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                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


                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


                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


                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


                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. <https://www.researchgate.net/figure/Sketch-illustrating-the-drag-origin-for-road-vehicles-a-aerodynamic-drag-skin_fig2_322640200> [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. <https://www.researchgate.net/figure/a-Side-view-and-upper-view-of-the-original-Ahmed-bo dy-with-a-30-rear-slant-b-Schematic_fig1_226408776> [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 h?gskola and Chalmers tekniska h?gskola. Institutionen f?r till?mpad 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


                [1]  HUBBLE SITE., 2004. The progenitor of a type Ia supernova. Available at: .

                [2] Paolo A. Mazzali., Friedrich K. R?pke., 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: . Accessed on 7-18-2016.

                [8]  Tim Pearson, 2002. PGPLOT Graphic Subroutine Library. California Institute of Technology. Available at: . 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: . 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


                [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


                [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


                [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


                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




                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






                function [ y ] =probability_of_incurable_desease( x )

                y = 2.695*exp(1.331*x)



                syms x












                function [ y ] = percentage( x )

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



                syms x







                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


                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


                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


                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


                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)


                Return a

                End Function

                End Class

                An Asymptotic Approach to the Analysis of Swings

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


                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



                胡湛,彭希哲.应对中ω 国人口老龄化的治理选择[J].中国藍家主社会科学,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 那大總管頓時兇光畢露全科医学,2017,20(07):842-851.


                An Integrated Intelligent Medical Infusion Device Based on PLC

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



                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.



                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. 一种沒有人不會感到震驚新型智能输液监护系统的研制[D].南京又是幾道人影落下航空航天大学,2012.



                The Design and Construction of an Intelligent Selfie Robot

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




                OpenCV-Python Tutorials,

                Supplementary materials

                Codes in Python:

                l Controlling the Servo:

                import RPi.GPIO as GPIO

                from time import sleep

                def rotate_to(angle):


                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)


                GPIO.output(3, False)



                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




                #set basic parameters

                def forward(t):


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

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


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

                def backward(t):






                def turnright(d):

                t = d * cc





                def turnleft(d):

                t = d * cc





                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


                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:


                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:


                 for (mx,my,mw,mh) in eyes:


                 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



                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


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                李敏, 胡美姣, 薛丁榕, 杨冬平, 杨波, 张正科, 赵超, 高兆银(2013). 火龙果仿佛是印證了銀角電鯊黑斑病菌 Bipolaris cactivora (Petrak) Alcorn 生物学特性研究. 热带作物学报, 34 (9): 1770~1775.

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                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.

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                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. 中国真菌学為什么不是全力一擊杂志, (6): 23.

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                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


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                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).

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                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


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                Molecular Tool to Improve the Root System for Stress Resistance

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


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                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


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                Appendix I. Profiles of Mood State (POMS) Questionnaire


                Sex:  M F


                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


                Quite a lot

















                Worn Out


































































































                Unable to concentrate























































































































                Full of Pep




























                Uncertain about things





















                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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                Siegel, Rebecca L., Kimberly D. Miller, and Ahmedin Jemal. "Cancer statistics, 2017." CA: a cancer journal for clinicians 67.1 (2017): 7-30.

                Sun, Yu, et al. "Treatment-induced damage to the tumor microenvironment promotes prostate cancer therapy resistance through WNT16B." Nature medicine 18.9 (2012): 1359.

                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


                崔涛, 高晶, 曾勇, 李薇, 伊秀林, 司端运,... 李大鹏. (2017). 薏苡仁油对人前列●腺癌PC-3细胞的抑制作用. 中草药, 48(21), 4460-4464

                毛小强, 那万里, 赵丹, 施朝龄, 于洋, 姜福全,... 王伟华. (2010). 茶多酚对前列腺癌PC-3M细胞增殖与凋亡的影响. 中国实验诊断学, 14(02), 170-173

                张星海, & 杨贤强. (2003). 茶多酚及儿茶↓素对前列腺癌细胞生长怎么的抑制作用. 茶叶(03), 142-144

                Balk, S. P., & Knudsen, K. E. (2018). AR, the cell cycle, and prostate cancer. Nuclear Receptor Signaling, 6(1), 6001.

                Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A. Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68(6), 394-424.

                Chen, W., Zheng, R., Baade, P. D., Zhang, S., Zeng, H., Bray, F. He, J. (2016). Cancer statistics in China, 2015. CA: A Cancer Journal for Clinicians, 66(2), 115-132.

                Gao, D., Vela, I., Sboner, A., Iaquinta, P. J., Karthaus, W. R., Gopalan, A.,... Arora, V. K. (2014). Organoid cultures derived from patients with advanced prostate cancer. Cell, 159(1), 176-187

                Johnson, J. J., Bailey, H. H., & Mukhtar, H. (2010). Green tea polyphenols for prostate cancer chemoprevention: A translational perspective. Phytomedicine, 17(1), 3-13.

                Khan, N., & Mukhtar, H. (2007). Tea polyphenols for health promotion. Life Sciences, 81(7), 519-533.

                Magee, J. A., Chang, L., Stormo, G. D., & Milbrandt, J. (2006). Direct, Androgen Receptor-Mediated Regulation of the FKBP5 Gene via a Distal Enhancer Element. Endocrinology, 147(1), 590-598.

                Markus, L., Atsushi, S., Andrea, B., Anna, F., Michael, E., Goodarzi, A. A. Jeggo, P. A. (2010). gammaH2AX foci analysis for monitoring DNA double-strand break repair: strengths, limitations and optimization. Cell Cycle, 9(4), 662-669.

                Puca, L., Bareja, R., Prandi, D., Shaw, R., Benelli, M., Karthaus, W. R. Beltran, H. (2018). Patient derived organoids to model rare prostate cancer phenotypes. Nature Communications, 9(1).

                Scully, R., & Xie, A. (2013). Double strand break repair functions of histone H2AX. Mutation Research/Fundamental and Molecular Mechanisms of Mutagenesis, 750(1-2), 5-14.

                Xia, X., Li, F., He, J., Aji, R., & Gao, D. (2019). Organoid technology in cancer precision medicine. [Journal Article]. Cancer Lett, 457, 20-27.

                Xu, L. L., Srikantan, V., Sesterhenn, I. A., Augustus, M., Dean, R., Moul, J. W. Srivastava, S. (2000). Expression profile of an androgen regulated prostate specific homeobox gene NKX3.1 in primary prostate cancer. [Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.]. J Urol, 163(3), 972-979.

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

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










                王媛. 轨道交通规划环境影响评价指标体系研究及其应用[D].吉林大学, 2008

                F. T. A. Office Of Planning And Environment, "Transit noise and vibration impact assessment. 2006.

                G. Lombaert, G. Degrande, S. Fran?ois, and D. J. Thompson. Ground-Borne Vibration due to Railway Traffic: A Review of Excitation Mechanisms, Prediction Methods and Mitigation Measures. Notes on Numerical Fluid Mechanics & Multidisciplinary Design, vol. 126, pp. 2015, 253-287.

                M. Sanayei, P. Maurya and J. A. Moore, "Measurement of building foundation and ground-borne vibrations due to surface trains and subways," Engineering Structures, vol. 53, pp. 2013, 102-111.

                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


                [1] Allot Andrew, and David Mindorff. Biology: Course Companion. Oxford University Press, 2014.

                [2] Brelsford, Craig. “Cape Nanhui.” Shanghai Birding, <www.shanghaibirding.com/sites/nanhui/>

                [3] Gregory, R.D., Noble, D.G., Custance, J. (2004). The state of play of farmland birds: population trends and conservation status of lowland farmland birds in the United Kingdom.

                [4] “Nanhui Dongtan.” ebird.org. The Cornell Lab of Ornithology. Web. 18 January 2019.


                [5] Newton, I. (1995). “The contribution of some recent research on birds to ecological understanding.” Journal of Animal Ecology 1995.

                [6] Zaiontz, Charles. “Simpson’s Diversity Index.” real-statistics.com. Web. 18 January 2019.

                <www.realstatistics.com.descriptive-statistics/diversity-indices/simpsons-diversity- index/>

                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


                1. Williams, J. A., Sathyanarayanan, S., Hendricks, J. C., & Sehgal, A. (2007). Interaction Between Sleep and the Immune Response in Drosophila: A Role for the NFκB Relish. Sleep, 30(4), 389–400. doi:10.1093/sleep/30.4.389

                2. Ju YE, Lucey BP, Holtzman DM. Sleep and Alzheimer disease pathology—a bidirectional relationship. Nat Rev Neurol. 2014 Feb;10(2):115–9.

                3. Mitolo, M., Tonon, C., La Morgia, C., Testa, C., Carelli, V., & Lodi, R. (2018). Effects of Light Treatment on Sleep, Cognition, Mood, and Behavior in Alzheimer’s Disease: A Systematic Review. Dementia and Geriatric Cognitive Disorders, 371–384. doi:10.1159/000494921

                4. Ancoli-Israel S, Gehrman P, Martin JL, Shochat T, Marler M, Corey-Bloom J, et al. Increased light exposure consolidates sleep and strengthens circadian rhythms in severe Alzheimer’s disease patients. Behav Sleep Med. 2003a;1(1):22–36.

                5. Ancoli-Israel S, Martin JL, Kripke DF, Marler M, Klauber MR. Effect of light treatment on sleep and circadian rhythms in demented nursing home patients. J Am Geriatr Soc. 2002 Feb;50(2):282–9.

                6. Dowling GA, Hubbard EM, Mastick J, Luxenberg JS, Burr RL, Van Someren EJ. Effect of morning bright light treatment for rest-activity disruption in institutionalized patients with severe Alzheimer’s disease. Int Psychogeriatr. 2005a Jun;17(2):221–36

                7. Satlin A, Volicer L, Ross V, Herz L, Campbell S. Bright light treatment of behavioral and sleep disturbances in patients with Alzheimer’s disease. Am J Psychiatry. 1992 Aug;149(8):1028–32.

                8. Yamadera H, Ito T, Suzuki H, Asayama K, Ito R, Endo S. Effects of bright light on cognitive and sleep-wake (circadian) rhythm disturbances in Alzheimer-type dementia. Psychiatry Clin Neurosci. 2000 Jun;54(3): 352–3

                9. Iaccarino, H. F., Singer, A. C., Martorell, A. J., Rudenko, A., Gao, F., Gillingham, T. Z., … Tsai, L. H. (2016). Gamma frequency entrainment attenuates amyloid load and modifies microglia. Nature, 540(7632), 230–235. doi:10.1038/nature20587

                10. Onega LL, Pierce TW, Epperly L. Effect of Bright Light Exposure on Depression and Agitation in Older Adults with Dementia. Issues Ment Health Nurs. 2016 Sep;37(9):660–7.

                11. Botanov, Yevgeny, and Stephen S Ilardi. “The acute side effects of bright light therapy: a placebo-controlled investigation.” PloS one vol. 8,9 e75893. 24 Sep. 2013, doi:10.1371/journal.pone.0075893

                12. Terman, M., & Terman, J. S. (2005). Light Therapy for Seasonal and Nonseasonal Depression: Efficacy, Protocol, Safety, and Side Effects. CNS Spectrums, 10(8), 647–663. doi: 10.1017/s1092852900019611

                13. Hanus J., Zhang H., Wang Z., Liu Q., Zhou Q., Wang S. Induction of necrotic cell death by oxidative stress in retinal pigment epithelial cells. Cell Death & Disease. 2013;4(12):p. e965. doi: 10.1038/cddis.2013.478.

                14. Peng IF. 2018 GanWei, a hepatic lipidosis medication, improves sleep loss via alleviating oxidative stress. Personal Communications.

                15. Collin F, Cheignon C, & Hureau C. (2018) Oxidative stress as a biomarker for Alzheimer’s disease. Biomarkers in Medicine, 12(3). doi: 10.2217/bmm-2017-0456.

                16. Markesbery, W. R. (1999). The Role of Oxidative Stress in Alzheimer Disease. Archives of Neurology, 56(12), 1449. doi: 10.1001/archneur.56.12.1449

                17. Pohanka, M. (2013). Alzheimer′s Disease and Oxidative Stress: A Review. Current Medicinal Chemistry, 21(3), 356–364. doi: 10.2174/09298673113206660258

                18. Iijima K, Liu HP, Chiang AS, Hearn SA, Konsolaki M, Zhong Y. Dissecting the pathological effects of human Abeta40 and Abeta42 in Drosophila: a potential model for Alzheimer's disease. Proc Natl Acad Sci U S A. 2004 Apr 27;101(17):6623-8. Epub 2004 Apr 6. PMID: 15069204

                19. Sehgal, A., Joiner, W., Crocker, A., Koh, K., Sathyanarayanan, S., Fang, Y., … Zheng, X. (2007). Molecular Analysis of Sleep: Wake Cycles in Drosophila. Cold Spring Harbor Symposia on Quantitative Biology, 72(1), 557–564. doi:10.1101/sqb.2007.72.018

                20. Joan C. Hendricks,* Stefanie M. Finn, Karen A. Panckeri, Jessica Chavkin, Julie A. Williams, Amita Sehgal, and Allan I. Pack. Rest in Drosophila Is a Sleep-like State. Neuron, Vol.

                21. Thimgan, M. S., Gottschalk, L., Toedebusch, C., McLeland, J., Rechtschaffen, A., Gilliland-Roberts, M., … Shaw, P. J. (2013). Cross-Translational Studies in Human and Drosophila Identify Markers of Sleep Loss. PLoS ONE, 8(4), e61016. doi:10.1371/journal.pone.0061016

                22. Sheetal Potdar, Danita K. Daniel, Femi A. Thomas, Shraddha Lall, Vasu Sheeba, Behavioural Neurogenetics Laboratory, Evolutionary and Organismal Biology Unit, Neuroscience Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, INDIA. Sleep deprivation negatively impacts reproductive output in Drosophila melanogaster. Journal of Experimental Biology. 4 January 2018. doi: 10.1242/jeb.174771.

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

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


                [1] BD Biosciences. Introduction to Flow Cytometry: A Learning Guide. <>, 2000.

                [2] Ferreira D, Adega F, Chaves R. “The Importance of Cancer Cell Lines as in vitro Models in Cancer Methylome Analysis and Anticancer Drugs Testing”. Oncogenomics and Cancer Proteomics - Novel Approaches in Biomarkers Discovery and Therapeutic Targets in Cancer. <>, 2013.

                [3] Futschik M, Jeffs A, Pattison S, Kasabov N, Sullivan M, Merrie A, Reeve A, (2002), “Gene Expression Profiling of Metastatic and Nonmetastatic Colorectal Cancer Cell Lines”, Genome Letters, vol. 1 (1), 26-34.

                [4] National Cancer Institute. Colorectal Cancer – Patient Version. <>, 2015.

                [5] National Cancer Institute. High-Dose Vitamin C. <>, 2015.

                [6] National Institutes of Health, Office of Dietary Supplements. Vitamin C Fact Sheet for Consumers. <>, 2011.

                [7] Padayatty SJ, Sun AY, Chen Q, Espey MG, Drisko J, Levin M, (2010), Vitamin C: intravenous use by complementary and alternative medicine practitioners and adverse effects, Public Library of Science One, 5(7).

                [8] Phelan MC, (1998), Basic Techniques for Mammalian Cell Tissue Culture, Current Protocols in Cell Biology.

                [9] Physical Sciences-Oncology Center Network Bioresource Core Facility. Thawing, Propagating, and Cryopreserving Protocol. <>, 2012.

                [10] R&D Systems. Flow Cytometry Protocol for Analysis of Cell Viability using Propidium Iodide. <>, 2012.

                [11] Riss TL, Moravec RA, Niles AL, et al. Cell Viability Assays – Assay Guidance Manual, National Center for Advancing Translational Sciences. <>, 2013.

                [12] Sigma. Use of Trypan Blue Stain and the Hemocytometer to Determine Total Cell Counts and Viable Cell Number. <www.researchgate.net/Ffile.PostFileLoader.html/Fid/D54f44c67d2fd64f57e8b4651/6assetKey/DAS/53A273719526658055/5401442271228688&usg=AFQjCNGxti0HFAZwPukCMnnOXLksYNgCfA&bvm=bv.115339255,d.eWE>, 2015.

                [13] Stewart BW, Wild CP, (2014), World Cancer Report, International Agency for Research on Cancer, World Health Organization Press.

                [14] Valko M, Leibfritz D, Moncol J, et al, (2007), Free radicals and antioxidants in normal physiological functions and human disease, International Journal of Biochemistry & Cell Biology, vol. 39 (1), 44-84.

                [15] Yun J, Mullarky E, Lu C, Bosch KN, Kavalier A, et al, (2015), Vitamin C selectively kills KRAS and BRAF mutant colorectal cancer cells by targeting GAPDH, Science, vol. 350 (6266), 1391-1396.

                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


                [1] Albrecht, Julie A. “Escherichia coli O157:H7”. Institute of Agriculture and Natural Resources. University of Nebraska-Lincoln. 23 July 2016. Web. <http://food.unl.edu/documents/Escherichinia%20coli%20O157%3AH7.pdf>.

                [2] Andrews, Jennifer M. “Determination of minimum inhibitory concentrations”. Journal of Antimicrobial Chemotherapy (2001) 48, Suppl. S1, 5-16. The British Society for Antimicrobial Chemotherapy. 2001. Web. <http://jac.oxfordjournals.org/content/48/suppl_1/5.abstract>.

                [3] Fothergill, P.G. and M.M. Yeomen. “The Mineral Nutrition of Rhizopus stolonifer”. J. gen. Microbiol. 17. Botany Department, King's College, Newcastle upon Tyne: 1957, p631-639. 23 July 2016. Web. <http://www.microbiologyresearch.org/docserver/fulltext/micro/17/3/mic-17-3-631.pdf?eexpire=1469250417&id=id&accname=guest&checksum=90E724A93EBBA62805D6942694ABDA96>.

                [4] Hsu, S.C., J.L. Lockwood. “Mechanisms of Inhibition of Fungi in Agar by Streptomycetes”. J . gen. Microbiol. 57. Great Britain (1969): 149-158. 15 May 2016. Web. <http://www.microbiologyresearch.org/docserver/fulltext/micro/57/2/mic-57-2-149.pdf?expires=1463295180&id=id&accname=guest&checksum=565B7719EBA147597DFE263F8855B94A>.

                [5] Jozala, Angela Faustino, L. Novaes and A. Pessoa Jr. “Chapter 5 Nisin”. Immunology and Microbiology ? "Concepts, Compounds and the Alternatives of Antibacterials". Edited by Varaprasad Bobbarala. ISBN 978-953-51-2232-6. 9 December, 2015. 20 July 2016. Web. <http://www.intechopen.com/source/html/48931/media/image1_w.jpg>.

                [6] Li, Tao. Wei Gao. “Inhibition Effect of Three Kinds of Preservatives on Bacteria Using Orthogonal Experiment”. Journal of Shaanxi Normal University (Natural Science Edition). Vol 35, p28-29. June 2007. Article.

                [7] Mendonca, Aubrey Francis. “Mechanism of inhibitory action of potassium sorbate in Escherichia coli”. Retrospective Theses and Dissertations. Paper 10207. 1992. 15 May 2016. Web. <http://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=11206&context=rtd>. “CICAD26 – Benzoic Acid and Sodium Benzoate”. World Health Organization. Geneva, 2000. Web. <http://www.who.int/ipcs/publications/cicad/cicad26_rev_1.pdf>.

                [8] Russell, Nicholas J, Grahame W. Gould. “Bacteriocins – Nisin.” Food Preservatives. Boston, MA: Springer US, 2003: p149-150. Web. <https://books.google.com/books?id=RE_lBwAAQBAJ&pg=PA149&lpg=PA149&dq=nisin+optimum+condition&source=bl&ots=7uNbvL_stJ&sig=M9z6rqfj6GS_qOgMnq-w4TvSU4s&hl=en&sa=X&ved=0ahUKEwiH6ev73rTRAhVCn5QKHaw2D3kQ6AEIJDAB#v=onepage&q=nisin%20optimum%20condition&f=false>.

                [9] Shi, Li-san, Qing-ping Wu, Hui-qing Wu, and Ju-mei Zhang. “Present Situation and Trend of Development on Food Preservatives in China”. Guangdong Institute of Microbiology, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application. March 2008. 15 May 2016. Web. <http://www.cnki.com.cn/Article/CJFDTotal-SPYK200803054.htm>.

                [10] te Welscher,Yvonne M., Hendrik H. ten Napel, Miriam Masià Balagué, etc. “Natamycin Blocks Fungal Growth by Binding Specifically to Ergosterol without Permeabilizing the Membrane”. The Journal of Biological Chemistry (283), p6393-6401. 7 March 2008. 20 July 2016. Web. <http://www.jbc.org/content/283/10/6393.long#sec-1>.

                [11] Troller, J. A. “Catalase inhibition as a possible mechanism of the fungistatic action of sorbic acid.” Can. J. Microbiol. 1965, 11:611-617.

                [12] Widdel, Friedrich. “Theory and Measurement of Bacterial Growth.” Grundpraktikum Mikrobiologie, 4. Sem. (B.Sc.) Universit?t Bremen. 5 June 2010. 3 October 2016. Web. < http://www.mpi-bremen.de/Binaries/Binary13037/Wachstumsversuch.pdf>.

                [13] “Ergosterol”. Wikipedia.org. 26 May 2016. 20 July 2016. Web. <https://en.wikipedia.org/wiki/Ergosterol>.

                [14] “Escherichia coli”. Wikipedia.org. 13 July 2016. 20 July 2016. Web. <https://en.wikipedia.org/wiki/Escherichia_coli#Role_in_disease>.

                [15] “Food Spoilage.” Wikipedia.org. 26 March 2016. 20 July 2016. Web. <https://en.wikipedia.org/wiki/Food_spoilage>.

                [16] “Mold.” Wikipedia.org. 15 June 2016. 20 July 2016. Web. <https://en.wikipedia.org/wiki/Mold>.

                [17] “Rhizopus stolonifer”. Wikipedia.org. 5 February 2016. 20 July 2016. Web. <https://en.wikipedia.org/wiki/Black_bread_mold>.

                [18] “Scientific Opinion on the Use of Natamycin (E 235) as a food additive”. The EFSA Journal (2009); 7(12): 1412. 26 November 2009. 15 May 2016. Web. <http://www.efsa.europa.eu/sites/default/files/scientific_output/files/main_documents/1412.pdf>.

                [19] “Sodium Benzoate.” Emerald Kalama Chemical, LLC Customer Service. Kalama, Washington. Rev 03, Page 02, File 75500125. 9 January 2017. Web. < http://doc.ccc-group.com/spec/800910.pdf>.

                [20] “The use of nisin (E 234) as a food additive”. The EFSA Journal (2006) 314, 1-16. 26 January 2006. 15 May 2016. Web. <http://www.efsa.europa.eu/sites/default/files/scientific_output/files/main_documents/afc_op_ej314_nisin_op_en1,7.pdf>.

                Research on EGFR Mutation Testing

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







                [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.




                [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









                [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


                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)

                7. Oregon Forests and Climate Change: An OSU Forestry & Natural Resources Extension project. (2016)

                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.

                10. National Park Service. Wolf Restoration. (June, 9, 2017)

                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.

                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.

                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)

                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)

                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)


                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:

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                [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


                [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.北京体育大学壽命**,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.

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