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In order to improve the accuracy of life prediction of lithium-ion battery, a life prediction method based on deep belief network and long short-term memory network is proposed. Combined with the capacity data of lithium-ion battery from the Prognostic Center of Excellence (PCoE) of NASA Ames Research Center, discuss the prediction accuracy of the network under different hidden layers, different hidden layer nodes and different optimizers, then choose the best for the fusion model. Combining the above two models, the experimental results show that: the fused model can effectively fit the decline trend of battery capacity, and the remaining useful life prediction error is only 1 cycle. Compared with using DBN or LSTM alone, the prediction accuracy of fusion model is better.

IntroductionSeveral adipokines are implicated in the pathophysiology of gestational diabetes mellitus (GDM), however, longitudinal data in early pregnancy on many adipokines are lacking. We prospectively investigated the association of a panel of adipokines in early and mid-pregnancy with GDM risk.Research design and methodsWithin the National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies-Singletons cohort (n=2802), a panel of 10 adipokines (plasma fatty acid binding protein-4 (FABP4), chemerin, interleukin-6 (IL-6), leptin, soluble leptin receptor (sOB-R), adiponectin, omentin-1, vaspin, and retinol binding protein-4) were measured at gestational weeks (GWs) 10–14, 15–26, 23–31, and 33–39 among 107 GDM cases (ascertained on average at GW 27) and 214 non-GDM controls. Conditional logistic regression was used to estimate ORs of each adipokine and GDM, controlling for known GDM risk factors including pre-pregnancy body mass index.ResultsThroughout pregnancy changes in chemerin, sOB-R, adiponectin, and high-molecular-weight adiponectin (HMW-adiponectin) concentrations from 10–14 to 15–26 GWs were significantly different among GDM cases compared with non-GDM controls. In early and mid-pregnancy, FABP4, chemerin, IL-6 and leptin were positively associated with increased GDM risk. For instance, at 10–14 GWs, the OR comparing the highest versus lowest quartile (ORQ4–Q1) of FABP4 was 3.79 (95% CI 1.63 to 8.85). In contrast, in both early and mid-pregnancy adiponectin (eg, ORQ4–Q1 0.14 (0.05, 0.34) during 10–14 GWs) and sOB-R (ORQ4–Q1 0.23 (0.11, 0.50) during 10–14 GWs) were inversely related to GDM risk. At 10–14 GWs a model that included conventional GDM risk factors and FABP4, chemerin, sOB-R, and HMW-adiponectin improved the estimated prediction (area under the curve) from 0.71 (95% CI 0.66 to 0.77) to 0.77 (95% CI 0.72 to 0.82).ConclusionsA panel of understudied adipokines including FABP4, chemerin, and sOB-R may be implicated in the pathogenesis of GDM with significant associations detected approximately 10–18 weeks before typical GDM screening.

Amino acids, fatty acids, and acylcarnitine metabolites play a pivotal role in maternal and fetal health, but profiles of these metabolites over pregnancy are not completely established. We described longitudinal trajectories of targeted amino acids, fatty acids, and acylcarnitines in pregnancy. We quantified 102 metabolites and combinations (37 fatty acids, 37 amino acids, and 28 acylcarnitines) in plasma samples from pregnant women in the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies—Singletons cohort (n = 214 women at 10–14 and 15–26 weeks, 107 at 26–31 weeks, and 103 at 33–39 weeks). We used linear mixed models to estimate metabolite trajectories and examined variation by body mass index (BMI), race/ethnicity, and fetal sex. After excluding largely undetected metabolites, we analyzed 77 metabolites and combinations. Levels of 13 of 15 acylcarnitines, 7 of 25 amino acids, and 18 of 37 fatty acids significantly declined over gestation, while 8 of 25 amino acids and 10 of 37 fatty acids significantly increased. Several trajectories appeared to differ by BMI, race/ethnicity, and fetal sex although no tests for interactions remained significant after multiple testing correction. Future studies merit longitudinal measurements to capture metabolite changes in pregnancy, and larger samples to examine modifying effects of maternal and fetal characteristics.

With the development of electronic assembly workshop to the direction of digitalization, intelligence and customization, surface mount technology (SMT), as an important process of electronic assembly, has higher and higher requirements for mixed production. A key supporting point of mixed production integration is the acquisition and analysis of production data. This paper is for the characters of multi-variety and small-batch of SMT production line in military electronic industry. And considering the problem of inconsistence of data files and difficulty of reading data in the process of production, this paper used the scheme of distributed data management with system registration drive to realize data real-time acquisition of manufacturing execution in SMT workshop automatic, and promote comprehensive analysis ability of military SMT production data and processing efficiency in the way of multi-variety and small-batch effectively.

The energy industry is undergoing a tremendous transformation from a centralized paradigm to a decentralized one with the employment of various distributed energy technologies and information technologies. Different from the traditional urban central heating system, the district heating system (DHS) with combined energy supply by multi-energy and multi-level energy stations is considered to be promising and can improve the efficiency and flexibility of the energy system. However, its complex system structure and multiple stakeholders bring new challenges to the operation optimization. Therefore, in the face of the DHS, two optimization models are applied in this study: 1) Independent Optimization Model (IOM): the operation strategies of each energy station are optimized independently, and each station then submits its own energy demand to the upper-level energy station; 2) Collaboration Optimization Model (COM): a model established for all energy stations for global optimization. A small number of existing papers on the operation optimization of such DHS have ignored the contradiction between social benefits and operators’ profits, and do not study the gap between the two models. Therefore, this paper takes COM as the benchmark, quantitatively evaluating the potential of IOM for improving the overall energy efficiency under the interest game, and grasps the key reasons that restrict its energy saving potential, aiming to improve its comprehensive benefits. An actual DHS in China was used as a case study to explore the above problems. The results show that, although COM is more energy-saving, it loses 18.3% of the profits of energy integrators, and increases the profits of energy suppliers by 13.8%. Compared with COM, the primary energy consumption of IOM increases by 19.1%. For the results, this paper finally discusses whether it is possible to further improve the energy efficiency of IOM by adjusting the heat price without impairing the profits of the operators. The results indicate that the system’s primary energy consumption reduces by 5.2% without impairing the profits of each operator.

At present, the global manufacturing industry is accelerating towards the era of digitalization, networking and intelligence. Aiming at the manufacturing characteristics of electronic information equipment, such as the integration of scientific research and production, multi variety and small batch, fast renewal, high coupling of multi disciplines, and huge supply chain, this paper studies and analyzes the technical hotspots of intelligent manufacturing of electronic information equipment, such as 3D printing of mechatronic system structures, design and manufacturing of microsystem, immersive virtual assembly and digital twin. At the same time, based on the guiding ideology of Model-Based Systems Engineering (MBSE), the exploration and practice of electronic information equipment design mode, production mode, support mode and management mode are carried out. The research shows that the application of new technology and mode of intelligent manufacturing effectively shortens the development cycle of electronic information equipment, greatly improves the production efficiency and comprehensive utilization of resources, and significantly reduces the operating cost and the rate of defective products.