Spatial modeling of plant distributions: coupling remote sensing with GIS-based models

Spatial species distributions and the relationship between species and environmental factors have been studied for several years. Climate change and habitat fragmentation can be considered as the factors effective in biodiversity changes. Therefore prediction of species range shifts under climate change and other physical processes is a crucial challenge for the management of natural resources. The major objective of this thesis was to integrate MigClim, SDM and CA-Markov chain models so as to assess the effects of future landscape fragmentation and climate change scenarios on the geographic distributions of three open-land plant species in 21st century. For all target plants, simulations were performed for four dispersal events (full dispersal, no dispersal, regular dispersal (short-distance dispersal), and regular dispersal along with long-distance dispersal), two landscape (static and dynamic change) and two climate change (RCP4.5 and RCP8.5) scenarios (chapter 5). In this investigation, it was shown that the predicted distribution areas for all the three species under RCP8.5 scenario will largely increase in the coming decades. Also, a significant difference appears to be between the simulations of realistic dispersal limitations and those considering full or no dispersal for projected future distributions during the 21st century. Besides, the results obtained by the limited projections of future plant distributions via realistic dispersal restrictions showed to be generally closer to no-dispersal than to full-dispersal scenario when compared with real dispersal scenarios. Overall, the results of this study indicate that dispersal limitations can have an important impact on the outcome of future projections of species distributions under climate change scenarios. Also our findings clearly showed that change in landscape fragmentation is more effective than the climate change impacts on species distributions in our study area

Numerical Simulation of Entropy Growth for a Nonlinear Evolutionary Model of Random Markets

In this communication, the generalized continuous economic model for random markets is revisited. In this model for random markets, agents trade by pairs and exchange their money in a random and conservative way. They display the exponential wealth distribution as asymptotic equilibrium, independently of the effectiveness of the transactions and of the limitation of the total wealth. In the current work, entropy of mentioned model is defined and then some theorems on entropy growth of this evolutionary problem are given. Furthermore, the entropy increasing by simulation on some numerical examples is verified

Use of Intensity Analysis to Characterize Land Use/Cover Change in the Biggest Island of Persian Gulf, Qeshm Island, Iran

In this study, land use/cover change was systematically investigated in the Qeshm Island to understand how human and nature interact in the largest island of Persian Gulf. Land-use maps were prepared for 1996, 2002, 2008, and 2014 using Landsat satellite imagery in six classes including agriculture, bare-land, built-up, dense-vegetation, mangrove, and water-body, and then dynamic of changes in the classes was evaluated using intensity analysis at three levels: interval, category, and transition. Results illustrated that, while the land changes were fast over the first and third time intervals (1996–2002 and 2008–2014), the trend of changes was slow in the second period (2002–2008). Driven by high demand for construction and population growth, the built-up class was identified as an active gainer in all the three time intervals. The class of bare-land was the main supplier of the land for other classes especially for built-up area, while built-up did not act as the active supplier of the land for other classes. The dense-vegetation class was active in all three time intervals. As for the mangrove class, drought and cutting by residents had negative effects, while setting up protected areas can effectively maintain this valuable ecosystem. High demands were observed for land change in relation to built-up and agriculture classes among other classes. The findings of this study can advance our understanding of the relationship and behavior of land use/cover classes among each other over 18 years in a coastal island with arid climate

Protocol for systematic review: peak bone mass pattern in different parts of the world

Copyright: © 2015 Mohammadi Z. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Peak bone mass, which can be defined as the amount of bone tissue present at the end of the skeletal maturation, and also it is an important determinant of osteoporotic fracture risk. The peak bone mass of a given part of the skeleton is directly dependent upon both its genetics and environmental factors. Therefore, the aim of the proposed research is a comprehensive systematic assessment of the pattern of peak bone mass in different countries across the globe. The present article explains the protocol for conducting such a research

Patterns of historical and future urban expansion in Nepal

Globally, urbanization is increasing at an unprecedented rate at the cost of agricultural and forested lands in peri-urban areas fringing larger cities. Such land-cover change generally entails negative implications for societal and environmental sustainability, particularly in South Asia, where high demographic growth and poor land-use planning combine. Analyzing historical land-use change and predicting the future trends concerning urban expansion may support more effective land-use planning and sustainable outcomes. For Nepal's Tarai region-a populous area experiencing land-use change due to urbanization and other factors-we draw on Landsat satellite imagery to analyze historical land-use change focusing on urban expansion during 1989-2016 and predict urban expansion by 2026 and 2036 using artificial neural network (ANN) and Markov chain (MC) spatial models based on historical trends. Urban cover quadrupled since 1989, expanding by 256 km2 (460%), largely as small scattered settlements. This expansion was almost entirely at the expense of agricultural conversion (249 km2). After 2016, urban expansion is predicted to increase linearly by a further 199 km2 by 2026 and by another 165 km2 by 2036, almost all at the expense of agricultural cover. Such unplanned loss of prime agricultural lands in Nepal's fertile Tarai region is of serious concern for food-insecure countries like Nepal

The financial cost of preventive and curative programs for breast cancer: a case study of women in Shiraz-Iran

Background: This cross-sectional study was conducted to compare the average costs of breast cancer screening and treatment among women with the age of 25 and over in Shiraz-Iran. Methods: Three majors hospitals affiliated with Shiraz University of Medical Sciences (SUMS) were selected for data collection. Financial documents and interviews with the hospitals’ financial officers were used for data collection. Results: Finding shows that the total cost of screening would be 5,847,544.96 US dollars for age groups of 25–34 and 35 and above, demonstrating the huge expense of screening programs. On the other hand, the average cost of breast cancer treatment for each patient would be 3608.47, 996.89, and 311.47 US dollars for mastectomy, radiotherapy, and chemotherapy, respectively. In addition, the total average cost for treatment of 2217 patients would be 1,466,988.9 US dollars, which is much less than screening programs expenses. Conclusion: It is concluded that although screening can be effective for improving quality of life and treatment effectiveness, considering the high costs of screening, it is not economical in Iran. Screening methods within suitable intervals, and also considering patients’ medical history have been recommended by the present study

Impact of climatic parameters on the extent of mangrove forests of southern Iran

Mangrove forests play a valuable role in maintaining the coastal ecosystem. Global warming alongside human activities has caused reduced extent and health of these ecosystems in recent years. This study aimed to examine the variability of the extent of mangrove forests and the sea surface area in response to changes in climatic parameters in the south of Iran. To achieve this, the climatic data recorded at Bandar Abbas Synoptic Weather Station and Landsat series of satellite images were used. To detect the trends of meteorological parameters during 1987-2017, the modified Man-Kendall test and the Sen’s slope estimator were employed. We investigated the regression relationship between climatic parameters as well as the sea surface area and the mangrove forest extent. The results showed that mangrove forest extent was about 73.08 km2 in the first year of study (1987), which increased to 88.73 km2 (21%) in 2017. The minimum temperature (Z = 2.77, β = 0.0186), maximum temperature (Z = 2.066, β = 0.0362), and the extent of the mangrove forests (Z = 2.58, β = 0.0405) displayed significantly growing trends. In contrast, the mean temperature, precipitation, relative humidity, and the sea surface area had no significant trends during the study period. The minimum temperature presented the highest correlation coefficient with the mangrove forest extent (61%). It is expected, therefore, along with global warming and increasing minimum temperature, the extent of mangrove forests would have a growing trend in the south of Iran in the future. The results of this study can be used by natural resources and forest managers to determine the best place for afforestation in order to perform better protection of these forests

A cost-effective and efficient framework to determine water quality monitoring network locations

A crucial part in designing a robustwater qualitymonitoring network is the selection of appropriatewater quality sampling locations. Due to cost and time constraints, it is essential to identify and select these locations in an accurate and efficientmanner. Themain contribution of the present article is the development of a practical methodology for allocating critical sampling points in present and future conditions of the non-point sources under a case study of the Khoywatershed in northwest Iran,where financial resources andwater quality data are limited. To achieve this purpose, the rivermixing lengthmethod (RML)was applied to propose potential sampling points. A new non-point source potential pollution score (NPPS) was then proposed by the analytic network process (ANP) to classify the importance of each sampling point prior to selecting the most appropriate locations for a river system. In addition, an integrated cellular automata–Markov chainmodel (CA–Markov)was applied to simulate future change in non-point sources during the period 2026–2036. Finally, by considering anthropogenic activities through land-usemapping, the hierarchy value, the non-point source potential pollution score values and budget deficiency in the study area, the seven sampling pointswere identified for the present and the future. It is not expected, however, that the present location of the proposed sampling pointswill change in the future due to the forthcoming changes in non-point sources. The current study provides important insights into the design of a reliable water quality monitoring network with a high level of assurance under certain changes in non-point sources. Furthermore, the results of this study should be valuable for water quality monitoring agencies looking for a cost-effective approach for selecting sampling locations

The Financial Cost of Preventive and Curative Programs for Breast Cancer: A Case Study of Women in Shiraz-Iran

Background: This cross-sectional study was conducted to compare the average costs of breast cancer screening and treatment among women with the age of 25 and over in Shiraz-Iran. Methods:Three majors hospitals affiliated with Shiraz University of Medical Sciences (SUMS) were selected for data collection. Financial documents and interviews with the hospitals’ financial officers were used for data collection. Results:Finding shows that the total cost of screening would be 5,847,544.96 US dollars for age groups of 25–34 and 35 and above, demonstrating the huge expense of screening programs. On the other hand, the average cost of breast cancer treatment for each patient would be 3608.47, 996.89, and 311.47 US dollars for mastectomy, radiotherapy, and chemotherapy, respectively. In addition, the total average cost for treatment of 2217 patients would be 1,466,988.9 US dollars, which is much less than screening programs expenses. Conclusion:It is concluded that although screening can be effective for improving quality of life and treatment effectiveness, considering the high costs of screening, it is not economical in Iran. Screening methods within suitable intervals, and also considering patients’ medical history have been recommended by the present study

Numerical Simulation of Entropy Growth for a Nonlinear Evolutionary Model of Random Markets

In this communication, the generalized continuous economic model for random markets is revisited. In this model for random markets, agents trade by pairs and exchange their money in a random and conservative way. They display the exponential wealth distribution as asymptotic equilibrium, independently of the effectiveness of the transactions and of the limitation of the total wealth. In the current work, entropy of mentioned model is defined and then some theorems on entropy growth of this evolutionary problem are given. Furthermore, the entropy increasing by simulation on some numerical examples is verified