رضا جعفری

This research delves deeply into the issues and challenges associated with artificial intelligence (AI) and moral judgment, seeking to answer the fundamental question of how AI can play a role in human moral decision-making. 

The study employs an analytical-descriptive approach, utilizing library research to examine existing literature and resources related to AI, ethics, and moral judgment. 

AI, through complex algorithms and machine learning, is capable of processing information and making decisions across various domains. The application of AI in moral judgment offers benefits such as increased accuracy, speed, and impartiality. However, several challenges persist, including a lack of understanding of moral nuances, bias, and a deficiency in creativity and innovation. 

Currently, fully entrusting moral judgment to AI is unreliable. AI can serve as a tool to assist humans in the moral judgment process, but final decision-making should remain with humans.

Infection of the lacrimal sac (dacryocystitis) can lead to serious complications, including orbital cellulitis, orbital abscess, meningitis, and cavernous sinus thrombosis, all of which pose significant threats to human health. This condition is often associated with nasolacrimal duct obstruction, which prevents normal tear drainage and creates an environment conducive to microbial growth. Timely identification of the causative agents of dacryocystitis is critical, as untreated or improperly managed infections can rapidly worsen, leading to systemic infections or even life-threatening conditions. This study aims to investigate the spectrum of microbiological agents causing dacryocystitis to control infection and provide appropriate treatment for patients with chronic nasolacrimal duct obstruction.

This cross-sectional study included patients diagnosed with chronic dacryocystitis who candidated for dacryocystorhinostomy (DCR) surgery. Routine ophthalmic examinations, including visual acuity, slit-lamp examination, and fundoscopy, were performed for all patients. Ocular discharge samples were sent to the laboratory at the Pediatric Infectious Disease Research Centre, Bu-Ali Sina Hospital, Sari. Each sample was incubated in blood agar, chocolate agar, EMB, and Sabouraud dextrose agar under aerobic conditions at 37°C for 24 hours and simultaneously in chocolate agar and blood agar under anaerobic conditions at 37°C for 48 hours. Catalase tests were used to distinguish Staphylococcus from Streptococcus bacteria, coagulase tests to differentiate Staphylococcus aureus from other Staphylococcus species, and oxidase tests to identify Pseudomonas and other Gram-negative bacteria. For detecting fungal infections, samples were stored on Sabouraud dextrose agar at 25-27°C for two weeks. Gram staining was performed on observed colonies, followed by microbiological diagnostic and differential tests based on bacterial species. Data were analyzed using SPSS software, described as means, and compared using the chi-squared test.

Of the 50 patients with chronic dacryocystitis included in the study, with a mean age of 56.9±15.28 years (range, 31 to 86 years), 34(68%) were female, and 16(32%) were male. Gram-positive bacteria accounted for 42 cases (84%) of isolated organisms, while gram-negative bacteria accounted for 8 cases (16%). Pus reflux was detected in 84% of cases, significantly more often with gram-positive organisms(P=0.03). The most common pathogens were Staphylococcus epidermidis (22 cases, 44%), Staphylococcus aureus (10 cases, 20%), and Streptococcus pneumoniae (6 cases, 12%). No significant correlations were found between sex and age with the number and type of organisms (P=0.77).

Gram-positive bacteria are the main cause of chronic dacryocystitis infections, and demographic factors such as age and gender do not influence the type of infection. Staphylococcus epidermidis and Staphylococcus aureus have been identified as the predominant pathogens responsible for this condition. Understanding the microbiological spectrum can improve disease diagnosis and lead to more effective treatment strategies. This information is essential to help clinicians select appropriate, targeted antimicrobial treatments to more effectively control infections, reduce the risk of antimicrobial resistance, prevent serious complications and systemic infections, and ultimately improve patient outcomes.

The first step in understanding basins is measuring different climatic and hydrological variables and examining their relationships. Primary variables include temperature, precipitation, evapotranspiration, water infiltration rate in the soil, flow discharge, etc. Meanwhile, precipitation is one of the most important and effective variables. The inappropriate distribution of rain gauge stations in different regions of developing countries, on the one hand, and the development of remote sensing sciences on the other hand, have led to the increasing use of precipitation products estimated from satellite images. Therefore, it is important to know the characteristics of these products and their possible errors. One of these products is Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) satellite-based precipitation data. Over time, this satellite product has been developed and introduced as PERSIANN-CCS and PERSIANN-CDR. One of the shortcomings of these products is that in humid and arid areas, they report  underestimation and overestimation of precipitation, respectively. To solve this problem, PERSIANN product development experts designed a new product, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Dynamic Infrared Rain Rate near real-time (PDIR-Now) product, in 2019. This product creates a dynamic relationship between the precipitation rate and cloud brightness temperature by consideringground conditions affecting the precipitation phenomenon. This has led tosignificant advantages in this algorithm compared to other quantitative precipitation estimation algorithms.

In this research, the performance of the PDIR-Now product was analyzed. Chaharmahal and Bakhtiari Provinces, despite their small area, have about 10% of the country's water resources and play an essential role in supplying water resources for the neighboring provinces; therefore, it was chosen for this research. Its area is 16421 km2, located in the southwest and part of the western mountainous belt of Iran, and with an average height of 2282.7 m above sea level, it is a high-altitude region in terms of topography. To conduct this research, 27 rain gauge, climatology, and synoptic stations that contained data for a common period (2005 to 2020) were selected and their precipitation information was collected monthly and annually, removing statistical deficiencies in some years. In the next step, PDIR-Now information was extracted at scales corresponding to the data of gauge stations on the reference site, and 208 received images were georeferenced and processed in the ArcMap environment. Then, these values were compared with the corresponding precipitation values of ground stations using three coefficients: Nash-Sutcliffe (NS), correlation (R), and relative bias (RB). In the next step, using the IDW geostatistics method, zoning maps of the province were created based on the correlation coefficient value, separately for each of the 12 months of the Gregorian year and also on an annual scale.

The results indicate that the best relationship between PDIR-Now and the precipitation data from ground stations is established in November, where 100% of the stations have an R higher than 0.5 at the 5% significance. Also, in this month, 88.8% of the stations recorded NS values greater than 0.5. The lowest level of correlation was related to May, where 33.3% of the stations had an R greater than 0.5, and 11.1% of the stations showed NS values greater than 0.5. In general, the best relationship is established between PDIR-Now data and ground station precipitation values in the rainy months, especially when rain dominates over other forms of atmospheric precipitation. The weakest relationship is related to January in the rainy season (November to April). In this province, atmospheric precipitation in January is mostly in the form of snow. Since the characteristics of clouds, including surface temperature and brightness temperature, differ in rain and snow conditions, this could resulted in the lack of occurrence in January. Additionally, on an annual scale, 74.07% of the stations have an R greater than 0.5, and 55.5% recorded NS values above 0.5. The best correlation is for Armand station with coefficients of 0.63, 0.83, and 0.01 for NS, R, and RB, respectively. In contrast, the same coefficients for Bardeh station were less than 0.00, 0.35, and 0.356, respectively, indicating the lowest fit between the two data groups at this station.

The general results show that PDIR-Now performs better in the rainy season in the southern and central regions, which are located in the low-altitude areas of the province, where precipitation amounts approach the average. In the low-rainy season, stations with precipitation levels close to the average, rather than the minimum, yield better results. These results were consistent with the findings of other research conducted in this field in Iran and the world in the following ways: 1) PDIR-Now, like many other products, underestimates heavy rainfall but performs better in estimating low and medium rainfall. 2) The performance of most satellite-based precipitation products decreases for altitudes above 1000 m. 3) PERSIAANN satellite-based products are more accurate at the monthly scale than at the annual scale. Additionally, the relationship between ground station data and satellite products in the central regions of the province, which receive average rainfall, consistent with the results of this research. In the present study, the density of rain gauge stations was low in the steep and inaccessible parts of the province, and errors in rain gauge data recording remain a Limitation. However, the number of long-term stations with sufficient statistical intervals in other regions is a significant advantage.

Fast collection, low cost and stability of tax revenues have caused countries to turn to this type of income as a main and stable income. In this research, the identification of the macro trends and future research of Iran's tax system from the perspective of science and technology as a basic priority in planning, policy-making and decisions in the tax field based on the future-making drivers affecting the tax system is examined. The data collection method is descriptive-survey type and based on the type of combined data and the future-shaping drivers affecting the field of Iran's tax system in the next 20 years were identified using expert panel methods, open questionnaire, interview and fuzzy Delphi in 1402. To select experts from professors and PhD students of accounting, employees of the Tax Affairs Organization, judgmental and snowball sampling methods were used. The development of intelligent accounting software and systems, business intelligence, and the expansion of personal technologies are the most important influential drivers in the future, and the increase in cybersecurity challenges in business and the expansion of the application of new sciences and technologies in business should be given serious attention. The results of the present study can lead to the development of guidelines for the use of new technologies that will reduce cybersecurity challenges, facilitate tax processes, and strengthen the exploitation of science and technology with the aim of improving the accuracy, transparency, and efficiency of tax processes and contributing to the transformation of the tax system in Iran,

and water management in the field. Therefore, its measurement has special importance. The surface soil has a great diversity in soil moisture and different methods were used to measure this property. Due to the problems of contact methods of soil moisture measurement, remote sensing has gained attention because of the possibility of analyzing and monitoring soil moisture on a large and global scale. In this research, satellite data and moisture measured in selected fields located in Hormoaz Abad Plain have been analyzed and compared. Sentinel-2 satellite data have been analyzed using the Google Earth Engine system. The results of this research showed that the use of triple indices in the OPTRAM model to estimate moisture is not very accurate, but the use of the EVI plant index has provided better results than the other two indices.

Awareness of vegetation and climate changes is necessary for the correct planning of ecosystem management. In the present study, the temporal and spatial changes of vegetation cover and also land surface temperature and precipitation parameters in the natural vegetation cover of different bioclimates of Isfahan province from 1986 to 2022 were investigated based on  Google Earth Engine system, Landsat satellite and PERSIANN-CDR data. The trend and the amount of changes of these parameters were obtained with the Mann-Kendall and Sen's slope tests. The results showed that the minimum amount of NDVI vegetation was 0.26 in arid and warm climate in 2022 and its maximum was 0.90 in humid and cold climate in 2002. The lowest and highest of land surface temperature was obtained in humid and cold climate and warm and arid climate as 290.61 and 328.54 Kelvin in 2003 and 2022, respectively. The minimum and maximum precipitation were 88.72 mm and 598.17 mm in 2004 and 2001, respectively, in the semi-arid and warm climate and humid and cold climate. The Z statistics of Mann -Kendall test and the amount of Sen's slope for the parameters of vegetation and temperature in most climates were decreasing and increasing, respectively. A decreasing trend with high statistical significance of precipitation was observed only in humid and cold climate (Z equal to -2.08 with a slope of -3.34). In general, the results showed that the vegetation of the province is decreasing, but the temperature is increasing.

Premature birth is common worldwide and can affect the development of brain structures, such as the optic nerve. Eye diseases are very common in low birth weight children and premature children. Vision defects associated with premature birth include Retinopathy of premature babies (ROP), decreased visual acuity, strabismus, amblyopia, and various types of refractive errors. Studies have shown that the occurrence of myopia in children born prematurely is negatively correlated with gestational age and birth weight and is directly related to the severity of retinopathy of prematurity(ROP). Advances in neonatal intensive care unit services have led to a significant increase in the survival rate of premature infants and subsequent medical problems in these children. This study was designed to determine the prevalence of refractive errors in 4-6-year-old children born with a birth weight of less than 2000 grams and gestational age of less than 34 weeks and normal children admitted to the neonatal department of Bou-Ali Sina Hospital in Sari in 2016-2018.

This study was a cross-sectional descriptive study on 43 children aged 4-6 years who were born with a weight of less than 2000 grams and gestational age of less than 34 weeks, and 17 normal children who were admitted in the neonatal department of Bou-Ali Sina Hospital due to neonatal jaundice. The study subjects were in four groups: 12 subjects in group A (ROP cases without injection), 14 subjects in group B (ROP cases with injection), 17 subjects in group C (cases with birth weight less than 2000 grams and gestational age less than 34 weeks without ROP), and 17 normal subjects in group D. Uncorrected distance visual acuity was measured using E-chart. Dry and cyclo refraction (using cyclopentolate 1%) were measured using an autorefractometer. Finally, the fundus was examined using a biomicroscope and 90 diopter lens. Statistical analysis was performed using SPSS software, version 26.

of the 60 studied subjects, 46.67% were females and 53.33% were males, and there was no significant difference in the prevalence of the two sexes between the groups (P=0.146). In total, 12.1% of the studied subjects were myopie, 34.5% were emmetrope, and 53/4% were hypermetrope. Although the prevalence of hypermetropia was higher than other types of refractive errors in all groups, the prevalence of myopia was higher in those with ROP than in groups without ROP, and there was a significant difference in the prevalence of refractive errors in different groups (P=0.025). There was no significant difference between the prevalence of the different types of astigmatism in the studied groups (P=0.304) and with the rule astigmatism was the most common type among all groups. There was a significant and direct relationship between gestational age and birth weight with cyclo-equivalent sphere, which means that the lesser gestational age (P=0.356, r=0.006) and birth weight (P=0.002, r=0.387), the higher the probability of myopia.

Considering the higher prevalence of myopia in low birth weight children, especially in children with ROP, it is necessary to formulate a regular program to screen this category of children to prevent refractive errors, especially myopia in the future.

In the context of Nahj al-Balaghah, the ruler is the person who has the authority to manage the affairs of the society, and government orders are issued or implemented by him. This person is referred to by terms such as Amir, Imam, Waali, Wali, and today, in the political systems of the world, according to the constitutional laws of the countries, the ruler is the leader, the president, the head of the government, prime minister, king, etc. The present research hypothesizes that Nahj al-Balaghah introduced a standard personality model for the leader of the Islamic society and mentioned his unique characteristics. Corresponding to this hypothesis, the main research question is what is the model of personal competence of the Islamic ruler in Nahj al-Balaghah? Therefore, by studying Nahj al-Balaghah using a library method, the primary research information was collected, analyzed, and coded using the content analysis method. Then, the concepts are divided into nine components, including self-restraint, being people-oriented and hard-working, being honest with people, appreciating people, creating moral virtues, being respectful and friendly with people, creating vitality in society, observing religious duties and consulting with the wise, were organized and presented in the form of a model for the personal qualification of the ruler. These components were evaluated using the export scale, and the scale's result was calculated using the Friedman test. The test output showed that the component creating moral virtues has the highest frequency, with 6.03%, and self-restraint, with 4.13%, has the lowest frequency.

The present study aimed to investigate  the spatio - temporal changes in  Leaf Area Index (LAI) and Land Surface Temperature (LST). This research examined the spatial and temporal trends of LST and LAI across the climatic zones of northern Iran from 2001 to 2022, analyzing the data both seasonally and annually using the remote sensing data from MODIS. To assess the trends in the indices studied, the prewhitened Mann-Kendall test was employed due to the significant autocorrelation present in the time series. The trend slope was calculated using  Sen's slope estimator. The results showed that during the study period, the leaf area index (LAI) increased across all bioclimatic zones. In forest ecosystems, a direct relationship between LAI and LST was observed in all seasons. In contrast,  rangeland ecosystems exhibited a  direct relationship in spring and summer, but an inverse relationship in autumn and winter. The Z-values were 2.825 for forests in humid temperate areas and 2.944 for rangelands in semi-arid, windy regions, both indicating a significant greening trend at a 99% confidence level. Analyzing the relationship between changes in LAI and LST can help develop effective management strategies to enhance ecosystem quality in northern regions and adapt to climate change.

 Antibiotic resistance is recognized as a major threat to global health that can result in increased morbidity and mortality. On February 27, 2017, the first list of antibiotic-resistant priority pathogens was published by the World Health Organization (WHO). These pathogens, including Pseudomonas aeruginosa, pose the greatest threat to human health. This research was conducted in order to introduce viable candidate vaccine proteins against P. aeruginosa outer membrane proteins (OMPs) using reverse vaccinology approaches.

 Fifty-eight clinical isolates and 9982 outer membrane protein sequences were retrieved for vaxign2 calculation of sequence-derived features. First, 30 proteins with the highest adhesion probability were selected, and in the next step, 10 candidates common among the 58 strains with the highest scores were introduced as candidates for further studies. After determining the physicochemical characteristics of these vaccine candidates, the presence of protected domains was predicted in 2 out of 10 proteins.

 Based on the results obtained from the bioinformatics analysis of the antigenic properties of these proteins, we identified 10 outer membrane proteins that have the highest antigenic scores, are common among all 58 clinical isolates, have no human protein homologs, and have less than 1 transmembrane helix. These candidate proteins have optimal physicochemical properties. The presence of conserved domains was predicted only in the outer membrane porin F and enterobactin iron receptor.

 We suggested 10 candidate proteins that showed suitable characteristics including outer membrane protein F (OprF) and ferric enterobactin receptor.

Psoriasis is a systemically pathogenic immune-mediated, chronic inflammatory disease that mostly affects the skin. Ocular diseases, which are frequently nonspecific or just slightly bothersome in psoriasis, are not well-known. This study investigated ocular manifestations in patients with psoriasis.

This cross-sectional study was conducted at Bu-Ali Sina hospital in Sari, Iran. Seventy-five patients with psoriasis matched by age and sex with 75 non-psoriatic controls were examined. Dermatology life quality index (DLQI) and psoriasis area and severity index (PASI) scores were calculated for every patient with psoriasis. Ocular surface disease index (OSDI) was measured in both groups. We did a slit-lamp examination, Schirmer I test, tear breakup time (TBUT) and fundoscopy in both groups.

The mean age of patients was 40.11±13.88 years. The most common findings on our examinations were conjunctival hyperemia (38%), blepharitis (36%), MGD (33%), and pinguecula (26%). MGD and low TBUT were more common in patients with higher PASI and DLQI scores. OSDI score was also higher in patients with psoriasis.

Ocular surface conditions such as dry eye were more common among the participants and were correlated with duration and severity of the disease. Therefore, psoriasis patients should get frequent and routine eye exams to detect ocular disorders quicly and avoid their unintended effects.

Temperature is the most important parameter for studying spatiotemporal phenological changes in plants. Thus, the current study was aimed to investigate the potential of MODIS land surface temperature (LST) data for mapping growing degree days (GDD) and different phenological stages of Bromus tomentllus and Astragalus effusus in Chaharmahal and Bakhtiari Province. MODIS extracted maps of maximum, minimum and mean temperature, GDD index and phenological stages from 2018 to 2019 during growing season were assessed against weather station data and also field-based phenilogical data using Pearson analysis in three regions with different altitudes. Results showed that MODIS LST and GDD maps had more than 91 and 99% correlations with field-based air temperature and GDD data, respectively (p<0.001). In early growing season, GDD values were less than 16 degree-days and they were more than 5200 degree-days in the late growing season which explained one and all the phenological stages of the studied species in the study area, respectively. The study findings indicated that MODIS data have high capability in spatiotemporal stratification of phenological stages of the Bromus tomentllus and Astragalus effuses plant species. The knowledge of different phenological stages is essential in species conservation and rangeland sustainable utilization, therefore, species phenology map can be used as an effective tool in rangeland management in the related organizations.

Nowadays, synthetic drugs used to treat inflammatory diseases are of the slightest interest due to their potential side effects and serious adverse effects. Therefore, finding new treatment approaches, especially herbal medicines, could help manage inflammation disorders. Olive and its products (oleuropein), as both antioxidants and inflammatory inhibitors, can effectively reduce the inflammatory process. This study aimed to investigate the effects of oleuropein on the cytokines profile of peripheral blood mononuclear cells of rheumatoid arthritis patients.

A 5 ml blood sample was taken from 40 patients with RA. Peripheral blood mononuclear cells (PBMCs) were isolated from each sample using Ficoll. Then, it cultured in 24 well tissue culture plates with and without oleuropein and phytohemagglutinin (PHA). The concentration of TNF-α and IL-4 was evaluated in the supernatant of treated and non-treated cells using immunoassay after 72 hours. The results were analyzed using t-test and ANOVA.

The results revealed a significant reduction of TNF-α in treated PBMCs with oleuropein compared to non-treated cells, especially in the 100 mg/ml of oleuropein (P<0.0001). In addition, a significant increase in IL-4 in a dose-dependent manner was indicated (P<0.0001).

Our findings demonstrated that oleuropein could effectively reduce the inflammation of inflammatory diseases, especially rheumatoid arthritis, by increasing the anti-inflammatory factor IL-4 production and decreasing the level of TNF-α.

Spatiotemporal changes of net primary production (NPP) is one of the essential indicators in determining rangeland ecosystem condition. Therefore, the aim of current research was to map and monitor the actual and potential NPP of rangeland ecosystems in Kohgiluyeh and Boyer-Ahmad province, using CASA and Miami models, MODIS and field data from 2009 to 2018. The accuracy of the produced NPP maps was assessed in 253 sampling sites located in different vegetation types with good, fair and poor rangeland conditions, using linear regression. Results showed that the difference between actual and potential NPP was greater than 56 gr C/m2/month, which can be a sign of human impacts and interferences in the rangeland ecosystems of the region. The highest and lowest relationships between modeled and field productions were observed in the Astragalus spp. - Bromus tomentellus vegetation type with good rangeland condition (R2=0.84) and Astragalus sieberi- Stipa capensis vegetation type with poor rangeland condition (R2=28), respectively. The present research findings indicated the importance of drought conditions, vegetation type and rangeland condition in estimating the actual and potential NPP and the difference of these productions can be used as an index to determine and monitor the condition of rangeland ecosystems.

This study aimed to evaluate and compare the structure and function status of some areas with different desertification classes around Gavkhoni International Wetland using Landscape Function Analysis (LFA), and Cover Directional Leakiness Index (CDLI) extracted from Landsat 8 OLI sensor and NDVI, SAVI-A, PD54, and STVI-1 vegetation indices. In this research, six different areas were selected, and two or three transects with a length of 50 meters were established in each area. The length and width of the patches and inter-patches were continuously measured. Then 11 soil surface indicators were assessed in all ecological patches, and inter-patch areas with three replications. The results showed that although range places did not differ significantly in terms of soil stability and nutrient cycling indices, the infiltration index in the areas covered by Haloxylon ammodendron, Seidlitzia rosmarinus and Halocnemum strobilaceum varied significantly compared with the other areas (p < 0.05). According to the results, there were no significant differences between the structure and function indices in the areas with various desertification classes (p > 0.05). The PD54 index showed the best performance among vegetation indices due to its better differentiation of resource leakage between different areas, and it had a high relationship with CDLI (R2 = 71.9 %). There was a negative correlation between CDLI and vegetation cover percentage (p < 0.05). The present study indicated that integrating the results of LFA method with CDLI can be used to evaluate the structure and function status of desert ecosystems appropriately.

Ectopic pregnancy is the most common cause of maternal mortality, while cesarean scar ectopic is one of the rarest of all ectopic pregnancies.Early diagnosis of this type ofpregnancyleads to better control of this situation.

A 28-year-old 28 weeks’ pregnant woman (G3P1Ab1L1) presented to the emergency department with the complaint of hematuria, labor pain, bleeding, and cervical dilation. The patient was taken to the operating room, considering her past surgical history of cesarean delivery. Due to the recreated placenta and ectopic pregnancy, hysterectomy and Cystorrhaphy were performed.

Cesarean scar ectopic is a type of extrauterine pregnancy that implants around the place of a previous pregnancy scar. This pregnancy may cause severe hemorrhage due to uterine rupture, accreta, and percreta placenta. Bladder invasion by the placenta percreta causes hematuria during pregnancy, so early diagnosis plays a critical role. Ultrasonography is a widely accessible method. As described in this case, a false negative result in ultrasonography causes extensive hemorrhage hysterectomy.

The ectopic pregnancy may occurs in the connective tissue around the site of a previous cesarean scar. This type of pregnancy can cause severe bleeding due to uterine rupture or placenta accreta and placental abruption. Placenta percarta invading the bladder causes hematuria during pregnancy and early diagnosis is very important and the most available diagnostic method is transvaginal ultrasound. A false negative diagnosis may lead to complications such as uncontrollable bleeding and hysterectomy as in this case.

In all organizations, management, as it is the most essential element in achievement of organizational goals, is also the most important and influential element in the ethics. However, Strategic managers are the most effective actors in the field of ethics in all countries, including the I.R. of Iran. Therefore, it is necessary for the country's strategic managers to be free from all moral diseases, such as hypocrisy; Because the contamination of managers with hypocrisy, due to their direct influence on people's behavior and attitudes, causes the moral decline of society, contamination of people with hypocrisy in social and administrative interactions, and the loss of public trust in the Islamic system and Islam. The purpose of this applied research, which gathers its information through both library and field methods, is to explain the hypocrisy of strategic managers with a qualitative approach, in three dimensions (what, why and how) and to present their solutions to this phenomenon. With a quantitative approach, indicators of hypocrisy of strategic managers have been analyzed with an expert point of view. The results of the research in the dimension of whatness indicate that strategic managers in three domains of faith, worship and ethics are likely to be infected with hypocrisy. in the why dimension; Weakness of faith, self-deprecation, arrogance, worldliness, Satan's influence and inability to confront directly are the reasons for hypocrisy in some managers. In terms of how, 20 types of possible hypocrisy of strategic managers are: bribery (in the form of a gift), pretending, smooth talking, reckless speaking, contradiction in words and actions, creating despair and doubt in people, lethargy, too much agreement, backbiting, betrayal, violation of right, envy and secretly harming others, pretending to believe, hatred against the righteous, false appearance, not acting according to one's knowledge, worldliness, flattery and duplicity. Also, 6 ways to be guided in the light of the teachings of Islam and the Quran include: persistence in religious education during service, paying attention to internal organizational supervision, observing organizational justice and paying attention to the principle of meritocracy, true understanding of the nature of the world and the hereafter and not giving a chance to hypocrites. In addition, the average opinion of the experts showed that suspecting the right of others by managers (19.75), False expression of regret (7.2), have the most and the least harmful effect on the society, respectively.

The objective of the present study was to investigate the performance of some of the extracted information for mapping land degradation using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land degradation map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land degradation covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS models increases the ability to identify land degradation.

Mapping land degradation is essential for appropriate management. In present study, the potential of revised MEDALUS model and its integration with fuzzy logic in land degradation crisis mapping has been investigated in the Semirom County of Isfahan Province with more than 380 mm of rainfall. The study area has a semi-arid climate. Most of annual precipitation occurs in winter while less in summer. Altitude in the area is relatively high, ranging from 1707 to 4393 m, with the peaks in the Zagros Mountains. Due to large altitudinal range and human activity influences, there are a large number of vegetation types and a range of conditions in the rangelands. In recent years, grazing and other activities, such as the construction of the Hana Dam and land use changes, have degraded the existing rangelands, especially those around the agricultural lands in Semirom County. First, based on the characteristics of the study area, six desertification indicators including climate, soil, vegetation, groundwater, water erosion and management quality were selected. Then, 25 indices affecting the quality of each indicator were mapped and assigned a value between 0 and 1 using fuzzy logic option in GIS environment at pixel- size scale. According to the results, a 7% of the study area was classified as slight, 84% as moderate and 14% as severe degradation. Results also showed that the management, water erosion and vegetation indicators and the indices of the rangeland land use, vegetation resistance against erosion and rainfall were the most important factors with more than 70% effectiveness in the land degradation of the study area. The findings of this study indicate that the conversion of rangelands to agricultural land, overgrazing and drought are main factors of land degradation crisis in the study area.

Dust storm is a major weather event that plays an important role in the Earth’s atmosphere and ocean surfaces. Dust storms affect marine phytoplankton, soil physical and chemical characteristics in entrainment and deposition areas, climate and radiative forcing, economic loss, and human health. They mostly originate from Introduction Dust storm is a major weather event that plays an important role in the Earth’s atmosphere and ocean surfaces. Dust storms affect marine phytoplankton, soil physical and chemical characteristics in entrainment and deposition areas, climate and radiative forcing, economic loss, and human health. They mostly originate from plains and playas across North Africa, the Middle East and Asia. One of the main sources of dust is relatively recent flood sediments deposited since the late tertiary. Asia, with approximately 60% of the population of the world, is an important source of dust that impacts the climate on a global scale. A dust storm is defined by the World Meteorological Organization (WMO) protocol as where ‘strong winds lift large quantities of dust particles, reducing visibility to between 200 and 1000 m’. Therefore, the aim of present study was to investigate the potential of desert dust mapping algorithms using satellite images in the west and western parts of Iran.

The study area has hot and arid climate; therefore, dust storms are usually occurred especially during dry seasons in this region including Iran, Iraq, Syria, Jordan, Kuwait, Saudi Arabia, Bahrain, Qatar, UAE, Oman, and Yemen. Although, some of dust in the study area comes from the Sahara in the Africa. In recent years, the most environmental problem in Iran is the dust crisis in the western half of the country in mountainous regions of Zagros. Zagros Mountains occupy a broad extent of western Iran, covering an area roughly 1300 km by 200 km. The mean annual rainfall is from 400 to 800 mm and mostly in the winter and spring. The average annual temperature ranges between 9°C and 25°C. The most common ecosystems in the region are the forest and semi-steppe areas. Forests with an area of 5 million hectares cover about 40% of Iran’s forests and are the widest forest regions of the country. This region with its semi-arid climate is generally dominated by broad-leaved trees with the dominant species of Quercus brantii that covers more than 50% of Zagros’ forest area. Dust storms from western neighbouring countries such as Iraq have significantly increased in recent years and much of Iran is affected. For example, 20 and 52 dusty days in 2008 with visibility less than 1000 m occurred in Somar and Abadan meteorological stations in Kermanshah and Khozestan provinces, which are the nearest stations to Iraq. Around 31% of Iraq’s total land is desert, which is the main source of soil-derived mineral dust in the region. There are three important sources of dust storms in Iraq: one is centred over Baghdad; the second is centred west of Basrah; and the third is the Southern Desert. Thus, dust cases are common in central and southern Iraq. The major hotspots for dust generation are aeolian deposits south of Baghdad, Karbula, Najaf, Nasiriya, Basrah, as well as Kuwait, and also alluvial deposits of Tigris and Euphrates Rivers from sandy clayey silt (72%) to clayey sandy silt (28%). In addition to the high potential of Iraq’s desert deposits for dust production, recent changes in the region such as war in Iraq, dam construction in Iraq and neighbouring countries, and intense drought conditions have increased the frequency of dust events. Saudi Arabia is rich in fine sediments from dry riverbeds and lakebeds and sand seas, and is another important dust source in the region. Therefore, for mapping this environmental crisis, MODIS level 1 B satellite images were acquired in winter and summer seasons and processed with MCT tool. Then, the BTD, Ackerman, Miller and TDI algorithms were applied to the images and dust regions were mapped with use of appropriate thresholds. The accuracy of the outputs maps were assessed against natural color composites and dust products including Aqua AOD and Aura AI.

Results showed that a single method for identifying different dust plumes cannot be used due to differences in mineralogy and weather conditions of the events and each algorithm needs different threshold based on the event type and characteristics. The algorithms worked best on dense dust, but they performed differently in cloudy regions and over bright desert surfaces. Most of the algorithms examined here misidentified thick cloud cover as dust. Despite the published dust/no-dust thresholds for the methods tested here; results indicated that it was not possible to use a single dust/no-dust threshold for any of the algorithms applied to the studied events. Therefore, it seems that for each dust event an event-specific threshold is needed.

Comparison of the studied algorithms showed that all of them produced almost similar results and, among them, the TDI index had relatively better performance over dust sources and showed its usefulness as an effective approach for dust detection and mapping in the region. It appears that the combination of these simple algorithms is the best way to overcome the limitations of different dust detection methods. By combining several algorithms used in this study, the performance of dust detection and mapping may improve. The finding indicated that free and available dust products are sufficient for assessment and monitoring dust storms in Iran if the maps less than 10 km are not required.plains and playas across North Africa, the Middle East and Asia. One of the main sources of dust is relatively recent flood sediments deposited since the late tertiary. Asia, with approximately 60% of the population of the world, is an important source of dust that impacts the climate on a global scale. A dust storm is defined by the World Meteorological Organization (WMO) protocol as where ‘strong winds lift large quantities of dust particles, reducing visibility to between 200 and 1000 m’. Therefore, the aim of present study was to investigate the potential of desert dust mapping algorithms using satellite images in the west and western parts of Iran.

Snow is one of the effective fators on vegetation rate and function in mountainous areas. The aim of this study was to investigate the impact of snow cover area index and snow cover duration index on two declining and dominant plant species including Astragalus adscendens and Quercus brantii in Chaharmal and Bakhtiari Province from 2003 to 2016. For this purpose, Normalized Difference Snow Index (NDSI) and Normalized Difference Vegetation Index (NDVI) were extracted from MODIS satellite images and compared using Pearson analysis in forest decline regions (Barz, Seveh, Helen and control area) and in rangeland decline regions including Astragalus adscendens decline and control areas. Results showed that about 32% of the snow cover in the study area has been reduced, although a constant trend was not observed. The studied snow indices showed the highest relationships with rangeland and forest vegetation cover in March. The decline region of Astragalus adscendens had the maximum relationship with the snow cover area (R>0.70) and snow cover duration (R>0.71) in March. According to the findings, snow precipitation in late winter season and its duration is more effective on the rangeland Astragalus adscendens species than forest vegetation cover and this pecies can be more sensitive to decline in case of snow reduction.

Spatial mapping of evapotranspiration (ET) rates can indirectly be very important for rangeland management and rehabilitation. The map of evapotranspiration can be an indicator of plant water requirement. Mapping of ET from remote sensing data is more appropriate than field methods, because the spatial and temporal coverage of this method is very extend and time and cost are optimized. Among the evapotranspiration mapping methods using remote sensing data, the FAO-56 model is one of the usual ones. Therefore, this study aimed to spatially estimate the amount of ET in semi-arid region of Isfahan province using the integration of FAO-56 and remote sensing techniques. To calculate ET, Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were obtained from geometrically corrected TM 2009 image and the standard crop evapotranspiration (ET0) was calculated using FAO-56 equation and then the crop coefficient (Kc), crop transpiration (Kcb) and soil evaporation (Ke) were extracted from NDVI data. Results showed that agricultural lands with 6/977 mm/day and bare ground with 0.012 mm/day had the highest and lowest ET in the study area. High relationships of ET map with well-known remote sensing maps such a NDVI (>99%) and LST (>92%) indicated that ET mapping in the region has been achieved with a high accuracy, therefore, it is a valuable supplementary technique to field methods in determining plant water requirements for rehabilitation purposes.

The southern regions of Kerman Province have repeatedly encountered dust storms. Therefore, the objective of this study was to identify dust sources using effective parameters such as vegetation cover, land surface temperature, soil moisture, soil texture, and slope as well as to detect dust storms originating from these regions based on 31 MODIS images in 2016 and SRTM data. After normalizing parameters, the dust source map was prepared by fuzzy logic and assessed with an error matrix and available dust source map. Results showed that 30.5% of the study area was classified as a low source of dust, 39.55% as moderate, and 29.85% as severe-very severe. The overall accuracy of the produced map was about 70% and the producer and user accuracy of the severe-very severe class was more than 87%. The detection of dust storms originated from the identified dust sources also confirmed a crisis situation in the region. Due to the repeatability and continuity of obtained dust source map at pixel scale, it can be used to update available dust source maps and manage dust crisis in the region, properly.