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BACKGROUND AND OBJECTIVES: In this research, our main objective is more accurate pricing of life insurance products with a new approach of predicting mortality or survival rates. Currently, a life table is used to calculate the current value of pensions, insurance premiums, etc. Therefore, to increase the accuracy of our calculations, we are looking for a mortality prediction model for such calculations. Therefore, in this research, instead of static pricing (only using the latest life table), we used life table prediction and dynamically rated life insurance products. METHODS: In this research, a new model proposed to predict the probability of human mortality (survival) based on the Markov process, a limited state with an absorption state (death). This model measured based on the physiological age, because the physiological age of each person can be checked based on different laboratory indicators, and finally it has led to the results of the individual health index. In addition, the parameters of this model are the initial probability vector and the sub-intensity matrix of a Markov chain that changes over time. In other words, in this model, according to a possible process in the model, the initial probability vector over time selects the possible interval of the physiological age equivalent to the chronological age. FINDINGS: To show the satisfactory performance of this model, the relevant data set from the United States of America was analyzed. The predicted results with the presented model are better than Lee Carter’s model. It should be noted that the number of parameters of the model introduced in this research is much less compared to the Lee Carter model and other mortality or survival prediction models. Based on this model, a closed form for life insurance pricing relationships is obtained, which simplifies these calculations for users. CONCLUSION: The relationships obtained for pricing were investigated based on two products, 5-year term life insurance and also a 5-year term pension. The fitted results for the model used in the predictions of the probability of mortality as well as the probability of survival and pricing are very satisfactory. [ABSTRACT FROM AUTHOR]

Wetlands are vital for human survival and constitute 40.6% of the total value of global ecosystem services. This study analyzed land cover changes in the Golpayegan Shoor wetland from 1988 to 2018 and then modeled it with a combination of artificial neural network, Markov chain, and multi-objective optimization for 2030 and 2040 for obtaining environmental sustainability. To obtain the highest accuracy in transition potential modeling, four scenarios were used with various calibration periods and sub-models. The FOM index was used to evaluate the accuracy of various scenarios using the approach by overlaying three maps for the years 2008, actual 2018, and predicted 2018 maps. Effective variables in describing the changes occurred in the case study selected using Cramer's v. The highest accuracy was shown in a scenario with the calibration period of 1988-1998 and 9 sub-models in land cover change modeling 2018 year. The evidence likelihood of change map and distance from agricultural lands showed the highest impact in the changes of the study area. Residential areas and agricultural lands increased by 0.6% and 1.8% in 2030, while 1% and 2.2% grew up in 2040 compared to 2018, respectively. Dense rangeland decreased by 13.1% and 18.6%, respectively in 2030 and 2040 compared to 2018. The water bodies showed an increase of 0.18% and 0.27% due to the conversion of agricultural areas and rangeland to Lake Golpayegan and Kochery dams. Taking water from unauthorized wells, reducing the flow of water entering the wetland, and the presence of decorative ore has caused serious problems for Golpayegan Shoor Wetland, which requires managers and land planners to consider compensatory ways to restore, water requirement, and ecological sustainability for the wetland. [ABSTRACT FROM AUTHOR]

One of the effective factors regarding the impact of natural resource rents on financial development is the role of institutional quality. Existence of natural resources can increase corruption through rent-seeking behaviors if there is no strong institutional framework. Therefore, the role of the institutional quality factor is undeniable. In the present study, the role of institutional quality in the impact of natural resource rents on the financial development of Iran in the period 1984-2019 has been investigated. For this purpose, first to extract the indicators of institutional quality and financial development from the model of principal component analysis and then from the Markov switching model to investigate the effect of natural resource rents on financial development in the country in two cases with and without considering Institutional quality index has been used. Increasing the rent of natural resources has had a negative and significant impact on financial development in low regime of the financial development. Economic growth and trade openness have also had a positive and significant impact on all levels and regimes of financial development. Increasing the rent of natural resources in terms of improving the institutional quality index has had a positive and significant effect on financial development in a low regime. Increasing the inflation has had a negative and significant effect on financial development in all regimes of financial development. [ABSTRACT FROM AUTHOR]

Most of businesses financinig in the country done by the banking network and banking network play a very important role in achieving macroeconomic goals. Accordingly, in the present study, the effect of fluctuations of asset markets (exchange rate, oil price and stock market index) on financial instability index over a period of 1388-1397 monthly is investigated by using the Markov Switching model. The wavelet transform model is used to extract exchange rate fluctuations, oil prices and stock market index. The results show that the effect of exchange rate fluctuations in different regimes and different time periods is different, so that in the short run the exchange rate fluctuations in the high regime of the financial instability index have a different effect than the other periods. Oil price fluctuations have a positive and significant effect in the medium- and long-term periods and in all regimes, and this effect will be stronger in the long run. Also, fluctuations in the stock market have a negative and significant effect only in the short run and under conditions of low regime of financial instability index. These results show that fluctuations have different effects with respect to time period as well as level of financial instability. Therefore, the management of foreign exchange rate and stock markets should take into the account of financial instability level and the timeframe for fluctuations. [ABSTRACT FROM AUTHOR]

Statement of the Problem: Land use is a dynamic phenomenon whose temporal and spatial changes are caused by human pressures and development. Knowledge of how to use user changes and their causes and factors over a period of time can be of great interest to planners and managers. The present study seeks to evaluate and extend eight sample villages in Mashhad during 2001-2019 and then predict changes to 2031 and 2046. Methodology: The research method is descriptive-analytical and it is applied in terms of the purpose. The required data were obtained through the documentary method; and Landsat 7 ETM + satellites (2001 and 2007) and Landsat 8 OLI satellites (2013 and 2019) were used to map land use and survey changes in the study area. After supervised classification of images with maximum similarity algorithm, Markov chain model was used to investigate land use changes in sample villages. Also, the CA Markov model and the Kappa index were used to predict the trend of changes up to 2031 and 2046 for confidence in classification. Results: The results showed that most of the user change occurred in the built ranges, and land use has increased sharply, and land and garden land use has fallen sharply, with 1008.67 hectares (241.57%) and 32.28 hectares of unused lands and 1060.55 hectares of fields and orchards, respectively. These changes were more frequent in the villages of Hossein Abad Gharqi, Georgian Sefli and Ghale Siah (Black Castle). According to Markov’s Model, the built area in 2031 and 2046 will change by 110.77% and 113.18%, respectively. Therefore, it is necessary to prevent unauthorized expansion of Mashhad and surrounding villages to gardens and agricultural lands with proper planning. Innovation: For the first time, the process of land use changes in rural settlements, especially peri-urban settlements, is studied and predicted by satellite imagery. [ABSTRACT FROM AUTHOR]

The growth of cities and their impacts on the environment have disastrous consequences. But, with having the knowledge of the areas, investigation, employment, and proper use of new technologies, sustainable development can be achieved. On the other hand, geography and its most important indicators, especially geomorphology as a specialized science, can play an important role in the growth and development of urban centers. The present study examines the development status of the city and the risks that affect its development. In the present study, using the remote sensing and geographic information system of land use changes in the city of Qaen, in order to estimate the increase in the urban area and the reduction of agricultural and horticultural lands in the period from 2000 and 2017, and also its effect on the geomorphological hazards (such as fault, waterway and lithology) has been studied in the region. The area of use was characterized by maximum similarity algorithm, supervised method and Markov chain model at 1404 horizons. The results of this study show that during the years 2000, 2010 and 2017, the area of use of the boundaries built in the city of Qaen has increased and this increase has brought the city closer to the clay formations, faults and main waterways. So that constructed lands (114.84%), gardens and agricultural lands (99.1%), uncultivated lands (99.71%) and rangeland (99.94%) will change. The combination of different data to obtain more accurate and coherent outcomes as well as a vision for identification of favorable areas for urban growth and development in the coming years will be considered as the innovation of this research. [ABSTRACT FROM AUTHOR]

Distance protection system as the main lines' protection, has an essential role in networks' reliability. Thus an optimized maintenance plan for distance protection can influence on network reliability and costs. Intrinsic hidden function of the protection systems cause some problem in the way of applying reliability-centered maintenance technique (RCM) as an effective planning method. In this study, the concept of systematic solution view in RCM is proposed instead of equipment-based solution view. The presented concept can well model the behavior of a hidden performance system. Also, Markov process and krill optimization algorithm is used for calculating the reliability and finding optimal time intervals between maintenance tasks. Increasing system reliability and decreasing costs are considered as optimization aims. The proposed method has been implemented and tested using real data of two distance protection systems. The output results show that the proposed method is capable of providing higher reliability and lower cost than other methods. [ABSTRACT FROM AUTHOR]

Condition assessment is one of the most significant techniques of equipment health, repairs, maintenance, and management. Prognostics and Health Managemen (PHM) methodology cycle is a developed form of Condition Based Maintenance (CBM). Condition assessment is the most important step of this cycle. In this study, based on the presented model, the Remaining Useful Life (RUL) is estimated using equipment condition assessment. Using the simulation and forecasting of a new feature for the vibration of the equipment (Kurtosis-Entropy) by Autoregressive Markov Regime Switching (AMRS) method, equipment health condition is determined. Prior to forecasting the condition of the equipment, the equipment degradation state is determined by the fuzzy C-means clustering method. Based on the current state of equipment and pre-determined state of degradation, the remaining useful life of the equipment is estimated. In order to evaluate the model, the experimental data from the FEMTO-ST Institute, which is provided to estimate the remaining useful life of the bearing, was used and the results of the study are compared with the rival models. The innovation of this paper is the use of fuzzy C-means, a new approach to evidence theory for data fusion, and the use of the Markov switching model for prediction. [ABSTRACT FROM AUTHOR]

Haftad-Gholleh Area has recently encountered many changes. In this study, by using landsat images of the years 1996 and 2016, landuse maps were classified into four classes including: agriculture, rangeland, residential areas, and rocks. Land Change Modeler (LCM) and Habitat and Biodiversity Modeler (HBM) modules in the Idrisi GIS software were used to analyze the land use changes and habitat evaluation for the prediction of the land uses status in 2016, based on the Artificial Neural Network (ANN), Markov Chain analysis and logistic regression. The results showed that most of the changes in the landscape of the region between 1996 and 2016 were related, respectively, to attrition, aggregation and creation indicators; between the years 2016 to 2041, they can be related, respectively, to the creation and dissection indicators. The habitat evaluation showed that 4.5% of the habitat was decreased in 2016, as compared to 1996. With the continuation of this trend, 6.5% of the habitat will fall in 2041, as compared to 2016. Receiver Operating Characteridtic (ROC) of the model also specified that the desirability model validity was equal to 0.9558, showing the excellent performance of logistic regression method. In general, this can be an important principle approach preventing from changes in the habitat of wild sheep to other land uses. [ABSTRACT FROM AUTHOR]

In this paper occurrences of precipitation in Iran was analyzed according to random processes by the applying of markov chain model. In order to do that occurrence and non-occurrence of rainfall of asfezari data base for the period of 1961/3/21 to 2004/12/31 was used. This data are for 7187 pixel and 15991 days. By using markov chain model first order two state dry and wet frequency matrix was made and then by the method of maximum likelihood transmission probability matrix was developed. By Chi-square goodness data following from chosen model was confirmed. By frequently powering this matrix stable probability and daily return period of rainy and non-rainy states was estimated. Period of daily returns and non-rainy days was estimated 7 and 1 respectively. So probability of precipitation was 0.1449 and probability of non-occurrence was estimated 0.8551. Return period of precipitation with lasting 1 to 3 days was calculated for all the months. The maximum likelihood of occurrence with precipitation was in March with the amount of 0.27. The shortest return period. The Shortest return period with lasting of 1 to 2 days was in February and March with the amount of 5/6 and 3/7 respectively. [ABSTRACT FROM AUTHOR]

Modeling and prediction of land use/cover changes is an essential need for planning sustainable use of land in country like Iran with its high level of changes in land uses and land covers. This study aimed to analyze the capability of CA Markov prediction model and Landsat satellite images for land use/cover change detection within the framework of "Iran at 1404 prospective" in Esfarayen region, North Khorasan province for year 2025. The results of CA Markov modeling showed a reduction about 5000 and 400 ha in irrigated agricultural lands and rangeland class in 2025, respectively. Furthermore, the model predicted that the poor rangeland class and Esfarayen city will be increased 30 and 450 ha in 13 years from 2009 to 2025. According to the results, if current land use continues to intensify regardless of sustainable development considerations, severe degradation in the natural resources of the study area is unavoidable in the future. Overall, the results demonstrate that satellite remote sensing data and CA Markov model can be successfully used to predict land use/cover changes and the extracted maps can help policy makers to make better decision for the future of the region. [ABSTRACT FROM AUTHOR]

Background: Hypothermia is an important determinant of survival in newborns, especially among low-birth-weight ones. Prolonged hypothermia leads to edema, generalized hemorrhage, jaundice and ultimately death. This study was undertaken to examine the factors affecting transition from hypothermic state in neonates. Methods: The study consisted of 439 neonates hospitalized in NICU of Valiasr in Tehran, Iran in 2005. The neonates' rectal temperature was measured immediately after birth and every 30 minutes afterwards, until neonates passed hypothermia stages. In order to estimate the rate of transition from neonatal hypothermic state, we used multistate Markov models with two covariates, birth weight and environmental temperature. We also used R package to fit the model. Results: Estimated transition rates from severe hypothermia and mild hypothermia were 0.1192 and 0.0549 per minute, respectively. Weight had a significant effect on transition from hypothermia to normal condition (95% CI: 0.1364-0.4165, P<0.001). Environmental temperature significantly affected the transition from hypothermia to normal stage (95% CI: 0.0439-0.4963, P<0.001). Conclusion: The results of this study showed that neonates with normal weight and neonates in an environmental temperature greater than 28 °C had a higher transition rate from hypothermia stages. Since birth weight at the time of delivery is not under the control of medical staff, keeping the environmental temperature in an optimum level could help neonates to pass through the hypothermia stages faster. [ABSTRACT FROM AUTHOR]

This study was performed with objective of predicting land cover change in the east of Mazandaran Province (Neka and Behshahr counties), using Artificial Neural Network in the GIS environment. Landsat imagery belonging to the years 1987 and 2001 was used for change detection. Then, using Multilayer Perceptron neural network transition potentials was implemented for 7 sub-models and finally, land cover change modeling for 2006 with 1987-2001 calibration period and by Markov Chain and hard prediction was run. The accuracy of the assessment model was determined by using the Null Successes, Hits, False Alarms, and Misses. Finally, land cover change prediction was done for 2015. The result showed that during 1987–2001, respectively, 1964 and 1197 ha from forest and orchard were reduced, and 1182 and 1978 ha agriculture and residential were added. The results showed high accuracy (67-89%) in all the sub-models. Total error prediction model was 9.98%. Furthermore, the prediction results showed that forest areas will be reduced and residential, agriculture and orchard will be increased in 2015 compared with 2006. [ABSTRACT FROM AUTHOR]