Use of phytase and citric acid supplementation on growth performance and nutrient digestibility of Cirrhinus mrigala fingerlings fed on canola meal based diet

Fishmeal; being a limited and costly feed ingredient is continuously been substituted with locally available plant proteins. However, the occurrence of anti-nutritional factors in plant meal suppresses its potential to be fully replaced. Therefore, in this study we aimed to study the synergistic effects of dietary additives like citric acid and phytase enzyme supplementation on growth performance and nutrient digestibility of Cirrhinus mrigala fingerlings. Canola meal (CM) was used as a test ingredient to replace fishmeal (FM) as; 0%, 25%, 50% and 75%. These four diets were further supplemented by varying levels of phytase (0 and 750 FTU kg-1) and citric acid (0% and 2.5%) to formulate total sixteen test diets as T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, T13, T14, T15 and T16. Each treatment contained three replicates; applied to fish groups having 15 fingerlings each; following 3×3 factorial arrangement. 1% of chromic oxide was added as an inert marker. Maximum weight gain% (288%) and the lowest value of FCR (1.07) were recorded when fish was fed on diet T12 as compared to fish fed control diet (T1). Similarly, optimum nutrient digestibility values such as crude protein (77%), crude fat (84%) and gross energy (70%) were noted on same level. It was concluded that 50% canola meal can optimally replace fishmeal when supplemented with phytase and citric acid at the levels of 750 FTU kg-1 and 2.5%, respectively.

Plasticine: A Cross-layer Approximation Methodology for Multi-kernel Applications through Minimally Biased, High-throughput, and Energy-efficient SIMD Soft Multiplier-divider

The rapid evolution of error-resilient programs intertwined with their quest for high throughput has motivated the use of Single Instruction, Multiple Data (SIMD) components in Field-Programmable Gate Arrays (FPGAs). Particularly, to exploit the error-resiliency of such applications, Cross-layer approximation paradigm has recently gained traction, the ultimate goal of which is to efficiently exploit approximation potentials across layers of abstraction. From circuit- to application-level, valuable studies have proposed various approximation techniques, albeit linked to four drawbacks: First, most of approximate multipliers and dividers operate only in SISD mode. Second, imprecise units are often substituted, merely in a single kernel of a multi-kernel application, with an end-to-end analysis in Quality of Results (QoR) and not in the gained performance. Third, state-of-the-art (SoA) strategies neglect the fact that each kernel contributes differently to the end-to-end QoR and performance metrics. Therefore, they lack in adopting a generic methodology for adjusting the approximation knobs to maximize performance gains for a user-defined quality constraint. Finally, multi-level techniques lack in being efficiently supported, from application-, to architecture-, to circuit-level, in a cohesive cross-layer hierarchy. In this article, we propose Plasticine , a cross-layer methodology for multi-kernel applications, which addresses the aforementioned challenges by efficiently utilizing the synergistic effects of a chain of techniques across layers of abstraction. To this end, we propose an application sensitivity analysis and a heuristic that tailor the precision at constituent kernels of the application by finding the most tolerable degree of approximations for each of consecutive kernels, while also satisfying the ultimate user-defined QoR. The chain of approximations is also effectively enabled in a cross-layer hierarchy, from application- to architecture- to circuit-level, through the plasticity of SIMD multiplier-dividers, each supporting dynamic precision variability along with hybrid functionality. The end-to-end evaluations of Plasticine on three multi-kernel applications employed in bio-signal processing, image processing, and moving object tracking for Unmanned Air Vehicles (UAV) demonstrate 41%–64%, 39%–62%, and 70%–86% improvements in area, latency, and Area-Delay-Product (ADP), respectively, over 32-bit fixed precision, with negligible loss in QoR. To springboard future research in reconfigurable and approximate computing communities, our implementations will be available and open-sourced at https://cfaed.tu-dresden.de/pd-downloads.

Identifying Sustainable Nitrogen Management Practices for Tea Plantations

Tea (Camellia sinensis L.) is the most widely consumed beverage in the world. It is mostly grown in the tropics with a heavy dependence on mineral nitrogen (N) fertilisers to maintain high yields while minimising the areas under cultivation. However, N is often applied in excess of crop requirements, resulting in substantial adverse environmental impacts. We conducted a systematic literature review, synthesising the findings from 48 studies to assess the impacts of excessive N application on soil health, and identify sustainable, alternative forms of N management. High N applications lead to soil acidification, N leaching to surface and groundwater, and the emission of greenhouse gases including nitrous oxide (N2O). We identified a range of alternative N management practices, the use of organic fertilisers, a mixture of organic and inorganic fertilisers, controlled release fertilisers, nitrification inhibitors and soil amendments including biochar. While many practices result in reduced N loading or mitigate some adverse impacts, major trade-offs include lower yields, and in some instances increased N2O emissions. Practices are also frequently trialled in isolation, meaning there may be a missed opportunity from assessing synergistic effects. Moreover, adoption rates of alternatives are low due to a lack of knowledge amongst farmers, and/or financial barriers. The use of site-specific management practices which incorporate local factors (for example climate, tea variety, irrigation requirements, site slope, and fertiliser type) are therefore recommended to improve sustainable N management practices in the long term.