XTE J0111.2-7317 : a nebula-embedded X-ray binary in the SMC

The observed characteristics of the nebulosity surrounding the SMC High Mass X-ray Binary XTE J0111.2-7317 are examined in the context of three possible nebular types: SNR, bowshock and HII region. Observational evidence is presented which appears to support the interpretation that the nebulosity surrounding XTE J0111.2-7317 is an HII region. The source therefore appears to be a normal SMC Be X-ray binary (BeXRB) embedded in a locally enhanced ISM which it has photoionised to create an HII region. This is supported by observations of the X-ray outburst seen with BATSE and RXTE in 1998-1999. It exhibited characteristics typical of a giant or type II outburst in a BeXRB including large spin-up rates, Lx~10E38 erg/sq.cm-s, and a correlation between spin-up rate and pulsed flux. However, the temporal profile of the outburst was unusual, consisting of two similar intensity peaks, with the first peak of shorter duration than the second.Comment: Accepted for publication by MNRA

Model and Sensor-Based Recommendation Approaches for In-Season Nitrogen Management in Corn

Nitrogen management for corn (Zea mays L.) may be improved by applying a portion of N in-season. This investigation was conducted to evaluate crop modeling (Maize-N) and active crop canopy sensing approaches for recommending in-season N fertilizer rates. These approaches were evaluated during 2012–2013 on 11 field sites, in Missouri, Nebraska, and North Dakota. Nitrogen management also included a no-N treatment (check) and a non-limiting N reference (all at planting). Nitrogen management treatments were assessed for two hybrids and at low and high seeding rates, arranged in a randomized complete block design. In 9 of 11 site-years, the sensor-based approach recommended lower in-season N rates than the model (collectively 59% less N), resulting in trends of higher partial factor productivity of nitrogen (PFPN) and higher agronomic efficiency (AE) than the model. However, yield was better protected by the model-based approach. In some situations, canopy sensing excelled at optimizing the N rate for localized conditions. With abnormally warm and moist soil conditions for the 2012 Nebraska sites and presumed high levels of inorganic N from mineralization, N application was appropriately reduced, resulting in no yield decrease and N savings compared to the non-limiting N reference. Depending on the site, both recommendation approaches were successful; a combination of model and sensor information may optimize in-season decision support for N recommendation

Sceptical Employees as CSR Ambassadors in Times of Financial Uncertainty

This chapter offers new insights into the understanding of internal (employee) perceptions of organizational corporate social responsibility (CSR) policies and strategies. This study explores the significance of employees’ involvement and scepticism upon CSR initiatives and focuses on the effects it may have upon word of mouth (WOM) and the development of employee–organisation relationships. Desk research introduces the research questions. Data for the research questions were gathered through a self-completion questionnaire distributed in a hardcopy form to the sample. An individual’s level of scepticism and involvement appears to affect the development of a positive effect on employees’ WOM. Involvement with the domain of the investment may be a central factor affecting relationship building within the organization, and upon generation of positive WOM. The chapter offers a conceptual framework to public relations (PR) and corporate communications practitioners, which may enrich their views and understanding of the use and value of CSR for communication strategies and practices. For-profit organisations are major institutions in today’s society. CSR is proffered as presenting advantages for (at macro level) society and (micro level) the organization and its employees. Concepts, such as involvement and scepticism, which have not been rigorously examined in PR and corporate communication literature, are addressed. By examining employee perceptions, managers and academic researchers gain insights into the acceptance, appreciation and effectiveness of CSR policies and activities upon the employee stakeholder group. This will affect current and future CSR communication strategies. The knowledge acquired from this chapter may be transferable outside the for-profit sector

Water channel pore size determines exclusion properties but not solute selectivity

Aquaporins (AQPs) are a ubiquitous family of transmembrane water channel proteins. A subgroup of AQP water channels also facilitates transmembrane diffusion of small, polar solutes. A constriction within the pore, the aromatic/arginine (ar/R) selectivity filter, is thought to control solute permeability: previous studies on single representative water channel proteins suggest narrow channels conduct water, whilst wider channels permit passage of solutes. To assess this model of selectivity, we used mutagenesis, permeability measurements and in silico comparisons of water-specific as well as glycerol-permeable human AQPs. Our studies show that single amino acid substitutions in the selectivity filters of AQP1, AQP4 and AQP3 differentially affect glycerol and urea permeability in an AQP-specific manner. Comparison between in silico-calculated channel cross-sectional areas and in vitro permeability measurements suggests that selectivity filter cross-sectional area predicts urea but not glycerol permeability. Our data show that substrate discrimination in water channels depends on a complex interplay between the solute, pore size, and polarity, and that using single water channel proteins as representative models has led to an underestimation of this complexity

Active-Optical Reflectance Sensing Corn Algorithms Evaluated over the United States Midwest Corn Belt

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Activeoptical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency, but locally derived (e.g., within a US state) AORS algorithms have not been tested simultaneously across a broad region. The objective of this research was to evaluate locally developed AORS algorithms across the US Midwest Corn Belt region for making in-season corn N recommendations. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and sidedress N rates (45–270 kg N ha–1 on 45 kg ha–1 increments) applied at approximately V9 corn development stage. Nitrogen recommendation rates from AORS algorithms were compared with the end-of-season calculated economic optimal N rate (EONR). No algorithm was within 34 kg N ha–1 of EONR \u3e 50% of the time. Average recommendations differed from EONR 81 to 147 kg N ha–1 with no N applied at planting and 74 to 118 kg N ha–1 with 45 kg of N ha–1 at planting, indicating algorithms performed worse with no N applied at planting. With some algorithms, utilizing red edge instead of the red reflectance improved N recommendations. Results demonstrate AORS algorithms developed under a particular set of conditions may not, at least without modification, perform very well in regions outside those within which they were developed

Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea mays L.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (~V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha–1 for treatments receiving 0 and 45 kg N ha–1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha–1. Without adjustment, 20 and 29% of sites were within 34 kg N ha–1 of EONR with 0 and 45 kg N ha–1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha–1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Improving an Active-Optical Reflectance Sensor Algorithm Using Soil and Weather Information

Active-optical reflectance sensors (AORS) use light reflectance characteristics from a crop canopy as an indicator of the plant’s N health. However, studies have shown AORS algorithms used in conjunction with measured reflectance characteristics for corn (Zea maysL.) N fertilizer rate recommendations are not consistently accurate. Our objective was to determine if soil and weather information could be utilized with an AORS algorithm developed at the University of Missouri (ALGMU) to improve in-season (∼V9 corn development stage) N fertilizer recommendations. Nitrogen response trials were conducted across eight states over three growing seasons, totaling 49 sites with soils ranging in productivity. Nitrogen fertilizer rates according to the ALGMU were compared to economic optimal nitrogen rate (EONR). Without soil and weather information included, the root mean square error (RMSE) of the difference between ALGMU and EONR (MUDIFF) was 81 and 74 kg N ha–1 for treatments receiving 0 and 45 kg N ha–1 applied at planting, respectively. When ALGMU was adjusted using weather (seasonal precipitation and distribution prior to sidedress) and soil clay content, the RMSE was reduced by 24 to 26 kg N ha–1. Without adjustment, 20 and 29% of sites were within 34 kg N ha–1 of EONR with 0 and 45 kg N ha–1 at planting, respectively. But with adjustment for soil and weather data, 45 and 51% of sites were within 34 kg N ha–1 of EONR. These results show that weather and soil information could be used to improve ALGMU N recommendation performance