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Experiment was conducted to study the effects of Mulberry Leaf (ML) powder on reproductive performance, serum and milk amino acid composition in sows. Fifty sows (D 85 at gestation) with parity 3 or 4 were randomly divided into 5 groups: C, M100, M200, M300 and M400, receiving 0, 100, 200, 300 and 400 g ML powder per sow per day. Blood and milk of sows at Days 1 and 21 of lactation were collected. Results showed that average daily feed intake (ADFI) during lactation was higher in groups supplemented ML compared with control group (p 0.01). Litter weight gain during lactation was higher in M400 than in groups M200 and C (p 0.05), with no significant difference compared with M100 and M300. Serum glucose concentration in groups M400 and M300 was higher than those in the other groups (p 0.01). Serum HDL-C concentration in group M400 was significantly greater than those in groups M100 and M200 (p 0.05), with no significant difference between group M400 and groups M300, control. Milk amino acid concentrations such as isoleucine, leucine, lysine and valine were all lower in group M400 than in control (p 0.01). Serum methionine (Met) concentration was higher in M300 than in other groups (p0.01). Milk Met concentration in group C was higher than those of the sows in the group M400, with no significant difference compared with groups M100, M200 and M300 (p 0.05). Serum Lys and Met concentrations were lower in M400 than in control group (p 0.05). In summary, our results have revealed the ML supplementation at a high dose such as 300 g/day during later gestation and lactation showed benefit in regulating lipid and amino acid metabolism in sows and then improved growth performance of their offspring.

Smart grids, the next generation of electricity systems, would be intelligent and self-aware of physical and cyber activity in the control area. As a cyber-embedded infrastructure, it must be capable of detecting cyberattacks and responding appropriately in a timely and effective manner. This article tries to introduce an advanced and unique intrusion detection model capable of classifying binary-class, trinary-class, and multiple-class CDs and electrical network incidents for smart grids. It makes use of the gray wolf algorithm (GWA) for evolving training of artificial neural networks (ANNs) as a successful machine learning model for intrusion detection. In this way, the intrusion detection model’s weight vectors are initialized and adjusted using the GWA in order to reach the smallest mean square error possible. With the suggested evolving machine learning model, the issues of cyberattacks, failure forecast, and failure diagnosing would be addressed in the smart grid energy sector properly. Using a real dataset from the Mississippi State Laboratory in the United States, the proposed model is illustrated and the experimental results are explained. The proposed model is compared to some of the most widely used classifiers in the area. The results show that the suggested intrusion detection model outperforms other well-known models in this field.

In order to solve the problem of the time offset between the supervisory control and data acquisition system and phasor measurement unit and the unknown distribution of non-Gauss noise, this paper proposes a robust state estimation method for power systems based on the Maximum Exponential Square and data fusion. Firstly, the robust Mahalanobis distance is used to detect system outliers and assign appropriate weights to the selected PMU buffer measurement. Then, the MES-based estimator uses these weights to filter out non-Gauss PMU measurement noise to generate a set of state estimation results. At the same time, the MES estimator is used to process the received SCADA measurement with unknown measurement noise, thereby generating another set of state estimation results. Finally, the two sets of estimation results from two independent MES estimators are fused by using data fusion theory to obtain the final optimal state estimation results. Based on IEEE-14 and 30-buses standard system, the simulation results prove the effectiveness and robustness of the method proposed in this paper.

A simulated acid rain (SAR) experiment on leaching of antimony (Sb) and arsenic (As) in three soil types including paddy soils (PS), vegetable soils (VS) and slag based soils (SS) from Xikuangshan (XKS) Sb mine area was conducted. The SAR at pH 2.5, 3.5, 4.5 and 5.6 were sprayed to soil columns with intermittent pattern in a period of 50 days. Through the spraying duration, leaching Sb in PS, VS and SS showed decreasing trends regardless of pH values in SAR and were in the ranges of 0.026-0.064 mg L

When conventional methods collect cross region interconnection detection data of power monitoring system, one data has multiple signal strengths, resulting in low detection rate and high false alarm rate of cross region interconnection abnormal data. Therefore, a cross region interconnection detection method of abnormal data in power monitoring system is proposed. Divide the cross region interconnection scene types of power monitoring system, make the detection end send data packets to the security partition corresponding to different scenes, and Gaussian filter the signals received in the security partition, and only retain the signals with probability distribution density function of [0.5,1]; Convert multiple signal strength indications of MAC address into one, so that the security partition only responds to the MAC address, obtains the MAC address detection data, and inputs it into the deep confidence network after preprocessing. If the MAC address matches successfully, it is judged that there is cross region interconnection in the power monitoring system, and the cross region interconnection detection of abnormal data is completed. The cross region interconnection alarm data provided by a power Co., Ltd. is selected as the experimental data, and the comparative experiment is set. The results show that the design method improves the abnormal data detection rate, reduces the false alarm rate, and ensures the detection accuracy of large-scale cross region interconnection data.

when the conventional system classifies the power distribution data, the data category is not fine enough, resulting in low accuracy of abnormal data identification, high false detection rate and large correction error. A power distribution abnormal data identification and processing system based on correlation classification is proposed. In terms of hardware, multi-layer framework is adopted, and the overall hardware structure of the system is composed of communication modules, so as to optimize the power distribution data and signal acquisition circuit and suppress signal noise; In terms of software, set the data classification convergence conditions, iteratively increase the number of distribution data clustering centers until the convergence conditions are met, realize the fine correlation classification of data, draw the classified data curve, obtain the normal data feasible region, judge that there are abnormalities in the data beyond the feasible region, and use the vertical comparison method for correction. The results show that the designed system improves the accuracy of abnormal data identification, reduces the false detection rate and correction error, and the accuracy of abnormal data identification and processing is better than that of the conventional system.

With the continuous development of big data era and cloud computing(CC) technology, there are still a series of problems in the planning performance and related algorithms of CC grid, such as unbalanced grid load and uneven grid spatial distribution. However, with the continuous development of social economy and the continuous improvement of people’s living standards and quality, the scale of power grid construction is also expanding, the load distribution of power grid is unbalanced, and the power distribution capacity is insufficient. In order to effectively analyze the load balance and scheduling of power grid, CC technology can accurately improve the accuracy of data analysis and processing. In the process of network load balancing and distribution, the use of CC can not only improve the scheduling efficiency, but also improve the quality and speed of work. Aiming at the problem of poor scheduling performance, this paper proposes a fuzzy iterative algorithm and applies it to CC environment. Plan load balancing for the current network. The experimental process of the algorithm is introduced in detail, the balanced distribution parameters of the load data are extracted, and the load model is established. Realize and improve the network load balance distribution. Experimental results show that the proposed method can improve the load balancing configuration of power grid, improve the management efficiency of power grid, and has good experimental performance.

In the traditional cloud computing environment, the power network load balancing scheduling method cannot adaptively track the time-varying coupling characteristics of load data, and the power data center is not efficient in processing massive data, so it is increasingly unable to meet the multi-service quality requirements of power users. The purpose of this paper is to improve the load balancing of power network, improve the efficiency of power network balance scheduling and power resource balance allocation in cloud computing environment. At the research method level, this paper analyzes the application status of data center and cloud computing task scheduling, and starts from the task scheduling demand analysis of cloud computing data center to determine load balancing and QoS as the technical goals of task scheduling. According to the parallel model framework of MapReduce, the task execution process is studied, and the design scheme of power cloud data center is given in view of the problems of low resource utilization, poor scalability and high energy consumption cost faced by the existing power data center. The experimental results show that the network load balancing scheduling method proposed in this paper can meet the multi-qos demand of power users to the maximum extent, effectively improve the efficiency of power data center operation, and achieve better load balancing effect than the traditional scheme.

To solve the current high energy consumption problem in cloud computing, the paper proposes an energy saving strategy--- Computing Resources Dynamic Power-aware CRDP. For the case of multiple tasks on one server in cloud computing system, the strategy tries to set processor supply voltage to lower level when a task is finished and the computing resource it possesses is idle to reduce the energy consumption caused by computing resource idle. And the lowing of supply voltage is within left unfinished tasks response time constraint to ensure the system performance. Based on process analysis of executing tasks on a server, energy consumption model and performance model of system are built. Simulation results show that the CRDP can improve the energy efficiency while meeting the quality of service requirement.

Mobile social network MSN with delay tolerant nature of intermittent network connectivity and the node behavior of the network has certain social characteristics, which is helpful to the data sharing and transmission between the nodes. At present, most social routing algorithms do not take into account the energy consumption of the nodes, and the energy consumption of the mobile nodes is not balanced, so the performance optimization can not be achieved. In this paper, we proposes the community-based energy-aware routing protocol----CBEAR, an MSN network dynamic partition into several different community, transferring and sharing news in the intra-community and inter-community, considering node energy consumption rate and the encounter probability between node and each community to make routing decisions, avoid some nodes' energy consumption is too fast problem, to realize load balance between nodes. By comparison with existing routing protocols through extensive simulations, the simulation study show that although the CBEAR protocol has some delay performance, it has achieved better performance in terms of data transmission success rate, protocol overhead ratio and energy balance.