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In a two-person perfect-information game, Conspiracy Number Search (CNS) was invented as a possible search algorithm but did not find much success. However, we believe that the conspiracy number, which is the core of CNS, has not been used to its full potential. In this paper, we propose a novel way to utilize the conspiracy number in the minimax framework. Instead of using conspiracy numbers separately, we combine them together. An example way of combining conspiracy numbers with the evaluation value is suggested. Empirical results obtained for the game of Othello show the potential of the proposed method. 2 RELATED WORKS In this section, we describe in more details about conspiracy numbers and their application in previous 400

Introduction: As hypertension (HTN) is a strong risk factor of coronary artery disease (CAD), what is an optimal blood pressure (BP) which could preclude CAD? Hypothesis: In industrial piping, laminar flow preserves equipment service life while turbulent flow damages the inner surface of pipes and pumps. Based on the same hydraulic principles, our hypothesis is built on the observations that turbulent flow injures the intima and triggers atherosclerosis. In order to identify an optimal BP, at which BP level, could we evidence VISUALLY the abolishment of turbulent flow at the iliac artery as proof of correction of the injuring mechanism precipitated by HTN? Methods: 210 patients with uncontrolled HTN who underwent coronary intervention and a repeat diagnostic angiogram a few months later because of unstable angina were enrolled. At the end each coronary procedure, patients underwent iliac angiogram for deployment of closure device. In a new technique of dynamic angiography, the contrast was injected until the iliac artery was completely opacified. As the injection stopped, the blood flew down and displaced the contrast. The movements of the blood in white color above a black background could be clearly identified and analyzed. At the same time, Artificial Intelligence (Machine Learning algorithms) program had 2 models built on Python. Model 1 was based on U-net and Densenet-121 for vessel segmentation. Model 2 was used for classification and movement of flow. Model 2 was trained based on the convolutional neural network. The main measurements were the type of flow (turbulent), directions (antegrade versus retrograde) and presence of collision from slamming of the retrograde against antegrade flow. Results: At baseline, 98% patients had prolonged reversed flow with turbulence. At the 2nd iliac angiogram, if the systolic BP was <110mmHg, the rate of turbulence was 10%. If the systolic BP >130mmHg, the turbulence was present in 70% (p<0.05) Conclusions: With the uncontrolled BP imaged as collision at the iliac artery secondary to heightened distal vascular resistance, the abolishment of turbulence and return of laminar flow confirmed that the ideal systolic BP was <110mmH in the application of personalized medicine. Larger studies need to confirm the above result.