Methodology to Solve Multi-Dimentional Sphere Packing Problems

This paper discusses the problem of optimally packing spheres of various dimensions into containers of arbitrary geometrical shapes. According to the international classification, this problem belongs to Sphere Packing Problems (SPPs). The aim of this work is to create an integrated methodology for solving SPPs.В статті розглядається задача оптимального розміщення куль різної розмірності в контейнерах довільних геометричних форм. Згідно з міжнародною класифікацією ця задача належить до класу SPP (Sphere Packing Problems). Метою даної роботи є створення єдиної методології розв’язання задач SPP.В статье рассматривается задача оптимального размещения шаров различной размерности в контейнерах произвольных геометрических форм. Согласно международной классификации эта задача относится к классу SPP (Sphere Packing Problems). Целью данной работы является создание единой методологии решения задач SPP

3D packing of balls in different containers by VNS

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityIn real world applications such as the transporting of goods products, packing is a major issue. Goods products need to be packed such that the smallest space is wasted to achieve the maximum transportation efficiency. Packing becomes more challenging and complex when the product is circular/spherical. This thesis focuses on the best way to pack three-dimensional unit spheres into the smallest spherical and cubical space. Unit spheres are considered in lieu of non-identical spheres because the search mechanisms are more difficult in the latter set up and any improvements will be due to the search mechanism not to the ordering of the spheres. The two-unit sphere packing problems are solved by approximately using a variable neighborhood search (VNS) hybrid heuristic. A general search framework belonging to the Artificial Intelligence domain, the VNS offers a diversification of the search space by changing neighborhood structures and intensification by thoroughly investigating each neighborhood. It is exible, easy to implement, adaptable to both continuous and discrete optimization problems and has been use to solve a variety of problems including large-sized real-life problems. Its runtime is usually lower than other meta heuristic techniques. A tutorial on the VNS and its variants along with recent applications and areas of applicability of each variant. Subsequently, this thesis considers several variations of VNS heuristics for the two problems at hand, discusses their individual efficiencies and effectiveness, their convergence rates and studies their robustness. It highlights the importance of the hybridization which yields near global optima with high precision and accuracy, improving many best- known solutions indicate matching some, and improving the precision and accuracy of others. Keywords: variable neighborhood search, sphere packing, three-dimensional packing, meta heuristic, hybrid heuristics, multiple start heuristics

Container Loading Problems: A State-of-the-Art Review

Container loading is a pivotal function for operating supply chains efficiently. Underperformance results in unnecessary costs (e.g. cost of additional containers to be shipped) and in an unsatisfactory customer service (e.g. violation of deadlines agreed to or set by clients). Thus, it is not surprising that container loading problems have been dealt with frequently in the operations research literature. It has been claimed though that the proposed approaches are of limited practical value since they do not pay enough attention to constraints encountered in practice.In this paper, a review of the state-of-the-art in the field of container loading will be given. We will identify factors which - from a practical point of view - need to be considered when dealing with container loading problems and we will analyze whether and how these factors are represented in methods for the solution of such problems. Modeling approaches, as well as exact and heuristic algorithms will be reviewed. This will allow for assessing the practical relevance of the research which has been carried out in the field. We will also mention several issues which have not been dealt with satisfactorily so far and give an outlook on future research opportunities

Imaging Biomarkers of Pulmonary Structure and Function

Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airflow limitations resulting from airway obstruction and/or tissue destruction. The diagnosis and monitoring of these pulmonary diseases is primarily performed using spirometry, specifically the forced expiratory volume in one second (FEV1), which measures global airflow obstruction and provides no regional information of the different underlying disease pathologies. The limitations of spirometry and current therapies for lung disease patients have motivated the development of pulmonary imaging approaches, such as computed tomography (CT) and magnetic resonance imaging (MRI). Inhaled hyperpolarized noble gas MRI, specifically using helium-3 (3He) and xenon-129 (129Xe) gases, provides a way to quantify pulmonary ventilation by visualizing lung regions accessed by gas during a breath-hold, and alternatively, regions that are not accessed - coined “ventilation defects.” Despite the strong foundation and many advantages hyperpolarized 3He MRI has to offer research and patient care, clinical translation has been inhibited in part due to the cost and need for specialized equipment, including multinuclear-MR hardware and polarizers, and personnel. Accordingly, our objective was to develop and evaluate imaging biomarkers of pulmonary structure and function using MRI and CT without the use of exogenous contrast agents or specialized equipment. First, we developed and compared CT parametric response maps (PRM) with 3He MR ventilation images in measuring gas-trapping and emphysema in ex-smokers with and without COPD. We observed that in mild-moderate COPD, 3He MR ventilation abnormalities were related to PRM gas-trapping whereas in severe COPD, ventilation abnormalities correlated with both PRM gas-trapping and PRM emphysema. We then developed and compared pulmonary ventilation abnormalities derived from Fourier decomposition of free-breathing proton (1H) MRI (FDMRI) with 3He MRI in subjects with COPD and bronchiectasis. This work demonstrated that FDMRI and 3He MRI ventilation defects were strongly related in COPD, but not in bronchiectasis subjects. In COPD only, FDMRI ventilation defects were spatially related with 3He MRI ventilation defects and emphysema. Based on the FDMRI biomarkers developed in patients with COPD and bronchiectasis, we then evaluated ventilation heterogeneity in patients with severe asthma, both pre- and post-salbutamol as well as post-methacholine challenge, using FDMRI and 3He MRI. FDMRI free-breathing ventilation abnormalities were correlated with but under-estimated 3He MRI static ventilation defects. Finally, based on the previously developed free-breathing MRI approach, we developed a whole-lung free-breathing pulmonary 1H MRI technique to measure regional specific-ventilation and evaluated both asthmatics and healthy volunteers. These measurements not only provided similar information as specific-ventilation measured using plethysmography, but also information about regional ventilation defects that were correlated with 3He MRI ventilation abnormalities. These results demonstrated that whole-lung free-breathing 1H MRI biomarker of specific-ventilation may reflect ventilation heterogeneity and/or gas-trapping in asthma. These important findings indicate that imaging biomarkers of pulmonary structure and function using MRI and CT have the potential to regionally reveal the different pathologies in COPD and asthma without the use of exogenous contrast agents. The development and validation of these clinically meaningful imaging biomarkers are critically required to accelerate pulmonary imaging translation from the research workbench to being a part of the clinical workflow, with the overall goal to improve patient outcomes

Leksell Gamma Knife Treatment Planning via Kernel Regression Data Mining Initialization and Genetic Algorithm Optimization

Gamma Knife is a medical procedure that is used to treat several types of intracranial disease. The system utilizes gamma rays from Cobalt-60 radiation sources focused at an isocenter and a stereotactic frame system that serves as an immobilization device coordinate system. Treatment is performed by localizing the patient’s disease with a medical imaging study and positioning the diseased area at the focused intersection of the beams. Patient treatment may require multiple treatment positions and varying beam sizes. The treatment position, time, and beam size is determined through a treatment planning process. Traditionally Gamma Knife treatment planning is performed manually by an expert planner. This process can be time consuming and arrival at an optimal plan may depend on the skill of the planner. This work automates the treatment planning process with a multi-module optimization system. First, a kernel regression data mining module compares the treatment volume to a database of past treatment plans to create a set of initial plans. These plans seed a genetic algorithm optimizer that produces an optimized plan. The cost function for the optimization is a weighted average of several traditional metric for assessing stereotactic radiosurgery plan quality. A gradient descent optimizer is utilized to further refine the optimized treatment plan. The developed system was applied to three Gamma Knife planning cases; a solitary metastasis, an acoustic schwannoma, and a pituitary adenoma. The system produced an average percent isodose coverage for the three plans of 94.5% and the average Paddick Conformity index was 0.76 in an average time of 17.16 minutes for the three plans. The system was compared to an expert planner and an optimizer included with the Gamma Knife planning software. The developed system and expert planner performance was essentially equivalent (average percent isodose coverage 95.8%, average Paddick Conformity index 0.70, optimization time 20.52). The developed system performed much better than the Gamma Plan optimizer (average percent isodose coverage 85.8%, average Paddick Conformity index 0.71) however the Gamma Plan optimizer result was obtained quicker (optimization time about 1 minute). The developed system can be utilized for efficient high-quality Gamma Knife treatment planning

Proof-of-Concept For Converging Beam Small Animal Irradiator

The Monte Carlo particle simulator TOPAS, the multiphysics solver COMSOL., and several analytical radiation transport methods were employed to perform an in-depth proof-ofconcept for a high dose rate, high precision converging beam small animal irradiation platform. In the first aim of this work, a novel carbon nanotube-based compact X-ray tube optimized for high output and high directionality was designed and characterized. In the second aim, an optimization algorithm was developed to customize a collimator geometry for this unique Xray source to simultaneously maximize the irradiator’s intensity and precision. Then, a full converging beam irradiator apparatus was fit with a multitude of these X-ray tubes in a spherical array and designed to deliver converged dose spots to any location within a small animal model. This aim also included dose leakage calculations for estimation of appropriate external shielding. The result of this research will be the blueprints for a full preclinical radiation platform that pushes the boundaries of dose localization in small animal trials

Radiobiology Textbook:Space Radiobiology

The study of the biologic effects of space radiation is considered a “hot topic,” with increased interest in the past years. In this chapter, the unique characteristics of the space radiation environment will be covered, from their history, characterization, and biological effects to the research that has been and is being conducted in the field. After a short introduction, you will learn the origin and characterization of the different types of space radiation and the use of mathematical models for the prediction of the radiation doses during different mission scenarios and estimate the biological risks due to this exposure. Following this, the acute, chronic, and late effects of radiation exposure in the human body are discussed before going into the detailed biomolecular changes affecting cells and tissues, and in which ways they differ from other types of radiation exposure. The next sections of this chapter are dedicated to the vast research that has been developed through the years concerning space radiation biology, from small animals to plant models and 3D cell cultures, the use of extremophiles in the study of radiation resistance mechanisms to the importance of ground-based irradiation facilities to simulate and study the space environment