high energy
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2022 ◽  
Vol 43 (2) ◽  
pp. 523-540
Jorge Augusto Dias da Costa Abreu ◽  
Mikael Neumann ◽  
Wagner Paris ◽  
André Martins de Souza ◽  

Essential oils and enzymes are alternatives to feed additives for ruminants that aim to replace the use of ionophores and improve animal performance, but their mechanisms of action are different. Therefore, the present study aimed to verify if there is a synergistic effect in the combined use of enzymes carbohydrates and essential oils on the performance, ingestive behavior and carcass traits of steers fed a high-energy diet. During the finishing period of 78 days, 40 steers were assigned to four treatments: CON- control; ENZ- enzymatic complex; EO- essential oil blend; ENZ+EO - enzymatic complex combined with essential oil blend. Regardless of the feedlot periods, the ENZ+EO treatment caused a reduction in the dry matter intake (12.48%) compared to the control. The ENZ+EO treatment resulted in the lowest mean fecal output and, consequently, the highest dry matter digestibility (DMD) and starch digestibility (SD), compared to the other treatments. Animals that received EO and ENZ+EO in the diet spent more time in feeding. As for the number of times animals visited the feeding trough, the highest values were presented by the animals in the EO, ENZ and ENZ+EO treatments. For the carcass parameters, only the subcutaneous fat thickness on the rib was significantly different between treatments, with the highest values obtained by adding EO and ENZ+EO (8.80 and 8.10 mm respectively). Thus, the combination of carbohydrate enzymes and essential oils proved to be synergistically beneficial in relation to better use of nutrients and productive performance of feedlot steers.

2022 ◽  
Vol 247 ◽  
pp. 106177
Jennifer L. Cudney ◽  
Charles W. Bangley ◽  
Andrea Dell’Apa ◽  
Eric Diaddorio ◽  
Roger A. Rulifson

Mohd Firdaus Mohd Ab Halim ◽  
Erwan Sulaiman ◽  
Mahyuzie Jenal ◽  
Raja Nor Firdaus Kashfi Raja Othman ◽  
Syed Muhammad Naufal Syed Othman

The inclusion of a high energy density permanent magnet into magnetic gear improves the machine's torque density. However, it also contributes to eddy current loss, especially in a high-speed application such in electric vehicle. In this paper, the losses from eddy current and iron loss are investigated on concentric magnetic gear (CMG). Torque multiplier CMG is designed with 8/3 gear ratio for this study. Iron loss and eddy current loss are compared and discussed. Based on this study, eddy current loss contributes to almost 96% of the total loss. This finding is hoped to direct the researcher to focus more on reducing loss associated with eddy current loss.

2022 ◽  
Vol 27 (2) ◽  
pp. 1-16
Ming Han ◽  
Ye Wang ◽  
Jian Dong ◽  
Gang Qu

One major challenge in deploying Deep Neural Network (DNN) in resource-constrained applications, such as edge nodes, mobile embedded systems, and IoT devices, is its high energy cost. The emerging approximate computing methodology can effectively reduce the energy consumption during the computing process in DNN. However, a recent study shows that the weight storage and access operations can dominate DNN's energy consumption due to the fact that the huge size of DNN weights must be stored in the high-energy-cost DRAM. In this paper, we propose Double-Shift, a low-power DNN weight storage and access framework, to solve this problem. Enabled by approximate decomposition and quantization, Double-Shift can reduce the data size of the weights effectively. By designing a novel weight storage allocation strategy, Double-Shift can boost the energy efficiency by trading the energy consuming weight storage and access operations for low-energy-cost computations. Our experimental results show that Double-Shift can reduce DNN weights to 3.96%–6.38% of the original size and achieve an energy saving of 86.47%–93.62%, while introducing a DNN classification error within 2%.

2022 ◽  
Vol 209 ◽  
pp. 114368
Jing Xu ◽  
Zhong Dong ◽  
Kejing Huang ◽  
Lina Wang ◽  
Zhengnan Wei ◽  

2022 ◽  
Vol 309 ◽  
pp. 118498
Seungyun Han ◽  
Roland Kobla Tagayi ◽  
Jaewon Kim ◽  
Jonghoon Kim

2022 ◽  
Vol 237 ◽  
pp. 111855
Ke-Juan Meng ◽  
Haorui Zhang ◽  
Shuai-Zhong Wang ◽  
Yi Wang ◽  
Qinghua Zhang ◽  

2022 ◽  
Vol 211 ◽  
pp. 114514
Xinzhong Zhang ◽  
Peng Zheng ◽  
Lili Li ◽  
Fei Wen ◽  
Wangfeng Bai ◽  

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