216 research outputs found
Motivation Practices, Employee Job Satisfaction And Employee Retention in MAB Bank (Win Lae Soe, 2023)
No full textThe objectives of the study are to identify the motivation practices implemented
by MAB Bank, their effect on employee job satisfaction and employee job satisfaction
effect on employee retention in MAB Bank. The descriptive method is employed in this
study. To achieve the study objectives, both primary and secondary data are utilized.
Primary data are collected by using structured questionnaires from select 204 staff
members. The data collection period is in May 2023. According to the regression
results, in the analysis of the effect of motivation practices on employee job satisfaction
at MAB Bank in Myanmar, rewards and recognition, work environment, relationships
with others, and challenging work are positively significant predictors of job
satisfaction. The findings indicate that employees who receive greater rewards and
recognition, work in a better environment, maintain positive relationships with
colleagues, and engage in challenging works tend to exhibit high levels of job
satisfaction. When analyzing the effect of employee job satisfaction on employee
retention at MAB Bank, the study's results reveal a strong positive correlation between
job satisfaction and employee retention. It is suggested that MAB Bank should provide
incentives such as bonuses and benefits to enhance the attractiveness of its
compensation package to employees. Additionally, the bank needs to offer more
training and development opportunities to employees, enabling them to enhance their
skills and advance in their careers. MAB Bank should also review its promotion policies
to ensure the existence of clear career progression paths for its employees
Does the spatial arrangement of urban landscape matter? Examples of urban warming and cooling in Phoenix and Las Vegas
No full textabstract: This study examines the impact of spatial landscape configuration (e.g., clustered, dispersed) on land-surface temperatures (LST) over Phoenix, Arizona, and Las Vegas, Nevada, USA. We classified detailed land-cover types via object-based image analysis (OBIA) using Geoeye-1 at 3-m resolution (Las Vegas) and QuickBird at 2.4-m resolution (Phoenix). Spatial autocorrelation (local Moran’s I ) was then used to test for spatial dependence and to determine how clustered or dispersed points were arranged. Next, we used Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data acquired over Phoenix (daytime on 10 June and nighttime on 17 October 2011) and Las Vegas (daytime on 6 July and nighttime on 27 August 2005) to examine day- and nighttime LST with regard to the spatial arrangement of anthropogenic and vegetation features. Local Moran’s I values of each land-cover type were spatially correlated to surface temperature. The spatial configuration of grass and trees shows strong negative correlations with LST, implying that clustered vegetation lowers surface temperatures more effectively. In contrast, clustered spatial arrangements of anthropogenic land-cover types, especially impervious surfaces and open soil, elevate LST. These findings suggest that city planners and managers should, where possible, incorporate clustered grass and trees to disperse unmanaged soil and paved surfaces, and fill open unmanaged soil with vegetation. Our findings are in line with national efforts to augment and strengthen green infrastructure, complete streets, parking management, and transit-oriented development practices, and reduce sprawling, unwalkable housing development.Corresponding Author:
Soe Win Myint
Arizona State University
[email protected]
Cross-sectional questionnaire-based study: diabetes mellitus patients’ stressors and perceptions towards the disease
No full textThis study aimed to identify the stressors among the Type 2 Diabetes Mellitus (T2D) and to investigate the perceptions of the patients towards T2D disease as a continuation from the previous study. A total of 99 patients from Diabetic Clinic in Pekan, Pahang with type 2 diabetes were enrolled in this study as the respondents. T2D patients that came to the clinic to get
routine treatment is requested to answer a set of hardcopy questionnaire that consist of perception towards T2D, feeling about Diabetes Mellitus and multi factors related stressors. Study on the perceptions towards T2D, highest positive respond (strongly agree and agree) is P1 (it is burdensome) with 15 and 41 while the lowest positive respond is P3 (it is associated with external factor such as paranormal and environment) with frequency of 5 and 14 respectively. The feeling about DM showed highest positive respond, 59 out of 99 respondents in F1 (feeling thankful) and F2 (feeling stress). Out of twelve stressors, the highest positive responds towards the S10 (regular
exercise), S11 (restricted to consume favorite food) and S12 (limited food consumption) represents more than 50 from total of 99 respondents. T2D patients need intensive guidance from the caregiver and the physicians to motivate and prevent diabetes-related distress.
Isolation and Characterization of Endophytic Bacteria from the Leaves of Carica papaya L.
No full textEndophytic bacteria were isolated from the leaves of Carica papaya L. (papaya). The leaf samples were collected from the Campus of University of Mandalay. This experiment was carried out at the Microbiology Laboratory, Department of Botany, University of Mandalay from December 2017 to August 2018. The six bacterial strains, TS 1 - TS 6 were isolated and characterized based on their colony morphology and biochemical tests. Each of isolates was characterized by the morphological characters (shape, colony, colour, cell size, gram staining, aerobic growth and motility) and biochemical tests (catalase, oxidase, starch hydrolysis, lysine decarboxylase, urea hydrolysis, sugar fermentation such as dextrose, glucose, manitol and sucrose, citrate utilization, methyl red, triple sugar iron (TSI) and nitrate reduction) were carried out. The isolated bacteria were confirmed TS 1 as Mycobacterium sp., TS 2 as Bacillus sp., TS 3 as Staphylococcus sp., TS 4 as Micrococcus sp. TS 5 as Streptococcus sp. and TS 6 as Enterobacter sp.
Case –Based Reasoning (CBR) Based on Fuzzy Set Approach For Rainfall Prediction
No full textRainfall predictions and warnings are the most important services provided by the meteorological profession. Predictions are used by government and industry to protect life and to improve the efficiency of operations, and by individuals to plan a wide range of daily activities. The basic idea of this system is that CBR (case –based reasoning) solves new case by using solution to past cases. Rainfall predictions for the present case are made from the outcomes of past cases. A fuzzy set approach based methodology for knowledge acquisition is developed and used for retrieval of temporal cases in a Case –Based Reasoning (CBR) system. This system is to predict daily and monthly Rainfall Amounts (RFA) and Rainfall Type (RFT)
GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data
No full textabstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201
Micro-scale urban surface temperatures are related to land-cover features and residential heat related health impacts in Phoenix, AZ USA
No full textabstract: Context
With rapidly expanding urban regions, the effects of land cover changes on urban surface temperatures and the consequences of these changes for human health are becoming progressively larger problems.
Objectives
We investigated residential parcel and neighborhood scale variations in urban land surface temperature, land cover, and residents’ perceptions of landscapes and heat illnesses in the subtropical desert city of Phoenix, AZ USA.
Methods
We conducted an airborne imaging campaign that acquired high resolution urban land surface temperature data (7 m/pixel) during the day and night. We performed a geographic overlay of these data with high resolution land cover maps, parcel boundaries, neighborhood boundaries, and a household survey.
Results
Land cover composition, including percentages of vegetated, building, and road areas, and values for NDVI, and albedo, was correlated with residential parcel surface temperatures and the effects differed between day and night. Vegetation was more effective at cooling hotter neighborhoods. We found consistencies between heat risk factors in neighborhood environments and residents’ perceptions of these factors. Symptoms of heat-related illness were correlated with parcel scale surface temperature patterns during the daytime but no corresponding relationship was observed with nighttime surface temperatures.
Conclusions
Residents’ experiences of heat vulnerability were related to the daytime land surface thermal environment, which is influenced by micro-scale variation in land cover composition. These results provide a first look at parcel-scale causes and consequences of urban surface temperature variation and provide a critically needed perspective on heat vulnerability assessment studies conducted at much coarser scales.Corresponding Author:
G. Darrel Jenerette
University of California Riverside
[email protected]
Wavelet Analysis and Classification of Urban Environment Using High -Resolution Multispectral Image Data.
No full textAttempts to analyze urban features and classify land use and land cover directly from high-resolution satellite data with traditional computer classification techniques have proven to be inefficient. The fundamental problem usually found in identifying urban land cover types from high-resolution satellite imagery is that urban areas are composed of diverse materials (metal, glass, concrete, asphalt, plastic, trees, soil, etc.). These materials, each of which may have completely different spectral characteristics, are combined in complex ways by human beings. Hence, each urban land cover type may contain several different objects with different reflectance values. Noisy appearance with lots of edges, and the complex nature of these images, inhibit accurate interpretation of urban features. Traditional classifiers employ spectral information based on single pixel value and ignore a great amount of spatial information. Texture features play an important role in image segmentation and object recognition, as well as interpretation of images in a variety of applications ranging from medical imaging to remote sensing. This study analyzed urban texture features in multi-spectral image data. Recent development in the mathematical theory of wavelet transform has received overwhelming attention by the image analysts. An evaluation of the ability of wavelet transform and other texture analysis algorithms in urban feature extraction and classification was performed in this study. Advanced Thermal Land Application Sensor (ATLAS) image data at 2.5 m spatial resolution acquired with 15 channel (0.45 mum--12.2 mum) were used for this research. The data were collected by a NASA Stennis LearJet 23 flying at 6600 feet over Baton Rouge, Louisiana, on May 7, 1999. The algorithms examined were the wavelet transforms, spatial co-occurrence matrix, fractal analysis, and spatial autocorrelation. The performance of the above approaches with the use of different window sizes, different channels, and different feature measures were investigated. Six types of urban land cover features were evaluated. Wavelet transform was found to be far more efficient than other advanced spatial methods. The results of this research indicate that the accuracy of texture analysis in classifying urban features in fine resolution image data could be significantly improved with the use of wavelet transform approach
Alcohol consumption among adult males in urban area of Thanlyin Township, Yangon Region, Myanmar
No full textAlcohol consumption is a major cause of morbidity and mortality worldwide. It is frequently related to health and behavioural problems as well as socio-economic hardship. Therefore, this study was conducted to determine the prevalence and risk factors of alcohol consumption among adult males residing in urban area of Thanlyin Township, Yangon Region.</p
Alcohol consumption among adult males in urban area of Thanlyin Township, Yangon Region, Myanmar
No full textBackground: Alcohol consumption is a major cause of morbidity and mortality worldwide. It is frequently related to health and behavioural problems as well as socio-economic hardship. Therefore, this study was conducted to determine the prevalence and risk factors of alcohol consumption among adult males residing in urban area of Thanlyin Township, Yangon Region.Methods: A cross-sectional study was conducted among 380 adult males. Multi-stage random sampling was applied. Data entry and analysis was done using Stata 11.0 statistical package.Results: The prevalence of current alcohol drinking, ex-drinking and never drinking were 20.5%, 9.0% and 70.5%, respectively. There was a significant decreasing trend of alcohol consumption across the levels of age-group. Age, education status and practicing other health-risk behaviours such as smoking and betel chewing were detected as significant risk factors of alcohol consumption. Ever smokers and ever betel chewers were about 4 times more likely to be ever alcohol user compared to their counterparts even if age and education level were adjusted. By controlling smoking and betel chewing habits, 79.2% and 76.6% of existing prevalence of alcohol consumption among respondents would be reduced, respectively.Conclusions: There is an urgent need to curb the habit of alcohol consumption among adult males living in urban area, especially young adults. Alcohol and tobacco control policies in Myanmar should be strengthened or reinforced. Tobacco control program also needs to be intensified. Health education and health promotion activities should be enhanced in order to reduce alcohol consumption in the country.
- …
