Structured assembler language for IBM computers

For courses on IBM 360/370 Assembly Language, Kindred offers extensive use of structured programming at the assembly language level. The continuity of examples using the same data provides a consistent theme. Programs begin with simple character operations and gradually move on to more advanced topics. Abundant laboratory assignments are included in the text itself; no additional manual is required. File creation and processing are covered with indexed sequential and VSAM files. A complete set of sample programs for each chapter allows students to see fully developed models

Informed consent and assent guide for paediatric clinical trials in Europe

Objective Clinical trial sponsors spend considerable resources preparing informed consent (IC) and assent documentation for multinational paediatric clinical trial applications in Europe due to the limited and dispersed patient populations, the variation of national legal and ethical requirements, and the lack of detailed guidance. The aim of this study was to design new easy-to-use guide publicly available on European Medicines Agency's, Enpr-EMA website for all stakeholders. Methods Current EU legal, ethical and regulatory guidance for paediatric clinical trials were collated, analysed and divided into 30 subject elements in two tables. The European Network of Young Person's Advisory Group reviewed the data and provided specific comments. A three-level recommendation using 'traffic light' symbols was designed for four age groups of children, according to relevance and the requirements. Results A single guide document includes two tables: (1) general information and (2) trial-specific information. In the age group of 6-9 years old, 92% of the trial-specific subject elements can be or should be included in the IC discussion. Even in the youngest possible age group (2-5 years old children), the number of elements considered was, on average, 52%. Conclusion The EU Clinical Trial Regulation (2014) does not contain specific requirements exclusively for paediatric clinical trials. This work is the first to extensively collate all the current legal, regulatory and ethical documentation on the IC process, together with input from adolescents. This guide may increase the ethical standards in paediatric clinical trials. Young people and researchers gathered together to synthesise and rate the advice from all the EU systems they could find about paediatric clinical trials to create a simpler, patient-led, framework for information to aid meaningful trial consent discussions.Peer reviewe

Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS) experiment: design, execution and science overview

We describe the design and execution of the BORTAS (Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites) experiment, which has the overarching objective of understanding the chemical aging of air masses that contain the emission products from seasonal boreal wildfires and how these air masses subsequently impact downwind atmospheric composition. The central focus of the experiment was a two-week deployment of the UK BAe-146-301 Atmospheric Research Aircraft (ARA) over eastern Canada, based out of Halifax, Nova Scotia. Atmospheric ground-based and sonde measurements over Canada and the Azores associated with the planned July 2010 deployment of the ARA, which was postponed by 12 months due to UK-based flights related to the dispersal of material emitted by the Eyjafjallajökull volcano, went ahead and constituted phase A of the experiment. Phase B of BORTAS in July 2011 involved the same atmospheric measurements, but included the ARA, special satellite observations and a more comprehensive ground-based measurement suite. The high-frequency aircraft data provided a comprehensive chemical snapshot of pyrogenic plumes from wildfires, corresponding to photochemical (and physical) ages ranging from 45 sr 10 days, largely by virtue of widespread fires over Northwestern Ontario. Airborne measurements reported a large number of emitted gases including semi-volatile species, some of which have not been been previously reported in pyrogenic plumes, with the corresponding emission ratios agreeing with previous work for common gases. Analysis of the NOy data shows evidence of net ozone production in pyrogenic plumes, controlled by aerosol abundance, which increases as a function of photochemical age. The coordinated ground-based and sonde data provided detailed but spatially limited information that put the aircraft data into context of the longer burning season in the boundary layer. Ground-based measurements of particulate matter smaller than 2.5 μm (PM2.5) over Halifax show that forest fires can on an episodic basis represent a substantial contribution to total surface PM2.5

Possible changes to arable crop yields by 2050

By 2050, the world population is likely to be 9.1 billion, the CO2 concentration 550 ppm, the ozone concentration 60 ppb and the climate warmer by ca 2°C. In these conditions, what contribution can increased crop yield make to feeding the world

Automating nitrogen fertiliser management for cereals (Auto-N). AHDB Project Report No.561

Uncertainty in estimating fertiliser N requirements is large, with differences between recommended and measured N optima frequently exceeding 50 kg/ha. Precision farming technologies including yield mapping, canopy sensing, satellite imaging and soil mapping are now common-place on farm. The Auto-N project sought to apply the information readily available from these technologies within an ‘Auto-N logic’ to improve the precision of N fertiliser decision making. The ‘Auto-N logic’ was derived from that used to estimate fertiliser N requirements as set out in the AHDB Cereals & Oilseeds guide Nitrogen for winter wheat – management guidelines; this guide suggests that N requirements should be calculated by subtracting Soil N Supply (SNS) from Crop N Demand (CND: grain yield x crop N content) and dividing by Fertiliser N Recovery (FNR); thus the ‘Auto-N logic’ uses yield and protein maps to inform estimates of CND, canopy sensing to inform estimates of SNS and soil sensing to inform estimates of FNR. Novel chessboard N response experiments were set up on six commercial fields between harvest years 2010 and 2012 to quantify spatial variation in N requirement, to explain it in terms of CND, SNS and FNR, hence to develop the ‘Auto-N logic’. At each site, each farmer applied N as liquid urea plus ammonium nitrate (UAN) using the farm sprayer twice, in perpendicular directions, to create a systematic grid of ~400 plots (~12m × 12m) fertilised with N rates of 0, 120, 240 or 360 kg/ha; the area of each experiment exceeded 4 ha. Grain yields were measured by small-plot combine, grain samples were analysed for protein, and N harvest index and total N uptake were determined from pre-harvest grab samples. Values were then estimated for all variates and all N levels for all plots by kriging. Response curves were fitted, and N optima and their components (SNS, CND, FNR) were derived assuming 5 kg grain would pay for 1 kg fertiliser N. Within field variation in optimum N exceeded 100 kg/ha at all sites; spatial variation in optimal yield was greater than 2 t/ha at all sites and variation in SNS was generally greater than 50 kg/ha. Some of the spatial variation in optimum N was explained in terms of SNS and CND. However, the tendency for positive correlations between SNS and optimum yield was striking, and hindered complete explanation of spatial variation in optimum N: i.e. high yielding areas tended to have greater SNS, so the increased requirement from higher crop N demand was counteracted by the reduced requirement from higher SNS. Spatial variation in CND and SNS was reasonably well estimated from the use of past yield maps and crop sensing, respectively; often, similar within-field patterns showed through for both. However, variation in FNR was also large and was unpredictable. Using clustering techniques, zoning, performance mapping or simple averaging of data from five farms, it was shown that past yield maps could be used usefully to estimate variation in CND. In addition, variation in SNS could be predicted from canopy sensing in early spring (an algorithm was developed based on sensed NDVI and thermal time since sowing). Calibrations for crop N uptake, biomass and crop N status (Nitrogen Nutrition Index) from canopy sensing were explored, but no rational basis could be found to justify their inclusion in the ‘Auto-N logic’. Validation trials were set up with farmers on 11 fields in 2013 & 2014; these used adjacent tramlines to compare the Auto-N logic with the farm’s own practice, 50 kg/ha more N and 50 kg/ha less N. Evaluation of these trials along with economic analysis of the chessboard trials showed the benefits of precision in judging N requirements to be modest, whereas benefits of accuracy (proximity to the measured mean) were much greater. Whilst this work demonstrated the feasibility of automating judgements of N requirements within fields using precision information, the variability in CND, SNS and FNR, and crucially the interactions between them, meant that the use of such systems would not guarantee increased accuracy or precision of N use. The evidence suggests that variable rate N management can give only modest returns, even with a system making perfect predictions, if the field is already receiving the right average N rate. The results showed that the most important decisions concern N use for whole farms, then for whole fields, then for areas within fields. Precision technologies can help with all of these, especially through comparisons of crops between and within farms. However, the most effective aspect of precision farming technologies is probably the empowerment of farmers to test retrospectively the effects of their N decisions (or indeed any decisions) on-farm. Given the variation in and unpredictability of N requirements between fields and between farms the only way farmers can know for sure whether their chosen N rates were right is to test yield effects of different N rates – this is relatively easy now, by simply applying (say) 60 kg/ha more and 60 kg/ha less to adjacent tramlines. The chessboard trials initiated here have transformed our understanding of N responses and shown new possibilities for spatial experimentation, not only to empower on-farm testing, but to understand how soil variation affects husbandry outcomes. These trials show that N use is not the major cause of the very large spatial variation seen in yield. Thus, understanding the soil-related causes of yield variation should, and can, now become a priority for soil and agronomic research