جستجوی مقالات مرتبط با کلیدواژه « عدم قطعیت » در نشریات گروه « فیزیک »

Proton therapy is one of the prostate cancer treatments. Bragg peak in proton depth-dose curve is the most important characteristic of this method. It causes the most amount deposition of energy in the tumor. Prostate is not a fixed organ and has involuntary movement in the range of 1-7mm due to bloating of the gastrointestinal tract in body, so this movement results in uncertainty in absorbed dose of particle. In this research absorbed dose are calculated regarding prostate movement using FLUKA code which works with Monte Carlo method. First, the phantom used is simulated. Regarding depth of 15cm of prostate in body the needed energy to form Bragg peak in tumor was calculated 148 MeV with 0.02% precision. Total Absorbed dose for one proton particle is 7.2 nGy when prostate was considered fixed in coordinate center .it was shown that 99.9% of total absorbed dose which is deposited in prostate and does not damage other tissues. The reason for this is the presence of Bragg Peak, which is all the energy absorbed in this area. with moving prostate for 7mm in perpendicular proton beam direction, the total dose absorbed by the prostate is reduced by 2.2%, also for moving prostate in line with proton beam, the total dose absorbed by the prostate is reduced by 6.66%.

Brachytherapy, is a method to treat prostate cancer in which a radiation source is placed inside or next to the cancer affected tissues. Purpose of this article is to determine the uncertainty level of received dose in prostate tissue due to the relocation of the placed radiation sources and inflation in prostate after positioning the radiation seeds. To simulate the model 6711 Amersham (activity level equal to 0.5 mCi) iodine-125 brachytherapy source, we used MCNPX 2.6 code and TG-43U1 protocol, and we used ORNL phantom. Treatment plan included 76 sources consisting iodine-125 radioactive, once placed in the form of seeds and once applied in the form of beads in three initial volume: 30.02, 38.01 and 52.01 cm3 and three different time steps were placed in prostate phantom. Three-time steps were investigated, including the moment after placing, the zero days and thirty days after placing. In the first step, the obtained dose of radiation in three different prostate volumes, for both seed distribution and bead distribution in healthy and cancerous tissue, was calculated. In the second step, position of the sources was relocated from the assumption, and causes a change in dose distribution. Considering the movements in 3 directions of left-to-right (1.8 mm), intra-exterior (2.1 mm), up-down (3.4 mm) after implantation the equivalent dose was calculated. The maximum uncertainty of received dose in a prostate with seed implanted sources and the volume of 52.01 cm3 and it was reported as +20%. At the third step, in addition to the change in the location of sources, inflation rate of 12% was observed in comparison with the primary volume at the first step of treatment process which resulted in a smaller dose rather than the previous stage.In this treatment plan, relocating of the sources and 12% inflation of the tumor were analyzed. Uncertainty of source relocating was calculated as ±20% in zero-day of positioning. Also, 12% inflation of the tumor and source relocation after 30 days, in a prostate with the volume of 58.25 cm3 curing by point sources, shown 21.04% reduction in received dose for healthy and cancer tissues since the moment of placing. The maximum error of MCNPX code was calculated 0.03%.

A useful benchmark for the reliability of gamma spectrometry results is to provide measurement uncertainty. Due to the importance and necessity of radiation monitoring in water resources, this research has been oriented toward providing measurements of the gamma- emitting radionuclides along with introducing the expression for the uncertainty that appeared in these measurements. To calculate the uncertainty of the activity measurements in water, a sample, where its radioactive elements were obtained from the PolAtom Company, was used. The calculations were performed based on the 661 keV peak of 137</sup>Cs radionuclide and a high-purity germanium detector (HPGe). The investigations show that the factors affecting the measurement uncertainty are composed of two parts, including counting with the gamma spectroscopy system and sample preparation, where each of these parts comes from several sources of uncertainty such as uncertainties associated with the counting process, efficiency calibration, emission probability, the counting correction factors, and the mass measurement. The results of analyses show that the relative uncertainty is about 3.7% (Coverage factor = 1) for a water sample with an activity of 425 Becquerel from 137</sup>Cs.

Extreme weather conditions have an important role on strategic planning of water resource and developing adaptation plans and natural disaster management. Therefore, it is necessary to present a detailed perspective of upcoming extreme patterns of rainfall events. In the context of climate change, pattern extraction of extreme events can only be achieved by using of daily downscaling methods. In the current paper, two single site downscaling methods SDSM, DMDM and a multisite approach based on Singular Value Decomposition (SVD) technique are used and their results are evaluated. The case study is located on Tehran province with over 10 precipitation and synoptic stations in the period 1985 to 2005. The used climate change scenarios were generated for (2021-2050) period. In addition, the daily NCEP/NCAR dataset and results of climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) were achieved from the Canadian Centre for Climate Modeling and Analysis. For each downscaling models, based on their own concepts, suitable predictors have been selected via backward stepwise regression as a preprocessing step (Hessami et al. 2008). The implemented multisite approach is based on combination of two multiple regression models to simulate precipitation amount and occurrence and also using SVD to capture stochastic behavior of precipitation to preserve accurately the space–time statistical properties of daily precipitation (Khalili and Nguyen, 2017). Beside the SDSM as a regression based downscaling method (Wilby et al. 2002), DMDM as a regression based tool box including Multiple Linear Regression (MLR), Ridge Regression (RR), Multivariate Adaptive Regression Splines (MARS) and Model Tree (MT) have been used as well (Tavakol et al. 2013b). To achieve the goal of the current research, temporal downscaling method to simulate extreme precipitation values is needed. In this regard, numerical model based on scaling invariant concept is used to do temporal downscaling (Nguyen et al. 2007). The sub daily extreme rainfall, are estimated from daily downscaled rainfalls by analyzing the non-central moments of observed rainfalls, single time regime (from 6 h to 24 h) and using scaling factor. Finally, as the major output of this study, Intensity Duration Frequency (IDF) curve is calculated affected by climate change in the period 2020 to 2050. The results based on statistical assessment both in calibration and validation periods of daily precipitation show the effectiveness of SDSM and DMDM models, respectively, in terms of long-term monthly average, and multisite model in preserving the trend of computed information in comparison with observed values. Based on uncertainty assessments results, DMDM provided the most precsion results versus the other methods over the study area. In addition, the models performance rank in estimating unseen station belong to the DMDM and multisite and SDSM methods, respectively. In the second step, quantities of IDF curves for return periods of (2-100) years and durations of (6, 12, 24 hour) at Mehrabad station are estimated using the results of coupled three different spatial downscaling and GEV distribution. Results show higher accuracy of DMDM and SDSM models respectively in comparison with multisite model. Based on the linear structure of SDSM and Multisite downscaling models versus the complex structure of DMDM, it seems that limitations of linear methods cause DMDM to be superior to the other ones. In addition, evaluation of the results of extreme values by three different climate change scenarios based on the DMDM downscaling model indicates an increase in rainfall intensity using scenario RCP8.5 and a decrease under scenarios RCP4.5 and RCP2.6.

In this paper, we will introduce soliton-like solutions of the complex ϕ^6 system as an example of the complex non-linear Klein-Gordon systems in 1+1 dimensions. Complex kink (anti-kink), radiative profiles and wave-packets are three different types of soliton-like solutions for complex ϕ^6 system. All relativistic energy-momentum relations can be satisfied generally for such solutions. For this special system, numerical calculations show that we can detect an apparent uncertainty in the outputs of collisions of the complex kink-anti-kink pairs and the pairs of wave-packets, which originate from an arbitrary initial phase. Also in collisions between out-of-phase kink-anti-kink pairs, we can always detect radiative profiles. The reverse can also occur, i.e. in collisions between two energetic radiative profiles with zero rest masses, it is possible to create a pair of kink-anti-kink with non-zero rest masses.

Increase greenhouse gases in the Earth's atmosphere have led to imbalances in the phenomenon of climate over the past decades, defined as Climate Change. Studies show that climate change can have negative effects on water resources, agriculture, environment, health, industry and economy. Global warming and climate change is happening and changing weather and climate volatility is associated with greater risk of damage. Since increasing the likelihood of future climate change could have devastating consequences for human societies, it is essential to examine the drought situation in the future periods in this area. For climate change effects on various resources in the future, climatic variables affected by greenhouse gases should be determined. Different techniques are available to simulate the future climatic variables under climate change effects; the most reliable data is atmospheric general circulation models. GCM models are three-dimensional models of the physical relationships that govern the atmosphere, crysphere, biosphere and hydrosphere. One of the weaknesses of GCM models is large spatial and temporal scales of the climatic variables. Therefore variables regarding hydrological and water resources studies are not sufficiently accurate. It should be downscaled by various techniques. Since different methods are available for downscaling, the uncertainty associated with these methods must be investigated. Various uncertainties affect the final outcome runoff simulation in a basin under the impact of climate change. The credibility of the results by ignoring any of these uncertainties would be reduced.
In this study, the GCM models uncertainty, methods of downscaling climate models and the SRES emission scenarios over the period 2069-2040 on Dez Ulya basin runoff were examined. For this purpose, the simulated temperature and precipitation of 10 GCM models, including BCM2.0, CGCM3T63, CNRMCM3, CSIROMK3.0, GFDLCM2.0, GISS-ER, HADCM3, INMCM3.0, IPSLCM4, MIROC3.2MEDRES, with two downscaling methods (Change factor and statistical using LARS-WG software) and three emission scenarios (A1B and A2 and B1) and artificial neural network model were used to simulate rainfall-runoff model. LARS-WG (Long Ashton Research Station Weather Generator) is a stochastic weather generator which can be used for the simulation of weather data at a single site, under both current and future climate conditions. These data are in the form of daily time-series for suitahle climate variables, namely, precipitation (mm), maximum and minimum temperature (°C) and solar radiation (MJm-2day-1). Stochastic weather generators were originally developed for two main 1) To provide means of simulating synthetic weather time-series with statistical characteristics corresponding to the observed statistics at a site, but which were long enough to be used in an assessment of risk in hydrological or agricultural applications.2) To provide means of extending the simulation of weather time-series to unobserved locations, through the interpolation of the weather generator parameters obtained from running the models at neighboring sites. It is worth noting that a stochastic weather generator is not a predictive tool that can be used in weather forecasting, but is simply a means of generating time-series of synthetic weather statistically ‘identical’ to the observations. New interest in local stochastic weather simulation has arisen as a result of climate change studies. At present, output from global climate models (GCMs) is of insufficient spatial and temporal resolution and reliability to be used directly in impact models. A stochastic weather generator, however, can serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily time scale which incorporate changes in both mean climate and in climate variability. It utilizes semi-empirical distributions for the lengths of wet and dry day series, daily precipitation and daily solar radiation. The rainfall-runoff models for the base period (2000-1971) has been calibrated and verified, then by downscaling of ten GCM-AR4 climate models for the study area and take into account each of them separately for rainfall-runoff models, changes of runoff in the period 2069-2040 under the three scenarios (A1B and A2 and B1) were determined.
Results from downscaling models showed that the rainfall for some models increase and others decrease in the future, compared to the base periods. Changing factors in downscaling method showed more decrease than statistical method. Results showed that the percentage change in long-term monthly simulated runoff for the two downscaling methods is about 5.11 percent, while a decreasing trend in the future compared to the base runoff was seen. Runoff simulation scenarios relative to each other in different months had the same difference. The results showed uncertainty in climate models used in this study is more than of uncertainty according to downscaling methods and emission scenarios.

In this paper, we first described briefly the non-linear system and its solitray kink and anti-kink solutions in a  dimensional space-time. We showed that there will be an internal channel for the low amplitude oscillations for the kink (anti-kink) solutions. For such oscillations with specific velocity dependent frequencies and wavelengths, there are the similar wave-particle relations to the standard quantum mechanical ones. Finally, we showed numerically that in the collisions between kink-ant kink pairs, the output results are completely dependent on the initial phases of the oscillations which are imposed on the kink-ant kink pairs. In other words, the uncertainty created in the output of the same collisions is due to a quantity that does not in any way play a role in the particle properties, which can be called a hidden variable.

One of the most important impact of climate change is reduction of precipitation in some areas including Iran. Hence, climate change studies are essential in these areas. Besides, according to IPCC, some meteorological stations of Iran, such as Tabriz (capital of East Azerbaijan Province) have showed a downward trend in precipitation. Therefore, East Azerbaijan Province was selected as the study area in this survey. It is one of the north-western provinces with cold dry climate. Firstly, monthly temperature and precipitation observed data over 1981-2012 were gathered from 15 meteorological stations of the region, and they were produced for 0.5-degree cells by interpolation methods and selecting the most appropriate one based on the amount of corresponding errors (RMSE and ME). Thereafter, monthly precipitation and temperature data for 2013-2022 were projected using 16 Atmosphere-Ocean General Circulation Models (AOGCMs) under A2B, A2 and B1 SRES scenarios, and downscaled by Bias Correction/Spatial Downscaling technique at 0.5-degree cells. After applying pattern scaling method on monthly temperature and precipitation data, in order to produce future data under more scenarios, monthly climatic variables were calculated for 10, 25 and 50 percent risk, and risk analysis was done based on the computed parameters. The pattern scaling technique used in this study calculates the variable under a desired scenario, from the base scenario (A2 in this study) with a linear equation in which the global temperature rise was calculated by a model named MAGICC. Assessing observed climatic variables showed that western parts of the province had lower precipitation and higher temperature, while eastern parts had higher precipitation. However, south-western cells also experienced a better situation. Mean annual temperature over 1981-2012 was between 7.5-13.5 degrees Centigrade, and annual precipitation was 260 to more than 310 millimeters. Moreover, despite precipitation fluctuations over 1981-2012, annual precipitation of the first years is higher than the last years. After applying pattern scaling method and accessing future monthly precipitation and temperature data under 49 scenarios for 16 AOGCMs, temperature and precipitation boxplots of each month were produced for each month. Results showed that precipitation is right-skewed in all months and all cells. The outliers of March and April are less than others, while August outliers are numerous. Comparing boxplots of temperature and precipitation indicated that outliers of temperature data are much less than precipitation, i.e. uncertainties of AOGCMs and downscaling to project temperature are less than precipitation. The monthly precipitation and temperature data were calculated for 10, 25 and 50 percent risk and the monthly temperature-risk and precipitation-risk line charts were produced for each cell. The amount of monthly temperature and precipitation with higher and lower risk showed a significant difference. Furthermore, projections with lower risk have less difference and they indicate almost one prediction. According to the areal interpolated maps of the future mean annual precipitation and temperature, the least temperature will be around Sarab station, and the highest temperature will be near Malekan and Bonab stations. Furthermore, maps showed that the amount of temperature will increase by moving west. Moreover, by moving from high risk to lower risks, the amount of temperature increases about one degree Centigrade. Western regions will experience lower precipitation with all levels of risk, and the maximum annual precipitation will be seen in north-eastern spots. The difference between the predicted and observed temperature and precipitation with 10, 25 and 50 percent risk for each cell was calculated and their spatial distribution maps were produced by applying different interpolation methods and selecting the best method. It is predicted that temperature will increase 2.9-3.15 degrees Centigrade with 10 percent risk, and the rise amount is bigger in the western areas. Precipitation will decrease about 75 to 150 millimeters. Temperature will increase 2.1-2.25 degree Centigrade with 25 percent risk, and the amount of precipitation in some areas will be lower and in some others will rise even up to 50 millimeters. The temperature with 50 percent risk is projected to increase about 1.2 degree Centigrade, and precipitation will also aggrandize. In conclusion, the temperature increase in the next decade will be bigger in the southern areas of the province, and precipitation amount of north-western and western areas will experience higher precipitation. The results of this study confirm other research done by others before, indicating the least amount of observed precipitation was in Sarab station. By having these results for future periods the decision makers of this field will have a better vision, ad so they will be able to sufficiently plan for the future. In addition to this research, some suggestions are proposed as follows to improve and strengthen the (i) past and future drought assessment in the area with different drought indexes, (ii) presenting a more logic relationship between temperature and precipitation because of relatively low correlation between temperature and precipitation and so not being linear, or applying models ensemble and comparing the results with this survey, (iii) using daily temperature and precipitation instead of monthly data to improve the results.