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The objectives of this study involve the optimization of longitudinal porous fins of square cross-section using metaheuristic algorithms. A generalized nonlinear ordinary differential equation is derived using Darcy and Fourier’s laws in the energy balance around a control volume and is solved numerically using RFK 45 method. The temperature of the base surface is higher than the fin surface, and the fin tip is kept adiabatic or cooled by convection heat transfer. The other pertinent parameters include Rayleigh number (100 ≤ Ra ≤ 104), Darcy number, (10−4 ≤ Da ≤ 10−2), relative thermal conductivity ratio of solid phase to fluid (1000 ≤ kr ≤ 8000), Nusselt number (10 ≤ Nu ≤ 100), porosity (0.1 ≤ φ ≤ 0.9). The impacts of these parameters on the entropy generation rate are investigated and optimized using metaheuristic algorithms. In computer science, metaheuristic algorithms are one of the most widely used techniques for optimization problems. In this research, three metaheuristic algorithms, including the firefly algorithm (FFA), particle swarm algorithm (PSO), and hybrid algorithm (FFA-PSO) are employed to examine the performance of square fins. It is demonstrated that FFA-PSO takes fewer iterations and less computational time to converge compared to other algorithms. [ABSTRACT FROM AUTHOR]

Loss given default (LGD) is a key parameter in credit risk management to calculate the required regulatory minimum capital. The internal ratings-based (IRB) approach under the Basel II allows institutions to determine the loss given default (LGD) on their own. In this study, we have estimated LGD for a credit portfolio data by using beta regression with precision parameter (O) and mean parameter (µ). The credit portfolio data was obtained from a banking institution in Jordan; for the period of January 2010 untilDecember 2014. In the first stage, we have used the "outstandingamount" and "amount of borrowing" to find LGD of each default borrower (494 out of 4393 borrower). In the second stage, we fit univariate parametric distributions to the LGD data to obtain the beta distribution. After that, we have estimated the values of O based on microeconomic variables (SPP, OE and LR). Moreover, we have estimated the values of µ based on macroeconomic variables (GDP and Inflation rate). Finally, we have compared between six different link functions (Logit, loglog, probit, cloglog, cauchit, and log), which have used with O and µ. The results show that Beta regression with probit link function has the highest R-squared with accepted measurements for logL, AIC and BIC. [ABSTRACT FROM AUTHOR]

One of the most significant considerations in the design of a heat sink is thermal management due to increasing thermal flux and miniature in size. These heat sinks utilize plate or pin fins depending upon the required heat dissipation rate. They are designed to optimize overall performance. Elliptical pin fin heat sinks enhance heat transfer rates and reduce the pumping power. In this study, the Firefly Algorithm is implemented to optimize heat sinks with elliptical pin-fins. The pin-fins are arranged in an inline fashion. The natureinspired metaheuristic algorithm performs powerfully and efficiently in solving numerical global optimization problems. Based on mass, energy, and entropy balance, three models are developed for thermal resistance, hydraulic resistance, and entropy generation rate in the heat sink. The major axis is used as the characteristic length, and the maximum velocity is used as the reference velocity. The entropy generation rate comprises the combined effect of thermal resistance and pressure drop. The total EGR is minimized by utilizing the firefly algorithm. The optimization model utilizes analytical/empirical correlations for the heat transfer coefficients and friction factors. It is shown that both thermal resistance and pressure drop can be simultaneously optimized using this algorithm. It is demonstrated that the performance of FFA is much better than PPA. [ABSTRACT FROM AUTHOR]