Abiotic-Biological Hybrid Systems for CO2 Conversion to Value-Added Chemicals and Fuels

Abiotic-biological hybrid systems that combine the advantages of abiotic catalysis and biotransformation for the conversion of carbon dioxide(CO2)to value-added chemicals and fuels have emerged as an appealing way to address the global energy and environmental crisis caused by increased CO2 emission.We illustrate the recent progress in this field.Here,we first review the natural CO2 fixation pathways for an in-depth understanding of the biological CO2 transformation strategy and why a sustainable feed of reducing power is important.Second,we review the recent progress in the construction of abiotic-biological hybrid systems for CO2 transformation from two aspects:(i)microbial electrosynthesis systems that utilize electricity to support whole-cell biological CO2 conversion to products of interest and(ii)photosynthetic semiconductor biohybrid systems that integrate semiconductor nanomaterials with CO2-fixing microorganisms to harness solar energy for biological CO2 transformation.Lastly,we discuss potential approaches for further improvement of abiotic-biological hybrid systems.

A Numerical Convex Lens for the State-Discretized Modeling and Simulation of Megawatt Power Electronics Systems as Generalized Hybrid Systems

Modeling and simulation have emerged as an indispensable approach to create numerical experiment platforms and study engineering systems.However,the increasingly complicated systems that engineers face today dramatically challenge state-of-the-art modeling and simulation approaches.Such complicated systems,which are composed of not only continuous states but also discrete events,and which contain complex dynamics across multiple timescales,are defined as generalized hybrid systems(GHSs)in this paper.As a representative GHS,megawatt power electronics(MPE)systems have been largely integrated into the modern power grid,but MPE simulation remains a bottleneck due to its unacceptable time cost and poor convergence.To address this challenge,this paper proposes the numerical convex lens approach to achieve state-discretized modeling and simulation of GHSs.This approach transforms conventional time-discretized passive simulations designed for pure-continuous systems into state-discretized selective simulations designed for GHSs.When this approach was applied to a largescale MPE-based renewable energy system,a 1000-fold increase in simulation speed was achieved,in comparison with existing software.Furthermore,the proposed approach uniquely enables the switching transient simulation of a largescale megawatt system with high accuracy,compared with experimental results,and with no convergence concerns.The numerical convex lens approach leads to the highly efficient simulation of intricate GHSs across multiple timescales,and thus significantly extends engineers’capability to study systems with numerical experiments.

Controlled unknown quantum operations on hybrid systems

Any unknown unitary operations conditioned on a control system can be deterministically performed if ancillary subspaces are available for the target systems [Zhou X Q, et al. 2011 Nat. Commun. 2 413]. In this paper, we show that previous optical schemes may be extended to general hybrid systems if unknown operations are provided by optical instruments. Moreover, a probabilistic scheme is proposed when the unknown operation may be performed on the subspaces of ancillary high-dimensional systems. Furthermore, the unknown operations conditioned on the multi-control system may be reduced to the case with a control system using additional linear circuit complexity. The new schemes may be more flexible for different systems or hybrid systems.

Max-plus-linear model-based predictive control for constrained hybrid systems: linear programming solution

In this paper, a linear programming method is proposed to solve model predictive control for a class of hybrid systems. Firstly, using the （max, ＋） algebra, a typical subclass of hybrid systems called max-plus-linear （MPL） systems is obtained. And then, model predictive control （MPC） framework is extended to MPL systems. In general, the nonlinear optimization approach or extended linear complementarity problem （ELCP） were applied to solve the MPL-MPC optimization problem. A new optimization method based on canonical forms for max-min-plus-scaling （MMPS） functions （using the operations maximization, minimization, addition and scalar multiplication） with linear constraints on the inputs is presented. The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization. The validity of the algorithm is illustrated by an example.

Semiconductor,molecular and hybrid systems for photoelectrochemical solar fuel production

The paper shortly reviews the basic direct approaches applied in searching for viable solutions to solar fuel production. These are generally distinguished in molecular and semiconductor(non-molecular)systems, however, hybrid strategies, proposed recently, have also been included. The most promising efforts are considered, highlighting key aspects and emerging critical issues. Special attention is paid to aspects such as electrode architecture, device design, and main differences in the scientific vision and challenges to directly produce solar fuels. This overview could be useful to orientate the readers in the wide panorama of research activities concerning water splitting, natural and artificial photosynthesis, and solar fuel production through the identification of common aspects, specialties and potentialities of the many initiatives and approaches that are developing worldwide in this field with the final aim to meet world energy demand.

High precision patternable liquid metal based conductor and adhesive substrate enabled stretchable hybrid systems

Stretchable hybrid systems have been attracting tremendous attention for their essential role in soft robotics,on-skin electronics,and implantable devices.Both rigid and soft functional modules are typically required in those devices.Consequently,ensuring stable electrical contact between rigid and soft modules is a vital part.Here,we propose a simple,universal,and scalable strategy for the stretchable hybrid system through a highly precise printable liquid metal particle-based conductor and adhesive fluorine rubber substrate.The properties of liquid metal particle-based conductors could be easily tuned to realize high-precision patterning,large-scale printing,and the ability to print on various substrates.Additionally,the fluorine rubber substrate could form strong interfacial adhesion with various components and materials through simply pressing and heating,hence enabling stable electrical contact.Furthermore,we prepared a stretchable hybrid light-emitting diode(LED)display system and employed it in on-skin visualization of pressure levels,which perfectly combined rigid and soft modules,thus demonstrating the promising potential applications in complex multifunctional stretchable hybrid systems for emerging technologies.

Hybrid Energy Systems and the Logic of Their Service-Dominant Implementation: Screening the Pathway to Improve Results

The transition of the global economy to a low-carbon development path has led to dramatic changes in the organization and functioning of energy markets around the world,where hybrid energy systems(HESs)are one of the decisive active agents.At the same time,a number of problems facing the modern HESs are primarily due to the stochastic nature of the renewable energy they use,require further profound changes not only in the technologies they use and how they manage them,necessary to meet the needs of end consumers and interact with the unified energy system,but also to preserve the ability of the environment to self-heal.In order to make the process of changes more efficient and eco-deep,the article proposes to use and discusses the approach based on service dominant(SD)logic,which opens up new opportunities for solving the problems of HESs.First of all through:the implementation of closer service interaction with other participants in the energy markets,as well as with the environment;a systemically organized process of transforming the“product”economic activity of HESs into a service-dominant one;developing the generalized and engineering models for solving the problems of optimizing the technical and economic indicators of HESs,operation in steady-state and transient modes.The calculations confirm the effectiveness of the proposed approach and its ability to reduce the average daily costs for the system as a whole by 14.7%compared to the costs with a uniform distribution of power between the modules.

Symbolic Algorithmic Analysis of Rectangular Hybrid Systems

This paper investigates symbolic algorithmic analysis of rectangular hybrid systems. To deal with the symbolic reachability problem, a restricted constraint system called hybrid zone is formalized for the representation and manipulation of rectangular automata state-spaces. Hybrid zones are proved to be closed over symbolic teachability operations of rectangular hybrid systems. They are also applied to model-checking procedures for verifying some important classes of timed computation tree logic formulas. To represent hybrid zones, a data structure called difference constraint matrix is defined. These enable us to deal with the symbolic algorithmic analysis of rectangular hybrid systems in an efficient way.

Derivation of the multi-model generalized labeled multi-Bernoulli filter:a solution to multi-target hybrid systems

In this study,we extend traditional(single-target)hybrid systems to multi-target hybrid systems with a focus on the multi-maneuvering-target tracking system.This system consists of a continuous state,a discrete and switchable state,and a discrete,time-constant,and unique state.By defining a new generalized labeled multi-Bernoulli density,we prove that it is closed under the Chapman-Kolmogorov prediction and Bayes update for multi-target hybrid systems.In other words,we provide the exact derivation of a solution to this system,i.e.,the multi-model generalized labeled multi-Bemoulli filter,which has been developed without strict proof.

Symbolic Control of Hybrid Systems from Signal Temporal Logic Specifications

In safety-critical systems,it is desirable to automatically synthesize controllers for complex tasks with guaranteed safety and correctness.Although much progress has been made through controller synthesis from temporal logic speci-cations,existing approaches generally require conservative assumptions and do not scale well with system dimensionality.We propose a scalable,provably complete algorithm that synthesizes continuous trajectories for hybrid systems to satisfy temporal logic speci-cations.Speci-cally,we harness highly e±cient Boolean satis-ability(SAT)and Linear Programming(LP)solvers to-nd trajectories that satisfy nonconvex Signal Temporal Logic(STL)speci-cations for a class of high dimensional hybrid systems.The proposed design algorithms are proven sound and complete,and are validated in simulation experiments.

Simple hybrid dithiafulvenes-triphenylamine systems as dopant-free hole-transporting materials for efficient perovskite solar cells

Two extended hybrid conjugated systems based on a triphenylamine(TPA) core with two and three peripheral 1,4-dithiafulvenes(DTF) units coded WH-2 and WH-3 as hole-transporting materials(HTMs) applied in perovskite solar cells(PSCs) are synthesized by facile one-step reaction in good yield over 75%. DTF unit as electron donor can enhance the electron donating ability and the fusion of benzenic ring of TPA with DTF unit may lead to reinforced intermolecular interactions in the solid state. In addition,WH-2 and WH-3 exhibit a pyramid shape containing partial planarity and quasi three-dimensionality features, which is also conducive to enhancing the π-π stacking of molecules in the solid state. The above-mentioned structural characteristics make the two HTMs have good hole mobilities. As a result,WH-2 and WH-3 obtained the high intrinsic hole mobilities of 4.69 × 10^(-4)and 2.18 × 10^(-3)cm^(2)V^(-1)s^(-1)respectively. Finally, the power conversion efficiencies(PCEs) of PSCs with WH-2 and WH-3 as cost-effective dopant-free HTMs are 15.39% and 19.22% respectively and the PCE of PSC with WH-3 is on a par with that of PSC with Li-TFSI/t-BP doped Spiro-OMe TAD(19.67%).

Finite time stability and stabilization of hybrid dynamic systems

Finite time stability and stabilization are studied for hy-brid dynamic systems. By combining multiple Lyapunov function and finite time Lyapunov function, a sufficient condition of finite time stability is given for the system. Compared with the previ-ous works, our results have less conservativeness. Furthermore, based on the state partition of continuous and resetting parts of system, a hybrid feedback controller is constructed, which stabi-lizes the closed-loop systems in finite time. Finally, a numerical example is provided to demonstrate the effectiveness of the pro-posed method.

Measure synchronization in hybrid quantum-classical systems

Measure synchronization in hybrid quantum-classical systems is investigated in this paper.The dynamics of the classical subsystem is described by the Hamiltonian equations,while the dynamics of the quantum subsystem is governed by the Schr¨odinger equation.By increasing the coupling strength in between the quantum and classical subsystems,we reveal the existence of measure synchronization in coupled quantum-classical dynamics under energy conservation for the hybrid systems.

Modelling Problems of Hybrid Event Systems

This paper discusses the mathematical description of hybrid event systems, consisting of continuous, discrete, even logic-judgment and inference-decision event systems. The description takes events as its core, and information interconnection as its tie. In this paper, an event, instead of a process, is regarded as an analyzed element. The hybrid event systems are described in mathematical models with the following tools: chains of time, generalized space and interconnecting information.

Frequency Control Approach and Load Forecasting Assessment for Wind Systems

Frequency deviation has to be controlled in power generation units when there arefluctuations in system frequency.With several renewable energy sources,wind energy forecasting is majorly focused in this work which is a tough task due to its variations and uncontrollable nature.Whenever there is a mismatch between generation and demand,the frequency deviation may arise from the actual frequency 50 Hz(in India).To mitigate the frequency deviation issue,it is necessary to develop an effective technique for better frequency control in wind energy systems.In this work,heuristic Fuzzy Logic Based Controller(FLC)is developed for providing an effective frequency control support by modeling the complex behavior of the system to enhance the load forecasting in wind based hybrid power systems.Frequency control is applied to reduce the frequency deviation due tofluctuations and load prediction information using ANN(Artificial Neural Network)and SVM(Support Vector Machine)learning models.The performance analysis of the proposed method is done with different machine learning based approaches.The forecasting assessment is done over various climates with the aim to decrease the prediction errors and to demote the forecasting accuracy.Simulation results show that the Mean Absolute Percentage Error(MAPE),Root Mean Square Error(RMSE)and Normalized Mean Absolute Error(NMAE)values are scaled down by 41.1%,9.9%and 23.1%respectively in the proposed method while comparing with existing wavelet and BPN based approach.

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.

Application of the Constrained Predicated Nets and Interval Logic to Production Systems

A method to model and analyze the hybrid systems is presented. The time to be considered in the plant is taken as an explicit parameter through the constrained predicated net (CPN). The CPN's basic structure is a Petri net with predicated transition. All components of the net are expressed by annotation which is defined on rational set Q. The analysis method for the plant is interval temporal logic represented by Petri nets. This paper combines the above two methods to synthesize the hybrid system, gives a simple and clear expression of the expected action of the studied plant.

Modelling and Optimal Design of Hybrid Power System Photovoltaic/Solid Oxide Fuel Cell for a Mediterranean City

This work presents a hybrid power system consisting of photovoltaic and solid oxide fuel cell(PV-SOFC)for electricity production and hydrogen production.The simulation of this hybrid system is adjusted for Bou-Zedjar city in north Algeria.Homer software was used for this simulation to calculate the power output and the total net present cost.The method used depends on the annual average monthly values of clearness index and radiation for which the energy contributions are determined for each component of PV/SOFC hybrid system.The economic study is more important criterion in the proposed hybrid system,and the results show that the cost is very suitable for the use of this hybrid system,which ensures that the area is fed continuously with the sufficient energy for the load which assumed to be 500 kW in the peak season.The optimized results of the present study show that the photovoltaic is capable of generating 8733 kW electricity while the SOFC produces 500 kW electricity.The electrolyzer is capable of producing 238750 kg of hydrogen which is used as fuel in the SOFC to compensate the energy lack in nights and during peak season.

Subtle Influence of the Weibull Shape Parameter on Homer Optimization Space of a Wind Diesel Hybrid Gen Set for Use in Southern Brazil

Wind power is an increasingly important alternative for obtaining energy supplies, both in large interconnected power systems and in smaller hybrid systems and even in backup systems. The temporal and spatial variability of the winds represent an obstacle to be overcome so that wind energy can be increasingly used. The capacity factor of wind farms shows how this variability impacts the operation of the plants and its value is of the order of 30% to 35%. The variability of the wind speed is influenced if the point of interest is on land or on sea, the shape of the surface, the proximity of water bodies, among other factors. The availability of wind is best described by the Weibull probability distribution, which has as one of its defining parameters one which is termed as shape parameter. This parameter is much higher as higher is the variability of the wind speed. This paper studies the subtle influence of Weibull shape parameter on the optimal combination of components in a wind diesel hybrid system, by means of computer simulations with the well known software Homer. The results indicate a relatively small influence (as expected) in the studied system, which appears particularly when the cost of diesel is higher and the availability of wind is lower.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.