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Abstrak :
Niall Ferguson follows the money to tell the human story behind the evolution of finance, from its origins in ancient Mesopotamia to the latest upheavals. To Christians, love of it is the root of all evil. To generals, it's the sinews of war. To revolutionaries, it's the chains of labor. But historian Ferguson shows that finance is in fact the foundation of human progress. What's more, he reveals financial history as the essential backstory behind all history. Through Ferguson's expert lens, for example, the civilization of the Renaissance looks very different: a boom in the market for art and architecture made possible when Italian bankers adopted Arabic mathematics. The rise of the Dutch republic is reinterpreted as the triumph of the world's first modern bond market over insolvent Habsburg absolutism. Yet the central lesson of financial history is that, sooner or later, every bubble bursts.--From publisher description.

Abstrak :
Many previous researches conveyed the superiority of Steepest Ascent (SA) method to find the optimal area in Response Surface Methodology (RSM) by shifting the experiment factor level. By using this method, Design of Experiment (DoE) is enabled to shift the factor level gradually in the right track, so that the global optimum can be reached. However, the response variable that is commonly optimized by using RSM cannot fulfill the classical statistics assumption of surface regression model. Taguchi’s orthogonal array, as alternative of RSM, gives loose statistics assumptions in performing the analysis. However, Taguchi’s orthogonal array has not yet been supported to shift the factor level to an optimum direction. Adopting the procedures of RSM in finding the optimal level combination using SA, integrating SA method in the Taguchi experiment is proposed in this paper. This procedure is applied into a simulated response surface. Then, the performance of this procedure is evaluated based on its direction to reach the optimum solution. The simulation data representing the real case is generated for two factors. Then, the proposed procedure is applied. The result of this simulation study shows that the integrated SA method in the Taguchi experiment successfully found the factor level combination that yields optimum response even though it is not as close as possible as the RSM results.

Abstrak :
Rumah unggas adalah salah satu penyumbang polutan amonia dan PM di udara. Penyebaran polutan dipengaruhi oleh kipas dan kondisi meteorologi di sekitar rumah unggas. Model Dispersi Gauss untuk Gas (MDGG) dengan modifikasi titik semu merupakan model dispersi atmosfer yang cocok digunakan untuk memprediksi konsentrasi polutan dan mengakomodasi kondisi spasial rumah unggas. Steepest ascent adalah metode optimasi untuk mencari nilai maksimal dari fungsi umum nonlinear dengan menggunakan gradien fungsi untuk menentukan arah pergerakan pencarian nilai maksimal. Optimasi MDGG dengan metode steepest ascent memberikan hasil jarak titik semu optimal untuk polutan amonia L = 2,396 m dan polutan PM L = 1,259 m. Kedua nilai tersebut memberikan prediksi yang lebih baik di beberapa eksperimen. Prediksi konsentrasi PM lebih baik dari amonia dan hasil prediksi kedua polutan pada malam hari lebih baik dibandingkan pada pagi hari. ......Poultry houses are one of the contributors to ammonia and PM pollutants in the air. Fans and meteorological conditions around the poultry house influence the spread of pollutants. The Gaussian Plume Model with virtual point modification is an atmospheric dispersion model suitable for predicting pollutant concentrations and accommodating the spatial conditions around poultry houses. Steepest ascent is an optimization method for finding the maximum value of a general nonlinear function by using the gradient of the function to determine the direction of movement to find the maximum value. Gaussian Plume Model optimization using the steepest ascent method results optimal virtual point distances for pollutants ammonia L = 2.396 m and PM L = 1.259 m. Both values provide better predictions in some experiments. PM concentration prediction was better than ammonia, and prediction results for both pollutants at night were better than in the morning.