Polyhydroxyalkanoates (PHAs) are a group of bioplastics that have a wide range of applications. Extensive progress has been made in our understanding of PHAs' biosynthesis, and currently, it is ...possible to engineer bacterial strains to produce PHAs with desired properties. The substrates for the fermentative production of PHAs are primarily derived from food-based carbon sources, raising concerns over the sustainability of their production in terms of their impact on food prices. This paper gives an overview of the current carbon sources used for PHA production and the methods used to transform these sources into fermentable forms. This allows us to identify the opportunities and restraints linked to future sustainable PHA production. Hemicellulose hydrolysates and crude glycerol are identified as two promising carbon sources for a sustainable production of PHAs. Hemicellulose hydrolysates and crude glycerol can be produced on a large scale during various second generation biofuels' production. An integration of PHA production within a modern biorefinery is therefore proposed to produce biofuels and bioplastics simultaneously. This will create the potential to offset the production cost of biofuels and reduce the overall production cost of PHAs.
This paper develops a hierarchical deep learning machine (HDLM) to efficiently achieve both quantitative and qualitative online transient stability prediction (TSP). For the sake of improving its ...online efficiency, multiple generators' fault-on trajectories as well as the two closest data-points in pre-/post-fault stages are acquired by PMUs to form its raw inputs. An anti-noise graphical transient characterization technique is tactfully designed to transform multiplex trajectories into 2-D images, within which system-wide transients are concisely described. Then, following the divide-and-conquer philosophy, the HDLM trains a two-level convolutional neural network (CNN) based regression model. With stability margin regressions hierarchically refined, it manages to perform reliable and adaptive online TSP almost immediately after fault clearance. Test results on the IEEE 39-bus test system and the real-world Guangdong Power Grid in South China demonstrate the HDLM's superior performances on both stability status and stability margin predictions.
This letter considers the problem of voltage regulation of distribution networks by optimally setting the reactive power of distributed energy resources. Based on the linearized DistFlow model, the ...problem is first formulated as a convex quadratic optimization problem with linear constraints. Then, a distributed accelerated dual descent algorithm is proposed to solve the optimization problem by employing the dual decomposition and accelerated gradient projected techniques.
As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term ...load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.
The human intestine is colonized by an estimated 100 trillion bacteria. Some of these bacteria are essential for normal physiology, whereas others have been implicated in the pathogenesis of multiple ...inflammatory diseases including IBD and asthma. This review examines the influence of signals from intestinal bacteria on the homeostasis of the mammalian immune system in the context of health and disease. We review the bacterial composition of the mammalian intestine, known bacterial-derived immunoregulatory molecules, and the mammalian innate immune receptors that recognize them. We discuss the influence of bacterial-derived signals on immune cell function and the mechanisms by which these signals modulate the development and progression of inflammatory disease. We conclude with an examination of successes and future challenges in using bacterial communities or their products in the prevention or treatment of human disease.
Due to the distributed structure of electric power networks (EPNs) and district heating networks (DHNs), distributed dispatch is favorable for coordination in an integrated electricity and heat ...system (IEHS). However, the feasibility issue of the dispatch solution remains unresolved. Without making this clear, critical threats may be posed to system operation, and there cannot be security guarantees. In this article, we propose a distributed method for real-time IEHS dispatch where feasibility is strictly ensured in each iteration. First, a network reduction method of DHNs retaining the temperature quasi-dynamics is derived. Based on that, a novel feasibility cut (FC) generation method is devised, which strictly maintains system security during the iterative process. Finally, modified Benders decomposition with guaranteed feasibility (MBD-GF) is proposed to tackle distributed IEHS dispatch. The case studies of two IEHSs validate the effectiveness and efficiency of the proposed method.
The mucus clearance system is the dominant mechanical host defense system of the human lung. Mucus is cleared from the lung by cilia and airflow, including both two-phase gas-liquid pumping and ...cough-dependent mechanisms, and mucus transport rates are heavily dependent on mucus concentration. Importantly, mucus transport rates are accurately predicted by the gel-on-brush model of the mucociliary apparatus from the relative osmotic moduli of the mucus and periciliary-glycocalyceal (PCL-G) layers. The fluid available to hydrate mucus is generated by transepithelial fluid transport. Feedback interactions between mucus concentrations and cilia beating, via purinergic signaling, coordinate Na
absorptive vs Cl
secretory rates to maintain mucus hydration in health. In disease, mucus becomes hyperconcentrated (dehydrated). Multiple mechanisms derange the ion transport pathways that normally hydrate mucus in muco-obstructive lung diseases, e.g., cystic fibrosis (CF), chronic obstructive pulmonary disease (COPD), non-CF bronchiectasis (NCFB), and primary ciliary dyskinesia (PCD). A key step in muco-obstructive disease pathogenesis is the osmotic compression of the mucus layer onto the airway surface with the formation of adherent mucus plaques and plugs, particularly in distal airways. Mucus plaques create locally hypoxic conditions and produce airflow obstruction, inflammation, infection, and, ultimately, airway wall damage. Therapies to clear adherent mucus with hydrating and mucolytic agents are rational, and strategies to develop these agents are reviewed.
Multi-microgrids (MMGs) are emerging as a cost-effective solution to provide ancillary services. To reconcile external reserve provision and internal risk hedging for MMGs, a novel comprehensive ...multi-area dynamic optimal power flow (MADOPF) model is established, where energy-reserve co-optimization, three-phase unbalanced network intrinsics and dual control time-scales are all addressed. To better hedge the uncertainties of distributed generation and loads, distributionally robust model predictive control (MPC) is applied to the MADOPF problem. To preserve operational independence and information privacy for each microgrid, decomposition of the nonconvex model is devised with guaranteed convergence. Numerical tests on a two-area system and a real large-scale 16-area system derived from Shandong Power Grid validate the effectiveness of the proposed method. The advantages are demonstrated by the comparison with the conventional MPC, stochastic and robust methods.
In this paper, we present a new prescribed-time distributed control method for consensus and containment of networked multiple systems. Different from both regular finite-time control (where the ...finite settling time is not uniform in initial conditions) and the fixed-time control (where the settling time cannot be preassigned arbitrarily), the proposed one is built upon a novel scaling function, resulting in prespecifiable convergence time (the settling time can be preassigned as needed within any physically allowable range). Furthermore, the developed control scheme not only ensures that all the agents reach the average consensus in prescribed finite time under undirected connected topology, but also ensures that all the agents reach a prescribed-time consensus with the root's state being the group decision value under the directed topology containing a spanning tree with the root as the leader. In addition, we extend the result to prescribed-time containment control involving multiple leaders under directed communication topology. Numerical examples are provided to verify the effectiveness and the superiority of the proposed control.
In this article, a novel distributed coordinated control framework is proposed to handle the uncertain voltage violations in active distribution networks. It addresses the problem of coordination of ...different types of devices in a distributed manner. In our control design, on-load tap changers (OLTCs) are firstly employed to handle the potential voltage violations based on the prediction of renewable outputs and load variations. During real-time operation, once an unmanageable voltage violation is detected, the reactive power of distributed energy resources (DERs) will be coordinated immediately to provide fast corrective control. The control schedules of OLTCs are calculated by solving a multitime-step constrained optimization problem via the alternating direction method of multipliers, whereas the reactive power injections of DERs are determined by a novel online distributed algorithm. The effectiveness of the proposed control framework is verified on the modified IEEE 34-bus and 123-bus test feeders.
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