Asymptotic approximations for the distribution of the product of correlated normal random variables

We obtain asymptotic approximations for the probability density function of the product of two correlated normal random variables with non-zero means and arbitrary variances. As a consequence, we deduce asymptotic approximations for the tail probabilities and quantile functions of this distribution, as well as an asymptotic approximation for the widely used risk measures value at risk and tail value at risk.Comment: 19 page

Learning-based Predictive Control via Real-time Aggregate Flexibility

Aggregators have emerged as crucial tools for the coordination of distributed, controllable loads. To be used effectively, an aggregator must be able to communicate the available flexibility of the loads they control, as known as the aggregate flexibility to a system operator. However, most of existing aggregate flexibility measures often are slow-timescale estimations and much less attention has been paid to real-time coordination between an aggregator and an operator. In this paper, we consider solving an online optimization in a closed-loop system and present a design of real-time aggregate flexibility feedback, termed the maximum entropy feedback (MEF). In addition to deriving analytic properties of the MEF, combining learning and control, we show that it can be approximated using reinforcement learning and used as a penalty term in a novel control algorithm -- the penalized predictive control (PPC), which modifies vanilla model predictive control (MPC). The benefits of our scheme are (1). Efficient Communication. An operator running PPC does not need to know the exact states and constraints of the loads, but only the MEF. (2). Fast Computation. The PPC often has much less number of variables than an MPC formulation. (3). Lower Costs. We show that under certain regularity assumptions, the PPC is optimal. We illustrate the efficacy of the PPC using a dataset from an adaptive electric vehicle charging network and show that PPC outperforms classical MPC.Comment: 13 pages, 5 figures, extension of arXiv:2006.1381

“Cultural and Creative IP” Empowerment Model for Red Culture under the New Media Environment

Boasting exemplary national qualities and high educational value, Red Culture is a unique spiritual wealth that make the Chinese nation stand out from the rest nations. Under the new media environment, however, the collision between the disparate characteristics of Red Culture and new media has given rise to the predicament plaguing the communication of Red Culture. With the advent of the 100th anniversary of the founding of the Communist Party of China, both tangible and intangible cultural heritage and resources are being valued, tapped and cultivated across the country. With the cultural and creative industry in the ascendant, the concept of intellectual property (“IP”) has seeped into all aspects of our everyday lives. Against that backdrop, this study attempts to explore novel ways to link up the Red Culture resources scattered across China, to combine education with cultural tourism through the “4+5+2” cultural and creative IP empowerment model, and to pass on and spread Red Culture and add new value to the industry on the strength of influential IPs

High-Salt Diet Has a Certain Impact on Protein Digestion and Gut Microbiota: A Sequencing and Proteome Combined Study

High-salt diet has been considered to cause health problems, but it is still less known how high-salt diet affects gut microbiota, protein digestion, and passage in the digestive tract. In this study, C57BL/6J mice were fed low- or high-salt diets (0.25 vs. 3.15% NaCl) for 8 weeks, and then gut contents and feces were collected. Fecal microbiota was identified by sequencing the V4 region of 16S ribosomal RNA gene. Proteins and digested products of duodenal, jejunal, cecal, and colonic contents were identified by LC-MS-MS. The results indicated that the high-salt diet increased Firmicutes/Bacteroidetes ratio, the abundances of genera Lachnospiraceae and Ruminococcus (P < 0.05), but decreased the abundance of Lactobacillus (P < 0.05). LC-MS-MS revealed a dynamic change of proteins from the diet, host, and gut microbiota alongside the digestive tract. For dietary proteins, high-salt diet seemed not influence its protein digestion and absorption. For host proteins, 20 proteins of lower abundance were identified in the high-salt diet group in duodenal contents, which were involved in digestive enzymes and pancreatic secretion. However, no significant differentially expressed proteins were detected in jejunal, cecal, and colonic contents. For bacterial proteins, proteins secreted by gut microbiota were involved in energy metabolism, sodium transport, and protein folding. Five proteins (cytidylate kinase, trigger factor, 6-phosphogluconate dehydrogenase, transporter, and undecaprenyl-diphosphatase) had a higher abundance in the high-salt diet group than those in the low-salt group, while two proteins (acetylglutamate kinase and PBSX phage manganese-containing catalase) were over-expressed in the low-salt diet group than in the high-salt group. Consequently, high-salt diet may alter the composition of gut microbiota and has a certain impact on protein digestion

Lipid engineering combined with systematic metabolic engineering of <i>Saccharomyces cerevisiae</i> for high-yield production of lycopene

Saccharomyces cerevisiae is an efficient host for natural-compound production and preferentially employed in academic studies and bioindustries. However, S. cerevisiae exhibits limited production capacity for lipophilic natural products, especially compounds that accumulate intracellularly, such as polyketides and carotenoids, with some engineered compounds displaying cytotoxicity. In this study, we used a nature-inspired strategy to establish an effective platform to improve lipid oil–triacylglycerol (TAG) metabolism and enable increased lycopene accumulation. Through systematic traditional engineering methods, we achieved relatively high-level production at 56.2 mg lycopene/g cell dry weight (cdw). To focus on TAG metabolism in order to increase lycopene accumulation, we overexpressed key genes associated with fatty acid synthesis and TAG production, followed by modulation of TAG fatty acyl composition by overexpressing a fatty acid desaturase (OLE1) and deletion of Seipin (FLD1), which regulates lipid-droplet size. Results showed that the engineered strain produced 70.5 mg lycopene/g cdw, a 25% increase relative to the original high-yield strain, with lycopene production reaching 2.37 g/L and 73.3 mg/g cdw in fed-batch fermentation and representing the highest lycopene yield in S. cerevisiae reported to date. These findings offer an effective strategy for extended systematic metabolic engineering through lipid engineering

Face mask integrated with flexible and wearable manganite oxide respiration sensor

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La0.7Sr0.3MnO3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices

Towards Balanced Three-phase Charging: Phase Optimization in Adaptive Charging Networks

We study the problem of phase optimization for electric-vehicle (EV) charging. We formulate our problem as a non-convex mixed-integer programming problem whose objective is to minimize the charging loss. Despite the hardness of directly solving this non-convex problem, we solve a relaxation of the original problem by proposing the PXA algorithm where "P", "X", and "A" stand for three variable matrices in the formed phase optimization problems. We show that under certain conditions, the solution is given by the PXA precisely converges to the global optimum. In addition, using the idea of model predictive control (MPC), we design the {PXA-MPC}, which is an online implementation of the PXA. Compared to other empirical phase balancing strategies, the PXA algorithm significantly improves the charging performance by maximizing energy delivery, minimizing charging price, and assisting future energy planning. The efficacy of our algorithm is demonstrated using data collected from a real-world adaptive EV charging network (ACN).Comment: 8 pages, 6 figures, accepted by PSCC 202

New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay

The robust stability of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By decomposing some connection weight matrices, new Lyapunov-Krasovskii functionals are constructed, and serial new improved stability criteria are derived. These criteria are formulated in the forms of linear matrix inequalities (LMIs). Compared with some previous results, the new results are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method