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Today, the Internet of Things (IoT) has an important role for deploying power and energy management in the smart grids as emerging trend for managing power stability and consumption. In the IoT, smart grids has important role for managing power communication systems with safe data transformation using artificial intelligent approaches such as Machine Learning (ML), evolutionary computation and meta-heuristic algorithms. One of important issues to manage renewable energy consumption is intelligent aggregation of information based on smart metering and detecting the user behaviors for power and electricity consumption in the IoT. To achieve optimal performance for detecting this information, a context-aware prediction system is needed that can apply a resource management effectively for the renewable energy consumption for smart grids in the IoT. Also, prediction results from machine learning methods can be useful to manage optimal solutions for power generation activities, power transformation, smart metering at home and load balancing in smart grid networks. This paper aims to design a new periodical detecting, managing, allocating and analyzing useful information regarding potential renewable power and energy consumptions using a context-aware prediction approach and optimization-based machine learning method to overcome the problem. In the proposed architecture, a decision tree algorithm is provided to predict the grouped information based on important and high-ranked existing features. For evaluating the proposed architecture, some other well-known machine learning methods are compared to the evaluation results. Consequently, after analyzing various components by solving different smart grids datasets, the proposed architecture’s capacity and supremacy are well determined among its traditional approaches.

Two kinds of novel poly(arylene ether nitrile)s (CPDP-DCBN and CHDP-DCBN) containing pendant aliphtatic ring were synthesized by 4,4′-cyclopentane-1,1′-diyldiphenol (CPDP) or 4,4′-cyclohexane-1,1′-diyldiphenol (CHDP) and 2,6-dichlorobenzonitrile (DCBN) in this work. The inherent viscosities of poly(arylene ether nitrile)s (PENs) were in the range of 0.701–0.806 dL g−1. The polymers showed high glass transition temperatures ( T g) of 185.4–196.4°C and weight-loss temperatures ( T5%) of 447.8–454.3°C. The obtained CPDP-DCBN and CHDP-DCBN could be hot pressed into the films, which showed the tensile strengths of 82.6 MPa and 86.8 MPa, respectively. And the storage modulus of CPDP-DCBN and CHDP-DCBN were about 1.0 GPa and 1.5 GPa at 150°C, respectively. Additionally, the PENs could be dissolved in many solutions at room temperature, such as NMP and concentrated H2SO4, indicating that they had good solubility; they can be processed by the solution method. Meanwhile, the optical transmittance of CPDP-DCBN was 78.1% at 450 nm; it has potential to be applied to the heat-resistant optical film.

In this article, a fully coupled multiphysical model of multichip press-pack insulated gate bipolar transistor (PPI) considering surface roughness is proposed. This model realizes the coupling of the electrical-, thermal- and mechanical domains through contact resistance. The simultaneous prediction of temperature and stress under various surface roughness is achieved. First, the fully coupled model and the contact resistance model are established. Second, both thermomechanical stress under the clamping phase and heating phase are analyzed. The influence of the surface roughness on the thermo-mechanical stress distribution is also investigated based on the proposed model. Results show that the surface roughness does not affect the thermomechanical stress distribution under the heating phase, while the amplitudes increase with surface roughness. Third, the correctness of the model is verified by the temperature measurement and deterioration observation results. The observed fretting scratches on the additional metallization area after the power cycling test validates the correctness of the deformation prediction. Finally, the strain-life fatigue model of PPI is established, it turns out that the surface roughness has an impact on the fatigue life of the chip metallization area.

Tea is a worldwide popular drink with high nutritional and medicinal values as it is rich in nutrients, such as polyphenols, amino acids, vitamins, glycosides, and so on. Among them, tea polyphenols (TPs) are the current research hotspot. TPs are known to have multiple biological activities such as anti-oxidation, anti-tumor, anti-inflammation, anti-bacteria, lowering lipid, and liver protection. By reviewing a large number of literatures, we explained the mechanism of TPs exerting biological activity and a wide range of applications. We also discussed the deficiencies and development potential of TPs, in order to provide theoretical reference and scientific basis for the subsequent development and utilization of TPs. PRACTICAL APPLICATIONS: We summarized the bioactivity mechanisms of TPs in anti-tumor, anti-oxidation, antibacterial, anti-inflammatory, lipid-lowering, and liver protection, focused on its application fields in food and medicine, and discussed the deficiency and development potential of current research on TPs, so as to provide a certain convenient way for scholars studying TPs. It is expected to contribute to the subsequent discovery of biological activity and the broadening of the field of TPs.

Current transformer (CT) with the structure of iron-core coil can detect the partial discharge to reflect the insulation state of the equipment, or measure the sweep current in the frequency response method to judge the mechanical state of the transformer winding. In these cases, the CT only need to measure the small current with high frequency in the conductor, but sometimes there will be large power frequency current in the measured conductor. The signal induced by this power frequency current will be superimposed on the output of the CT, so that the high frequency signal is easily submerged in the interference of the power frequency signal. To improve the signal-to-noise ratio of measurement, this paper proposes the double coils compensation method and the double integral resistors method from the perspectives of coil and integral circuit respectively, and conducts experimental verification. The experimental results show that due to the nonlinearity of iron-core, the double coils compensation method cannot obtain satisfactory measurement results. While the double integral resistors method can effectively suppress the interference caused by the power frequency current, and has little effect on the measurement of high frequency current, which significantly improves the signal-to-noise ratio of measurement.

Customer cancellations and no‐shows have an important impact on the performance of an airline's revenue management system. In this study, we study airline network revenue management problems with customer cancellations and no‐shows, considering two types of demand models: independent‐demand and choice‐based‐demand models. In independent‐demand models, the booking request rates for different products are independent of the assortment of products offered by the airline (exogenous), whereas in choice‐based‐demand models they depend on the airline's assortment (endogenous). The stochastic problems of optimizing the booking policies for the two demand models are not tractable because of the high‐dimensional state space. Therefore, for each model, we use the corresponding deterministic, continuous‐time, and continuous‐state model, known as the fluid model. A booking policy based on the fluid solution can be implemented for the original stochastic, discrete model. This fluid policy is shown to be asymptotically optimal when the arrival rates become high and the seat capacities become large. In the independent‐demand setting, an optimal fluid solution can be computed by solving a convex optimization problem for which efficient algorithms exist, whereas for some choice‐based‐demand models, the fluid control problems are known to be intractable. We also develop mixed fluid policies for the independent‐demand setting, taking advantage of the real‐time booking state, to improve the performance over the fluid policy. In a factorial experiment of 20 small‐sized single‐leg problems, the revenue loss under one of the mixed fluid policies is demonstrated to be less than 1% of the optimal value for all problems.

To investigate the effect of the integration pattern of hospital-community on the grade-based management for hypertension in elders. We randomly chosen 218 (male, n=121; female, n=97) primary senile hypertension patients from a Community Health Service Center of District in Changsha City, from June, 2013 to December, 2013. Based on the risky factors, the subjects were divided into three groups and every group received grade-based management on blood pressure with a integration pattern of hospital-community for six months. According to the HILL-BONE high blood pressure compliance scale and the self- designed blood pressure monitoring form, we assessed the effect of compliance and blood pressure control on senile hypertension patient. Hypertension treatment rate for the elders ranged from 22.9% to 88.1% (P0.01). The levels of blood pressure of the subjects were significantly decreased compared with baseline. The level of diastolic blood pressure in the low, average, high and very high-risk group was decreased by 17, 20 and 23 mmHg, respectively (P0.01). The level of systolic blood in the low, average, high and very high-risk group was decreased by 6, 5 and 7 mmHg, respectively (P0.01). The compliance rate of HILL-BONE hypertension rose from 54.5% to 87.4% (P0.01). The integration pattern of hospital-community with the grade-based management for hypertension significantly improved the senile hypertension control rate and compliance of drug treatment. The rational for drug usage rate rose obviously. The integration pattern of hospital-community with the grade-based management for elders deserves to spread.目的：评价医院-社区一体化分级管理模式对社区老年高血压患者的管理效果，探索社区老年高血压有效管理模式。方法：2013年6月采取随机抽样的方法从长沙市某社区卫生服务中心随机抽取218名原发性老年高血压患者(男121名，女97名)，根据心血管风险分级，将研究对象随机分为3个管理组(低危、中危、高危/很高危)，对各组分别展开6个月医院-社区一体化分级管理。采用《HILL-BONE高血压依从性评估表》及自行设计的《血压监测登记表》，评估老年高血压患者的血压控制情况和就医依从性。结果：干预6个月后，血压控制率由22.9%提高至88.1%(P0.01)；与基线水平比较，低危组、中危组、高危组/很高危组收缩压水平分别下降17，20和23 mmHg(P0.01)，舒张压水平分别下降6，5和7 mmHg (P0.01)；HILL-BONE高血压依从性由54.5%提高至87.4%(P0.01)。结论：医院-社区一体化分级管理模式能提高社区老年高血压患者血压控制率、治疗依从性及合理用药率，值得在社区老年高血压病防治中推广。.