Dr. Brenda J. Little Essays - Mississippi, Electrochemistry

Dr. Brenda J. Little Dr. Brenda J. Little of the Naval Research Laboratory Stennis Space Center is the recipient of the 1995 Women in Science and Engineering Award. This award recognizes specific or special scientific or technical contributions by a woman scientist in the Federal service and specific contributions made by a woman scientist toward encouraging young girls and women to pursue science or engineering careers, or enhance employment, promotional or development opportunities for women scientists in their field. Dr. Little, head of the Microbiologically Influenced Corrosion Section at NRL, has worked on MIC projects for the Department of Energy and the U. S. Army, and has served as a consultant to the U. S. National Aeronautics and Space Administration and the Atomic Energy Laboratory of Canada. In addition to her accomplishments in basic research, Dr. Little also works on U. S. Navy platforms to identify and control MIC. Her research has been used to determine the cause of corrosion failures in weapons systems, seawater piping systems, storage tanks and other U. S. Navy equipment. She is currently investigating fungal growths on wooden spools and corrosion of wire ropes used to transfer people and weapons between ships. In 1988 Dr. Little received a patent award for an innovative dual-cell corrosion measuring device, the only published technique for quantifying the electrochemical impact of microorganisms on metal surfaces. In 1985 she was selected by the National Science Foundation as one of eight American Scientists to attend a workshop on biodeterioration in La Plata, Argentina, and to attend a similar NSF workshop in Paris, France in 1988. Dr. Little is an adjunct professor at the University of Southern Mississippi and Montana State University, and has collaborative research with investigators at Harvard University, University of Southern California, University of South Alabama, University of Tennessee, Texas A&M University, Naval Surface Warfare Center, Naval Undersea Warfare Center, and the Nuclear Regulatory Commission. The Singing River Chapter of the American Business Women's Association selected Dr. Little as one of the ten 1990 Women of the Year on the Gulf Coast for her participation in programs for women in science and technology. She has been keynote speaker for several Women in Science and Technology Conferences for the Mississippi Gulf Coast Community College, has participated in career day programs sponsored by the Girl Scouts Chapters of Mississippi, and has presented lectures at honors classes in chemistry and biology and local schools. She is a member of the American Chemical Society, the Adhesion Society, the Electrochemical Society, the National Association of Corrosion Engineers, Sigma Xi, Federally Employed Women, and the Mississippi Academy of Science. She has served the Gulf Coast Chapter of Sigma Xi as secretary, president-elect, president and past president. In addition to numerous performance awards, Dr. Little was selected for "Who's Who in Technology Today" and "American Men and Women in Science" in 1986, received NRL publications awards in 1981 and 1986, received an invention award and Best NRL Patent Award for 1989, and the NRL Alan Berman Research Publication Award in 1994.

A Four-Month Oscillation Detected from Advanced Microwave Sounding Unit-A Measurements in the Arctic and Antarctic The WritePass Journal

A Four-Month Oscillation Detected from Advanced Microwave Sounding Unit-A Measurements in the Arctic and Antarctic References A Four-Month Oscillation Detected from Advanced Microwave Sounding Unit-A Measurements in the Arctic and Antarctic Abstract1. Introduction2. Satellite Brightness Temperature Data3. ERA-Interim Reanalysis Data4. Arctic Four-Month Oscillation5. Discussions of the oscillation AcknowledgementReferencesRelated Abstract Satellite microwave measurements can penetrate through clouds and therefore provide unique information of surface and near-surface temperatures and surface emissivity. In this study, the brightness temperatures from NOAA-15 Advanced Microwave Sounding Unit (AMSU-A) are used to analyze the surface temperature variation in the Arctic and Antarctic regions during the past 13 years of period from 1998 to 2010. The data from four AMSU-A channels sensitive to surface are analyzed with wavelet and Fourier spectrum techniques. A very pronounced maximum is noticed in the period range centered around four months. Application of a statistical significance test confirms that it is a dominant mode of variability over polar regions besides the annual and semi-annual oscillations. No evidence of this feature could be found in middle and low latitudes. The four-month oscillation is 90o out of phase at Arctic and Antarctic, with the Arctic four-month oscillation reaching its maximum in the beginning of March, July and November and the Antarctic four-month oscillation in the middle of April, August and December. The intensity of the four-month oscillation varies inter-annually. The years with pronounced four-month oscillation were 2002-2003, 2005-2006 and 2008-2009. The strongest year for the Arctic and Antarctic four-month oscillations occurred in 2005-2006 and 2008-2009, respectively. The sign of four-month oscillation is also found in the surface-skin temperatures and two-meter air temperatures from ERA-Interim reanalysis. It is hypothesized that the Arctic and Antarctic four-month oscillations are a combined result of unique features of solar radiative forcing and snow/sea ice formation and metamorphosis. 1. Introduction The spectrum analysis of wind time series revealed a 40-50 day Madden and Julian oscillation (MJO) in the zonal wind in the tropical Pacific (Madden and Julian 1971). In the course of an investigation of Advanced Microwave Sounding Unit-A (AMSU-A) for global climate change and global warming, we stumbled upon an apparent four-month oscillation in the surface-sensitive channels in the Arctic and Antarctic. It is not a â€Å"periodicity† in the sense of tidally induced oscillation, but certainly a broadband phenomenon. It passes a statistical significance test with more than 95% confidence. A spectral analysis of both surface skin temperature and two meter air temperature from ERA-Interim reanalysis also confirms the existence of a four-month oscillation in the Arctic and Antarctic. It is our understanding that the AMSU-A observations can be strongly influenced by variable surface emissivity in polar environment and have not been effectively utilized through the ERA data assimil ation. Thus, the confirmation of a four-month oscillation signal from ERA-Interim reanalysis is significant and believed to be mostly associated with the physical process. 2. Satellite Brightness Temperature Data NOAA-15 AMSU-A has 15 channels and is a cross-track scanning radiometer, providing 30 field of views (FOVs) along each scan line. Near the nadir of satellite observations, the FOV size is at best of 48 km. There are a total of four AMSU-A surface-sensitive channels: channel 1 (23.8 GHz), channel 2 (31.4 GHz), channel 3 (50.3) and channel 15 (89 GHz) (Mo 1999; Goodrum et al. 2009). Over land where the surface emissivity is high, the measurements from these surface-sensitive channels are primarily affected by surface emissivity and surface temperature. Over oceans where the emissivity is relatively low, the channels are also a function of temperature, water vapor and liquid water in the lower troposphere. Channels 1,  2 and 15 are located at frequencies away from the major oxygen gaseous absorption lines and can thus see through the atmosphere. The radiation at these channels mainly comes from the  earth’s surface, which is proportional to the product of surface emissivity a nd surface temperature. For a  cloudy atmosphere, a portion of surface emission at these channels can be attenuated by cloud and the rest transmitted through the cloud. The cloud also emits additional radiation. Channel 3 is near an oxygen absorption line and contains the upwelling microwave radiation from both the earth’s surface and the near surface atmosphere. Satellite measurements and their retrieval products were used for studying climate variability and decadal trends (Christy et al. 1998, 2000, 2003; Izaguirre et al. 2010; Johannessen et al. 1995, 1999; Mears et al. 2003, Mears and Wentz 2009; Schneider et al. 2004; Vinnikov and Grody 2003; Zou et al. 2009). In these study, the AMSU-A brightness temperatures onboard NOAA-15 from October 26, 1998 to August 7, 2010 are analyzed for various applications including climate trend and global change. 3. ERA-Interim Reanalysis Data The ERA-Interim reanalysis is produced by European Center for Medium-Range Forecast (ECMWF) (Simmons et al. 2007). By employing an advanced four-dimensional variational data assimilation (4D-Var) approach with improved data quality control, satellite bias correction, and fast radiative transfer model, conventional surface and upper air observations and satellite brightness temperatures and cloud motion winds from Television InfraRed Observational Satellite (TIROS) Operational Vertical Sounder (TOVS), Special Sensor Microwave/Imager (SSM/I), ESA Remote-Sensing Satellites (ERS-1 and ERS-2), and Advanced TOVS (ATOVS) are optimally combined with model forecasts in ERA-Interim reanalysis. The ERA-Interim reanalysis products are thus suitable for use in studies of climate variability and decadal trends (Agudelo and Curry 2004; Chelliah et al. 2004; Frauenfeld et al. 2005). The ERA-Interim analyses consist of a high quality set of global analyses of the state of the atmosphere, land, and ocean-wave conditions from 1989 to present time. The surface-skin temperatures and two-meter air temperatures from ERA-Interim are used in this study. These data has 1.5 ° resolution and 37 pressure levels and is publicly available on the ECMWF Data Server. 4. Arctic Four-Month Oscillation A wavelet analysis is applied to global daily mean, nadir only, surface-sensitive brightness temperatures observed by the NOAA-15 AMSU-A over the time period from October 26, 1998 to August 7, 2010, as well as daily mean surface skin and surface air (two-meter) temperatures from ERA-Interim reanalysis. Specifically, the brightness temperature measurements at the surface-sensitive channel 1 (, 23.8 GHz), channel 2 (, 31.4 GHz), channel 3 (, 50.3 GHz) and channel 15 (, 89 GHz) near the nadir direction (FOVs 15 and 16), at both descending and ascending nodes, and north of 75oN and south of 70oS are averaged to provide eight daily time series from October 26, 1998 to August 7, 2010. Surface skin temperatures () and two-meter surface air temperature () from ERA-Interim north of 75oN and south of 70oS are also averaged to provide four more time series in the same time period. Using the Morlet wavelet analysis with statistical significant testing, each time series is decomposed into time-fr equency space, from which the dominant modes of variability and their temporal evolution can be determined with great confidence (Torrence and Compo 1998). The wavelet transform is chosen for this study as it can be used to analyze time series that contain non-stationary power at many different frequencies. Figure 1 shows the wavelet power spectrum (shaded) of daily mean nadir only brightness temperatures from NOAA-15 AMSU-A surface-sensitive channel two in the Arctic and Antarctic, and the surface skin and 2-m air temperature of ERA-Interim from October 26, 1998 to August 13, 2010. To show the significance of a peak in the wavelet power spectra, regions of greater than 95% confidence is indicated (line). For period less than semi-annual oscillation, most of the power is concentrated around the four-month period within the 95% confidence level. The existence of the four-month oscillation is also confirmed using the Fourier spectrum analysis technique and is shown in Fig. 2. However, with wavelet analysis, one can see variations in the frequency occurrence and amplitude of the Arctic/Antarctic four-month oscillations shown in Fig. 1. Large amplitude four-month oscillation events occurred at a period about 3 years. The strongest years were 2002-2003, 2005-2006 and 2008-2009. Similar wavel et power spectra are seen in other AMSU-A surface-sensitive channels in the Northern Hemisphere and AMSU-A channel 1 in the Southern Hemisphere (Figure omitted). Due to the fact the Antarctic is covered mostly by land, the Antarctic four-month oscillation is very weak in channels 15 and 3. A four-month oscillation is also found in daily mean surface skin temperatures and surface air temperatures from ERA-Interim reanalysis in the Arctic (Fig. 1c-d), but not in the Antarctic (figure omitted). The reduced power of surface and near-surface temperatures (Fig. 1c-d) compared to satellite observations (Fig. 1a-b) is possibly due to the fact that most of surface channels observations are excluded from data assimilation in high latitudes owing to large impacts of surface emissivity uncertainty on radiance simulations. From Fig. 1, it is seen that the ERA-Interim captures the four-month surface oscillation better during 2005-2006 and 2008-2009 than earlier years. Figure 3 presents the temporal evolution of Arctic (75oN-90oN) and Antarctic (70oS-90oS) daily mean brightness temperatures in 2005 (black line), in which mean values, annual and semi-annual components are removed, as well as the corresponding four-month oscillation (red curve). The four-month oscillation of AMSU-A channel 2 has the largest amplitude at the beginning of March, July and November. No significant phase difference is found between this and other three surface sensitive channels (Figures omitted). The Antarctic four-month oscillation is 90o out of phase with the Arctic oscillation. It peaks in the middle of April, August and December. A weak four-month oscillation is also found in the daily mean surface-skin temperatures and two-meter air temperatures from ERA-Interim reanalysis. However, a significant phase difference is found between the AMSU-A surface-sensitive channels and the ERA-Interim surface skin temperature and surface air temperatures. The four-month oscillation of both surface skin temperature and surface air temperatures peaks in late June, about one and half months earlier than satellite observations. Given the fact that the brightness temperatures at the four surface-sensitive channels approximately equal to the product of surface emissivity and surface skin temperature, with a small contribution from the atmosphere in a shallow layer above the Earth’s surface, the phase differences between the ERA-Interim surface variables and AMSU-A surface channel brightness temperatures suggest that the brightness temperature change is delayed by surface emissivity change. It is worth mentioning that the four-month oscillation is not found in the brightness temperature measurements of the other 11 AMSU-A channels, which approximately represent the air temperature in a broad layer centered in the troposphere or stratosphere. Figure 4 provides the percentage of explained variances by annual (black), semi-annual (red) and four-month (yellow) oscillations in middle and high latitudes for NOAA-15 AMSU-A channel 2. It is seen that the annual variation increases toward high latitudes from 20oN to 70oN or from 20oS to 70oS. The annual oscillation becomes a dominant feature with 50oN-70oN and 50oS-70oS. The sum of annual and semi-annual oscillations explains more than 80% of the total variances within the latitudinal band 60oN-70oN or 60oS-70oS, which reduces to below 60% in higher latitudes 70oN-90oN and 70oS-80oS. The four-month oscillation explains about 10% of the total variance in the Arctic and Antarctic. Figure 5 presents the annual cycles of Arctic four-month oscillation in three selected years (1999, 2003 and 2009) from all the six time series (, , , , , and ). The four-month oscillations of all four surface-sensitive channels have the largest amplitude at the beginning of March, July and November. No significant phase difference is found among these four channels. However, a significant phase difference is found between the AMSU-A surface-sensitive channels and the ERA-Interim surface skin temperature and surface air temperatures. The four-month oscillation of both surface skin temperature and surface air temperatures peaks in late June, about one and half months earlier than satellite observations. Given the fact that the brightness temperatures at the four surface-sensitive channels is a sum of the surface term (approximately equals to surface emissivity times surface skin temperature) and the atmosphere term (about equal to the air temperature in a shallow layer (~1 km) above t he Earth’s surface), the phase differences between the ERA-Interim surface variables and AMSU-A surface channel brightness temperatures suggest that the four-month oscillation started from the surface. In fact, the four-month oscillation is not found in the brightness temperature measurements of the other 11 AMSU-A channels, which approximately represent the air temperature in a broad layer centered in the troposphere or stratosphere. Wave structures with periods between 60 days and 150 days are shown in Fig. 6 based on the daily mean brightness temperatures in 75oN-90oN latitudes at nadir of NOAA-15 AMSU-A channel one, two, three, fifteen, skin and 2-m surface air temperature of ERA-Interim from January 1, 2004 to January 1, 2007. The four-month oscillation is a dominant feature in all years. A weak 90-day oscillation is also found in satellite measurements. The intensity of the Arctic four-month oscillation varies inter-annually. 5. Discussions of the oscillation A four-month oscillation is found in the satellite microwave measurements in the Arctic and Antarctic for the first time. The ERA-Interim reanalysis data confirms the existence of such an oscillation. Such oscillation is not found in other regions over the globe and nor in other AMSU-A atmospheric sounding channels. The surface temperature in polar regions is determined by surface heat budget equation, which relates changes in surface upward long-wave radiations to changes in (i) the surface downward short-wave radiation, (ii) surface downward long-wave radiations, (iii) heat storage for both land surface and ocean, (iv) surface sensible heat flux, and (v) surface latent heat flux. The presence of polar day/night is a unique feature that makes the annual variation of solar radiative forcing within the frigid zone[1] substantially different from middle and low latitudes. Since solar radiation is a major source of energy for the snow/ice melting in polar regions, the unique annual variation of solar radiation can modulate microwave surface emissivity and thermodynamic and dynamic processes near the surface boundary. The responses of surface-sensitive brightness temperature to solar radiation can also be delayed due to the time for the snow and ice metamorphosis process to occur. The combined effec t of polar day and night during the year and snow/ice metamorphosis process probably gives birth to a four-month oscillation in the Arctic and Antarctic. The fact that the four-month oscillation is stronger in higher latitudes is consistent with the increase of the length of the time when the sun is below the horizon from the Arctic Circle (20  hours) to North Pole (179  days). Acknowledgement This work was supported by Chinese Ministry of Science and Technology under 973 project no. 2010CB951600 and the NOAA/NESDIS grant to Florida State University. References Agudelo, P. A., and J. A. Curry, 2004: Analysis of spatial distribution in tropospheric temperature trends, Geophys. Res. Lett., 31, L22207, doi: 10.1029/ 2004GL020818. Chelliah, M., and G. D. Bell, 2004: Tropical Multidecadal and Interannual Climate Variability in the NCEP–NCAR Reanalysis.  J. Climate,  17, 1777–1803. Christy, J. R., R. W. Spencer, and E. S. Lobel, 1998: Analysis of the merging procedure for the MSU daily temperature time series. J. Climate, 11, 2016–2041. Christy, J. R., R. W. Spencer, and W. D. Braswell, 2000: MSU tropospheric temperatures: Dataset construction and radiosonde comparisons. J. Atmos. Oceanic Technol., 17, 1153–1170. Christy, J. R., R. W. Spencer, W. B. Norris, W. D. Braswell, and D. E. Parker, 2003: Error estimates of version 5.0 of MSU–AMSU bulk atmospheric temperature. J. Atmos. Oceanic Technol., 20, 613–629. Frauenfeld, O. W., T. Zhang, and M. C. Serreze, 2005: Climate change and variability using European Centre for Medium-Range Weather Forecasts reanalysis (ERA-40) temperatures on the Tibetan Plateau.  J. Geophys. Res.,  110, D02101, doi: 10.1029/2004JD005230. Goodrum, G., K. Kidwell, and W. Winston, 2009: NOAA KLM Users Guide with NOAA-N, -N-Prime supplement. NOAA, [available from: ncdc.noaa.gov/docs/klm/cover.htm]. Izaguirre C., Mendez F. J., Menendez M., and I. J., Losada, 2010: Global extreme wave height variability based on satellite data, Geophys. Res. Lett.,  doi:10.1029/2011GL047302. Johannessen, O. M., M. W. Miles, and E. Bjorgo, 1995: The Arctic’s shrinking sea ice. Nature, 376, 126-127. Johannessen, O. M., E. V. Shalina, and M. W. Miles, 1999: Satellite evidence for an Arctic sea ice coverage in transformation. Science, 286, 1837-1939. Madden, R. A., and P. R. Julian, 1971: Detection of a 40-50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci.,  28, 702-708. Mears, C. A., M. C. Schabel, and F. J. Wentz, 2003: A reanalysis of the MSU channel 2 tropospheric temperature record. J. Climate, 16, 3650–3664. Mears, C. A., and F. J. Wentz, 2009: Construction of the Remote Sensing Systems V3.2 atmospheric temperature records from the MSU and AMSU microwave sounders. J. Atmos. Oceanic Technol., 26, 1040–1056. Mo, T., 1999: AMSU-A antenna pattern corrections, IEEE Trans. Geosci. Remote Sens., 37, 103-112. Schneider, D. P., E. J. Steig, and J. C. Comiso, 2004: Recent Climate Variability in Antarctica from Satellite derived temperature data, J. Climate, 17, 1569-1583. Simmons, A., S. Uppala, D. Dee, and S. Kobayashi, 2007: ERA-Interim: New ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter, No. 110, 25–35. Torrence, C., and G. P.  Compo, 1998: A Practical Guide to Wavelet Analysis. Bull. Amer. Meteor. Soc., 79, 61-78. Vinnikov K. Y. and Norman C. Grody, 2003: Global Warming Trend of Mean tropospheric temperature observed by satellites, Science, 269-272. Zou, C.-Z., M. Gao, and M. Goldberg, 2009: Error structure and atmospheric temperature trend in observations from the Microwave Sounding Unit. J. Climate, 22, 1661–1681.

A Dialogue between a Hindu Upanishad and a Jain Essay

A Dialogue between a Hindu Upanishad and a Jain - Essay Example It is because I am free from suffering as my state (moksha) has liberated me from it. I do not wish anyone or anything ill and so this forest shall not harm me. I am pleased wherever I am. Religious A (Hindu): Wherever you are is not actually where you are. You see my friend, what you thought as you is not actually you because what you think you are that is trapped in this forest is just the body that houses the atman, which is your soul or the real you. The real you actually cannot perish and it does not matter where you are whether you are pleased or not because the real you cannot be harmed. Religious A (Hindu): I am confident because my atman will unite with its natural universe which is the Brahman. Brahman my friend could be this forest because it is all that permeates all things where it held all being and existence. Right now, in this forest with all its peace and tranquility, the atman becoming the Brahman is beginning to become more apparent to me. Religious B (Jain): That is so wonderful of you my friend. But your conviction and confidence may lead you to suffering because it entertains and contains the mana or ego that leads you to perish and suffer. Religious A (Hindu): No my friend, I don’t perish. I will just be reborn again to improve my karma so I am not worried about any harm. Sometimes death, or perishing as you call it is necessary to complete the cycle of birth and death. This is to allow me to improve my karma through their lifetime of action until I achieved enlightenment and vijnana. Religious B (Jain): Looks like we share the same sentiment my friend. I too strive to achieve nirvana and be released by the karmic bondage. Hey look! The town is already ahead. It looks like it is not our day after all to begin the cycle of

Love.Rapid weight gain during infancy and obesity in young adulthood Research Paper

Love.Rapid weight gain during infancy and obesity in young adulthood in a cohort of african American - Research Paper Example It involved selecting a cohort of 300 African Americans born at full term and their progress followed from birth to 20 years of age. The study was slow since it took the researchers two decades to get the appropriate outcome and results. The subjects used in the study were living and made for a more informative interview. The outcome of the research was general and gave satisfying results. The statistical analyses used in the study included; finding the weight of the subjects as recommended by Center for Disease Control and Prevention (CDCP) using the LMS method and a representative sample of the US population (Stettler et al, 2003). In analyzing, a pattern of quick rate of weight gain in the first 4 months of life, was defined as an increase in weight-for-age z score>b SD between birth and 4 months. The major result in table 1 show that the population attributes risk of young adulthood obesity was 30% for a pattern of rapid weight gain during infancy. 1 Â ½ of the obese young adults in the early infancy gained a quick weight (Stettler et al, 2003). 15% of African americans with and 6% without a quick mass increase throughout untimely infancy became ‘overweight –overfat’ in childish adulthood. In table 2, a quick weight gain in the early infancy and adult obesity was found out by sexual category, delivery weight, gestational time, firstborn condition, maternal BMI, motherly smoking condition or education (Stettler et al, 2003). OR information represents ‘odds ratios’ while CI represents ‘Confidence Intervals’. OR of 5.22 signifies harm in relation to the risk of quick weight gain as infant compared to weight as an immature adult, since gaining weight will affect the internal organs by fats blocking the body tissues. The biological facts of clinical knowledge are significant in comprehending and analyzing the results. The clinical implications of the study included using alternate definitions based on BMI or BMI combining it with

Texting While Driving Essay Example for Free

Texting While Driving Essay Texting while driving is one of the most common causes of accidents on roads. This is because texting while driving results in physical, visual and cognitive distraction. It greatly increases the amount of time a driver spends not looking at the road. It is a very serious distraction that can cost you your life or the lives of other people. According to recent research by Queensland’s RACQ, using a mobile phone in general can relay reaction time as much as having a blood alcohol content of 0. 08% which is well over the legal limit of 0.05% in Australia. It reduces your reaction time by 35%, even when using hands-free, so texting is obviously going to be even worse because you are effectively driving blind for however long you look at your phone. In fact, every second you spend texting, you double your chances of being in a crash, so why take the chance? If you are ever driving a car and are about to send a text message or use your phone, think about how important it really is. Is it really worth risking your life to tell someone â€Å"lunch was nice† or â€Å"I’ll be home soon†? Is it worth leaving a child fatherless and asking questions like â€Å"Mum, who will look after me if you go to heaven like dad?† as was the case for 5 year-old Harry Stortz after his Dad Jason was killed by an under-age, unlicensed texting driver? Harry will never get to see his Dad again just so someone could make arrangements to pick a girl up and go to his mate’s house. This is just one horrible example of the vast number of deaths caused by texting while driving. We are all disgusted by drink driving, and for good reason, and using a mobile while driving has been proven in many studies to be just as bad, if not worse. Despite this, around 40% of drivers between 18-24 routinely admit to sending or reading texts while driving. This shows that there is an urgent need for greater fines and punishments for drivers who use their mobiles while driving. Texting while driving is one of the most common causes of accidents on roads. This is because texting while driving results in physical, visual and cognitive distraction. It greatly increases the amount of time a driver  spends not looking at the road. It is a very serious distraction that can cost you your life or the lives of other people. According to recent research by Queensland’s RACQ, using a mobile phone in general can relay reaction time as much as having a blood alcohol content of 0.08% which is well over the legal limit of 0.05% in Australia. It reduces your reaction time by 35%, even when using hands-free, so texting is obviously going to be even worse because you are effectively driving blind for however long you look at your phone. In fact, every second you spend texting, you double your chances of being in a crash, so why take the chance? If you are ever driving a car and are about to send a text message or use your phone, think about how important it really is. Is it really worth risking your life to tell someone â€Å"lunch was nice† or â€Å"I’ll be home soon†? Is it worth leaving a child fatherless and asking questions like â€Å"Mum, who will look after me if you go to heaven like dad?† as was the case for 5 year-old Harry Stortz after his Dad Jason was killed by an under-age, unlicensed texting driver? Harry will never get to see his Dad again just so someone could make arrangements to pick a girl up and go to his mate’s house. This is just one horrible example of the vast number of deaths caused by texting while driving. We are all disgusted by drink driving, and for good reason, and using a mobile while driving has been proven in many studies to be just as bad, if not worse. Despite this, around 40% of drivers between 18-24 routinely admit to sending or reading texts while driving. This shows that there is an urgent need for greater fines and punishments for drivers who use their mobiles while driving. Texting while driving is one of the most common causes of accidents on roads. This is because texting while driving results in physical, visual and cognitive distraction. It greatly increases the amount of time a driver spends not looking at the road. It is a very serious distraction that can cost you your life or the lives of other people. According to recent research by Queensland’s RACQ, using a mobile phone in general can relay reaction time as much as having a blood alcohol content of  0.08% which is well over the legal limit of 0.05% in Australia. It reduces your reaction time by 35%, even when using hands-free, so texting is obviously going to be even worse because you are effectively driving blind for however long you look at your phone. In fact, every second you spend texting, you double your chances of being in a crash, so why take the chance? If you are ever driving a car and are about to send a text message or use your phone, think about how important it really is. Is it really worth risking your life to tell someone â€Å"lunch was nice† or â€Å"I’ll be home soon†? Is it worth leaving a child fatherless and asking questions like â€Å"Mum, who will look after me if you go to heaven like dad?† as was the case for 5 year-old Harry Stortz after his Dad Jason was killed by an under-age, unlicensed texting driver? Harry will never get to see his Dad again just so someone could make arrangements to pick a girl up and go to his mate’s house. This is just one horrible example of the vast number of deaths caused by texting while driving. We are all disgusted by drink driving, and for good reason, and using a mobile while driving has been proven in many studies to be just as bad, if not worse. Despite this, around 40% of drivers between 18-24 routinely admit to sending or reading texts while driving. This shows that there is an urgent need for greater fines and punishments for drivers who use their mobiles while driving. Texting while driving is one of the most common causes of accidents on roads. This is because texting while driving results in physical, visual and cognitive distraction. It greatly increases the amount of time a driver spends not looking at the road. It is a very serious distraction that can cost you your life or the lives of other people. According to recent research by Queensland’s RACQ, using a mobile phone in general can relay reaction time as much as having a blood alcohol content of 0.08% which is well over the legal limit of 0.05% in Australia. It reduces your reaction time by 35%, even when using hands-free, so texting is obviously going to be even worse because you are effectively driving blind for however long you look at your phone. In fact, every second you spend texting, you double your chances of being in a crash, so why take the chance? If you are ever driving a car and are about to send a text message or use your phone, think about how important it really is. Is it really worth risking your life to tell someone â€Å"lunch was nice† or â€Å"I’ll be home soon†? Is it worth leaving a child fatherless and asking questions like â€Å"Mum, who will look after me if you go to heaven like dad?† as was the case for 5 year-old Harry Stortz after his Dad Jason was killed by an under-age, unlicensed texting driver? Harry will never get to see his Dad again just so someone could make arrangements to pick a girl up and go to his mate’s house. This is just one horrible example of the vast number of deaths caused by texting while driving. We are all disgusted by drink driving, and for good reason, and using a mobile while driving has been proven in many studies to be just as bad, if not worse. Despite this, around 40% of drivers between 18-24 routinely admit to sending or reading texts while driving. This shows that there is an urgent need for greater fines and punishments for drivers who use their mobiles while driving. Texting while driving is one of the most common causes of accidents on roads. This is because texting while driving results in physical, visual and cognitive distraction. It greatly increases the amount of time a driver spends not looking at the road. It is a very serious distraction that can cost you your life or the lives of other people. According to recent research by Queensland’s RACQ, using a mobile phone in general can relay reaction time as much as having a blood alcohol content of 0.08% which is well over the legal limit of 0.05% in Australia. It reduces your reaction time by 35%, even when using hands-free, so texting is obviously going to be even worse because you are effectively driving blind for however long you look at your phone. In fact, every second you spend texting, you double your chances of being in a crash, so why take the chance? If you are ever driving a car and are about to send a text message or use your phone, think about how important it really is. Is it really worth risking your life to tell someone â€Å"lunch was nice† or â€Å"I’ll be home soon†? Is it worth leaving a child fatherless and asking questions like â€Å"Mum, who  will look after me if you go to heaven like dad?† as was the case for 5 year-old Harry Stortz after his Dad Jason was killed by an under-age, unlicensed texting driver? Harry will never get to see his Dad again just so someone could make arrangements to pick a girl up and go to his mate’s house. This is just one horrible example of the vast number of deaths caused by texting while driving. We are all disgusted by drink driving, and for good reason, and using a mobile while driving has been proven in many studies to be just as bad, if not worse. Despite this, around 40% of drivers between 18-24 routinely admit to sending or reading texts while driving. This shows that there is an urgent need for greater fines and punishments for drivers who use their mobiles while driving.

The Vietnam War :: Vietnam War Essays

The Vietnam War The Vietnam War is truly one of the most unique wars ever fought by the Unites States of by any country. It was never officially declared a war (Knowll, 3). It had no official beginning nor an official end. It was fought over 10,000 miles away in a virtually unknown country. The enemy and the allies looked exactly the alike, and may by day be a friend but by night become an enemy (Aaseng 113). It matched the tried and true tactics of World War Two against a hide, run, and shoot technique known as "Guerrilla Warfare." It matched some of the best trained soldiers in the world against largely an untrained militia of untrained farmers. The United States' soldiers had at least a meal to look forward to unlike the Communist Vietnamese soldiers who considered a fine cuisine to be cold rice and, if lucky, rat meat. The Vietnam War matched the most technically advanced country with one of the least advanced, and the lesser advanced not only beat but humiliated the strongest military in the wo rld (Aaseng, 111). When the war was finally showing signs of end, the Vietnamese returned to a newly unified communist country while the United Stated soldiers returned to be called "baby killers", and were often spat upon. With the complexities of war already long overdrawn because of the length of the war it is no wonder the returning solders often left home confused and returned home insane. Through an examination of the Vietnam War, in particular an event know as the My Lai Massacre, and the people involved with both, it can be proven that when the threshold for violence of a person is met or exceeded, the resulting psychological scarring becomes the most prominent reason for war being hell. Although officially, the Vietnam Conflict had neither a beginning nor an end, for the purpose of this paper it can be best examined through the decade the United States was involved: February 6, 1965 - August 30, 1975. During World War Two the French had been a major ally to the United States in the defeat of Adolph Hitler and the Axis Powers. France occupied and claimed the small coastline country of Vietnam in Indochina. In this region there had been recent Communist uprisings funded by the USSR The Vietnamese were willing to accept Communism in return for what they had been fighting for over 2000 years: self rule.

Causes of Water Pollution Essay

Nowadays, water pollution is a big problem in Vietnam and many researchers have been studying it. Accordingly, there are two main reasons of water pollution in Vietnam. The weakness in industrial wastewater management is the main cause of water pollution in Vietnam. Many industrial facilities use freshwater to carry away waste from their plants into canals, rivers, and lakes. Most of the enterprises don’t have any wastewater treatment system and many industrial zones don’t have a central wastewater treatment plant. Industrial wastewater is directly discharged into canals, lakes, ponds, and rivers, causing serious pollution of surface water. Besides this, the growing number of factories along the river and their untreated waste disposal is causing a lot of diseases and intestinal sicknesses among people living in the vicinity of the river. The water pollution is clearly visible, and residents can see a tarred black color and smell a pungent odor from the river. Another cause of water pollution in Vietnam is the lack of awareness among citizens. Every day people generate a lot of trash, and they throw it directly into canals, rivers, and ponds. They collect water from these sources to do their laundry, wash dishes, and bathe, and then they throw the dirty water that contained detergent and shampoo directly into them. Moreover, villages in Vietnam involve in paper production, livestock slaughtering, weaving and dyeing also produce a huge volume of wastewater and solid waste, all of which is discharged into the environment in a careless manner. As a result, it causes particularly serious levels of water pollution and poison many forms of aquatic life such as fish, shrimp, crabs, and plant life, slowing their development, and even resulting in their death.