Artificial neural network modeling

Introduction

Renewable energy plays a crucial role in reducing greenhouse gas emissions and combating global climate change. Among the various renewable energy sources, wind energy stands out due to its production capacity and rapid technological advancement. This study aims to evaluate the capabilities and uncertainties of CMIP5 and CMIP6 models under two scenarios—RCP4.5 and RCP8.5 for CMIP5, and SSP2-4.5 and SSP5-8.5 for CMIP6—in simulating wind speed. Additionally, it will forecast future changes in wind speed (2014-2100) at six synoptic stations in Sistan and Baluchestan, focusing on the differences between CMIP5 and CMIP6 reports regarding wind energy. This research is the first to examine future wind characteristics in Iran using CMIP6 model outputs while comparing the performance of both CMIP5 and CMIP6 in simulating wind speed in the study area. Wind energy is highly sensitive to climate change, as future alterations in wind flow characteristics will significantly impact electricity generation potential. Therefore, understanding future climate scenarios, especially under varying global warming conditions, is vital for estimating changes in wind energy resources over the coming decades.

Method

Wind speed data were obtained from six synoptic stations operated by I.R. of Iran Meteorological Organization (IRIMO), covering the period from 1990 to 2014, with all stations maintaining continuous data records throughout this timeframe. The data from CMIP5 and CMIP6 climate models were downloaded from the respective websites. This study selected outputs from six general circulation models for the historical period (1990-2014) and the future period (2014-2100) under the emission scenarios SSP2-4.5 and SSP5-8.5 for CMIP6 and RCP4.5 and RCP8.5 for CMIP5. The ability of the CMIP6 and CMIP5 climate models to simulate historical wind speed was evaluated against observational data from Sistan and Baluchestan using statistical criteria, including bias, correlation, and standard deviation. This evaluation determined the capability and accuracy of the models and assessed the uncertainty in their wind speed simulations before applying them to future climate forecasts. A multi-model averaging approach was employed to reduce uncertainties associated with individual models, utilizing the CDFT package in RStudio for downscaling and output bias correction based on cumulative distribution function transformation.

Results and Discussion

Most CMIP models simulated wind speed effectively. The CanESM5 model in CMIP6 demonstrated improved performance compared to CMIP5, yielding results closer to observational data. In contrast, the CMCC-ESM2 and CNRM-CM6-1 models in CMIP6 were less efficient than their CMIP5 counterparts. CMIP5 indicated a decrease in wind speed, while CMIP6 suggested an increase in annual projection, although these changes were not statistically significant. The projected average wind speeds by 2100 are 3.58 m/s and 3.57 m/s for the SSP2-4.5 and SSP5-8.5 scenarios, respectively, while the RCP4.5 and RCP8.5 scenarios predict averages of 3.1 m/s and 3.2 m/s, respectively. The baseline average wind speed from observational data is 3.49 m/s. CMIP5 indicated a decrease in wind speed across all months under both scenarios, with the most significant reductions occurring in spring (-0.47 m/s) and the least in autumn (-0.29 m/s). Conversely, CMIP6 projected increases in wind speed across all seasons, with the highest increase in spring (0.49 m/s) and the lowest in summer (0.32 m/s) under the SSP2 scenario. Under the SSP5 scenario, the highest increase was observed in winter (0.43 m/s) and the lowest in summer (0.37 m/s). The annual average for CMIP5 models showed a decrease in wind speed at all stations compared to the baseline period, particularly at Khash and Zabul stations. In CMIP6, all stations except Chabahar exhibited increased wind speeds, with Chabahar recording the highest average wind speed and other stations showing minimal differences. The results indicate varying model performance in simulating climate variables, with the historical wind speed uncertainty in CMIP5 models potentially attributed to differences in grid resolution, atmospheric components, and convection scheme parameterization.

Conclusion

Given the observed biases in the models, future research should involve a comprehensive study utilizing additional models from the CMIP6 and CMIP5 families. These findings are significant for assessing the potential of the wind energy sector in Sistan and Baluchestan, a region known for its wind resources, which may inform future development strategies. It is also recommended that similar assessments be conducted in other regions of Iran to determine whether identified sites with suitable wind power may experience future resource fluctuations. Also, it is suggested that different methods of bias correction and downscaling should be investigated and the best method should be suggested. On the other hand, using satellite data instead of observational data and comparing their results can be considered as another research proposal.

Introduction
Climate is variable in time and space, so detecting a significant trend is a major challenge for researchers. Calculating the trends of climatic elements such as average temperature, maximum, and minimum temperature has been the subject of many studies in recent years. It has been carried out in different regions around the world. Most studies in this field have focused on large-scale temperature trends. However, more research is needed to focus on the change that occurs at the regional level. In this way, the research conducted on the decadal trends of the average temperature in different regions provides impressive results. In order to study how global warming affects life at the regional level, climate change models and assessments often assume that the impact will be uniform. However, temperature does not increase uniformly in space or time.

Materials and Methods
In this research, the analyzed monthly data of ERA-Interim with a resolution of 0.25*0.25 degrees during the time period of 1979-2019 from the ECMWF website (European Center for Weather Forecasting) has been used. According to the area of the Abraqo-Sirjan catchment area and the resolution of the studied data, 338 points of the entire basin were covered and studied. Also, the data of the link indices from the North Atlas and South Atlas regions, which were extracted from the NOA site at the same time as the mentioned period, were used. Pearson's correlation and linear regression tests were used to investigate the relationship between the average temperature of the watershed and the remoteness indices.

Results and Discussion
Examining the average temperature trend of the Abrago-Sirjan basin area showed that except for the months of April, May, August, and December, the other months only have an increasing trend. In April, the northern areas and parts of the west and east of the basin have an increasing trend. Other parts of the basin have no trend. In this month, 37.05% of the area of the basin is covered by the increasing trend zone and 62.94% by the non-trending zone. In May, the northern half of the basin has an increasing trend and covers 48.95% of the area of the basin. The southern half with an area of 29252.85 square kilometers is without trend and covers 51.04% of the area of the basin. In August, only a very small part of the north of the basin showed an increasing trend. This area with an area of 5728.55 square kilometers includes 9.99% of the area of the basin. Other parts of the basin, which includes about 90% of the area of the basin, do not have any trend. In the month of December, the area has been expanding and has covered more parts of the area of the basin. In this month, an increasing trend has been observed in the northern half of the basin, parts of the center, and south of the basin. The area of increasing trend in this month with an area of 43,886.6 km2 has covered 76.57 km2 of the area of the basin. The no-trend area with an area of 13422.44 km2  includes 23.42% of the area of the basin. Correlation between the average temperature of the basin and teleconnection patterns also showed that only the NAO pattern in February showed an inverse correlation at a 95% significance level with the temperature of the basin and other patterns had a direct correlation with the temperature of the basin. In January, TSA and AMO patterns, in February, NAO, TNA, AMO, AMM and NTA patterns; in March TNA, TSA, AMM and NTA patterns; In April, TNA, TSA, AMO patterns; In May, TSA and AMO patterns; In June, the AMO pattern; In July, NTA and TNA patterns; In August, AMO and NTA patterns; In September, AMM and NAO patterns; In November, AMM, AMO and TNA patterns and in December, AMM and TNA patterns have shown correlation with average temperature. In the months of February, March, May, July and September, in addition to the correlation at the significance level of 95%, correlations were also observed at the 99% level, and a major part of the basin has correlations with distant teleconnection patterns.

Conclusion
The results of investigating the average temperature trend of the Abarqo-Sirjan basin showed that the temperature in this basin has only an increasing trend, and during the period of 1979-2019, there was no decreasing trend in the average temperature of this basin. In the months of January, February, March, June, July, September, October, and November, and on an annual scale, all basins have an increasing trend. In the months of April, May, August, and December, apart from the increasing trend zone, the no-trend zone is also observed at the basin level. Among the studied patterns, the AMO pattern has shown more correlation with the average temperature of the basin than other patterns. After that, TNA, AMM, NTA, and TSA patterns have the highest correlation. In the cold months of the year, the lowest correlation between the distance and average temperature of the basin was observed, and in the warm months of the year, a larger area of the basin showed a correlation.

Introduction: Purple nutsedge (Cyperus rotundus L.) is one of the problematic weeds worldwide prevalent in tropical and subtropical regions. Tubers are major tools through which purple nutsedge is propagated, whereas its seeds have a low ability to germinate. Therefore, evaluation of the response of tubers against environmental agents is great of importance to know the germination and emergence time. Germination, in turn, is mostly affected by temperature, among other environmental factors. Various models that are recognized as the Thermal Time model have been introduced to describe the seed germination pattern against temperature. Since predicting the emergence of reproductive organs through the modeling is great of importance for improving the control strategies; the present study was carried out to investigate the response of tuber sprouting of purple nutsedge (Cyperus rotundus) against temperature using thermal time models. Material and methods: The experiment was carried out as a randomized complete block design with three replications in a germinator. Each replicate was placed on a separate shelf. For each replicate, 15 tubers were placed inside a 20 cm Petri dish on a filter paper and then 100 ml of water was added. The experiment was performed separately for constant temperatures of 10, 15, 20, 25, 30, 35, and 40 °C in absolute darkness. To analyze the data as modeling, five thermal time models were evaluated based on the statistical distributions of normal, Weibull, Gumble, logistic and log logistic. Indices such as R 2 , RMSE, RMSE%, and AICc were used to evaluate the models. Results: The results showed that all models predicted the germination response of purple nutsedge tuber with high accuracy (R 2 = 0.95). A comparison of models based on AICc values showed significant superiority of the Gumble model over other models. According to this index, there was no difference between logistic and log logistic models with normal. Among the models, Weibull was identified as the most inappropriate model. Different models estimated the final germination (Gmax) between 0.93 to 0.94 (93 to 94%). The base temperature was estimated through different models from 7.10 to 7.47 °C. Among the models, the model based on the Gumble distribution proved the skew to the right of the thermal time and Tm. According to the Gumble model, the thermal time parameters required to reach 50% germination (θT (50)) equals 123.8 ° C day and the maximum temperature for germination at 50% probability (Tc (50)) was estimated to be 46.10 ° C. Conclusion: the thermal time model based on the Gumble probability distribution was most plausible among the models. Also, a distributed right skewness related to the thermal time and Tm was proved through the Gumble model. The parameters obtained from the Gumble model can be used to predict the sprouting of purple nutsedge tubers.

This study leverages satellite imagery and on-site meteorological data to empirically assess reservoir evaporation using the PM FAO 56 method and an artificial neural network. Focused on Sistan and Baluchistan provinces, it categorizes indicators into meteorological factors—such as wind speed, air pressure, relative humidity, and lake surface temperature—and hydrological connectivity indices, including the topographic wetness and flow length indices. These indices are evaluated under various hydrological conditions like the 120-day wind period, non-windy periods, and flood discharge periods. Results highlight the significant influence of the topographic wetness and flow length indices on evaporation, especially during flood discharge periods where their impact is 5% higher than in water storage periods. Additionally, meteorological indices have a 10% greater effect during windy conditions, with wind speed being notably more influential during the 120-day wind period. This research underlines the importance of integrating meteorological and hydrological data for comprehensive water resource management and suggests the potential of using similar approaches in other regions and under different climatic conditions, paving the way for future studies in water conservation and management strategies in response to global environmental changes.

Global Solar Radiation (GSR) is one of the most widely used parameters in many fields of agriculture engineering, meteorology, and hydrology. To design a reliable solar energy system, knowledge of the radiation data is essential. Because of high costs of maintenance, calibration, and measurement of global solar radiation, the application of commonly used radiation methods such as Angström models has become popular. In spite of the simplicity and advantages of Angström models, there are many weaknesses in this simple model which may result in unreliable predictions. For instance, Angström models ignore some important factors such as site elevation, relative humidity, and atmospheric turbidity. Since 1924, many attempts have been made for solving such difficulties. During recent years, numerous works have attempted to develop statistical methods, physical approaches (Sabziparvar, 2008), empirical relations, and artificial neural network (ANN) models to predict global solar radiation a...

In this study a convective precipitation event in southwestern Iran was examined using the aerosolaware bulk microphysical scheme implemented in the Weather Research and Forecasting (WRF) model. Two simulations were conducted for this event, which included the control and polluted simulations. In the control simulation, the concentration of aerosols in the current climate was not considrable. In contrast, the aerosol concentration was increased by a factor of 5 at all grid points in the polluted simulation. The main aim was to study the effects of aerosols on cloud microphysics and precipitation. The simulated vertical temperature and wind speed profiles were compared with the radiosonde data, and the model well simulated temperature and wind speed. During the convective event, southerly to southwesterly warm and dry winds dominated, causing a substantial transport of aerosols and humidity. The reflection of shortwave radiation by clouds in the innermost domain was increased in the polluted experiment, indicating that the first indirect effect of aerosols had a significant impact on the radiative balance of the atmosphere. In contrast to the effect of clouds on shortwave radiation, the effect of clouds on longwave radiation was positive at the top of the atmosphere (TOA) because clouds reflect longwave radiation emitted by the earth's surface. The impact of an increase in concentration of aerosols on cloud development was substantial in this simulation, which contained a high convergence of vertical moisture flux and strong winds over the region. The convergence of the vertical moisture flux indicates that more water vapor is available to be condensed on aerosols, which increases the cloud water content. Thus, the number density of cloud droplets is higher in the polluted compared to the control simulation. The altitude of the maximum mass density of cloud droplets is between 3 and 6 km; due to higher specific humidity at these altitudes, higher water vapor can be condensed on condensation nuclei. Also, the mass density of rain drops is higher in the polluted compared to the control simulation up to the altitude of 3 km, which is due to a higher collision of cloud droplets in the polluted simulation. An increase in ice and snow, which indicates a higher lifting of droplets to the freezing level, is seen in this simulation with the negative convergence of vertical moisture flux. This indicates that these regions may help the large-scale collection of moisture and its lifting. On the other hand, with a divergence of moisture in the northern and the whole domain, the cloud water content decreases. In addition, with a high moisture difference, there is higher precipitation in the polluted compared to control simulations because in the humid atmosphere, there is enough water vapor to be condensed on aerosols, that leads to the formation of larger cloud droplets. Thus, the collision of cloud droplets is more efficient, and precipitation increases. In addition, due to a lower cloud base, there is less chance for the evaporation and melting of precipitation.

هدف از این پژوهش واکاوی اثر گرمایش جهانی بر درجه روزهای رشد گیاهان در دهه‌های آینده می باشد.جهت این امر نخست داده‌های دمای روزانه شبیه‌سازی شده از پایگاه داده EH5OM و تحت سناریو A1B، طی بازه زمانی(2050-2015) از موسسه ماکس پلانک آلمان استخراج شد. سپس داده‌های دمای روزانه با تفکیک 27/0*27/0 درجه طول و عرض جغرافیایی که حدودا نقاطی با ابعاد 30*30 کیلومتر مساحت ایران را پوشش می‌دهند توسط مدل اقلیم منطقه‌ای طراحی شده در مرکزفیزیک نظری عبدالسلام (ایتالیا) ریزمقیاس گردید. درنهایت آرایه‌ای به ابعاد 2140*13140 بدست آمد که سطرها بیانگر دمای روز و ستون‌ها ایستگاه‌ها (سلول) می‌باشند. جهت محاسبه درجه روز رشد آستانه دمایی 5 درجه انتخاب شد .در نهایت میانگین ماهانه درجه روز رشدگیاهان در ماتریسی به ابعاد 2140*12 در نرم‌افزار MATLAB محاسبه شد.سپس نقشه‌های میانگین ماهانه درجه روز رشد گیاهان در نرم‌افزار سورفر ترسیم گردید. نتایج نشان داد که ماه‌های تیر ومرداد بیشترین میزان میانگین ماهانه رشد گیاهان را به میزان 1000-100 درجه روز در بین ماه‌های سال دارا می‌باشند. در ماه‌های تابستان بیشینه درجه روز رشد گیا...

The aim of this study was to analyze the effects of global warming on plant growth degree day in the future decades. To this aim, simulated daily temperature data were drawn from EHOM database and under A1B scenario during 2015-2050 from Germany Plank Max. Then, daily degree data were downscaled with a separation of 0.27*.027 degrees longitude and latitude, covering around 30*30 km of Iran, by regional climate model designed at the Abdul Salam Center for Theoretical Physics, Italy. Finally, a matrix with dimensions of 13140*2140 was obtained that rows represent degree day and columns represent stations (cells). The threshold of 5 ° was selected to calculate the growth degree day. Finally, the monthly average of growth degree day was calculated in a matrix with dimensions of 12*2140 in MATLAB software. Then, the map of average of growth degree day was designed through Surfer software. The results showed that June and July had the highest average monthly growth rate in the 100-1000 degree-day. In summer months, the highest degree day was observed in Khozestan plain. With regard to the degree day in spring and summer, Iran can be divided into three areas of mountains and interior foothills, internal pits and external foothills, and plains and southern coast.

The maximum range of long-term operational predictions was limited to one year until 2018, while with the implementation of the Decadal Climate Prediction Project (DCPP), their range was increased to a decade. These predictions are made by initializing global climate models using observational data. The current study aims to predict the precipitation and air temperature of Iran and neighboring countries for the next 5 years (2022 to 2026) in three time scales of seasonal, annual and five-year, using the output of DCPP models initialized in November 2021. For this purpose, the precipitation and temperature coarse data of the MPI-ESM1.2-LR (Max Plank Institute in Germany), MIROC6 (Japan Agency for Marine-Earth Science and Technology) and CNRM-ESM2-1 (Centre National de Recherches Meteorologiques, France) models were used. The horizontal resolution of the MPI-ESM1.2-LR, MIROC6 and CNRM-ESM2-1 models are 200×200, 250×250 and 250×250 km, respectively. The correction of the output of the climate models was done based on the standard methodology proposed by the WCRP (World Climate Research Program) working group of World Meteorological Organization (WMO). To correct the raw output of precipitation and temperature of the DCPP models, the GPCC and ERA5 datasets were used for precipitation and temperature, respectively.
The results showed that Iran's precipitation is unlikely to be more than normal in any of the next five years. The highest decrease in precipitation will likely occur in 2022 and 2025, and the precipitation of the 2023 will most likely be normal. The high predictability of ENSO and the expectation of El Niño occurrence in 2023 confirm that the precipitation of Iran and neighboring countries is within the normal range for 2023. It is more likely that, in none of the next 5 years, the average temperature of the Iran will be below normal, and the temperature anomaly is at least in the range of 0.3-0.5 degree Celsius, and the largest increase is expected in the western half of Iran and the northeast region under study. The minimum and maximum temperature increase will most likely occur in 2022 and 2026 over Iran. In the studied period, the precipitation of West Asia, especially the areas adjacent to the Arabian Sea and the Red Sea, is most likely more than normal and other countries are estimated to be within the normal range. Also, the average air temperature of the next five years in West Asia will be between 0.3 and 1.2 degree of Celsius above normal, with the largest increase of 1 to 1.2 degrees occurring in eastern Turkmenistan, Tajikistan and Kyrgyzstan. It is expected that the air temperature anomaly in the Arabian Peninsula will be in the range of 0.3 to 0.5 degrees, which will be about 0.5 degrees lower than other countries in the region.

الگوی رفتاری دمای سطوح در طول شبانه‎روز و نیز در شرایط آب‎وهوایی گوناگون در محیط‎های شهری، شاید در زمینۀ مدیریت و برنامه­ریزی شهری کارآمد باشد. در این پژوهش، روند تغییرات دمای اندازه­گیری‎شدۀ چهار پوشش آسفالت، سیمان، خاک و سنگ، در محدودۀ ایستگاه هواشناسی ژئوفیزیک در شرایط جوی گوناگون واکاوی شد. با توجه به ارتباط بین دمای سطح و دمای هوا، مدل رگرسیونی محاسبۀ دمای سطوح با دمای هوا انتخاب و دمای برآوردشده با دمای اندازه­گیری‎شده در ایستگاه، واسنجی شد. نتایج روش­های آماری نشان داد اختلاف دمای کمینه و بیشینۀ سطوح (به جز آب به‎دلیل تأثیر تبخیر) در شرایط آفتابی، حدود 30 درجۀ سلسیوس است؛ در حالیکه اختلاف آنها در شرایط بارانی، ابری و بادی، به‎ترتیب به حدود 6، 10 و 20 درجۀ سلسیوس می‎رسد. همچنین، در شرایط جوی همراه با بارندگی، علاوه‎بر وقوع کمترین دامنۀ دمایی، رفتار دمایی سطوح نسبت به یکدیگر نیز دچار تغییر می‎شود. نتایج مدل رگرسیونی نشان داد که بیشترین همبستگی بین میانگین دمای هوا و میانگین دمای سطوح وجود دارد. براساس ضرایب همبستگی و ضریب کارایی ناش ـ ساتکلیف، رابطۀ رگرسیونی مورد استفاده برای ت...

Paying attention to using systems with higher efficiency in buildings is increasingly necessary due to high energy consumption in buildings and lack of energy sources. On the other hand, air conditioning systems and cooling equipment accounted for the largest percentage of building energy consumption. In this study some changes in direct evaporative cooling has been performed to reduce energy and water consumptions. A rotary cylindrical channel is assumed instead of the fixed evaporating chamber. Thus the pump is eliminated and the heat transfer coefficient is enhanced due to rotational water flow. The results are evaluated by changing input parameters such as different input speeds and different rotational speeds. The energy analysis is done and the effects of input parameters are investigated on cooling capacity, dew point effective, coefficient of performance and exergy loss.

This paper is an open access and licenced under the CC BY NC licence.
Glacier cirques show the characteristics of past glaciers and climates. In this research, the analysis of 39 glacial cirques in the catchment area of Jajrud River was done. For this purpose, Arc GIS software and Google Earth images were used. The parameters of length, width, height of the top of the circus, height of the floor of the cirques , area, perimeter, ratio of length to width, ratio of length to height of floor and ratio of width to height of floor were used to check the morphometry of cirques. For each of the morphometric parameters, statistical factors of coefficient of variation, standard deviation, average, maximum and minimum were calculated and estimated in Excel. Then R2 values or coefficient of determination were estimated for each of the parameters and a scatter diagram was drawn. Finally, the correlation matrix was estimated using the Pearson correlation coefficient for all factors. The highest abundance of cirques is located in the southwest direction. The maximum height of the cirques is 3800 meters and belongs to the geographical direction of the south.The highest correlations between length and width parameters were observed at the rate of 0.9936. The results show that the cirques in the north-facing slopes have a lower height. This indicates the high nutrition of these cirques and their significant volume in the Pleistocene period. Investigations showed that more developed cirques have more area, less height and less length to width ratio than less developed cirques.

The use of fossil fuels to supply energy to the buildings is associated with many constraints, including the reduction of the resources and the environmental degradation, and the use of free and renewable sources of energy is noteworthy to provide the heat needed for the buildings. One of the free, clean and renewable energy sources is the energy in the underground layers of Earth. Although the soil temperature in the surface layers may be influenced by the temperature of the environment, the temperature during the year at sublayers is almost constant, and in the summer the temperature of the soil is lower and in winter it is higher than the ambient air temperature. This potential can be used for the cooling and heating of buildings. In this research, the spiral coiled tube air-earth heat exchanger has to be evaluated and its performance in Tehran should be evaluated. Three dimensional, steady state numerical simulations are performed using Ansys-Fluent software. The system is simul...

In this study, the temporal and spatial variation of snow depth over the mountainous region of Zagros, in the western Iran, for the period 1979-2010 was investigated for the cold season when the probability of snow occurrences was high. For this purpose, daily gridded snow depth data relative to Era-Interim/land were retrieved from the European Centre for Medium-Range Weather Forecasts (ECMWF) and used for spatiotemporal analysis of snow in the region. Furthermore, monthly maximum, minimum and mean air temperature relative to the weather stations distributed over the region were also used to investigate the relationship between snow depth and air temperature variability in the region. In each grid point, the rate of temporal changes in the snow depth was estimated using the Sen's slope estimator, while the modified Mann-Kendall Test was applied to assess if the change identified was statistically significant. The results showed that in almost all of the studied months, especially February and March, the snow depth was significantly reduced in the region, which was statistically significant at 5% significant level. Unlike the observed statistically significant decreasing trend in the depth snow in the region, a significant increase in the maximum, minimum and average temperature was observed for all the studied months and the stations. The result suggested that the observed decrease in the snow depth in the region was related to the increasing trend in the temperature during the study period, which could be attributed to the global warming and climate change.

Low-level Jets play a key role in the formation of dust storms in arid regions. The purpose of this study was to investigate the ability of the WRF_Chem model to simulate low-level jet and determine its criteria in local dust formation in Khuzestan province. For this purpose, dust was selected during March 13 and 14, 2012. The synoptic structure and wind field were analyzed using ERA-Interim data. The simulation was also perfoed with dust shceme GOCART and AFWA and planetary boundary layer scheme ACM, Bo, MYN, MYJ, UW and YSU. Wind profiles with LLJ characteristics were illustrated of those stations that located to the right of the maximum wind 925 hPa. The LLJ broke in the midday results in a increasing surface wind speed up to 13 1 ms − and local dust emissions. The model simulated low-level jet metrics, including wind speeds 20 to 23, positive wind shear between 8 and 18 and negative shear 4 to 13 1 ms − .The WRF_Chem model simulated stronger LLJ than the ERA_I data. The correlation coefficients of 0.83 and 0.7 for surface wind speed and of 0.68 and 0.72 for PM10 obtained by MYN scheme with GOCART and AFWA models, respectively. The most matching daily variation of PM10 was obtained with the MYN boundary layer and the AFWA dust scheme. The MYN boundary layer scheme was chosen as the best scheme for local dust simulation in this dust strom. However, all of the schemes showed a surface PM10 concentration of more than PM10 observations.

بسیاری از شرایط هیدرولوژیک در دریاها به تغییر دما بستگی داشته و میزان این پارامتر عامل تعیین کننده مهمی در شرایط محیطی هر منطقه می‌باشد. تغییرات دما و باد سطحی سبب تغییر چگالی آب دریا شده و تغییر چگالی در میزان پایداری و اختلاط ستون آب دریا مؤثر است .دراین تحقیق اثر متقابل دمای سطح آب دریا (SST) بر سرعت باد سطحی درمنطقه خزر جنوبی (استان مازندران) بررسی شده است. ابتدا داده‌های دمای سطح آب دریا،توسط سنجنده AVHRR ماهواره NOAA  وداده‌های سرعت بادسطحی،توسط ماهوارهQuikSCAT برای منطقه‌ای به ابعاد 220 ×340 کیلومتر مربع در خزر جنوبی گردآوری شد. پس از تجزیه وتحلیل داده‌های ماهواره‌ای دمای سطح دریا ، تغییرات ماهانه و فصلی آن توسط نرم افزار Tecplot برای این منطقه رسم گردید.مشخص شد که میانگین دمای فصلی(بهار و تابستان) سواحل شرقی خزرجنوبی  c 87/0 ازنواحی غربی آن بیشتراست. برای بررسی اثر متقابل دمای سطح دریا برسرعت باد سطحی، چهار ایستگاه A وD (درناحیه غربی)،B وC (درناحیه شرقی)درخزرجنوبی انتخاب گردید.سپس نمودار سری زمانی دما، اختلاف دمابین چهار ایستگاه، سری زمانی سرعت بادو سری زمانی اختلاف سرعت باد...

در مطالعة حاضر به پیش‌نگری اثرهای تغییرات آب و هوایی بر درجة روزهای نیاز سرمایشی و گرمایشی ایستگاه‌های منتخب ایلام و دهلران از طریق دادة مشاهده‌ای و دادة شبیه‌سازی‌شدة دورة آینده براساس برون‌داد مدل‌های CMIP5 پرداخته شد. بررسی دادة واقعی دورة پایه نشان داد که روند افزایشی و کاهشی به‌ترتیب برای نیاز سرمایشی و گرمایشی در ایستگاه‌های ایلام و دهلران وجود دارد. در دورة آینده نیاز گرمایشی در هر دو سناریوی RCP8.5 و RCP4.5 نسبت به دورة پایه کاهش خواهد یافت؛ به‌طوری‌که تا افق 2090 در سناریوی بدبینانه در ایستگاه ایلام به‌طور متوسط 20 درجة روز و در ایستگاه دهلران 10 درجة روز نسبت به دورة پایه کاهش رخ خواهد داد. همچنین، در دورة آینده نیاز سرمایشی نسبت به دورة پایه افزایش خواهد یافت. در ایستگاه‌های ایلام و دهلران در دورة آیندة میانی و دور به‌طور متوسط به‌ترتیب 9، 14، 10، و 20 درجة روز بر مقدار نیاز سرمایشی افزوده خواهد شد. تحت شرایط تغییر اقلیم دورة آینده، تعداد روزهای همراه با نیاز سرمایشی در این ایستگاه‌ها به جلو خواهد افتاد و، از طرفی دیگر، بازة زمانی همراه با نیاز سرمایشی مورد نیاز در طی سال...

هدف این مقاله واکاوی همدیدی هماهنگی نوسانات دما در ایران و اروپا در راستای استفاده از این ارتباط برای تحلیل اقلیم گذشتة ایران است. بدین منظور از داده‌های سی ایستگاه در داخل کشور و نوزده ایستگاه در اروپا با طول دورة آماری پنجاه سال و بیشتر (1951-2010) استفاده شد. داده‌ها عبارت بود از متوسط دماهای حداقل، حداکثر و میانگین روزانه؛ فشار تراز دریا؛ ارتفاع ژئوپتانسیلی؛ و مؤلفة نصف‌النهاری (v). به منظور گروه‌بندی الگوهای گردشی از حالت S تحلیل مؤلفه‌های اصلی و تحلیل خوشه‌ای سلسه‌مراتبی وارد (Ward) استفاده شد. نتایج نشان ‌داد چهار حالت مختلف دمایی (سرد/ گرم هماهنگ و دوره‌های سرد/ گرم مخالف) بین ایران و اروپا مشاهده می‌شود. در ایران، سال 2010 گرم‌ترین و سال 1972 سردترین سال شناخته شد. واکاوی نقشه‌های همدید در تراز‌های مختلف جوی طی دوره‌های سرد نقش مهم شکل‌گیری سامانه‌های بندالی و پشتة قوی روی اروپا و اطلس را در وقوع دمای حداقل فرین در ایستگاه‌های ایران نشان داد، به‌طوری که شکل‌گیری این سامانه‌ها در ابتدا سبب رخداد سرما برای اروپا و در ادامه به‌دنبال جابه‌جایی سامانه‌های جوی، سبب فرارفت سرمای ش...

To assess thermal comfort of residences, several different factors as meteorological, physiological, and socio-cultural, should be considered. The integrated effect of these variables on thermal stress can be obtained and evaluated using thermal comfort indices. Thermal comfort as a biometeorological index is of special importance. The purpose of this research is to analyze the seasonality and trend of heat stress in Iran in the period from 1981 to 2020. In this research, the Universal Thermal Climate Index (UTCI) index as a common thermal index was evaluated for outdoor thermal comfort, using ERA5 dataset. ERA5 is the fifth generation ECMWF reanalysis data for the global climate and weather for the past 4 to 7 decades. The ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave, and land-surface quantities. An uncertainty estimate is evaluated by an underlying 10-member ensemble at three-hour intervals. Such uncertainty estimates are closely related to the information content of the available observing system which has improved considerably over time. They also indicate flow-dependent sensitive areas (Hersbach et al., 2020). Also, root mean square error (RMSE), Percent bias (PBIAS), Nash-Sutcliffe model efficiency coefficient (NSE), and Root Mean Standard Deviation Ratio (RSR) metrics were used to evaluate the quality of ERA5 dataset. The seasonal variability of the UTCI index shows that this index has significant regional heterogeneity in Iran. The increase in UTCI from the north to the south of Iran leads to an increase in thermal stress. The spatial distribution of areas that do not have thermal stress during the hot period of the year are mainly consistent with the altitudes. During the cold seasons of the year, areas with elevations of more than 3,000 meters in Iran have moderate cold stress. The investigation of the trend of thermal stress during the last four decades, which was analyzed with the modified Mann-Kendall test, shows that the UTCI in Iran has a dominant increasing trend. The UTCI index shows an increasing trend in the spring and autumn seasons by 100%, and in the winter and summer seasons at 99.83 and 99.75% of the country, respectively. The maximum significant increasing trend of the index at the level of 0.05 was achieved in the spring. In the same way, the highest value of Sen's slope estimator test of the area-averaged trend is also seen with the value of 0.52 o C in this season in the study period. The results of this study for climatology and the trend of the UTCI index in Iran show that: 1-There is a close relationship between heat and cold stresses in Iran and topography, but this relationship is not a linear; 2-Along with global warming, the UTCI index in Iran during the years 1981-2020 has shown an increasing trend; 3-In general, areas with UTCI cold stress in the country are decreasing and areas with heat stress are increasing; 4-One of the key findings in this study is the significant increase in trend of the UTCI index in the spring season.

Knowledge of spatial and temporal variations of soil pore-water pressure in a slope is vital in hydrogeological and hillslope related processes (i.e., slope failure, slope stability analysis, etc.). Measurements of soil pore-water pressure data are challenging, expensive, time consuming, and difficult task. This paper evaluates the applicability of artificial neural network (ANN) technique for modeling soil pore-water pressure variations at multiple soil depths from the knowledge of rainfall patterns. A multilayer perceptron neural network model was constructed using Levenberg-Marquardt training algorithm for prediction of soil pore-water pressure variations. Time series records of rainfall and pore-water pressures at soil depth of 0.5 m were used to develop the ANN model. To investigate applicability of the model for prediction of spatial and temporal variations of pore-water pressure, the model was tested for the time series data of pore-water pressure at multiple soil depths (i.e...

Information of soil pore-water pressure changes due to climatic effect is an integral part for studies associated with hill slope analysis. Soil pore-water pressure variations in a soil slope due to rainfall were predicted using Artificial Neural Network (ANN) technique with Thin Plate Spline (TPS) radial basis function. A radial basis function (RBF) neural network with network architecture of 8-36-1 (input-hidden-output) was selected to develop RBF model. Number of hidden neurons was selected using trial and error procedure whereas spread of the basis function was established using normalization method. Time series data of rainfall and pore-water pressure was used for training and testing the RBF model. The performance of the model was evaluated using root mean square error, coefficient of correlation and coefficient of efficiency. The results of the model prediction revealed that the model produced promising results indicating that TPS basis function is able to predict time series of pore-water pressure responses to rainfall. Comparison with other studies showed that the RBF model using TPS basis function can be used as alternate of Gaussian basis function for prediction of soil pore-water pressure variations.

Knowledge of soil pore-water pressure variation due to climatic changes is fundamental for slope stability analysis and other problems associated with slope stability issues. This study is an application of Radial Basis Function Neural Network (RBFNN) modeling for prediction of soil pore-water pressure responses to rainfall. Time series data of rainfall and pore-water pressures were used to develop the RBFNN prediction model. The number of input neurons was decided by the analysis of auto-correlation between pore-water pressure data and cross-correlation between rainfall and pore-water pressure data. Establishing the number of hidden neurons by method of self learning network architecture determination and also by trial and error method was examined. A number of statistical measures were used for the evaluation of the network performance. Prediction results with a network architecture of 8-10-1 and a spread σ = 3.0 produced the lowest error measures (MSE, RMSE, MAE), highest coefficient of efficiency (CE) and coefficient of determination (R 2). The results suggest that RBFNN is suitable for mapping the non-linear, complex behavior of porewater pressure responses to rainfall. Guidelines for choosing the number of input neurons and eliminating possibility of model over-fitting are also discussed.

Agriculture sector has had a significant grow thing role in the Iran, but growth and development in this sector is not possible without a suitable risk management. This study has tried to predict and measure the risk from changing weather condition on agriculture products of Gorgan County using Mont Carlo Stimulation as one of the powerful tools on risk analysis. The results showed that the highest level of production risk is for the crop such as barley, soya and wheat and the least level of the risk is for the crops such as bean, clover and pea, respectively. To sum up, the research results indicated a significant effect of production risk in this area and therefore managers and policy makers need to notice it.

One of the ways to assess the rainfall trends in the past and present is through trend analysis of time series of rainfall at different scales of time. The aim of this study was to analyze the spatial and temporal trend of annual rainfall in Iran using Aphrodite's database. In this study, using the quality- controlled resulting outputs of Aphrodite cell precipitation database in order to assess the trend of the country's Iran's annual rainfall. The base data for the period of 1950 to 2007, and with the spatial resolution is of 0.25 × 0.25 and 0.5 × 0.5 degrees were used. Iran's annual precipitation trend was calculated using non-parametric Mann-Kendall method and in order to estimate the slope of trend the age line slope method was conducted. The results of the analysis of these data showed that in the time series of mean cell and Iran precipitation stations of Iran precipitation, there is was no significant increasing or decreasing trend at confidence levels of 99 and 95%. Age test statistics at the assessed confidence levels showed that the average rainfall of Iran has increased by about 0.439 mm/yr per year. In addition, the minimum and maximum decrease in rainfall at 99% level was -0.476 and 1.321 mm respectively. While the minimum and maximum Iran cellular precipitation at 95% level was estimated to be -0.221 and 1.088 mm respectively.

کچ هدي شيپ هنيمز و فده : هب از ي و دعوم زا رتدوز نام ي از ا ي لبق نام زا ٣٧ رادراب لماک هتفه ي از ي قلاطا سردوز نام م ي دوش ضعب . ي از للع زا ي دودح ات سردوز نام ي اما تسا هدش هتخانش ناکم ي مس اه ي ب ي ژولو ي نآ ًلاماک ن هدش هتخانش ي دهاوش .تس ي ناشن هک دراد دوجو م ي دهد از ي طبترم لاس فلتخم لوصف اب سردوز نام م ي دشاب زا . ماگ اه ي از شهاک رد مهم ي سردوز نام ي ، حم لماوع نتفا ي ط ي رثؤم ا رب ي ن ناکم ي مس اه ي ب ي ژولو ي .تسا ا ي قحت ن ي ق فدهاب عت يي ن زوت ي لصف ع ي از ي نام بدرا ناتسرهش رد سردوز ي ماجنا ل ي .تسا هتفا و داوم شور اه : ا ي اب شهوژپ ن تيهام صوت ي ف ي لحت ي ل ي ور اب ي درک هتشذگ ،رگن ط ي نامز هزاب ي درورف ي ن هام لاس ١٣٩٠ ات هامدنفسا ١٣٩٤ ، عضو ي ت ٧٥٩ رادراب نز ) ١٠٨٦٠ = n ( هک ط ي هتفه اه ي ٣٦ ٢٠ راچد هضراع ي از ي هب هجوت اب دندوب هدش سردوز نام هدنورپ اه ي ب ي ناتسرام ي و هداد اه ي لقا ي م ي ب رد ي نلابس ناتسرام بدرا ناتسرهش ي ل ، دروم هيزجت و ليلحت .تسا هداد رارق ا رد ي ن شهوژپ هيزجت و ليلحت هداد اه ي رامآ ي زا هدافتسا اب شور ياه رامآ ي صوت ي ف ي گتسبمه طباور و ي ، ...

Stream flow forecasting on a monthly time scale is essential for optimal water resources management and planning. In this paper using the predictions obtained from the ECMWF climate model, monthly stream flow forecast was made in Shahroud river Subbasin, part of Sefidrood basin northwest of Iran. To this end, using monthly precipitation forecasts from ECMWF climate model in tandem with SVR data-driven modelling, as a rainfall-runoff model, the stream flow was predicted based on the predicted precipitations. First, the results of precipitation forecast for the desired historical period and up to a 3-month forecast horizon for the study area were obtained from the Climate Data Store. Then, by using the SVR driven model, a linked Climate-Data-driven model was developed to predict the flow up to a 3-month forecast horizon. The results showed that flow forecasting based on climate forecasting models for the forecast horizon of the next month is more accurate than that of two and three mo...

The current study sought to understand and explore the field of technological advancements in these modern times, especially after the covid-19 surge, from the perspective of Business and Management. The development of science and technology will define a new meaning and application of the management and administration of the firm. Due to modernization in technology and automation of work in managerial work, the processes of businesses become easy to handle, maintain and control. Technologies have made different elements of business such as manufacturing, communication, purchase and sales, marketing, trading and advertising became easier than before. With the present technology, machines are able to process the functions and give the desired outputs after feeding the respective commands in that machine. Technology also aids the productivity of the employees, to work better, easier and with convenience. With the betterment of AI (Artificial Intelligence), businesses are able to provide satisfaction to the customer, giving response at time, reviewing, analyzing their issues, etc., which saves their overhead costs and expands the capabilities of the firms. With many upcoming advancements like MR (Mixed Reality), Metaverse, IOT (Internet of Things), Robotic Process Automation, Quantum Computing and many others, it will surely impact the management work in the work in both positive and negative ways. This study focuses to cover both the impacts and make the future even better, majorly comprising of the Metaverse and upcoming digitalization and technological evolutions and the rise of new tech-gen.

Pore-water pressure responses are vital in many aspects of slope management, design and monitoring. Its measurement however, is difficult, expensive and time consuming. Studies on its predictions are lacking. Support vector machines with linear kernel was used here to predict the responses of pore-water pressure to rainfall. Pore-water pressure response data was collected from slope instrumentation program. Support vector machine meta-parameter calibration and model development was carried out using grid search and k-fold cross validation. The mean square error for the model on scaled test data is 0.0015 and the coefficient of determination is 0.9321. Although pore-water pressure response to rainfall is a complex nonlinear process, the use of linear kernel support vector machine can be employed where high accuracy can be sacrificed for computational ease and time.

Osmotic dehydration can be viewed as an alternative method for drying of food materials with advantages of retention of gloss, texture & colour of dried products. Artificial neural network is emerging as a modeling tool for complex operations involving non linear multivariable relationships. The present work is aimed at estimation of the osmotic drying rates & weight reduction of beetroot slices as a function of concentration of sodium chloride, time & temperature using artificial neural network. Based on the observations, results & discussion, it can be said that, beetroot slices can be partially dewatered by osmotic dehydration in salt solution and percent weight loss is from 10 to 29 % depending upon the operating parameters. It can be concluded that the present work has successfully demonstrated the potential of ANN in modeling of osmotic dehydration of beetroot slices with high accuracy.

In this research a prediction model for the cellular uptake efficiency of nanoparticles (NPs), which is the rate that NPs adhere to a cell surface or enter a cell, is investigated via an artificial neural network (ANN) method. An appropriate mathematical model for the prediction of the cellular uptake rate of NPs will significantly reduce the number of time-consuming experiments to determine which of the thousands of possible variables have an impact on NP uptake rate. Moreover, this study constitutes a basis for targeted drug delivery and cell-level detection, treatment and diagnosis of existing pathologies through simulating NP-cell interactions. Accordingly, this study will accelerate nanomedicine research. Our research focuses on building a proper ANN model based on a multilayered feed-forward back-propagation algorithm that depends on NP type, size, surface charge, concentration and time for prediction of cellular uptake efficiency. The NP types for in-vitro NP-healthy cell interaction analysis are polymethyl methacrylate (PMMA), silica and polylactic acid (PLA), all of whose shapes are spheres. The proposed ANN model has been developed on MATLAB Programming Language by optimizing a number of hidden layers (HLs), node numbers and training functions. The datasets are obtained from in-vitro NP-cell interaction experiments conducted by Nanomedicine and Advanced Technology Research Center. The dispersion characteristics and cell interactions with different NPs in organisms are explored using an optimal ANN prediction model. Simulating the possible interactions of targeted NPs with cells via an ANN model will be faster and cheaper compared to the excessive experimentation currently necessary.

Neural networks are well known for their ability to model non linear functions, but as statistical methods usually does, they use a no parametric approach thus, a priori knowledge is not obvious to be taken into account no more than the a posteriori knowledge. In order to deal with these problematics, an original way to encode the knowledge inside the architecture is proposed. This method is applied to the problem of the evapotranspiration inside karstic aquifer which is a problem of huge utility in order to deal with water resource.

Employee disengagement has become a big concern for retail companies to increase productivity. In the United States, retail companies lose at least $96 billion in revenue every year. Using transformational leadership theory, the purpose of this qualitative multiple case study was to explore strategies needed by retail companies to improve employee engagement and increase workforce productivity. From data collected using face-to-face open-ended interview questions with 4 retail business leaders located in the eastern region of the United States and who have experienced this phenomenon; three major themes emerged. The primary themes that emerged were professional development, collaboration, and work-life balance. Based on the tenets of transformational leadership, the findings indicated that the 3 major themes were strategic to promote employee engagement, improve workforce productivity and financial stability. The findings also reveal that potential leaders could use transformational...

The purpose of this qualitative multiple case study was to explore strategic managerial tools small business owners or managers use to sustain growth over time through Schumpeter’s innovation theory. Five small business owners located in North America were interviewed through face-to-face open-ended interview questions. The results from data analysis collected through interview responses indicated that small business owners or managers attested that their businesses were developed when using innovation as a strategic managerial tool. By contrast, small business owners or managers agreed that the lack of using innovation and understanding the reality of the current business context had an adverse impact on business growth. The findings of this study may also provide small business leaders or managers with critical information to ensure success and better understand strategies small businesses need for long-term growth.

The purpose of this qualitative multiple case study was to explore strategic managerial tools small business owners or managers use to sustain growth over time through Schumpeter’s innovation theory. Five small business owners located in North America were interviewed through face-to-face open-ended interview questions. The results from data analysis collected through interview responses indicated that small business owners or managers attested that their businesses were developed when using innovation as a strategic managerial tool. By contrast, small business owners or managers agreed that the lack of using innovation and understanding the reality of the current business context had an adverse impact on business growth. The findings of this study may also provide small business leaders or managers with critical information to ensure success and better understand strategies small businesses need for long-term growth.

Osmotic dehydration can be viewed as an alternative method for drying of food materials with advantages of retention of gloss, texture & colour of dried products. Artificial neural network is emerging as a modeling tool for complex operations involving non linear multivariable relationships. The present work is aimed at estimation of the osmotic drying rates & weight reduction of beetroot slices as a function of concentration of sodium chloride, time & temperature using artificial neural network. Based on the observations, results & discussion, it can be said that, beetroot slices can be partially dewatered by osmotic dehydration in salt solution and percent weight loss is from 10 to 29 % depending upon the operating parameters. It can be concluded that the present work has successfully demonstrated the potential of ANN in modeling of osmotic dehydration of beetroot slices with high accuracy.

Pore-water pressure responses are vital in many aspects of slope management, design and monitoring. Its measurement however, is difficult, expensive and time consuming. Studies on its predictions are lacking. Support vector machines with linear kernel was used here to predict the responses of pore-water pressure to rainfall. Pore-water pressure response data was collected from slope instrumentation program. Support vector machine meta-parameter calibration and model development was carried out using grid search and k-fold cross validation. The mean square error for the model on scaled test data is 0.0015 and the coefficient of determination is 0.9321. Although pore-water pressure response to rainfall is a complex nonlinear process, the use of linear kernel support vector machine can be employed where high accuracy can be sacrificed for computational ease and time.

Knowledge of spatial and temporal variations of soil pore-water pressure in a slope is vital in hydrogeological and hillslope related processes (i.e., slope failure, slope stability analysis, etc.). Measurements of soil pore-water pressure data are challenging, expensive, time consuming, and difficult task. This paper evaluates the applicability of artificial neural network (ANN) technique for modeling soil pore-water pressure variations at multiple soil depths from the knowledge of rainfall patterns. A multilayer perceptron neural network model was constructed using Levenberg-Marquardt training algorithm for prediction of soil pore-water pressure variations. Time series records of rainfall and pore-water pressures at soil depth of 0.5 m were used to develop the ANN model. To investigate applicability of the model for prediction of spatial and temporal variations of pore-water pressure, the model was tested for the time series data of pore-water pressure at multiple soil depths (i.e., 0.5 m, 1.1 m, 1.7 m, 2.3 m, and 2.9 m). The performance of the ANN model was evaluated by root mean square error, mean absolute error, coefficient of correlation, and coefficient of efficiency. The results revealed that the ANN performed satisfactorily implying that the model can be used to examine the spatial and temporal behavior of time series of pore-water pressures with respect to multiple soil depths from knowledge of rainfall patterns and pore-water pressure with some antecedent conditions. eventually trigger a slope failure [1]. Time series of soil porewater pressures in response to climatic conditions, therefore, exhibits highly dynamic, nonlinear, and complex behavior [2]. Knowledge of the variation in soil pore-water pressure is of prime importance in hydrogeological studies like slope stability analyses, seepage analyses, engineered slope design, and studies related to hillslope hydrological responses [1, 3, 4]. However, field instrumentation to get knowledge of porewater pressure in a slope is challenging and difficult task. Usually, pore-water pressure information needed for such purposes either is obtained from previous field measurements made on a different site or is measured through a field instrumentation program. Such practices pose some concerns, like appropriateness of the use of pore-water pressure data from a different site whose climatic and geographic

330 Website: www.ijbmr.forexjournal.co.in Innovative Strategies for Small Retail Companies' ░ ABSTRACT: Forty-one percent of small American retail companies fail to succeed beyond 5 years. The purpose of this multiple case study is to explore strategies small retail business owners need for sustainability beyond 5 years. This study's data were collected through face-to-face interviews with 4 small successful retail business owners located in the United States southeastern region. Based on the conceptual framework of Schumpeter’s innovation theory, the paper provides 3 strategies that may prevent failure and lead to small business sustainability: passion and determination, market development and customer satisfaction, and business model innovation.

Temperature assessment and forecasting is one of the most practical estimates of climatic elements. Today, the agricultural and industrial sectors are highly dependent on the temperature conditions. Temperature is one of the most important climatic meters that is one of the main factors in the climate identity of each region. The purpose of this study is to make a model for predicting the average monthly seasonal temperature of selected stations in Lorestan province, including Al-Shatrami region. Identification and detection of vulnerabilities in the infrastructure of Aleshtar districts in the conditions of climate change. And due to the inadequacy of the 30-year time series of Al-Ashtarl, neighboring cities such as Khorramabad-Aleshtar-Borujerd synoptic stations have been used, because the artificial neural network method has a great ability to simulate and predict atmospheric elements. And the weather, especially the temperature. To model and predict the seasonal monthly temperature, the r programming tool software of the fOre gast package has been used. Two tests of estimator trend analysis have been used. The 30-year time series trend of these elements was examined during the basic statistical period (1989-2019). The climate cycle was reported and extracted under two scenarios: NNAR and forEgast. The artificial neural network is one of the most powerful models capable of receiving and displaying complex Data input and output is one of the most widely used neural network (NNA) models to determine the best network inputs.

Forty-one percent of small American retail companies fail to succeed beyond 5 years. The purpose of this multiple case study is to explore strategies small retail business owners need for sustainability beyond 5 years. This study's data were collected through face-to-face interviews with 4 small successful retail business owners located in the United States southeastern region. Based on the conceptual framework of Schumpeter's innovation theory, the paper provides 3 strategies that may prevent failure and lead to small business sustainability: passion and determination, market development and customer satisfaction, and business model innovation.

It has been recognized that pore water pressure (PWP) changes in response to precipitation play a critical role in rainfall-triggered landslides. Tank models as a kind of undetermined model are widely applied for estimating groundwater levels in slopes. Most of these applications treat the tank models as a theoretical model. Therefore, in this study, physical tank experiments are reported, indicating an evaluation of three typical conceptual tank models (i.e., simple tank model, surface runoff tank model, and lateral water flow supply tank model). To reduce the slope structure controlling affection, the study takes homogenous soil material as the simulation of the slope mass. The experimental results demonstrated how the groundwater tables producing pore water pressure were affected by infiltration time lags, surface runoff, and lateral flow.