Chung-Ang University

About: Chung-Ang University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Thin film. The organization has 13381 authors who have published 26978 publications receiving 416735 citations. The organization is also known as: CAU & Chung.

Mental health in elite athletes: International Olympic Committee consensus statement (2019).

Abstract: Mental health symptoms and disorders are common among elite athletes, may have sport related manifestations within this population and impair performance. Mental health cannot be separated from physical health, as evidenced by mental health symptoms and disorders increasing the risk of physical injury and delaying subsequent recovery. There are no evidence or consensus based guidelines for diagnosis and management of mental health symptoms and disorders in elite athletes. Diagnosis must differentiate character traits particular to elite athletes from psychosocial maladaptations.Management strategies should address all contributors to mental health symptoms and consider biopsychosocial factors relevant to athletes to maximise benefit and minimise harm. Management must involve both treatment of affected individual athletes and optimising environments in which all elite athletes train and compete. To advance a more standardised, evidence based approach to mental health symptoms and disorders in elite athletes, an International Olympic Committee Consensus Work Group critically evaluated the current state of science and provided recommendations.

Human exposure and health effects of inorganic and elemental mercury.

Abstract: Mercury is a toxic and non-essential metal in the human body. Mercury is ubiquitously distributed in the environment, present in natural products, and exists extensively in items encountered in daily life. There are three forms of mercury, i.e., elemental (or metallic) mercury, inorganic mercury compounds, and organic mercury compounds. This review examines the toxicity of elemental mercury and inorganic mercury compounds. Inorganic mercury compounds are water soluble with a bioavailability of 7% to 15% after ingestion; they are also irritants and cause gastrointestinal symptoms. Upon entering the body, inorganic mercury compounds are accumulated mainly in the kidneys and produce kidney damage. In contrast, human exposure to elemental mercury is mainly by inhalation, followed by rapid absorption and distribution in all major organs. Elemental mercury from ingestion is poorly absorbed with a bioavailability of less than 0.01%. The primary target organs of elemental mercury are the brain and kidney. Elemental mercury is lipid soluble and can cross the blood-brain barrier, while inorganic mercury compounds are not lipid soluble, rendering them unable to cross the blood-brain barrier. Elemental mercury may also enter the brain from the nasal cavity through the olfactory pathway. The blood mercury is a useful biomarker after short-term and high-level exposure, whereas the urine mercury is the ideal biomarker for long-term exposure to both elemental and inorganic mercury, and also as a good indicator of body burden. This review discusses the common sources of mercury exposure, skin lightening products containing mercury and mercury release from dental amalgam filling, two issues that happen in daily life, bear significant public health importance, and yet undergo extensive debate on their safety.

Applied Numerical Methods Using MATLAB

Abstract: Preface. 1. MATLAB Usage and Computational Errors. 1.1 Basic Operations of MATLAB. 1.1.1 Input/Output of Data from MATLAB Command Window. 1.1.2 Input/Output of Data Through Files. 1.1.3 Input/Output of Data Using Keyboard. 1.1.4 2-D Graphic Input/Output. 1.1.5 3-D Graphic Output. 1.1.6 Mathematical Functions. 1.1.7 Operations on Vectors and Matrices. 1.1.8 Random Number Generators. 1.1.9 Flow Control. 1.2 Computer Errors Versus Human Mistakes. 1.2.1 IEEE 64-bit Floating-Point Number Representation. 1.2.2 Various Kinds of Computing Errors. 1.2.3 Absolute/Relative Computing Errors. 1.2.4 Error Propagation. 1.2.5 Tips for Avoiding Large Errors. 1.3 Toward Good Program. 1.3.1 Nested Computing for Computational Efficiency. 1.3.2 Vector Operation Versus Loop Iteration. 1.3.3 Iterative Routine Versus Nested Routine. 1.3.4 To Avoid Runtime Error. 1.3.5 Parameter Sharing via Global Variables. 1.3.6 Parameter Passing Through Varargin. 1.3.7 Adaptive Input Argument List. Problems. 2. System of Linear Equations. 2.1 Solution for a System of Linear Equations. 2.1.1 The Nonsingular Case (M = N). 2.1.2 The Underdetermined Case (M N): Least-Squares Error Solution. 2.1.4 RLSE (Recursive Least-Squares Estimation). 2.2 Solving a System of Linear Equations. 2.2.1 Gauss Elimination. 2.2.2 Partial Pivoting. 2.2.3 Gauss-Jordan Elimination. 2.3 Inverse Matrix. 2.4 Decomposition (Factorization). 2.4.1 LU Decomposition (Factorization): Triangularization. 2.4.2 Other Decomposition (Factorization): Cholesky, QR, and SVD. 2.5 Iterative Methods to Solve Equations. 2.5.1 Jacobi Iteration. 2.5.2 Gauss-Seidel Iteration. 2.5.3 The Convergence of Jacobi and Gauss-Seidel Iterations. Problems. 3. Interpolation and Curve Fitting. 3.1 Interpolation by Lagrange Polynomial. 3.2 Interpolation by Newton Polynomial. 3.3 Approximation by Chebyshev Polynomial. 3.4 Pade Approximation by Rational Function. 3.5 Interpolation by Cubic Spline. 3.6 Hermite Interpolating Polynomial. 3.7 Two-dimensional Interpolation. 3.8 Curve Fitting. 3.8.1 Straight Line Fit: A Polynomial Function of First Degree. 3.8.2 Polynomial Curve Fit: A Polynomial Function of Higher Degree. 3.8.3 Exponential Curve Fit and Other Functions. 3.9 Fourier Transform. 3.9.1 FFT Versus DFT. 3.9.2 Physical Meaning of DFT. 3.9.3 Interpolation by Using DFS. Problems. 4. Nonlinear Equations. 4.1 Iterative Method Toward Fixed Point. 4.2 Bisection Method. 4.3 False Position or Regula Falsi Method. 4.4 Newton(-Raphson) Method. 4.5 Secant Method. 4.6 Newton Method for a System of Nonlinear Equations. 4.7 Symbolic Solution for Equations. 4.8 A Real-World Problem. Problems. 5. Numerical Differentiation/Integration. 5.1 Difference Approximation for First Derivative. 5.2 Approximation Error of First Derivative. 5.3 Difference Approximation for Second and Higher Derivative. 5.4 Interpolating Polynomial and Numerical Differential. 5.5 Numerical Integration and Quadrature. 5.6 Trapezoidal Method and Simpson Method. 5.7 Recursive Rule and Romberg Integration. 5.8 Adaptive Quadrature. 5.9 Gauss Quadrature. 5.9.1 Gauss-Legendre Integration. 5.9.2 Gauss-Hermite Integration. 5.9.3 Gauss-Laguerre Integration. 5.9.4 Gauss-Chebyshev Integration. 5.10 Double Integral. Problems. 6. Ordinary Differential Equations. 6.1 Euler's Method. 6.2 Heun's Method: Trapezoidal Method. 6.3 Runge-Kutta Method. 6.4 Predictor-Corrector Method. 6.4.1 Adams-Bashforth-Moulton Method. 6.4.2 Hamming Method. 6.4.3 Comparison of Methods. 6.5 Vector Differential Equations. 6.5.1 State Equation. 6.5.2 Discretization of LTI State Equation. 6.5.3 High-Order Differential Equation to State Equation. 6.5.4 Stiff Equation. 6.6 Boundary Value Problem (BVP). 6.6.1 Shooting Method. 6.6.2 Finite Difference Method. Problems. 7. Optimization. 7.1 Unconstrained Optimization [L-2, Chapter 7]. 7.1.1 Golden Search Method. 7.1.2 Quadratic Approximation Method. 7.1.3 Nelder-Mead Method [W-8]. 7.1.4 Steepest Descent Method. 7.1.5 Newton Method. 7.1.6 Conjugate Gradient Method. 7.1.7 Simulated Annealing Method [W-7]. 7.1.8 Genetic Algorithm [W-7]. 7.2 Constrained Optimization [L-2, Chapter 10]. 7.2.1 Lagrange Multiplier Method. 7.2.2 Penalty Function Method. 7.3 MATLAB Built-In Routines for Optimization. 7.3.1 Unconstrained Optimization. 7.3.2 Constrained Optimization. 7.3.3 Linear Programming (LP). Problems. 8. Matrices and Eigenvalues. 8.1 Eigenvalues and Eigenvectors. 8.2 Similarity Transformation and Diagonalization. 8.3 Power Method. 8.3.1 Scaled Power Method. 8.3.2 Inverse Power Method. 8.3.3 Shifted Inverse Power Method. 8.4 Jacobi Method. 8.5 Physical Meaning of Eigenvalues/Eigenvectors. 8.6 Eigenvalue Equations. Problems. 9. Partial Differential Equations. 9.1 Elliptic PDE. 9.2 Parabolic PDE. 9.2.1 The Explicit Forward Euler Method. 9.2.2 The Implicit Backward Euler Method. 9.2.3 The Crank-Nicholson Method. 9.2.4 Two-Dimensional Parabolic PDE. 9.3 Hyperbolic PDE. 9.3.1 The Explicit Central Difference Method. 9.3.2 Two-Dimensional Hyperbolic PDE. 9.4 Finite Element Method (FEM) for solving PDE. 9.5 GUI of MATLAB for Solving PDEs: PDETOOL. 9.5.1 Basic PDEs Solvable by PDETOOL. 9.5.2 The Usage of PDETOOL. 9.5.3 Examples of Using PDETOOL to Solve PDEs. Problems. Appendix A: Mean Value Theorem. Appendix B: Matrix Operations/Properties. Appendix C: Differentiation with Respect to a Vector. Appendix D: Laplace Transform. Appendix E: Fourier Transform. Appendix F: Useful Formulas. Appendix G: Symbolic Computation. Appendix H: Sparse Matrices. Appendix I: MATLAB. References. Subject Index. Index for MATLAB Routines. Index for Tables.

Gold Nanoparticles for Photothermal Cancer Therapy.

Abstract: Gold is a multifunctional material that has been utilized in medicinal applications for centuries because it has been recognized for its bacteriostatic, anticorrosive, and antioxidative properties. Modern medicine makes routine, conventional use of gold and has even developed more advanced applications by taking advantage of its ability to be manufactured at the nanoscale and functionalized because of the presence of thiol and amine groups, allowing for the conjugation of various functional groups such as targeted antibodies or drug products. It has been shown that colloidal gold exhibits localized plasmon surface resonance (LPSR), meaning that gold nanoparticles can absorb light at specific wavelengths, resulting in photoacoustic and photothermal properties, making them potentially useful for hyperthermic cancer treatments and medical imaging applications. Modifying gold nanoparticle shape and size can change their LPSR photochemical activities, thereby also altering their photothermal and photoacoustic properties, allowing for the utilization of different wavelengths of light, such as light in the near-infrared spectrum. By manufacturing gold in a nanoscale format, it is possible to passively distribute the material through the body, where it can localize in tumors (which are characterized by leaky blood vessels) and be safely excreted through the urinary system. In this paper, we give a quick review of the structure, applications, recent advancements, and potential future directions for the utilization of gold nanoparticles in cancer therapeutics.