Investigating the Effects of Authentic Leadership of Managers on Organizational Commitment of Teachers with Organizational Justice as the Mediator Variable

The present study was conducted by the aim of investigating the fit of the presented model for the relationship between authentic leadership and organizational commitment of staff with a mediating role of organizational justice. The population of the research included all the teachers in high schools (for male students) in Education district 2 in Qom city. From the population, 300 individuals were selected through cluster sampling. For gathering the data, authentic leadership questionnaire (Avolio et al., 2007), organizational justice questionnaire of Niehoff & Moorman (1993), and Allen & Meyer's Organizational Commitment Questionnaire (2002) were used. For analyzing the data, structural equation modeling – fit indices and path coefficients – was used. The results of the analysis showed that authentic leadership has a direct and significant effect on the organizational commitment of teachers. Also, authentic leadership has an indirect effect, through organizational justice, on organizational commitment. The other finding of the research is that organizational justice has a direct and significant effect on organizational commitment of teachers, and the offered conceptual model has a significant statistical fit, that means the explanatory model for organizational commitment based on authentic leadership and organizational justice has fitness with empirical data. Finally, based on the information obtained from structural equations model, it can also be said that all the components existing in the 3 variables of the research have positive and significant relationships with one another

Environmental Knowledge of and Training Methods for Physicians and Nurses of Pars-e-Jonoubi Company, Iran

Background & Objective: Having employees with appropriate environmental behaviors and paying attention to their environmental knowledge and training is an important issue especially for industrial companies. However, the lack of prioritization of this important issue is one of the effective factors in the declining trend of environmental performance improvement of industrial companies. Therefore, the main purpose of the present study was to evaluate the environmental knowledge of physicians and nurses working at Pars-e-Jonoubi Company, Iran, and propose appropriate methods for their environmental training. Methods: This research was a descriptive survey. The statistical population included all physicians and nurses working at Pars-e-Jonoubi Company, from among which 135 individuals were selected through random stratified sampling method and based on the Morgan Table. The data collection tool was a researcher-made environmental knowledge questionnaire based on the view of Frick et al. Data was analyzed using descriptive statistics, t, independent t, Friedman, and chi-square tests. Results: The knowledge of physicians and nurses, in their own view, regarding environmental systems was near average (3.1 and 3.3, respectively). Physicians’ and nurses’ knowledge on environmental action was near average (3.2), and below average (2.6), respectively. Their knowledge of effectiveness was higher than average (physicians: 3.8, nurses: 4.3). Generally, physicians estimated their environmental knowledge as slightly higher than average (3.6) and nurses as nearly average (3.2). Moreover, there was a significant difference between the average of the three aforementioned dimensions of their environmental knowledge and the criterion average. In addition, there was a significant difference between the average of the three dimensions of environmental knowledge of physicians and nurses. They also ranked environmental training methods differently. Descriptive statistics and chi-squared test showed that 76 subjects (61%) preferred the compulsive environmental training method and 48 subjects (39%) preferred the optional method. Conclusion: The results of this research indicated that physicians and nurses working at Pars-e-Jonoubi Company felt the need for obtaining, generally, more environmental knowledge, and specifically, more action-related knowledge. Thus, it is suggested that the necessary requirements be provided for the environmental training of physicians and nurses working at this company according to each group’s preferred training method. Key Words: Knowledge, Training, Environmen

Using the Hair Removal Laser in the Axillary Region and its Effect on Normal Microbial Flora

Introduction: The axillary hair removal laser is one of the most often used procedures to treat unwanted hairs in that region. Employing this technology can be helpful in decreasing the bromhidrosis.Methods: In the present research, a clinical trial study over the effect of the hair removal laser on normal microbial flora at the axillary region is presented. The intervention group consisted of 30 women referred to the dermatologic clinic for the purpose of removing axillary hair by the alexandrite 755 nm laser and the control group consisted of 30 women referred to the same clinic for any other reasons. Both groups were evaluated for the type of bacterial strains on the first visit and after three and six months.Results: The results showed that the sense of sweat smell improved by about 63% after the last laser session. The frequency of all bacterial strains decreased in the intervention group except Staphylococcus epidermidis which was significant. In the control group, there was no significant decrement in any bacterial strains and even the prevalence of more strains including Staphylococcus aureus and S. epidermidis increased. Counting the mean bacterial colon showed a slight decrement of the bacterial count following the laser.Conclusion: The use of laser radiation, even with the aim of hair removal, can alter the microbial flora, and it can be accompanied by the improvement of the smell of sweat. The effect of the laser on different bacterial strains is quite different, which can depend on the amount of energy, the wavelength, the characteristics of the area under the laser, and also the structural properties of the membrane of the microorganism itself

Improving Mountain Snowpack Estimation Using Machine Learning With Sentinel‐1, the Airborne Snow Observatory, and University of Arizona Snowpack Data

Abstract Accurate mapping of snow amount in the mountains is critical as mountain snowpacks are water supply for millions of people. Satellite remote sensing has been largely unable to reliably detect the amount of snowpack in these areas. Recently, C‐band Synthetic Aperture Radar (SAR) data from the Sentinel‐1 (S1) satellites have shown potential for measuring snow depth in the mountains. However, their spatiotemporal coverage is incomplete, and their evaluation with robust, aerial snow depth data is limited. Here, we evaluate two S1 snowpack datasets with some of the best available gridded snowpack data over the Colorado Rockies and Sierra Nevada mountains in the western US: the Airborne Snow Observatory (ASO) and the University of Arizona (UA) snowpack datasets. Compared to ASO and UA data, the S1 data are biased high when snow is shallow, and biased low when snow is deep (particularly later in spring when there is wet snow), though these biases are reduced for deep snow areas when wet snow pixels are removed. We then apply corrections based on machine learning that account for physiographic characteristics to improve the accuracy of the S1 data. Furthermore, we fill gaps in the S1 data by using snow persistence, but also account for potential snow accumulation and ablation, to generate temporally complete snow depth maps over mountainous areas. Corrected and gap‐filled S1 snow depth mapping could be especially important for snow monitoring in remote mountain areas where other techniques for snow mapping do not work or are logistically infeasible or cost‐prohibitive

Comparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methods

Accurate quantification of snowfall rate from space is important but has remained difficult. Four years (2007-2010) of NOAA-18 Microwave Humidity Sounder (MHS) data are trained and tested with snowfall estimates from coincident CloudSat Cloud Profiling Radar (CPR) observations using several machine learning methods. Among the studied methods, random forest using MHS (RF-MHS) is found to be the best for both detection and estimation of global snowfall. The RF-MHS estimates are tested using independent years of coincident CPR snowfall estimates and compared with snowfall rates from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), Atmospheric Infrared Sounder (AIRS), and MHS Goddard Profiling Algorithm (GPROF). It was found that RF-MHS algorithm can detect global snowfall with approximately 90% accuracy and a Heidke skill score of 0.48 compared to independent CloudSat samples. The surface wet bulb temperatures, brightness temperatures at 190 GHz, and 157 GHz channels are found to be the most important features to delineate snowfall areas. The RF-MHS retrieved global snowfall rates are well compared with CPR estimates and show generally better statistics than MERRA-2, AIRS, and GPROF products. A case study over the United States verifies that the RF-MHS estimated snowfall agrees well with the ground-based National Center for Environmental Prediction (NCEP) Stage-IV and MERRA-2 product, whereas a relatively large underestimation is observed with the current GPROF product (V05). MHS snowfall estimated based on RF algorithm, however, shows some underestimation over cold and snow-covered surfaces (e.g., Greenland, Alaska, and northern Russia), where improvements through new sensors or retrieval techniques are needed.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products

Atmospheric rivers (ARs) play an important role in the total annual precipitation regionally and globally, delivering precious freshwater to many arid/semiarid regions. On the other hand, they may cause intense precipitation and floods with huge socioeconomic effects worldwide. In this study, we investigate AR-related precipitation using 18 years (2001-2018) of globally gridded AR locations derived from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). AR precipitation features are explored regionally and seasonally using remote sensing (Integrated Multi-satellitE Retrievals for GPM version 6 [IMERG V6], daily Global Precipitation Climatology Project version 1.3 [GPCP V1.3], bias-adjusted CPC Morphing Technique version 1 [CMORPH V1], and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [PERSIANN-CDR]) and reanalysis (MERRA-2 and ECMWF Reanalysis 5th Generation [ERA5]) precipitation products. The results show that most of the world (except the tropics) experience more intense precipitation from AR-related events compared to non-AR events. Over the oceans (especially the Southern Ocean), the contribution of ARs to the total precipitation and extreme events is larger than over land. However, some coastal areas over land are highly affected by ARs (e.g., the western and eastern United States and Canada, Western Europe, North Africa, and part of the Middle East, East Asia, and eastern South America and part of Australia). Although spatial correlations for pairs of IMERG/CMORPH and GPCP/PERSIANN-CDR are fairly high, considerable discrepancies are shown in their estimation of AR-related events (i.e., overall IMERG and CMORPH show a higher fraction of AR-related precipitation). It was found that the degree of consistency between reanalysis and satellite-based products is highly regionally dependent, partly due to the uneven distribution of in situ measurements.6 month embargo; first published online 12 October 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Application of machine learning and remote sensing for gap-filling daily precipitation data of a sparsely gauged basin in East Africa

Abstract Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicitly developed for filling the gaps of daily precipitation records. This study evaluated all approaches over a sparsely gauged basin in East Africa. Among the examined precipitation products, PERSIANN-CDR outperformed other satellite products in terms of root mean squared error (7.3 mm), and correlation coefficient (0.46) while having a large bias (50%) compared to the available in situ precipitation records. PERSIANN-CDR also demonstrates the highest skill in distinguishing rainy and non-rainy days. On the other hand, Random Forest outperformed all other approaches (including PERSIANN-CDR) with the least relative bias (-2%), root mean squared error (6.9 mm), and highest correlation coefficient (0.53)

2019–2020 Australia Fire and Its Relationship to Hydroclimatological and Vegetation Variabilities

Wildfire is a major concern worldwide and particularly in Australia. The 2019–2020 wildfires in Australia became historically significant as they were widespread and extremely severe. Linking climate and vegetation settings to wildfires can provide insightful information for wildfire prediction, and help better understand wildfires behavior in the future. The goal of this research was to examine the relationship between the recent wildfires, various hydroclimatological variables, and satellite-retrieved vegetation indices. The analyses performed here show the uniqueness of the 2019–2020 wildfires. The near-surface air temperature from December 2019 to February 2020 was about 1 °C higher than the 20-year mean, which increased the evaporative demand. The lack of precipitation before the wildfires, due to an enhanced high-pressure system over southeast Australia, prevented the soil from having enough moisture to supply the demand, and set the stage for a large amount of dry fuel that highly favored the spread of the fires

Evaluating the evolution of ECMWF precipitation products using observational data for Iran:from ERA40 to ERA5

Abstract European Center for Medium-Range Weather Forecasts Reanalysis (ERA), one of the most widely used precipitation products, has evolved from ERA-40 to ERA-20CM, ERA-20C, ERA-Interim, and ERA5. Studies evaluating the performance of individual ERA products cannot adequately assess the evolution of the products. We compared the performance of all ERA precipitation products at daily, monthly, and annual data (1980–2018) using more than 2100 Iran precipitation gauges. Results indicated that ERA-40 performed worst, followed by ERA-20CM, which showed only minor improvements over ERA-40. ERA-20C considerably outperformed its predecessors, benefiting from the assimilation of observational data. Although several previous studies have reported full superiority of ERA5 over ERA-Interim, our results revealed several shortcomings in ERA5 compared with the ERA-Interim estimates. Both ERA-Interim and ERA5 performed best overall, with ERA-Interim showing better statistical and categorical skill scores, and ERA5 performing better in estimating extreme precipitations. These results suggest that the accuracy of ERA precipitation products has improved from ERA-40 to ERA-Interim, but not consistently from ERA-Interim to ERA5. This study employed a grid-grid comparison approach by first creating a gridded reference data set through the spatial aggregation of point source observations, however, the results from a point-grid approach showed no change in the overall ranking of products (despite the slight changes in the error index values). These findings are useful for model development at a global scale and for hydrological applications in Iran