PUL-Mediated Plant Cell Wall Polysaccharide Utilization in the Gut Bacteroidetes

Plant cell wall polysaccharides (PCWP) are abundantly present in the food of humans and feed of livestock. Mammalians by themselves cannot degrade PCWP but rather depend on microbes resident in the gut intestine for deconstruction. The dominant Bacteroidetes in the gut microbial community are such bacteria with PCWP-degrading ability. The polysaccharide utilization systems (PUL) responsible for PCWP degradation and utilization are a prominent feature of Bacteroidetes. In recent years, there have been tremendous efforts in elucidating how PULs assist Bacteroidetes to assimilate carbon and acquire energy from PCWP. Here, we will review the PUL-mediated plant cell wall polysaccharides utilization in the gut Bacteroidetes focusing on cellulose, xylan, mannan, and pectin utilization and discuss how the mechanisms can be exploited to modulate the gut microbiota.     

Adaptive robust control-based energy management of hybrid PV-Battery systems with improved transient performance

Energy management of hybrid photovoltaic (PV)-battery systems still serve as a challenging task owing to their complex and nonlinear characteristics, multicomponent structures, and the extensive range of environmental factors disturbing their nominal performance. The hybrid energy system developed in this study encompasses PV arrays, a battery component, one boost converter, and one bidirectional boost converter. In this paper, we propose a novel adaptive robust control framework for the optimal energy management of the PV-battery systems under many operating conditions and subject to unmodelled dynamics. An improved exponential-like adaptive integral sliding mode (EISM) control coupled to neural network approximator is introduced using a multi-rate convergence tweaking mechanism for the sliding surface to improve the transient performance of the closed-loop system. Furthermore, the entire dynamics of the hybrid energy system is considered unknown, unlike the previous studies that only assumed the parametric uncertainties. The global asymptotic stability of the system is guaranteed, and the effectiveness of this novel framework is compared to benchmark studies.     

Adaptive finite-time tracking control for parameterized nonlinear systems with full state constraints

In this article, the issue of adaptive finite-time dynamic surface control (DSC) is discussed for a class of parameterized nonlinear systems with full state constraints. Using the property of logarithmic function, a one-to-one nonlinear mapping is constructed to transform a constrained system into an unconstrained system with the same structure. The nonlinear filter is constructed to replace the first-order linear filter in the traditional DSC, and the demand on the filter time constant is reduced. Based on finite-time stable theory and using modified DSC, the finite-time controller is designed via DSC. Theoretical analysis shows that all the signals in the closed-loop system are semiglobal practical finite-time stable. Furthermore, none of the states are outside the defined open set. In the end, simulation results are presented to demonstrate the effectiveness of the proposed control schemes with both linear filters and nonlinear filters.     

Comparative performance analysis of solar powered supercritical-transcritical CO2 based systems for hydrogen production and multigeneration

CO₂ based power and refrigeration cycles have been developed and analyzed in different existing studies. However, the development of a CO₂ based comprehensive energy system and its performance analysis have not been considered. In this study, the integration of a CO₂ based solar parabolic trough collector system, a supercritical CO₂ power cycle, a transcritical CO₂ power cycle, and a CO₂ based cascade refrigeration system for hydrogen production and multigeneration purpose is analyzed thermodynamically. This study aims to analyze and compare the difference in the thermodynamic performance of comprehensive energy systems when CO₂ is used as the working fluid in all the cycles with a system that uses other working fluids. Therefore, two comprehensive energy systems with the same number of subsystems are designed to justify the comparison. The second comprehensive energy system uses liquid potassium instead of CO₂ as a working fluid in the solar parabolic trough collector and a steam cycle is used to replace the transcritical CO₂ power cycle. Results of the energy and exergy performance analysis of two comprehensive energy systems showed that the two systems can be used for the multigeneration purpose. However, the use of a steam cycle and potassium-based solar parabolic trough collector increases the comprehensive energy systems’ overall energy and exergy efficiency by 41.9% and 26.7% respectively. Also, the use of liquid potassium as working fluid in the parabolic trough collectors increases the absorbed solar energy input by 419 kW and 2100 kW thereby resulting in a 23% and 90.7% increase in energetic and exergetic efficiency respectively. The carbon emission reduction potential of the two comprehensive energy systems modelled in this study is also analyzed.     

Design optimization of grid-connected PV-Hydrogen for energy prosumers considering sector-coupling paradigm: Case study of a university building in Algeria

Integrating sector coupling technologies into Hydrogen (H₂) based hybrid renewable energy systems (HRES) is becoming a promising way to create energy prosumers, despite the very little research work being done in this largely unexplored field. In this paper, a sector coupling strategy (building and transportation) is developed and applied to a grid-connected PV/battery/H₂ HRES, to maximise self-sufficiency for a University campus and to produce power and H₂ for driving electric tram in Ouargla, Algeria. A multi-objective size optimization problem is solved as a single objective problem using the ε-constraint method, in which the cost of energy (COE) is defined as the main objective function to be minimized, while both loss of power supply probability (LPSP) and non-renewable usage (NRU) are defined as constraints. Particle swarm optimization and HOMER software are then employed for simulation and optimization purposes. Prior to the two scenarios investigated, a sensitivity study is performed to determine the effects of H₂ demand by tram and NRU on the techno-economic feasibility of the proposed system, followed by a new reliability factor introduced in the optimization, namely loss of H₂ supply probability (LHSP). The results of the first scenario show that by setting NRU^max = 100%, the system without H₂ provides the best solution with COE of 0.016 $/kWh that reaches grid parity and has 13% NRU. However, by setting NRU^max = 1% in the second scenario, an optimized configuration consisting of grid/PV/Electrolyzer/Fuel cell/Storage tank is obtained, which has 0% NRU and COE of 0.1 $/kWh. In the second scenario, it is also observed that an increased number of trams (i.e. increased H₂ demands) causes a significant reduction in LHSP, COE, NRU and CO₂ emissions. It is thus concluded that the grid/PV combination is the optimal choice for the studied system when considering economic aspects. However, taking into account the growing requirements of future energy systems, grid-connected PV with H₂ will be the best solution.     

A personal retrospect on three decades of high temperature fuel cell research; ideas and lessons learned

In1986 the Dutch national fuel cell program started. Fuel cells were developed under the paradigm of replacing conventional technology. Coal-fired power plants were to be replaced by large-scale MCFC power plants fuelled by hydrogen in a full-scale future hydrogen economy. With today's knowledge we will reflect on these and other ideas with respect to high temperature fuel cell development including the choice for the type of high temperature fuel cell. It is explained that based on thermodynamics proton conducting fuel cells would have been a better choice and the direct carbon fuel cell even more so, with electrochemical gasification of carbon as the ultimate step. The specific characteristics of fuel cells and multisource multiproduct systems were not considered, whereas we understand now that these can provide huge driving forces for the implementation of fuel cells compared to just replacing conventional combined heat and power production technology.     

食物致敏原表位定位技术的研究进展

食物致敏原是引起食物过敏的元凶，多为蛋白质。抗原表位是在抗原分子中与抗体反应或被抗原受体识别，并引发机体免疫应答的特殊化学基团。从表位水平认识食物致敏原，能揭示食物过敏的物质基础，为解决食物过敏问题提供精准的靶标。本文基于表位结构和定位方法的不同，介绍了食物致敏原表位定位技术的发展，并进一步展望了致敏原表位信息对于改进食物过敏鉴定技术和低致敏食物加工方法的应用前景。     

Hydrogen based Multi Energy Systems: Assessment of the marginal utility of increasing hydrogen penetration on system performances

The potential role of hydrogen as a tool for the decarbonization of the energy sector is widely recognized. In this paper, a comprehensive analysis of the marginal utility of increasing the hydrogen penetration on the energy and environmental performances of a multi-energy system is developed to assess its potential. A distributed energy system satisfying the energy requirements for an existing building is chosen as test case, assumed as representative of a wide set of applications. Starting from real energy consumption profiles, representative scenarios and optimized design configurations are defined using a methodology that takes into account uncertainties both in demand and renewable production profiles and in techno-economic parameters assumptions. For each multi energy system configuration, an optimal dispatch control strategy is achieved through a mixed-integer linear programming method to maximize the building self-independence from the grid. Results show how an increase of hydrogen technologies penetration allows a reduction of the specific carbon footprint of the system down to 100 grCO₂/kWh while increasing the system resilience up to 85%. Detailed information about the marginal effect of H₂ technologies on resilience, carbon footprint, and fossil energy savings are presented and can be used for extensions to similar applications.     

Extended-state-observer-based dynamic surface control of flexible-joint robot systems with input saturation

This article presents an extended-state-observer-based dynamic surface control approach for flexible-joint robot systems with asymmetric input saturation and large unknown dynamic knowledge. Traditional controllers for flexible-joint robot systems usually use approximation technology to deal with unknown dynamics knowledge. Unlike the traditional control algorithm, this article utilizes an extended state observer to estimate the unknown dynamics. For the closed-loop system, the delay strategy handles the time-scale separation issue, the filtering system overcomes the “explosion of differentiation” caused by the repeated differentiation of auxiliary control signals, and the mean-value-theorem solves the input saturation problem of the actuator. The stability analysis implies that estimation errors of extended state observers (ESOs) and other state variables are semiglobally uniformly ultimately bounded. Compared with fuzzy control algorithms, the novel ESO-based dynamic surface control approach not only omits online learning time but also uses only a few control parameters to obtain satisfactory tracking performance. Finally, a comparison simulation experiment is provided to illustrate the effectiveness of the gained conclusions.     

Driving behavior recognition and prediction based on Bayesian model

Since the existing intelligent driving systems are lack of efficiency and accuracy when processing huge number of driving data,a brand new approach of processing driving data was developed to identify and predicate human driving behavior based on Bayesian model.The approach was proposed to take two steps to deduce the specific driving behavior from driving data correspondingly without any supervision,the first step being using Bayesian model segmentation algorithm to divide driving data that inertial sensor collected into near-linear segments with the help of Bayesian model segmentation algorithm,and the second step being using extended LDA model to aggregate those linear segments into specific driving behavior (such as braking,turning,acceleration and coasting).Both offline and online experiments are conducted to verify this approach and it turns out that approach has higher efficiency and recognition accuracy when dealing with numerous driving data.