Radiation exposure from diagnostic nuclear medicine examinations in golestan province

Introduction: The aim of present study was to estimate effective dose from most common procedures performed in nuclear medicine departments of Golestan province. Methods: Data of nuclear medicine procedures performed in 2 nuclear medicine departments in Golestan province were collected during 4 years. Effective dose, collective effective dose and effective dose per examination were calculated using standard dosimetry tables. Results: Based on the data of this study, results of 10437 nuclear medicine procedures performed during 4 years have lead to 3.97 mSv as average effective dose per examination and 10.37 human-Sv as mean collective effective dose. It was also revealed that Tc-99m was the main source of effective dose (98.3%), bone scan was the most common procedure (25.9%) and cardiac scan (MIBI-rest) has the highest collective effective dose (33.5%) during 4 years. Conclusion: Beside the cardiac scan which was the most common nuclear medicine procedure and the main contributor of effective dose in patients, due to geographical condition of the northeast of Iran, bone scan was the highest performed nuclear medicine examination in the Golestan province

A study of the time of hospital discharge of differentiated thyroid cancer patients after receiving iodine-131 for thyroid remnant ablation treatment

The aim of this study was to measure the radiation exposure rate from differentiated thyroid carcinoma (DTC) patients who had received iodine-131 (131I) treatment, and to evaluate hospital discharge planning in relation to three different sets of regulations. We studied 100 patients, 78 females and 22 males, aged 13 to 79 years (mean 44.40±15.83 years) with DTC, in three Groups who were treated with 3.7, 5.5 or 7.4GBq of 131I, respectively. The external whole-body dose rates following oral administration of 131I were measured after each one of the first three hospitalization days. A multivariant linear analysis was performed, considering exposure rates as dependent variables to the administered dose for treatment, age, gender, regional and/or distant metastases, thyroglobulin (Tg), antibodies to Tg and thyroid remnant in the three dose groups. We found that the exposure rates after each of the three first days of hospitalization were 30, 50 and 70μSvh-1 at 1m. All our DTC patients had an acceptable dose rate on days 2 and 3 that allowed their hospital discharge. After only 1 day of hospitalization, just 3/11 cases showed not permissible exposure rates above 70μSvh-1. In conclusion, it is the opinion of the authors that after measuring the exposure rates, most treated, DTC patients could be discharged after only one day of hospitalization, even some of those treated with high doses of 131I (7.4GBq). Patients, who received the higher doses of 131I, should not be released before their individual exposure rate is measured

Baseline study in environmental risk assessment – Escalating need for computer models to be whole-system approach

Despite landfills having the potential to pollute the environment both during their operation and long after they have ceased to receive waste, they remain a dominant waste management option, particularly in the UK. In order to combat the environmental pollution caused by landfills, risk analysis is increasingly being employed through computer models. However, for a risk analysis process to be successful, its foundation has to be well established through a baseline study. This paper aims to identify knowledge gaps in software packages regarding environmental risk assessments in general, and especially those that have been developed specifically for landfills and landfill leachate. The research establishes that there is no holistic computer model for the baseline study of landfills, which risk assessors can use to conduct risk analyses specifically for landfill leachate. This paper also describes a number of factors and features that should be added to the baseline study system in order to render it more integrated – thereby enhancing quantitative risk analysis, and subsequently environmental risk management

Simulated Optimisation of Disordered Structures with negative Poisson’s ratios

Copyright © 2009 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Mechanics of Materials. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mechanics of Materials, Vol. 41 Issue 8 (2009). DOI: 10.1016/j.mechmat.2009.04.008Two-dimensional regular theoretical units that give a negative Poisson’s ratio (NPR) are well documented and well understood. Predicted mechanical properties resulting from these models are reasonably accurate in two dimensions but fall down when used for heterogeneous real-world materials. Manufacturing processes are seldom perfect and some measure of heterogeneity is therefore required to account for the deviations from the regular unit cells in this real-life situation. Analysis of heterogeneous materials in three dimensions is a formidable problem; we must first understand heterogeneity in two dimensions. This paper approaches the problem of finding a link between heterogeneous networks and its material properties from a new angle. Existing optimisation tools are used to create random two-dimensional topologies that display NPR, and the disorder in the structure and its relationship with NPR is investigated

Laboratory investigation of the effect of cyclic wetting and drying on the behaviour of an expansive soil

AbstractThe behaviour of an expansive soil was studied through a number of experiments involving cycles of wetting and drying. Laboratory tests were conducted on compacted samples of an expansive soil (a mixture of bentonite and kaolin, CH) in a modified oedometer under different constant surcharge pressures. The void ratio and water content of samples were determined at different stages. The results show that the swelling–shrinkage was reversible after the soil reached the equilibrium condition where the deformations were the same and the wetting and drying paths (in the water content–void ratio space) converged to an S-shaped curve. The hysteresis phenomenon was studied through the variation of the void ratio with suction, and the results showed that it diminished gradually with the increase in cycles of wetting and drying. The effect of fabric on the behaviour of soil samples was studied during cycles of wetting and drying. The results show that the samples with lower initial water content (on the dry side of optimum) have more swelling potential than samples with a higher initial water content (on the wet side of optimum)

Analytical reliability analysis of soil-water characteristic curve

The Soil Water Characteristic Curve (SWCC), also known as the soil water-retention curve, is an important part of any constitutive relationship for unsaturated soils. Deterministic assessment of SWCC has received considerable attention in the past few years. However the uncertainties of the parameters which affect SWCC dictate that the problem is of a probabilistic nature rather than being deterministic. In this research, a Gene Expression Programming (GEP)-based SWCC model is employed to assess the reliability of SWCC. For this purpose, the Jointly Distributed Random Variables (JDRV) method is used as an analytical method for reliability analysis. All input parameters of the model which are initial void ratio, initial water content, silt and clay contents are set to be stochastic and modelled using truncated normal probability density functions. The results are compared with those of the Monte Carlo (MC) simulation. It is shown that the initial water content is the most effective parameter in SWCC

An evolutionary approach to modelling the soil-water characteristic curve in unsaturated soils

In this paper a new approach is presented based on evolutionary polynomial regression (EPR) for modelling of soil–water characteristic curve in unsaturated soils. EPR is an evolutionary data mining technique that generates a transparent and structured representation of the behaviour of a system directly from data. This method can operate on large quantities of data in order to capture nonlinear and complex relationships between variables of the system. It also has the additional advantage that it allows the user to gain insight into the behaviour of the system. Results from pressure plate tests carried out on clay, silty clay, sandy loam, and loam are used for developing and validating the EPR model. The model inputs are the initial void ratio, initial gravimetric water content, logarithm of suction normalised with respect to atmospheric air pressure, clay content, and silt content. The model output is the gravimetric water content corresponding to the assigned input suction. The EPR model predictions are compared with the experimental results as well as the models proposed by previous researches. The results show that the proposed approach is very effective and robust in modelling the soil–water characteristic curve in unsaturated soils. The merits and advantages of the proposed approach are highlighted

An intelligent finite element method

This paper presents a unified framework for constitutive modelling of complex materials in finite element analysis using evolutionary polynomial regression (EPR). EPR is a data-driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. A procedure is presented for construction of EPR-based constitutive model (EPRCM) and its integration in finite element procedure. The main advantage of EPRCM over conventional and neural network-based constitutive models is that it provides the optimum structure for the material constitutive model representation as well as its parameters, directly from raw experimental (or field) data. It can learn nonlinear and complex material behaviour without any prior assumption on the constitutive relationship. The proposed approach provides a transparent relationship for the constitutive material model that can readily be incorporated in a finite element model. A procedure is presented for efficient training of EPR, computing the stiffness matrix using the trained EPR model and incorporation of the EPRCM in a commercial finite element code. The application of the developed EPR-based finite element method is illustrated through an example and advantages of the proposed method over conventional and neural network-based FE methods are highlighted

Thermo-mechanical modelling of unsaturated soils using EPR

A new approach is presented for modelling of the thermo-mechanical behaviour of unsaturated soils using Evolutionary Polynomial regression (EPR). EPR is a data-driven technique based on evolutionary computing developed with the aim of searching for polynomial structures representing the behaviour of a system. In this technique, a combination of a genetic algorithm (GA) and the least square method is used to find feasible structures and the appropriate parameters of those structures. An EPR model was developed and validated based on results from a comprehensive set of triaxial tests conducted on an unsaturated silt. The developed model was used to predict the shear behaviour of the soil considering the effects of temperature. The results of the EPR model predictions were compared with experimental results. Comparison of the results reveals that the proposed model is highly accurate and robust in predicting the thermo-mechanical behaviour of unsaturated soils