A Linac Simulation Code for Macro-particles Tracking and Steering Algorithm Implementation

In this paper, a linac simulation code written in Fortran90 is presented and several simulation examples are given. This code is optimized to implement linac alignment and steering algorithms, and evaluate the accelerator errors such as RF phase and acceleration gradient, quadrupole and BPM misalignment. It can track a single particle or a bunch of particles through normal linear accelerator elements such as quadrupole, RF cavity, dipole corrector and drift space. One-to-one steering algorithm and a global alignment (steering) algorithm are implemented in this code

Research on the impact of asteroid mining on global equity

In the future situation, aiming to seek more resources, human beings decided to march towards the mysterious and bright starry sky, which opened the era of great interstellar exploration. According to the Outer Space Treaty, any exploration of celestial bodies should be aimed at promoting global equality and for the benefit of all nations. Firstly, we defined global equity and set a Unified Equity Index (UEI) model to measure it. We merge the factors with greater correlation, and finally, get 6 elements, and then use the entropy method (TEM) to find the dispersion of these elements in different countries. Then use principal component analysis (PCA) to reduce the dimensionality of the dispersion, and then use the scandalized index to obtain the global equity. Secondly, we simulated a future with asteroid mining and evaluated its impact on Unified Equity Index (UEI). Then, we divided the mineable asteroids into three classes with different mining difficulties and values, identified 28 mining entities including private companies, national and international organizations. We considered changes in the asteroid classes, mining capabilities and mining scales to determine the changes in the value of minerals mined between 2025 and 2085. We convert mining output value into mineral transaction value through allocation matrix. Based on grey relational analysis (GRA). Finally, we presented three possible versions of the future of asteroid mining by changing the conditions. We propose two sets of corresponding policies for changes in future trends in global fairness with asteroid mining. We test the separate and combined effects of these policies and find that they are positive, strongly supporting the effectiveness of our model.Comment: 19 page

Revive, Restore, Revitalize: An Eco-economic Methodology for Maasai Mara

The Maasai Mara in Kenya, renowned for its biodiversity, is witnessing ecosystem degradation and species endangerment due to intensified human activities. Addressing this, we introduce a dynamic system harmonizing ecological and human priorities. Our agent-based model replicates the Maasai Mara savanna ecosystem, incorporating 71 animal species, 10 human classifications, and 2 natural resource types. The model employs the metabolic rate-mass relationship for animal energy dynamics, logistic curves for animal growth, individual interactions for food web simulation, and human intervention impacts. Algorithms like fitness proportional selection and particle swarm mimic organism preferences for resources. To guide preservation activities, we formulated 21 management strategies encompassing tourism, transportation, taxation, environmental conservation, research, diplomacy, and poaching, employing a game-theoretic framework. Using the TOPSIS method, we prioritized four key developmental indicators: environmental health, research advancement, economic growth, and security. The interplay of 16 factors determines these indicators, each influenced by our policies to varying degrees. By evaluating the policies' repercussions, we aim to mitigate adverse animal-human interactions and equitably address human concerns. We classified the policy impacts into three categories: Environmental Preservation, Economic Prosperity, and Holistic Development. By applying these policy groupings to our ecosystem model, we tracked the effects on the intricate animal-human-resource dynamics. Utilizing the entropy weight method, we assessed the efficacy of these policy clusters over a decade, identifying the optimal blend emphasizing both environmental conservation and economic progression.Comment: 25 pages, 16 figure

Deep Learning Computed Tomography based on the Defrise and Clack Algorithm

This study presents a novel approach for reconstructing cone beam computed tomography (CBCT) for specific orbits using known operator learning. Unlike traditional methods, this technique employs a filtered backprojection type (FBP-type) algorithm, which integrates a unique, adaptive filtering process. This process involves a series of operations, including weightings, differentiations, the 2D Radon transform, and backprojection. The filter is designed for a specific orbit geometry and is obtained using a data-driven approach based on deep learning. The approach efficiently learns and optimizes the orbit-related component of the filter. The method has demonstrated its ability through experimentation by successfully learning parameters from circular orbit projection data. Subsequently, the optimized parameters are used to reconstruct images, resulting in outcomes that closely resemble the analytical solution. This demonstrates the potential of the method to learn appropriate parameters from any specific orbit projection data and achieve reconstruction. The algorithm has demonstrated improvement, particularly in enhancing reconstruction speed and reducing memory usage for handling specific orbit reconstruction

Emittance Growth in the NLCTA First Chicane

In this paper, the emittance growth in the NLCTA (Next Linear Collider Test Accelerator) first chicane region is evaluated by simulation studies. It is demonstrated that the higher order fields of the chicane dipole magnet and the dipole corrector magnet (which is attached on the quadrupoles) are the main contributions for the emittance growth, especially for the case with a large initial emittance ({gamma}{epsilon}{sub 0} = 5 {micro}m for instance). These simulation results agree with the experimental observations

Convergence of regional economic cycles in Turkey

Dissimilar economic fluctuations and asymmetric shocks across the regions of a country might create severe policy distortions that, under these circumstances, aggregate policy interventions (such as taxation and interest rates), are likely to be sub-optimal for at least a fraction of the regions. For instance, monetary policy can hardly satisfy the needs of all regions when some of the regions are experiencing a boom while others are in a recession phase. For these reasons, similarity of regional business cycles and their convergence are highly desirable from a policy viewpoint. The aim of this paper is, therefore, to provide empirical evidence and policy implications in that context. In particular, I analyze business cycle correlations across Turkish provinces and the tendency of these cycles to converge over the period of analysis between 1975-2000 and 2004-2008 (for Nomenclature of Territorial Units for Statistics [NUTS]-2 regions). I find that regional business cycle asymmetries have tended to decrease in recent decades. This result, although it seems to provide evidence in favor of rising correlations, shows that the convergence process is rather slow and there still exist asymmetries across the regional business cycles