Selecting Site Suitable for Animal Waste Application using a Vector GIS

Due to the increase in the number and size of intensive animal industries (IAI) in many parts of the world including Australia, the disposal of animal waste has become a pressing environmental problem. Frequently the wastes generated at IAI are conveniently, favourably, and cost-effectively applied to the nearby agricultural fields to recycle manure nutrients. However, excessive application of wastes in the nearby fields without due consideration of site-specific factors (eg. slope, soil, and watercourses) has resulted in the run-off and leaching losses of manure nutrients causing agricultural non-point source (NPS) pollution (He and Shi, 1998). The agricultural NPS pollution has contributed significantly to the eutrophication and toxic blue green algae blooms in many river systems including Murray-Darling, where world's largest toxic riverine algal bloom was recorded in 1991 (Kuhn, 1993). Hence it has become crucial to develop an animal waste application guide (i.e. a site suitability map) by considering biophysical and socio-economic factors to minimise the environmental hazards. Developing such a map requires consideration of many factors and their spatial variability. Geographic information system (GIS)offers site suitability analysis techniques that are capable of processing large volumes of spatial data (Davis, 1996). The objective of this study is to develop a suitability map using a vector GIS, and to evaluate the factor sensitivity and aptness of this technique in selecting suitable sites for animal waste application

Developing a scalable training model in global mental health: pilot study of a video-assisted training Program for Generalist Clinicians in Rural Nepal.

BackgroundIn low- and middle-income countries, mental health training often includes sending few generalist clinicians to specialist-led programs for several weeks. Our objective is to develop and test a video-assisted training model addressing the shortcomings of traditional programs that affect scalability: failing to train all clinicians, disrupting clinical services, and depending on specialists.MethodsWe implemented the program -video lectures and on-site skills training- for all clinicians at a rural Nepali hospital. We used Wilcoxon signed-rank tests to evaluate pre- and post-test change in knowledge (diagnostic criteria, differential diagnosis, and appropriate treatment). We used a series of 'Yes' or 'No' questions to assess attitudes about mental illness, and utilized exact McNemar's test to analyze the proportions of participants who held a specific belief before and after the training. We assessed acceptability and feasibility through key informant interviews and structured feedback.ResultsFor each topic except depression, there was a statistically significant increase (Δ) in median scores on knowledge questionnaires: Acute Stress Reaction (Δ = 20, p = 0.03), Depression (Δ = 11, p = 0.12), Grief (Δ = 40, p < 0.01), Psychosis (Δ = 22, p = 0.01), and post-traumatic stress disorder (Δ = 20, p = 0.01). The training received high ratings; key informants shared examples and views about the training's positive impact and complementary nature of the program's components.ConclusionVideo lectures and on-site skills training can address the limitations of a conventional training model while being acceptable, feasible, and impactful toward improving knowledge and attitudes of the participants

Analysis of Yield Attributing Characters of Different Genotypes of Wheat in Rupandehi, Nepal

Field experiment was conducted at National Wheat Research Program, Bhairahawa, Rupandehi with the objective to identify high yielding superior wheat genotypes for Rupandehi district of Nepalduring 2014. Experiment was laid out in one factorial Randomized completely block design with ten wheat genotypes including both released and promising; Annapurna 1, Annapurna 3, Pasang Lahmu, Bijaya, BL 3623, Bhirkuti, NL 297, BL 4316, BL 3978 and BL 4347with three replications. The results showed that the grain yield of BL 3978 was found higher (4.03 t ha-1) than other genotypes followed by BL 4347 (3.93t ha-1). BL 3978 have also higher number of effective tillers m-2 and test weight. Among release varieties, NL 297 show higher yield (4 t ha-1) followed by Bhirkuti (3.43 t ha-1)and Bijaya (3.37 t ha-1). From this experiment it can be concluded that BL 3978 was found promising among all genotypes however should be tested at on-farms before promoted for general cultivation in Rupandehi district of Nepal

[Accepted Manuscript] Formative qualitative research to develop community-based interventions addressing low birth weight in the plains of Nepal.

To explore the factors affecting intra-household food allocation practices to inform the development of interventions to prevent low birth weight in rural plains of Nepal. Qualitative methodology using purposive sampling to explore the barriers and facilitating factors to improved maternal nutrition. Rural Dhanusha District, Nepal. We purposively sampled twenty-five young daughters-in-law from marginalised groups living in extended families and conducted semi-structured interviews with them. We also conducted one focus group discussion with men and one with female community health volunteers who were mothers-in-law. Gender and age hierarchies were important in household decision making. The mother-in-law was responsible for ensuring that a meal was provided to productive household members. The youngest daughter-in-law usually cooked last and ate less than other family members, and showed respect for other family members by cooking only when permitted and deferring to others' choice of food. There were limited opportunities for these women to snack between main meals. Daughters-in-law' movement outside the household was restricted and therefore family members perceived that their nutritional need was less. Poverty affected food choice and families considered cost before nutritional value. It is important to work with the whole household, particularly mothers-in-law, to improve maternal nutrition. We present five barriers to behaviour change: poverty; lack of knowledge about cheap nutritional food, the value of snacking, and cheap nutritional food that does not require cooking; sharing food; lack of self-confidence; and deference to household guardians. We discuss how we have targeted our interventions to develop knowledge, discuss strategies to overcome barriers, engage mothers-in-law, and build the confidence and social support networks of pregnant women

17th International Congress on Modelling and Simulation (MODSIM07)

Site suitability analysis is performed to identify suitable land units (i.e. grid cells) for a specific purpose so that management decisions can be made in a site-specific manner. However, these grid cells are rarely equally suitable in the real world. They may vary substantially in their degree (or level) of suitability. Yet, the discrimination between suitable cells is often beyond the scope of conventional site suitability analysis. Widening the scope of conventional site suitability analysis to include a degree of site suitability (DoSS) measurement is therefore crucial for managing sites in a truly site-specific manner. Conventionally, site suitability analysis involves weighted linear combination (WLC) of standardised input factors (e.g. land use, slope, distance from stream, etc.) within a Geographic Information Systems (GIS) framework. In a conventional site suitability analysis, factor attributes are standardised using discrete classification method. Yet, the effect of this standardisation method on the DoSS measurement is unknown. Therefore, the objective of this study was to quantify the effect of the discrete classification methods of input factor attribute standardisation on the DoSS measurement. In this study, seven input factors affecting the suitability of an agricultural land for site-specific application of animal waste as fertiliser were selected, pre-processed and standardised. Discrete classification method of standardisation, which replaced continuous or discrete factor attributes with a fixed number of differentially weighted classes, was employed. Three different classification and weighting schemes were adopted. Firstly, the attributes of each input factor were classified in up to five equal-sized classes to examine the effect of class number on the DoSS measurement. These classes were weighted with equally incremented weights that added up to 100. Secondly, they were classified into three sets of three classes each using equal area, equal interval and defined interval methods of classification to examine the effect of the class size on the DoSS measurement. These classes were also weighted with equally incremented weights that added up to 100. Thirdly, the attributes of each input factor were classified into two sets of three classes each, using equal area method of classification to examine the effect of differential weighting on the DoSS measurement. These sets were respectively weighted with equally and unequally incremented weights that added up to 100. Finally, the standardised input factors were correspondingly combined within GIS framework to produce 10 different composite maps (i.e. five for varying class number, three for varying class size and two for varying class weight). The DoSS measurements of each of the composite maps was quantified using the descriptive statistical parameters such as weighted average (WA), coefficient of variation (CV), value range (VR), and coefficient of skewness (CS) to make them comparable. The conventional discrete classification method of standardisation resulted in a series of suitability maps that varied widely depending on the class number, the class size, and the method of weighting the classes. The WA varied between 700 (CV=0 & VR=0) and 221.9 (CV=6.31 & VR=100) for class number ranging between one and five. The WA for various class sizes and weight distribution between classes were less dramatic. However, they have resulted in DoSS measurements that were clustered and skewed. The comparisons of results from these tests have highlighted the inconsistencies in the DoSS measurement when using various discrete classification methods of input factor attribute standardisation. It was found that the variations in terms of the class number, the class size, and the weight distribution between classes were the major contributing elements towards measurement inconsistencies. Therefore, it was concluded that the usefulness of this method of standardisation is limited for obtaining a comparable and repeatable DoSS measurement unless a more robust technique could be developed through further research

Relating Satellite Imagery with Grain Protein Content

Satellite images, captured during the growing seasons of barley, sorghum and wheat were analysed to establish a relationship between the spectral response and the harvested grain protein content. This study was conducted near Jimbour (approx. 151 degrees 10'E and 27 degrees 05'S) in southern Queensland. Grain protein contents of the geo-referenced samples, collected manually during the harvest, were determined using a laboratory-based near-infrared spectrophotometer. Grain protein contents in grain varied between 7.4 - 15.2% in barley, 6.2 - 10.6% in sorghum and 13.1 - 15.6% in wheat. The Landsat images of 18 September 1999 (a week after barley flowering), 5 March 2000 (three weeks before sorghum harvest), and 15 August 2001 (two weeks before wheat flowering) were analysed. Additionally, an ASTER image of 24 September 2001 (three weeks after wheat flowering) was also examined. Digital numbers, extracted from raw image bands and derived indices, were correlated with grain protein contents. The grain protein content in barley was correlated strongly (r>0.80) with bands 2, 4 and 5 of the Landsat scene, first principal component, and the tasselled cap brightness and greenness indices. Similarly, wheat protein content was well correlated (r>0.75) with the near infrared band (band 4) of the Landsat scene, first principal component, and the tasselled cap brightness, greenness and wetness indices. The band 3 (near infrared band) of the ASTER image, captured well after flowering, was moderately correlated (

DeltaPhish: Detecting Phishing Webpages in Compromised Websites

The large-scale deployment of modern phishing attacks relies on the automatic exploitation of vulnerable websites in the wild, to maximize profit while hindering attack traceability, detection and blacklisting. To the best of our knowledge, this is the first work that specifically leverages this adversarial behavior for detection purposes. We show that phishing webpages can be accurately detected by highlighting HTML code and visual differences with respect to other (legitimate) pages hosted within a compromised website. Our system, named DeltaPhish, can be installed as part of a web application firewall, to detect the presence of anomalous content on a website after compromise, and eventually prevent access to it. DeltaPhish is also robust against adversarial attempts in which the HTML code of the phishing page is carefully manipulated to evade detection. We empirically evaluate it on more than 5,500 webpages collected in the wild from compromised websites, showing that it is capable of detecting more than 99% of phishing webpages, while only misclassifying less than 1% of legitimate pages. We further show that the detection rate remains higher than 70% even under very sophisticated attacks carefully designed to evade our system.Comment: Preprint version of the work accepted at ESORICS 201

Middle mountains forests of Nepal

Publication no. 3201

Unsupervised morpheme segmentation in a non-parametric Bayesian framework

Learning morphemes from any plain text is an emerging research area in the natural language processing. Knowledge about the process of word formation is helpful in devising automatic segmentation of words into their constituent morphemes. This thesis applies unsupervised morpheme induction method, based on the statistical behavior of words, to induce morphemes for word segmentation. The morpheme cache for the purpose is based on the Dirichlet Process (DP) and stores frequency information of the induced morphemes and their occurrences in a Zipfian distribution. This thesis uses a number of empirical, morpheme-level grammar models to classify the induced morphemes under the labels prefix, stem and suffix. These grammar models capture the different structural relationships among the morphemes. Furthermore, the morphemic categorization reduces the problems of over segmentation. The output of the strategy demonstrates a significant improvement on the baseline system. Finally, the thesis measures the performance of the unsupervised morphology learning system for Nepali

High mountains and high Himal forests of Nepal

Publication no. 4201