Effects of Different Dietary Energy and Protein Levels on Meat Performance and Meat Quality of Jinghai Yellow Chickens

To investigate the effect of different dietary energy and protein levels on meat performance and meat quality of Jinghai yellow chickens, 480 43-day old Jinghai yellow chickens with similar weight were randomly divided into four experimental groups： experimental group 1 （protein 15%, metabolic energy 9.95 MJ/kg）, experimental group 2 （protein 16%, metabolic energy 10.95 MJ/kg）, experimental group 3 （protein 17%, metabolic energy 12.65 MJ/kg） and experimental group 4 （ protein 18%, metabolic energy 13.95 MJ/kg）, respectively. All chickens were slaughtered at 112-day old. The breast and leg muscles of Jinghai yellow chickens were collected, to determine the slaughter performance, conventional meat quality and muscle chemical indicators. The results indicated that dressing-out percentage and eviscerated yield percentage in four experimental groups were above 87.27% and 67.00%, respectively; other slaughter performance indicators exhibited no significant differences among various groups （P 〉 0.05 ） ; breast muscle color of hens in experimental group 4 varied significantly from that in other three groups （ P 〈 0.05 ） ; leg muscle color of hens in experimental group 2 varied extremely significantly from that in other three groups （ P 〈 0.01 ） ; water-holding capacity of breast muscles of hens in experimental group 3 was significantly higher than that in experimental group 4 （P 〈 0.05 ） ; thiamine content of breast muscles of cocks in experimental group 3 was significandy higher than that in experimental group 2 （ P 〈 0.05 ） ; however, other properties exhibited no significant differenees among various groups （P 〉 0.05 ）.

E3GCAPS: Efficient EEG-Based Multi-Capsule Framework with Dynamic Attention for Cross-Subject Cognitive State Detection

Cognitive state detection using electroencephalogram(EEG)signals for various tasks has attracted significant research attention.However,it is difficult to further improve the performance of crosssubject cognitive state detection.Further,most of the existing deep learning models will degrade significantly when limited training samples are given,and the feature hierarchical relationships are ignored.To address the above challenges,we propose an efficient interpretation model based on multiple capsule networks for cross-subject EEG cognitive state detection,termed as Efficient EEG-based Multi-Capsule Framework(E3GCAPS).Specifically,we use a selfexpression module to capture the potential connections between samples,which is beneficial to alleviate the sensitivity of outliers that are caused by the individual differences of cross-subject EEG.In addition,considering the strong correlation between cognitive states and brain function connection mode,the dynamic subcapsule-based spatial attention mechanism is introduced to explore the spatial relationship of multi-channel 1D EEG data,in which multichannel 1D data greatly improving the training efficiency while preserving the model performance.The effectiveness of the E3GCAPS is validated on the Fatigue-Awake EEG Dataset(FAAD)and the SJTU Emotion EEG Dataset(SEED).Experimental results show E3GCAPS can achieve remarkable results on the EEG-based cross-subject cognitive state detection under different tasks.

Fuzzy Control of LED Tunnel Lighting and Energy Conservation

Current highway tunnel lighting control systems are often manually controlled, resulting in significant energy waste. This article designs a fuzzy control algorithm for tunnel lighting energy control systems. The system uses LED （Light Emitting Diode） lighting, so the fuzzy control algorithm is designed for LED lights. The traffic and the natural illumination level are used as parameters in the intelligent lighting control algorithm. This system has been deployed in the Lengshui tunnel on the 49th provincial highway of Zhejiang province and operated for more than six months. The performance results show that the energy conservation system provides sufficient lighting levels for traffic safety with significant energy conservation.

Fault Tolerant Data Aggregation Scheduling with Local Information in Wireless Sensor Networks

We study the problem of efficient data aggregation in unreliable wireless sensor networks by designing a fault tolerant data aggregation protocol. A fault tolerant data aggregation protocol consists of two parts： basic aggregation scheduling and amendment strategies. On default, data is aggregated according to the basic aggregation scheduling strategy. The amendment strategy will start automatically when a middle sensor node is out of service. We focus our attention on the amendment strategies and assume that the network adopts a connected dominating set （CDS） based aggregation scheduling as its basic aggregation scheduling strategy. The amendment scheme includes localized aggregation tree repairing algorithms and distributed rescheduling algorithms. The former are used to find a new aggregation tree for every child of the corrupted node, whereas the latter are used to achieve interference free data aggregation scheduling after the amendment. These amendment strategies impact only a very limited number of nodes near the corrupted node and the amendment process is transparent to all the other nodes. Theoretical analyses and simulations show that the scheme greatly improves the efficiency of the data aggregation operation by reducing both message and time costs compared to rebuilding the aggregation tree and rescheduling the en- tire network.