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Abstract Background This paper investigated the link between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and diabetic kidney disease (DKD) in adult diabetic patients and identified the optimal NHHR value for impacting DKD. Methods This cross-sectional research made use of records from the National Health and Nutrition Examination Survey (NHANES) executed between 2005 and 2016. The link of NHHR to DKD risk was analyzed by logistic regression and restricted cubic spline (RCS) models. The stability and reliability of the results were assessed by subgroup analysis and sensitivity analysis. Results In total, 4,177 participants were involved. As a continuous variable, NHHR was markedly connected to an increased risk of DKD (OR 1.07, 95% CI 1.02, 1.12, P

In the field of substation operation and maintenance, real-time detection and precise segmentation of tools play an important role in maintaining the safe operation of the power grid and guiding operators to work safely. To improve the accuracy and real-time performance of semantic segmentation of substation operation and maintenance tools, we have proposed an improved, light-weight, real-time, semantic segmentation network based on an efficient image cascade network architecture (ICNet). The network uses multiscale branches and cascaded feature fusion units to extract rich multilevel features. We designed a semantic segmentation and purification module to deal with redundant and conflicting information in multiscale feature fusion. A lightweight backbone network was used in the feature extraction stage at different resolutions, and a recursive gated convolution was used in the upsampling stage to achieve high-order spatial interactions, thereby improving segmentation accuracy. Due to the lack of a substation tool semantic segmentation data set, we constructed one. Training and testing on the data set showed that the proposed model improved the accuracy of tool detection while ensuring real-time performance. Compared with the currently popular semantic segmentation network, it had better performance in real-time and accuracy, and provided a new semantic segmentation method for embedded platforms.

Grapholita molesta (Busck) is a pest of rosaceous fruit plants worldwide. Due to a combination of monandry and promiscuity in G. molesta, the age and mating history of both sexes significantly affected the mating and reproductive success. In this study, the interactions of different ages (3, 5, or 7 days) and mating history (unmated or mated) in each sex on the mating selection, reproductive system, and offspring production were investigated in the laboratory. The results showed that these differences mainly occurred in young females or males, associated with unmated or mated state. Especially, the 3-day-old unmated females were preferred by the 7-day-old males but discriminated against by the 3- or 5-day-old unmated males, whereas the 3-day-old mated males were preferred by the 3-day-old mated or 7-day-old females but discriminated against by the 3- or 5-day-old unmated females. The lengths of the ovarian ducts were affected by age in the unmated females, with the greatest length being found at 7 days old. The size of testes varied with age in the unmated males, being the largest at 3 days old. At 3 days old, the testes size of the unmated males was larger than that of the mated males. The pairing of 5-day-old unmated females × 3-day-old mated males maximized the successful matings. The least productive pairing was 7-day-old unmated females × 5-day-old mated males. The pairing of 5-day-old mated males × 3-day-old mated females had the lowest number of matings and the highest number of offspring. The pairing of 3-day-old mated females × 3-day-old mated males had a high rate of mating success and the most offspring. These results revealed the different roles between females and males because of physiological states in terms of the reproductive biology in G. molesta.

The well-known dispatcher training simulator (DTS), as a good tool to train power system dispatchers, has been widely used for over 40 years. However, with the high-speed development of the smart grid, the traditional DTSs have struggled to meet the power industry’s expectations. To enhance the effectiveness of dispatcher training, technical innovations in DTSs are becoming more and more demanding. Meanwhile, the ever-advancing artificial intelligence (AI) technology provides the basis for the design of intelligent DTSs. This paper systematically reviews the traditional DTS in terms of its origin, structure, and functions, as well as limitations in the context of the smart grid. Then, this paper summarizes the AI techniques commonly used in the field of power systems, such as expert systems, artificial neural networks, and the fuzzy set theory, and employs them to develop intelligent DTSs. Regarding a less studied aspect of DTSs, i.e., intelligent training control, we introduce the Adaptive Learning System (ALS) to develop a personalized training program, which will also be an important aspect of future research.

When transnationalized electricity trade is conducted in the context of Global Energy Interconnection (GEI), the transaction settlement usually has a long cycle and high cost and is influenced by the volatility of the exchange rate. It is thus necessary to overcome the problems associated with the transaction settlement, change in the trading model data, and trading strategy in the transnational transaction deduction. To overcome the problem of trade settlement, this paper proposes the use of a digital currency (energy currency) for the cross-border electricity trading settlement based on the special drawing rights of the International Monetary Fund, which is controlled by the Global Energy Interconnection Development and Cooperation Organization (GEIDCO), to enable the proposed currency to become a stable digital currency. The traders can use the energy coins as a unit of currency for quotes, combined with the data pertaining to the changes in the energy information obtained from the GEI framework and data regarding the optimally extrapolated reference trading indicators. To realize the implementation of the multi-trader concurrent transaction deduction using a microservice architecture, this paper proposes a method of computing the microservice and synchronous interaction among the traders, based on the database table data, because the large amount of computation is required to be accomplished asynchronously with a single process. The key technology behind these cross-national electricity trading simulations can not only enable the GEI transnational traders to performed daily real-time trading, but it also demonstrates the advantages of the rapid settlement of the energy currency and the realization of a stable payment in the global energy interconnection cross-border electricity trading. Keywords: Global Energy Interconnection, Multinational power trading deduction, Micro-service, Digital currency

As the integration of distributed generations (DGs) transforms the traditional distribution network into the active distribution network, voltage stability assessments (VSA) of transmission grid and distribution grid are not suitable to be studied separately. This paper presents a distributed continuation power flow method for VSA of global transmission and distribution grid. Two different parameterization schemes are adopted to guarantee the coherence of load growth in transmission and distribution grids. In the correction step, the boundary bus voltage, load parameter and equivalent power are communicated between the transmission and distribution control centers to realize the distributed computation of load margin. The optimal multiplier technique is used to improve the convergence of the proposed method. The three-phase unbalanced characteristic of distribution networks and the reactive capability limits of DGs are considered. Simulation results on two integrated transmission and distribution test systems show that the proposed method is effective.