No abstract

An effective innovative pedagogy for sexual health education is required to meet the demands of technology savvy digital natives. This study investigates the extent to which game-based learning (GBL) and gamification could improve the sexual health education of adolescent students. We conducted a randomized control trial of GBL and gamification experimental conditions. We made a comparison with traditional teaching as a control condition in order to establish differences between the three teaching conditions. The sexual health education topics were delivered in a masked fashion, 40-min a week for five weeks. A mixed-method research approach was uses to assess and analyze the results for 120 students from a secondary school in Dar Es Salaam, Tanzania. Students were divided into groups of 40 for each of the three teaching methods: GBL, gamification, and the control group (the traditional teaching method). The average post-test scores for GBL (Mean = 79.94, SD = 11.169) and gamification (Mean = 79.23, SD = 9.186) were significantly higher than the control group Mean = 51.93, SD = 18.705 (F (2, 117) = 54.75, p = 0.001). Overall, statistically significant differences (p ≤ 0.05) were found for the constructs of Motivation, Attitude, Knowledge, and Engagement (MAKE). This study suggests that the two innovative teaching approaches can be used to improve the sexual health education of adolescent students. The methods can potentially contribute socially, particularly in improving sexual health behaviour and adolescents’ knowledge in regions plagued by years of sexual health problems, including HIV/AIDS.

No abstract

Denitrification is known as an important pathway for nitrate loss in agroecosystems. It is important to estimate denitrification fluxes to close field and watershed N mass balances, determine greenhouse gas emissions (N 2 O), and help constrain estimates of other major N fluxes (e.g., nitrate leaching, mineralization, nitrification). We compared predicted denitrification estimates for a typical corn and soybean agroecosystem on a tile drained Mollisol from five models (DAYCENT, SWAT, EPIC, DRAINMOD-N II and two versions of DNDC, 82a and 82h), after first calibrating each model to crop yields, water flux, and nitrate leaching. Known annual crop yields and daily flux values (water, nitrate-N) for 1993-2006 were provided, along with daily environmental variables (air temperature, precipitation) and soil characteristics. Measured denitrification fluxes were not available. Model output for 1997-2006 was then compared for a range of annual, monthly and daily fluxes. Each model was able to estimate corn and soybean yields accurately, and most did well in estimating riverine water and nitrate-N fluxes (1997-2006 mean measured nitrate-N loss 28 kg N ha -1 year -1 , model range 21-28 kg N ha -1 year -1 ). Monthly patterns in observed riverine nitrate-N flux were generally reflected in model output (r 2 values ranged from 0.51 to 0.76). Nitrogen fluxes that did not have corresponding measurements were quite variable across the models, including 10-year average denitrification estimates, ranging from 3.8 to 21 kg N ha -1 year -1 and substantial variability in simulated soybean N 2 fixation, N harvest, and the change in soil organic N pools. DNDC82a and DAYCENT gave comparatively low estimates of total denitrification flux (3.8 and 5.6 kg N ha -1 year -1 , respectively) with similar patterns controlled primarily by moisture. DNDC82h predicted similar fluxes until 2003, when estimates were abruptly much greater. SWAT and DRAINMOD predicted larger denitrification fluxes (about 17-18 kg N ha -1 year -1 ) with monthly values that were similar. EPIC denitrification was intermediate between all models (11 kg N ha -1 year -1 ). Predicted daily fluxes during a high precipitation year (2002) varied considerably among models regardless of whether the models had comparable annual fluxes for the years. Some models predicted large denitrification fluxes for a few days, whereas others predicted large fluxes persisting for several weeks to months. Modeled denitrification fluxes were controlled mainly by soil moisture status and nitrate available to be denitrified, and the way denitrification in each model responded to moisture status greatly determined the flux. Because denitrification is dependent on the amount of nitrate available at any given time, modeled differences in other components of the N cycle (e.g., N 2 fixation, N harvest, change in soil N storage) no doubt led to differences in predicted denitrification. Model comparisons suggest our ability to accurately predict denitrification fluxes (without known values) from the dominant...

Reliable water quality models are needed to forecast the water quality consequences of different agricultural nutrient management scenarios. In this study, the Soil and Water Assessment Tool (SWAT), version 2000, was applied to simulate streamflow, riverine nitrate (NO(3)) export, crop yield, and watershed nitrogen (N) budgets in the upper Embarras River (UER) watershed in east-central Illinois, which has extensive maize-soybean cultivation, large N fertilizer input, and extensive tile drainage. During the calibration (1994-2002) and validation (1985-1993) periods, SWAT simulated monthly and annual stream flows with Nash-Sutcliffe coefficients (E) ranging from 0.67 to 0.94 and R(2) from 0.75 to 0.95. For monthly and annual NO(3) loads, E ranged from -0.16 to 0.45 and R(2) from 0.36 to 0.74. Annual maize and soybean yields were simulated with relative errors ranging from -10 to 6%. The model was then used to predict the changes in NO(3) output with N fertilizer application rates 10 to 50% lower than original application rates in UER. The calibrated SWAT predicted a 10 to 43% decrease in NO(3) export from UER and a 6 to 38% reduction in maize yield in response to the reduction in N fertilizer. The SWAT model markedly overestimated NO(3) export during major wet periods. Moreover, SWAT estimated soybean N fixation rates considerably greater than literature values, and some simulated changes in the N cycle in response to fertilizer reduction seemed to be unrealistic. Improving these aspects of SWAT could lead to more reliable predictions in the water quality outcomes of nutrient management practices in tile-drained watersheds.

Mood is an emerging metadata type and access point in music digital libraries (MDL) and online music repositories. In this study, we present a comprehensive investigation of the usefulness of lyrics in music mood classification by evaluating and comparing a wide range of lyric text features including linguistic and text stylistic features. We then combine the best lyric features with features extracted from music audio using two fusion methods. The results show that combining lyrics and audio significantly outperformed systems using audio-only features. In addition, the examination of learning curves shows that the hybrid lyric + audio system needed fewer training samples to achieve the same or better classification accuracies than systems using lyrics or audio singularly. These experiments were conducted on a unique large-scale dataset of 5,296 songs (with both audio and lyrics for each) representing 18 mood categories derived from social tags. The findings push forward the state-of-the-art on lyric sentiment analysis and automatic music mood classification and will help make mood a practical access point in music digital libraries.

No abstract

No abstract