. Portland: Timber Press, 776.

Ben-Ze’ev IS, Assouline I, Levy E, Elkind G (2011). First report of Bipolaris cactivora causing fruit blotch and stem rot of pitaya (pitaya) in Israel. Phytoparasitica, 39 (2): 195~197.

Cosgrove DJ (2005). Growth of the plant cell wall. Nat Rev Mol Cell Biol, 6 (11): 850~861.

Durbin RD, Davis LH, Baker KF (1955). A Helminthosporium stem rot of cacti. Phytopathology, 45 (9): 509~512.

Ellis MB (1971). Dematiaceous hyphomycetes. Dematiaceous hyphomycetes.

Merten S (2003). A review of Hylocereus production in the United States. J Prof Assoc Cactus Dev, 5: 98~105

Mizrahi Y, Nerd A, Nobel PS (1997). Cacti as crops. Hort Rev, 18: 291~319.

Nakamura S (1970). Helminthosporium stem rot of cacti in Japan. Cuban J Agr Sci, 15: 66~72.

Nerd A, Sitrit Y, Kaushik RA, Mizrahi Y (2002). High summer temperatures inhibit flowering in vine pitaya crops (Hylocereus spp.). Sci Hortic, 96 (1): 343~350.

Petrak F (1931). In Cactacearum variarum plantis juvenilibus cultis, Moravia. Gartenbauwissenschaft, 5: 226.

Taba S, Miyahira N, Nasu K, Takushi T, Moromizato ZI (2007). Fruit rot of Strawberry pear (pitaya) caused by Bipolaris cactivora. J Gen Plant Pathol, 73 (5): 374~376.

White TJ, Bruns T, Lee SJWT, Taylor JW (1990). Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR protocols: a guide to methods and applications, 18: 315~322.


The Effect of Oral Administered Grape-Seed Extract on Cerebral Hypoperfusion Dementia in Mice

Class 8(10) Jiaxiao Gabriel Zhang, Class 8(1) Emily Jiang, Class 9(4) Qingyi Zhou

2016/12, Middle School

References

李齐欢. 补阳还五汤改善慢性脑缺血大鼠学习记忆能力的研究[D].南方医科大学,2010.

王侠,崔健,陈新.葡萄籽的化学成分及提取方法概述[J].吉林中医药,2007(10):68-69.

Ahn, S.H., et al., Grape seed proanthocyanidin extract inhibits glutamate-induced cell death through inhibition of calcium signals and nitric oxide formation in cultured rat hippocampal neurons. BMC Neurosci, 2011. 12: p. 78.

Bagchi, D., et al., Free radicals and grape seed proanthocyanidin extract: importance in human health and disease prevention. Toxicology, 2000. 148(2-3): p. 187-97.

Chen, C., et al., Oral administration of grape seed polyphenol extract restores memory deficits in chronic cerebral hypoperfusion rats. Behav Pharmacol, 2016.

"Chinafooding | Grape seed Extract, supplier, OPC, 84929-27-1, Herbal Extract, food additive". Chinafooding | Grape seed Extract,supplier,OPC,84929-27-1,Herbal Extract, food additive. Accessed January 15, 2017. http://www.foodchem.com/Herbal_Extracts/Grape_seed_Extract.

Dementia Fact sheet N°362. who.int. April 2012.

Ferruzzi, M.G., et al., Bioavailability of gallic acid and catechins from grape seed polyphenol extract is improved by repeated dosing in rats: implications for treatment in Alzheimer's disease. J Alzheimers Dis, 2009. 18(1): p. 113-24.

Gu, Y., et al., Potassium Aspartate Attenuates Brain Injury Induced by Controlled Cortical Impact in Rats Through Increasing Adenosine Triphosphate (ATP) Levels, Na+/K+-ATPase Activity and Reducing Brain Edema. Med Sci Monit, 2016. 22: p. 4894-4901.

Lian, Q., et al., Effects of grape seed proanthocyanidin on Alzheimer's disease in vitro and in vivo. Exp Ther Med, 2016. 12(3): p. 1681-1692.

Liang, Y., et al., Beneficial effects of grape seed proanthocyanidin extract on arterial remodeling in spontaneously hypertensive rats via protecting against oxidative stress. Mol Med Rep, 2016. 14(4): p. 3711-8.

Long, M., et al., The Protective Effect of Grape-Seed Proanthocyanidin Extract on Oxidative Damage Induced by Zearalenone in Kunming Mice Liver. Int J Mol Sci, 2016. 17(6).

Narita, K, et al. Differential nueroprotective activity of two different grape seed extracts. 2011. 6(1)

Ren, Q., et al., Effects of erythropoietin on neonatal hypoxia-ischemia brain injury in rat model. Physiol Behav, 2016. 169: p. 74-81.

Sarkaki, A., et al., Improvement in Memory and Brain Long-term Potentiation Deficits Due to Permanent Hypoperfusion/Ischemia by Grape Seed Extract in Rats. Iran J Basic Med Sci, 2013. 16(9): p. 1004-10.

Wang, Y.J., et al., Consumption of grape seed extract prevents amyloid-beta deposition and attenuates inflammation in brain of an Alzheimer's disease mouse. Neurotox Res, 2009. 15(1): p. 3-14.

Zhang, Z., Y. Li and Y. Li, Grape seed proanthocyanidin extracts prevent hyperglycemia-induced monocyte adhesion to aortic endothelial cells and ameliorates vascular inflammation in high-carbohydrate/high-fat diet and streptozotocin-induced diabetic rats. Int J Food Sci Nutr, 2015. 67(5): p. 524-34.



Bacterium Improves the Efficiency of Root Regeneration in A. thaliana

Class 8(1), Yi-Ting Chen, 2017/12, Middle School

Reference

Birnbaum, K. D., and Sanchez Alvarado, A. (2008). Slicing across kingdoms: regeneration in plants and animals. Cell 132, 697–710.

Correa Lda, R., Troleis, J., Mastroberti, A. A., Mariath, J. E., and Fett-Neto, A. G. (2012). Distinct modes of adventitious rooting in Arabidopsis thaliana. Plant Biol. (Stuttg.) 14, 100–109.

Cunxi Wang, Christopher A. Zien, et al. (2000) Involvement of Phospholipase D in Wound-Induced Accumulation of Jasmonic Acid in Arabidopsis. The Plant Cell, Vol.12, 2237-2246.

De Klerk, G.-J., Van Der Krieken, W., and De Jong, J.C. (1999). The formation of adventitious roots: new concepts, new possibilities. In Vitro Cell. Dev. Biol. Plant 35, 189–199.

Liu, J., Sheng, L., Xu, Y., Li, J., Yang, Z., Huang, H., et al. (2014). WOX11 and 12 are involved in the first-step cell fate transition during de novo root organogenesis in Arabidopsis. Plant Cell 26, 1081–1093.

Sugimoto, K., Gordon, S. P., and Meyerowitz, E. M. (2011). Regeneration in plants and animals: dedifferentiation, transdifferentiation, or just differentiation? Trends Cell Biol. 21, 212–218.


Molecular Tool to Improve the Root System for Stress Resistance

Liang Kaiqing, Tan, Jiaxin, 2017/12, Class 8(3), Middle School

References

范智勇, et al., 盐胁迫和干旱胁迫对蓝花子种子萌发和幼苗生长的影响. 北方园艺, 2011. 2: p. 7-10.

李树斌, et al., 转杉木14-3-3基因拟南芥根系对干旱胁迫的反应. 分子植物育种, 2016. 14(12): p. 3370-3376.

李文娆, et al., 干旱胁迫下紫花苜蓿根系形态变化及与水分利用的关系. 生态学报, 2010. 30(19): p. 5140-5150.

宋宗忆, 我国荒漠化防治如何走出困境. 科技导报, 1999. 17(10): p. 30-33.

俞仁培, 对盐渍土资源开发利用的思考. 土壤通报, 2001. 32: p. 138-140.

Bellini, C., D.I. Pacurar, and I. Perrone, Adventitious roots and lateral roots: similarities and differences. Annu Rev Plant Biol, 2014. 65: p. 639-66.

Briske, D.D. and A.M. Wilson, Drought Effects on Adventitious Root Development in Blue Grama Seedlings. Journal of Range Management, 1980. 33(5): p. 323-327.

Liu, J., et al., WOX11 and 12 are involved in the first-step cell fate transition during de novo root organogenesis in Arabidopsis. Plant Cell, 2014. 26(3): p. 1081-93.

Pascale, S.D., A. Maggio, and G. Barbieri, Soil salinization affects growth, yield and mineral composition of cauliflower and broccoli. European Journal of Agronomy, 2005. 23(3): p. 254-264.

Sheng, L., et al., Non-canonical WOX11-mediated root branching contributes to plasticity in Arabidopsis root system architecture. Development, 2017. 144(17): p. 3126-3133.

Steffens, B. and A. Rasmussen, The Physiology of Adventitious Roots. Plant Physiol, 2016. 170(2): p. 603-17.

Zhan, A., H. Schneider, and J.P. Lynch, Reduced Lateral Root Branching Density Improves Drought Tolerance in Maize. Plant Physiol, 2015. 168(4): p. 1603-15.

Zhao, Y., et al., The WUSCHEL-Related Homeobox Gene WOX11 Is Required to Activate Shoot-Borne Crown Root Development in Rice. Plant Cell, 2009. 21(3): p. 736-48.



EEG Results Indicate Quality of Sleep and Mood in the Elderly

Class 8(9) Christopher Cao, Class 8(5) Yusuke Atsuta, 2018/12, Middle School

References

Brenner, R. P., Ulrich, R. F., Spiker, D. G. , Sclabassi, R. J. , Iii, C. F. R. , & Marin, R. S. , et al. (1986). Computerized eeg spectral analysis in elderly normal, demented and depressed subjects. Electroencephalography & Clinical Neurophysiology, 64(6), 0-492.

Ishii R, Canuet L, Aoki Y, Hata M, Iwase M, Ikeda S, Nishida K, Ikeda M. Healthy and Pathological Brain Aging: From the Perspective of Oscillations, Functional Connectivity, and Signal Complexity. Neuropsychobiology. 2017, 75(4):151-161.

Ma J. Cause of Sleeping Disorder in the elders and Nursing Care. Chinese J. Modern Nursing 2011, 11:1114-1116.

Reynolds III, C. F., Kupfer, D. J. , Taska, L. S. , Hoch, C. C. , Spiker, D. G. , & Sewitch, D. E. , et al. (1985). Eeg sleep in elderly depressed, demented, and healthy subjects. Biological Psychiatry, 20(4),431-442.

Short MA, Louca M. Sleep deprivation leads to mood deficits in healthy adolescents. Sleep Med. 2015, 16(8):987-93.

The Report on Chinese Aging Marketing and Development during 2017-2022. Report Number 493919Intelligence Research Group 2017.

Wu Z, Li J, Xu S. A Comparative Study on Mental Health of the Elderly in Different Style of Providing for the Aged. Chinese J. Gerontology 2003, 11:1-3.

XU Wei, JIANG Luoluo, & WANG Binghong. (2018). The brain age prediction based on the power spectrum entropy feature extraction. Science & Technology Review, 36(8), 40-47.



Appendix I. Profiles of Mood State (POMS) Questionnaire

Date:

Sex:  M F

Age:


Below is a list of words that describe feelings people have. Please CIRCLE THE NUMBER THAT BEST DESCRIBES HOW YOU FEEL DURING THE PAST WEEK (INCLUDING TODAY).



Not at all

A little

Moderately

Quite a lot

Extremely

1

Tense

0

1

2

3

4

2

Angry

0

1

2

3

4

3

Worn Out

0

1

2

3

4

4

Unhappy

0

1

2

3

4

5

Proud

0

1

2

3

4

6

Lively

0

1

2

3

4

7

Confused

0

1

2

3

4

8

Sad

0

1

2

3

4

9

Active

0

1

2

3

4

10

On-edge

0

1

2

3

4

11

Grouchy

0

1

2

3

4

12

Ashamed

0

1

2

3

4

13

Energetic

0

1

2

3

4

14

Hopeless

0

1

2

3

4

15

Uneasy

0

1

2

3

4

16

Restless

0

1

2

3

4

17

Unable to concentrate

0

1

2

3

4

18

Fatigue

0

1

2

3

4

19

Competent

0

1

2

3

4

20

Annoyed

0

1

2

3

4

21

Discouraged

0

1

2

3

4

22

Resentful

0

1

2

3

4

23

Nervous

0

1

2

3

4

24

Miserable

0

1

2

3

4

25

Confident

0

1

2

3

4

26

Bitter

0

1

2

3

4

27

Exhausted

0

1

2

3

4

28

Anxious

0

1

2

3

4

29

Helpless

0

1

2

3

4

30

Weary

0

1

2

3

4

31

Satisfied

0

1

2

3

4

32

Bewildered

0

1

2

3

4

33

Furious

0

1

2

3

4

34

Full of Pep

0

1

2

3

4

35

Worthless

0

1

2

3

4

36

Forgetful

0

1

2

3

4

37

Vigorous

0

1

2

3

4

38

Uncertain about things

0

1

2

3

4

39

Bushed

0

1

2

3

4

40

Embarrassed

0

1

2

3

4


Scoring

Scores for each item is recorded as follows: 0 for “not at all”, 1 for “a little”, 2 for “moderately”, 3 for “quite a lot” and 4 for “extremely”.

Tension; items 1, 8, 15, 21, 28, 35

Anger: items 2, 9, 16, 22, 29, 36, 37

Fatigue: items 3, 10, 17, 23, 30

Depression: items 4, 11, 18, 24, 31, 38

Vigor: items 5, 12, 19, 25, 32, 39

Confuse: items 6, 13, 20, 26, 33

Esteem-related affect: items 7, 14, 27, 34, 40


A Total Mood Disturbance (TMD) score = sum of the 5 negative subscales (tension, depression, fatigue, confuse, anger) – sum of the 2 positive subscales (vigor and esteem-related affect) + 100




Appendix IIThe Pittsburgh Sleep Quality Index (PSQI)

Instructions: The following questions relate to your usual sleep habits during the past week only. Your answers should indicate the most accurate reply for the majority of days and nights in the past week. Please answer all questions. During the past week,

1. When have you usually gone to bed? ______________

2. How long (in minutes) has it taken you to fall asleep each night? ______________

3. When have you usually gotten up in the morning? ______________

4. How many hours of actual sleep do you get at night? (This may be different than the number of hours you spend in bed) ______________

Please elect the best fit answer for the following questions:

5. During the past week, , how often have you had trouble sleeping   because you….

Not during the past week (0)

Less than once a week (1)

Once or twice a week (2)

Three or more times a week (3)

a. Cannot get to sleep within 30 minutes





b. Wake up in the middle of the night or early   morning





c. Have to get up to use the bathroom





d. Cannot breathe comfortably





e. Cough or snore loudly





f. Feel too cold





g. Feel too hot





h. Have bad dreams





i. Have pain





j. Other reason(s), please describe, including   how often you have had trouble sleeping because of this reason(s):






Very good (0)

Fairly good (1)

Fairly bad (2)

Very bad (3)

6. During the past week, how would you rate your sleep quality overall?






Not during the past week (0)

Less than once a week (1)

Once or twice a week (2)

Three or more times a week (3)

7. During the past week, how often have you taken medicine (prescribed or   “over the counter”) to help you sleep?





8. During the past week, how often have you had trouble staying awake   while driving, eating meals, or engaging in social activity?






No problem at all (0)

Only a very slight problem (1)

Somewhat of a problem (2)

A very big problem (3)

9. During the past week, how much of a problem has it been for you to   keep up enthusiasm to get things done?





The Pittsburgh Sleep Quality Index (PSQI) is a self-rated questionnaire and was used to assess the subject’s sleep quality and disturbances over a one-week interval. Nineteen individual items generate seven component scores. The sum of these component scores yields one global PSQI score, with a range of 0 to 20 points. The higher the PSQI scores, the worse the sleep quality.

Scoring proceed as follows:

Component 1: Sleep quality

Examine question 6, and assign the scores as follows:

Response   Score

Very good 0

Fairly good 1

Fairly bad 2

Very bad 3

Component 2: Sleep latency

1. Examine question 2, and assign the scores as follows:

Response  Score

≤ 15min 0

16 – 30 min 1

31 – 60 min 2

> 60 min   3

2. Examine question 5a, and assign the scores as follows:

Response   Score

Not during the past week 0

Less than once a week 1

Once or twice a week 2

Three or more times a week 3

3. Assign component 2 score as follows:

Sum of 2 and 5a Score

0   0

1 – 2   1

3 – 4   2

5 – 6   3

Component 3: Sleep duration

Examine question 4, and assign the scores as follows:

Response   Score

> 7 hours  0

6 – 7 hours 1

5 – 6 hours 2

< 5 hours  3

Component 4: Habitual sleep efficiency

1. Calculate the number of hours spent in bed:

getting up time (Q3) – bed time (Q1)

2. Calculate habitual sleep efficiency (%):

(number of hours slept/number of hours spent in bed) × 100

3. Assign component 4 score as follows:

Habitual sleep efficiency Score

>85%  0

75 – 84%  1

65 – 74%   2

<65%   3

Component 5: Sleep disturbance

1. Examine question 5b – 5j, and assign scores for each question as follows:

Response   Score

Not during the past week 0

Less than once a week 1

Once or twice a week 2

Three or more times a week 3

2. Assign component 5 score as follows:

Sum of scores of 5b – 5j Score

0  0

1 – 9   1

10 – 18   2

19 – 27   3

Component 6: Use of sleep medication

Examine question 4, and assign the scores as follows:

Response  Score

Not during the past week 0

Less than once a week 1

Once or twice a week 2

Three or more times a week 3

Component 7: Daytime dysfunction

1. Examine question 8, and assign the scores as follows:

Response  Score

Not during the past week 0

Less than once a week 1

Once or twice a week 2

Three or more times a week 3

2. Examine question 9, and assign the scores as follows:

Response  Score

No problem at all 0

Only a very slight problem 1

Somewhat of a problem 2

A very big problem  3

3. Assign component 7 score as follows:

Sum of 8 and 9 Score

0  0

1 – 2   1

3 – 4   2

5 – 6   3

Global PSQI score = sum of scores of 1 - 7

Appendix III. The Brain Networks Differed Among Subjects

The four circles represent the four brain regions. We used arrows to indicate that the phases of EEG activity between the two brain regions are significantly synchronous (p<0.05). The top three indicate the brain networks of the first day during the 1st-3rd recordings, and the below three indicate the brain networks of the second day in the corresponding time periods.


The Effect of Excretory Factor EREG Released by Stromal Cells during Chemotherapy on the Malignant Phenotype of Prostate Cancer

Class 8(8) Run Yi Liu, Class 8(3) Helen Xiong, Middle School

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Weichselbaum, Ralph R., et al. "An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer." Proceedings of the National Academy of Sciences 105.47 (2008): 18490-18495.


An Organoid Culture Based Investigation: The Prevention Mechanism of Tea Polyphenols on Prostate Cancer

Class 8(2), Ivan Yuan Junior, 2019/12, Middle School

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Decay of Urban Rail Transit-induced Ground-borne Vibration and Rapid Prediction Methods

Class 7(2), Ivan Yuan Junior, 2018/12, Middle School

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An Evaluation of Avian Species Diversity at Microforests of Nanhui Dongtan Wetlands in Terms of Human Disturbance and Edge Effect

Class 12(1A), Yasuhiko Komatsu Senior, 2018/12, High School

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Effects of Sleep Intervention and Herb Medication Ganwei on Behavioral and Biochemical Responses in Drosophila Alzheimer’s Disease Model

Class 11(1B), Lily Peng and Tina Mengting Liu, 2018/11, High School

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The Effects of Vitamin C on SW480 Colon Cancer Cells In Vitro

Class 11(1B), Jayden Raymond Liu, 2015/5, High School

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The Inhibitory Effect of Chemical and Biological Food Preservatives on growth of Escherichia coli and Rhizopus stolonifer

Class 12(1B), Jessica Qu, 2017/1, High School

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Research on EGFR Mutation Testing

Class 11(5), Tian Yang Zhou, 2017/09, High School

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[1]  https://www.ncbi.nlm.nih.gov/gene/1956

[2]  https://www.mycancergenome.org/content/disease/lung-cancer/egfr/4/

[3]  http://www.medscape.com/viewarticle/813269_2

[4]  http://www.medscape.com/viewarticle/813269_3

[5]  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4830020/

[6]  Cross DA, Ashton SE, Ghiorghiu S, Eberlein C, Nebhan CA, Spitzler PJ, Orme JP, Finlay MR, Ward RA, Mellor MJ, Hughes G, Rahi A, Jacobs VN, Red Brewer M, Ichihara E, Sun J, Jin H, Ballard P, Al-Kadhimi K, Rowlinson R, Klinowska T, Richmond GH, Cantarini M, Kim DW, Ranson MR, Pao W. AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer. Cancer Discov. 2014;4(9):1046–1061. doi: 10.1158/2159-8290.CD-14-0337.

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[8]  http://bitesizebio.com/13516/how-dna-extraction-rna-miniprep-kits-work/

[9]  https://www.thermofisher.com/cn/zh/home/life-science/pcr/real-time-pcr/qpcr-education/essentials-of-real-time-pcr.html

[10]  https://www.thermofisher.com/cn/zh/home/life-thermofisher science/dna-rna-purification-analysis/dna-extraction/genomic-dna-extraction/dna-extractions-working-with-ffpe-samples.html

[11]  https://jeccr.biomedcentral.com/articles/10.1186/s13046-014-0104-7

[12]  http://www.wisegeek.org/what-is-blood-plasma.htm

[13]  https://medlineplus.gov/ency/article/000086.htm

[14]  https://www.aati-us.com/applications/cfdna-cell-free-dna/

[15]  http://www.ema.europa.eu/docs/en_GB/document_library/EPAR_-_Product_Information/human/004124/WC500202022.pdf#page=28

[16]  https://www.astrazeneca-us.com/media/press-releases/2016/us-fda-approves-tagrisso-osimertinib-blood-based-t790m-companion-diagnostic-test-09292016.html

[17]  http://www.bio-rad.com/en-au/applications-technologies/droplet-digital-pcr-ddpcr-technology

[18]  http://onlinelibrary.wiley.com/doi/10.1002/gcc.22047/abstract

[19]  http://www.bio-rad.com/zh-cn/applications-technologies/digital-pcr-next-generation-sequencing-ngs


Mitigating Biodiversity Loss

Class 11(1B), Mingyi Suo, 2017/4, High School

References

1. Foreman, David. Rewilding North America: A Vision for Conservation in the 21st Century”. Island Press (2004).

2. Quammen, David. Planet of Weeds: Tallying the losses of Earth’s animals and plants. Harper’s Magazine (1998).

3. Wilson, E.O. Half-Earth: Our Planet's Fight for Life. Liveright Publishing Corporation (2016)

4. Pearce, Fred. The New Wild: Why Invasive Species Will Be Nature’s Salvation. Beacon Press (2015)

5. Fraser, Caroline. Rewilding the World: Dispatches from the conservation revolution. Metropolitan Books (2009)

6. Pounds, J Alan; Fogden, Michael P L; Campbell, John H. Biological response to climate change on a tropical mountain. Nature 398, 608–610 (1999) http://www.nature.com/nature/journal/v398/n6728/full/398611a0.html?foxtrotcallback=true

7. Oregon Forests and Climate Change: An OSU Forestry & Natural Resources Extension project. (2016) http://blogs.oregonstate.edu/orforestscc/2016/04/26/considering-assisted-migration-for-trees-in-a-changing-climate/

8. Blumstein, Daniel T. Isolation from mammalian predators differentially affects two congeners. Behavioral Ecology Vol. 13 No. 5: 657–663 (2002) https://www.eeb.ucla.edu/Faculty/Blumstein/pdf%20reprints/Blumstein%26Daniel_2002_BE.pdf

9. The Nature Conservancy. Reforestation Project Bears Fruit for Local Communities. https://www.nature.org/ourinitiatives/regions/asiaandthepacific/china/explore/china-tengchong-reforestation-project.xml?redirect=https-301

10. National Park Service. Wolf Restoration. (June, 9, 2017) https://www.nps.gov/yell/learn/nature/wolf-restoration.htm

11. Meachen, Julie A., and Joshua X. Samuels. "Evolution in coyotes (Canis latrans) in response to the megafaunal extinctions.." Proceedings of the National Academy of Sciences of the United States of America 109.11 (2012): 4191-4196.

12. Zimmer, Carl. Bringing them back to life. Magazine, National Geographic. http://www.nationalgeographic.com/magazine/2013/04/species-revival-bringing-back-extinct-animals/

13. Dolan et al. Pleistocene Rewilding: An Optimistic Agenda for Twenty-First Century Conservation. The American Naturalist, Vol. 168, No. 5 (November 2006), pp. 660-681. The University of Chicago Press. http://www.jstor.org/stable/10.1086/508027

14. Marris, Emma. Rambunctious Garden: Saving Nature in a Post-Wild World. Bloomsbury (2011)

15. Rohland et al. Genomic DNA Sequences from Mastodon and Woolly Mammoth Reveal Deep Speciation of Forest and Savanna Elephants. (December 2010) https://www.researchgate.net/publication/49726374_Genomic_DNA_Sequences_from_Mastodon_and_Woolly_Mammoth_Reveal_Deep_Speciation_of_Forest_and_Savanna_Elephants

16. Agenbroad, Larry D. North American Proboscideans: Mammoths: The state of Knowledge. Quaternary International. Volumes 126–128 react-text: 71, /react-text react-text: 72 2005 /react-text react-text: 73, Pages 73-92. (2003) https://doi.org/10.1016/j.quaint.2004.04.016

17. Zimov, Sergey A.; Zimov N. S.; Chapin F. S. III. The Past and Future of the Mammoth Steppe Ecosystem. Paleontology in Ecology and Conservation pp 193-225. (2012)

18. https://link.springer.com/chapter/10.1007/978-3-642-25038-5_10/fulltext.html


Investigate the Effect of E-Liquid, High Temperature Stress and UV-C Radiation Exposure on the Growth of Saccharomyces cerevisiae (Yeast)

Class 11(1B), Karen Mei Song, 2017/11, High School

References and Bibliography

[1] Centers for Disease Control and Prevention, USA.Heat-related deaths among crop workers--United States, 1992—2006. Mortal. Wkly. Rep. 2008; 57(24): 649-653.

[2] Choi K, Lazovich D, Southwell B, Forster J, Rolnick SJ, Jackson J, Arch Dermatol. Prevalence and characteristics of indoor tanning use among men and women in the United States. 2010 Dec; 146(12):1356-61.

[3] Clontech Laboratories. Yeast Protocols Handbook. Published July 2009. E-version.

[4] Engineering Statistics Handbook Critical Values of the Chi-Square Distribution.

[5] Environmental Protection Agency. Respiratory health effects of passive smoking: lung cancer

[6] Farsalinos KE, Polosa R. Safety evaluation and risk assessment of electronic cigarettes as tobacco cigarette substitutes: a systematic review. Ther Adv Drug Saf. 2014;5(2):67– 86. doi: 10.1177/2042098614524430

[7] Halliwell B, Gutteridge J. Free Radicals in Biology and Medicine. 4th edition. Oxford, UK: Oxford University Press; 2007.

[8] Laugesen M. (2008) Safety Report on the Ruyan® e-cigarette Cartridge and Inhaled Aerosol. 2008.

[9] Lin Xiang Qian. Technical Paper, UV Lamps in Laminar Flow and Biological Safety Cabinets. Singapore. 2002 October.

[10] Rastogi RP, Richa Kumar A, Tyagi MB, Sinha RP. Molecular mechanisms of ultraviolet radiation-induced DNA damage and repair. J Nucleic Acids. 2010; 2010:592980.

[11] Scientific Review of Ultraviolet (UV) Radiation, Broad Spectrum and UVA, UVB, and UVC.  National Toxicology Program, U.S. Department of Health and Human Service. Available at:

[12] Sigmoid Population Growth Curve.

[13] Valko M, Rhodes CJ, Moncol J, Izakovic M, Mazur M.Free radicals, metals and antioxidants in oxidative stress-induced cancer. Chem Biol Interact. 2006 Mar 10; 160(1):1-40.10.

[14] Westenberger B. Evaluation of e-Cigarettes. St. Louis, MO: Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Division of Pharmaceutical Analysis. 2009. 


Surface Electromyogram Analysis of Muscle Reactivity During Tennis Top-spin Serve

Jiayi Zhang, Huayi Zhang, High School

References

[1] Matsunaga, N., Imai, A., &Kaneoka, K. (2017). Comparison of muscle synergies before and after 10 minutes of running. Journal of physical therapy science, 29(7), 1242-1246.

[2] Sorbie, G. G., Grace, F. M., Gu, Y., Baker, J. S., &Ugbolue, U. C. (2018). Electromyographic analyses of the erector spinae muscles during golf swings using four different clubs. Journal of sports sciences, 36(7), 717-723.

[3] Yanjun, Liu. Electromyogram of Table Tennis Basic Techniques. Journal of Tianjin Institute of Physical Education. 1995(03):18-21.

[4] 周萌然. 网球上旋发球技术的生物力学分析.西南大学,2011

[5] 郭全清. 青年男子网球运动员主要动作的肌电分析与应用.北京体育大学,2006.

[6] Yongdong, Qian. Surface EMG Analysis of Tennis Players’ Service. Journal of Jilin Institute of Physical Education,2013,29(01):66-68.

[7] Hui, Liu. Sports Biomechanical Principles of Power Serve Technique in Tennis,Journal of Beijing University of Physical Education,2000(02):173-176+180.

[8] Bo-tao,YAN, &Zao, LI Tennis Serve Movement and Its Basic Technique Patterns. China Sport Science And Technology, 2001(10):37-41.

[9] 陈锴. 网球发球技术技巧分析.《体育科研》2004年第2期(总第94期).:甘肃省体育科学学会,2004:3.

[10] Chun-lin,Jin,&Feng,QU. A Biomechanical Analysis of Bai Yan's Tennis Service Technique, Journal of Beijing Sport University,2008(02):271-274.

[11] Zhuoshi, Wang. Man Shot Athletes’ Main Muscle Mass Surface EMG Analysis During Sliding Stage of Shot-putting. Capital Institute of Physical Education, 2013.