Technical note: Evaluation of a novel enzymatic method to predict in situ undigested neutral detergent fiber of forages and nonforage fibrous feeds.

ABSTRACT The purpose of this study was to optimize the conditions of a previously proposed enzymatic method used to estimate in situ undigested neutral detergent fiber (uNDF). We used a multi-step enzymatic approach, in which samples were first solubilized in NaOH solutions as a preincubation (PreInc) phase. After rinsing, samples were incubated (24 h at 39°C) in a buffered solution (pH 6) containing hemicellulase, cellulase, and Viscozyme L enzymes (Sigma-Aldrich s.r.l., Milan, Italy), followed by incubation (24 h at 39°C) in a buffered solution (pH 5) containing xylanase. Two sets of experiments were performed: a calibration trial (that tested different PreInc conditions on 9 selected forages) and a validation trial (that verified the results by testing multiple samples of 6 different forage types and a group of fibrous by-products). In the calibration trial, samples (300 mg in Ankom F57 filter bags; Ankom Technology Corp., Fairport, NY) were preincubated at 39°C in a 0.1 M NaOH solution for 90, 180, or 240 min, or in 0.2, 0.5, 1.0, or 2.0 M NaOH solution for 90 min. The results indicated that the best PreInc method, in terms of intra-laboratory repeatability and estimation of reference in situ values, was 90 min in a 0.2 M NaOH solution. Thus, we used this PreInc condition to determine enzymatic uNDF of 257 samples in the validation trial. Although the selected method generally had good accuracy in predicting in situ uNDF, inconsistencies were noted for certain forage types. Overall, when enzymatic uNDF was used to predict the in situ uNDF of all samples, the regression was satisfactory (intercept = 7.098, slope = 0.920, R2 = 0.73). The regression models developed for alfalfa hays, corn silages, and small grain silages had also acceptable regression performances and mean square error of prediction (MSEP) values, and the main sources of MSEP variation were error due to incomplete (co)variation and random error. Even when R2 values were >0.70, the MSEP value of the regression model for grass hays was 149.55, and that for nonforage fibrous feeds was 155.16. Although enzymatic uNDF partially overestimated the in situ uNDF, particularly in grass silages, the proposed procedure seems to be promising for accurately predicting in situ uNDF, because it generally had good repeatability and provided satisfactory estimates of in situ uNDF

Temperature Effects on Organic Lubricants in Cold Forging of AA1050 Alloy

Abstract Lubricants have a key role in cold forging when pressures can reach extreme levels, since they contribute to reduce the high frictional forces occurring at the interface between the tools and billet. However, the adiabatic heating due to the high deformation rates may influence their performances with unpredictable consequences on the process stability. The objective of the research work is to investigate the friction behaviour of new environmental-friendly solid lubricants under process conditions with particular attention to the dies temperatures. The case study refers to the impact backward extrusion of AA1050 alloy cans. The newly developed testing set-up allows heating up the dies and the billet in order to reproduce controlled conditions of the tool temperature in the range 20-200° C. By matching the extrusion loads from the experiments carried out at different temperatures and the results of numerical simulations, the friction factors for each lubricant were determined

Integrated smart gas flow sensor with 2.6 mW total power consumption and 80 dB dynamic range

A thermal flow sensor including sensing structures and a read-out interface in a single chip is proposed. The sensing structure is a microcalorimeter based on a double heater configuration while the low noise electronic interface performs signal reading and offset compensation. The device has been fabricated with a commercial CMOS process followed by a post-processing procedure. Post-processing has been customized in order to increase the thermal insulation of the sensing structures from the silicon substrate and improve the heat exchange between the sensor and the gas flow. Device characterization confirms the effectiveness of the proposed fabrication method in increasing the sensitivity at constant power consumption without affecting the dynamic range

AN INVERTIBLE TRANSFORMATION AND SOME OF ITS APPLICATIONS

Several applications of an explicitly invertible transformation are reported. This transformation is elementary and therefore all the results obtained via it might be considered trivial; yet the findings highlighted in this paper are generally far from appearing trivial until the way they are obtained is revealed. Various contexts are considered: algebraic and Diophantine equations, nonlinear Sturm–Liouville problems, dynamical systems (with continuous and with discrete time), nonlinear partial differential equations, analytical geometry, functional equations. While this transformation, in one or another context, is certainly known to many, it does not seem to be as universally known as it deserves to be, for instance it is not routinely taught in basic University courses (to the best of our knowledge). The main purpose of this paper is to bring about a change in this respect; but we also hope that some of the findings reported herein — and the multitude of analogous findings easily obtainable via this te..

Automatic compensation of pressure effects on smart flow sensors in the analog and digital domain

Two different approaches for the automatic compensation of pressure effects on thermal flow sensors are investigated. One approach operates in the analog domain and it is based on a closed-loop circuit that uses a pressure dependent signal to keep the sensor output constant. The digital approach operates in an open loop fashion and is capable of producing also a pressure reading. The effectiveness of the proposed methods has been verified by means of a smart flow sensor integrating on the same chip the sensing structures and a configurable electronic interface performing signal reading and non idealities compensation. The chip has been designed with a commercial CMOS process and fabricated by means of a post-processing technique. The experimental results performed in nitrogen confirm that both methods are capable of reducing the sensitivity of the flow sensor output signal to pressure variation

Enabling Mixed-Precision Quantized Neural Networks in Extreme-Edge Devices

The deployment of Quantized Neural Networks (QNN) on advanced microcontrollers requires optimized software to exploit digital signal processing (DSP) extensions of modern instruction set architectures (ISA). As such, recent research proposed optimized libraries for QNNs (from 8-bit to 2-bit) such as CMSIS-NN and PULP-NN. This work presents an extension to the PULP-NN library targeting the acceleration of mixed-precision Deep Neural Networks, an emerging paradigm able to significantly shrink the memory footprint of deep neural networks with negligible accuracy loss. The library, composed of 27 kernels, one for each permutation of input feature maps, weights, and output feature maps precision (considering 8-bit, 4-bit and 2-bit), enables efficient inference of QNN on parallel ultra-low-power (PULP) clusters of RISC-V based processors, featuring the RV32IMCXpulpV2 ISA. The proposed solution, benchmarked on an 8-cores GAP-8 PULP cluster, reaches peak performance of 16 MACs/cycle on 8 cores, performing 21x to 25x faster than an STM32H7 (powered by an ARM Cortex M7 processor) with 15x to 21x better energy efficiency.Comment: 4 pages, 6 figures, published in 17th ACM International Conference on Computing Frontiers (CF '20), May 11--13, 2020, Catania, Ital

Enabling mixed-precision quantized neural networks in extreme-edge devices

The deployment of Quantized Neural Networks (QNN) on advanced microcontrollers requires optimized software to exploit digital signal processing (DSP) extensions of modern instruction set architectures (ISA). As such, recent research proposed optimized libraries for QNNs (from 8-bit to 2-bit) such as CMSIS-NN and PULP-NN. This work presents an extension to the PULP-NN library targeting the acceleration of mixed-precision Deep Neural Networks, an emerging paradigm able to significantly shrink the memory footprint of deep neural networks with negligible accuracy loss. The library, composed of 27 kernels, one for each permutation of input feature maps, weights, and output feature maps precision (considering 8-bit, 4-bit and 2-bit), enables efficient inference of QNN on parallel ultra-low-power (PULP) clusters of RISC-V based processors, featuring the RV32IMCXpulpV2 ISA. The proposed solution, benchmarked on an 8-cores GAP-8 PULP cluster, reaches peak performance of 16 MACs/cycle on 8 cores, performing 21 7 to 25 7 faster than an STM32H7 (powered by an ARM Cortex M7 processor) with 15 7 to 21 7 better energy efficiency