On the Measurement of the Government Spending Multiplier in the United States: an ARDL Cointegration Approach

This is the final version. Freely available on open access from Weissberg Publishing via the link in this record.This paper applies annual data from 1962 to 2011 to investigate the long run relationship between government spending and Gross Domestic Product (GDP). The common approach only considers defense government spending to estimate the multiplier to overcome the identification problem and endogeneity in isolating the effect of changes in government spending on GDP, I use the Autoregressive Distributed Lag (ARDL) approach to cointegration, which works despite having endogenous regressors to estimate the spending multiplier. The results confirm that government spending can be treated as a ‘long-run forcing’ variable for the explanation of real GDP and the long-run multiplier is found to be 1.94

Galactic Axion Laser Interferometer Leveraging Electro-Optics: GALILEO

We propose a novel experimental method for probing light dark matter candidates. We show that an electro-optical material's refractive index is modified in the presence of a coherently oscillating dark matter background. A high-precision resonant Michelson interferometer can be used to read out this signal. The proposed detection scheme allows for the exploration of an uncharted parameter space of dark matter candidates over a wide range of masses -- including masses exceeding a few tens of microelectronvolts, which is a challenging parameter space for microwave cavity haloscopes.Comment: 6+4 pages, 2 figure

Solving an Optimal Control Problem of Cancer Treatment by Artificial Neural Networks

Cancer is an uncontrollable growth of abnormal cells in any tissue of the body. Many researchers have focused on machine learning and artificial intelligence (AI) based on approaches for cancer treatment. Dissimilar to traditional methods, these approaches are efficient and are able to find the optimal solutions of cancer chemotherapy problems. In this paper, a system of ordinary differential equations (ODEs) with the state variables of immune cells, tumor cells, healthy cells and drug concentration is proposed to anticipate the tumor growth and to show their interactions in the body. Then, an artificial neural network (ANN) is applied to solve the ODEs system through minimizing the error function and modifying the parameters consisting of weights and biases. The mean square errors (MSEs) between the analytical and ANN results corresponding to four state variables are 1.54e-06, 6.43e-07, 6.61e-06, and 3.99e-07, respectively. These results show the good performance and efficiency of the proposed method. Moreover, the optimal dose of chemotherapy drug and the amount of drug needed to continue the treatment process are achieved

Report of Telenomus chrysopae (Hym.: Scelionidae) from Iran

در تحقیقی که به منظور شناسایی دشمنان طبیعی شته‌ها در سال‌های 1384-1383در اصفهان انجام شد، زنبور پارازیتوئید Telenomus chrysopae Ashmead از خانواده‌ی Scelionidae به عنوان پارازیتوئید تخم بالتوری C‏hrysoperla sp. (Neur.: Chrysopidae) مورد شناسایی قرار گرفت. این زنبور که برای اولین بار از ایران گزارش می‌شود، قبلاً از آمریکای شمالی و اروپا گزارش شده است