Mutual Constraints Research Articles

Design of control laws for timed event graphs networks subject to mutual exclusion constraints in Min-Plus algebra

The right control of systems, especially when different constraints exist, can avoid many problems of cost and time loss. This paper relates to the resolution of the mutual exclusion problem for discrete event systems modeled with a Network of Timed Event Graphs (NTEGs). It aims to propose formal approaches based on Min-Plus algebra formalisms for the synthesis of control laws to guarantee mutual exclusion constraints in NTEGs. In these studies, the behavior of the network is described by a set of linear Min-Plus equations and mutual exclusion specifications are represented by inequalities that are also linear in Min-Plus algebra. Computed laws are modeled by control places which are connected to the initial NTEGs in order to prevent the violation of mutual exclusion constraints.

Axial compression behaviours of tubular sectioned C-shape continuous-supported steel walls in MSB

The ultimate strength behaviour of C-shaped continuous-supported walls employing hot-rolled structural hollow section (SHS) columns in modular steel buildings (MSB) was evaluated using two full-scale axial compression tests. The columns' width-to-length (D/B), width-to-thickness (D/t), and material strength were considered experimental parameters. A three-dimensional (3D) finite element (FE) model was validated against test results, accounting for geometry and material nonlinearities while projecting ultimate strength and failure behaviour. The size, spacing, number of columns, stiffener thickness and sidewall effects were evaluated in a parametric analysis. The columns compressive resistance was predicted using prediction equations from EC3:1-1, AISC360, and GB50017. The results revealed that the column nominal strength was reduced to achieve compression yielding due to columns stiffness differences, mutual constraints, and elastic buckling. If the buckling length is considered or omitted, the EC3:1-1 slenderness limit for Class 3 steel members becomes less conservative. Reducing column size and spacing or increasing stiffener size and number improves column and wall compressive behaviour; however, the sidewalls improved the corner columns constraint while weakening the front wall. GB50017 predictions were the safest; whereas, EC3:1-1 made the most unsafe predictions when buckling was not taken into account.

High-Resolution Cooperate Density-Integrated Inversion Method of Airborne Gravity and Its Gradient Data

Airborne (or satellite) gravity measurement is a commonly used remote sensing method to obtain the underground density distribution. Airborne gravity gradiometry data have a higher horizontal resolution to shallower causative sources than airborne gravity anomaly, so joint exploration of airborne gravity and its gradient data can simultaneously obtain the anomaly feature of sources with different depths. The most commonly used joint inversion method of gravity and its gradient data is the data combined method, which is to combine all the components into a data matrix as mutual constraints to reduce ambiguity and non-uniqueness. In order to obtain higher resolution results, we proposed a cooperate density-integrated inversion method of airborne gravity and its gradient data, which firstly carried out the joint inversion using cross-gradient constraints to obtain two density structures, and then fused two recovered models into a result through Fourier transform; finally, data combined joint inversion of airborne gravity, and gradient data were reperformed to achieve high-resolution density result using fused density results as a reference model. Compared to the data combined joint inversion method, the proposed cooperate density-integrated inversion method can obtain higher resolution and more accurate density distribution of shallow and deep bodies meanwhile. We also applied it to real data in the mining area of western Liaoning Province, China. The results showed that the depth of the skarn-type iron mine in the region is about 900–1300 m and gives a more specific distribution compared to the geological results, which provided reliable data for the next exploration plan.

Performance Analysis of Dipole Antenna Based Planar Arrays With Mutual Coupling and Antenna Position Error in mmWave Hybrid System

In this work, a hybrid architecture called fully connected sub-array (FCSA) is considered for uplink data decoding with mutual coupling and space constraints at the base station (BS). The above architecture achieves the same system performance with a lower hardware complexity than a fully connected array. The packing of many antenna elements on a small surface area leads to the mutual coupling effect. Moreover, having antenna elements at the BS with finite directivity is more practical than having isotropic antenna elements at the BS. In the present work, we have considered uplink data decoding with dipole antennas arranged in a uniform planar array (UPA) grid at the BS with the error in separation between the antenna elements and mutual coupling as additional impairments. An approximate expression for the mutual coupling matrix (MCM) is derived and verified. A geometry-based stochastic model (GBSM) for the channel is considered between the user equipment (UE) and the BS. The performance of the hybrid architecture in terms of spectral efficiency (SE) and bit error rate (BER) of the system, with the mutual coupling effect and antenna separation error, is analyzed. Numerical simulations show that when the BS has no information on the MCM, SE and the system's BER performances with the FCSA architecture degrade. In the absence of any information on the coupling effect, the system performs better when a more extensive array size is used at the BS. Both the SE and BER degraded when longer dipoles were used in the array.

An approach for enforcing a class of GMECs on time Petri nets with uncontrollable transitions

Real-time control is of great significance for practical plants such as intelligent manufacturing systems and transportation systems, and timing information should be integrated in their discrete models. The existing specifications expressed by generalized mutual exclusion constraints (GMECs) do not consider timing specifications in the control design of these models, which would be restrictive for the control specifications of a timed system. In the framework of time Petri net (TPN) models, this paper introduces a class of GMECs, called time GMECs (TGMECs), in which each GMEC is associated with a restricted time interval. By restricting the firing intervals of controllable transitions, an efficient method based on control functions is proposed to implement TGMECs on a TPN system in presence of uncontrollable transitions. The core of the reported method is to employ the transitions-related timing constraints in a partial modified state class graph (PMSCG) to solve mathematical programming problems. Such a PMSCG is a graph recently introduced to enhance the supervisor permissiveness for a TPN system with GMECs. In particular, the terminal condition for the construction of a PMSCG is modified. By taking advantage of the PMSCG, an online control procedure is presented to implement the supervision for TGMECs in a maximally permissive way.

Mathematical formulation and a new metaheuristic for the constrained double-floor corridor allocation problem

A layout plan for a manufacturing system that is designed without any facility constraints will most likely be infeasible when confronted with reality. Additionally, considering that land available for building industrial plants is limited and its cost is high, it is necessary to investigate the layout planning of two and multi-floor facilities. To address these shortages in the scientific literature, we focus on the double-floor corridor allocation problem (DFCAP) which covers a wide range of complex facility constraints, such as fixed floor constraints, fixed row constraints, fixed positioning constraints, mutual floor constraints, mutual row constraints, sequencing constraints and adjacency constraints. For the model mentioned above, we term it as a constrained DFCAP (cDFCAP). A mixed-integer linear programming model is formulated for the cDFCAP. In order to solve larger realistic problems, a constrained metaheuristic with the memetic algorithm framework customised for solving the cDFCAP is introduced in this work. In our algorithm, four problem-specific heuristic rules to construct a set of initial solutions are developed. In addition, an ideal parameter combination for our constrained memetic algorithm is determined through a Taguchi experimental design. Finally, the results of a set of cDFCAP instances with different sizes (n = 10∼80) report that our provided approach is effective for the considered problem.

The Micro-genesis of Improvisational Co-creation

ABSTRACT Recent voices in creativity research emphasize the vital role that processes of active engagement and interaction play. These distributed, interactivity-based, and ecological accounts critique the reduction of creativity to mental mechanisms and eschew the methodological individualism that underlies this view. Instead, they elevate socio-material couplings to a genuine causal mechanism, hereby explaining why others “are good to be creative with” beyond mutual inspiration or mere constraints on creativity. With improvised dance duets as our example, we introduce a micro-analysis that unpacks these mechanisms from a first-person viewpoint: We describe how the embodied communication at the sub-second scale, a continuous “give and take” between dancers, mediates the co-creation process and how reciprocal micro-actions cumulate into more complex creative effects. Secondly, we distinguish convergent and divergent co-creation dynamics, degrees of playful exploration vs. adaptive pressures, and degrees of individual creative autonomy. Thirdly, we argue that the mix of external constraints, joint exploration, and individual ideation provides a fertile mechanism for co-creation. Finally, we discuss forms of individual creative vision that respect and exploit the dynamic ecology, which can manifest in immediate micro-inspirations, but also in subtle modulations of ongoing process, and in “cultivating” the wider system in ways that benefit novelty.

Effect of global constraint on the mechanical behavior of gradient materials

The gradient structured (GS) samples studied here consists of gradient layer (s) with grain size gradient and a coarse-grained (CG) layer. Gradient materials have been found to have superior mechanical properties due to the hetero-deformation induced (HDI) strengthening and strain hardening. However, the effect of the interaction between the GS layer and the CG layer on the mechanical properties of the gradient materials remains to be studied. In this paper, single-sided GS Cu and double-sided GS Cu plates were prepared by surface mechanical attrition treatment (SMAT) on one side and both symmetrical sides respectively. It is found that GS samples have higher yield strength than CG samples, and the Cu samples with double-sided GS has an uniform elongation comparable to CG Cu. In addition, the global mutual constraint among different GS layers affect the mechanical behavior of gradient materials, which is revealed in the comparative study of double-sided and single-sided GS Cu. Specifically, the double-sided GS Cu exhibit higher yield strength and ductility than the single-sided GS Cu, because the former is better constrained. This study demonstrates that in addition to the local structural gradient, the symmetric constraint produce more effective HDI effect to enhance the mechanical properties. The global mutual constraints of GS layers played a positive role in producing superior mechanical properties.

Implementation of Generalized Mutual Exclusion Constraints Using Critical Places and Marking Estimation

Generalized mutual exclusion constraints (GMECs) are a class of state specifications on Petri nets (PNs). They are generally enforced on the nets by a simple control structure called control places (monitors). Unfortunately, this conventional procedure is implemented in an offline and monolithic manner, which suffers from computational difficulty and is arduous for the control of a system in real time. Additionally, the flexibility and fault tolerance of such a method are somewhat unacceptable, and the method suffers from a lack of adaptability to net variations incurred by system reconfiguration, communication failure, and constraint integration. This article aims to enforce GMECs for a live PN model by using critical places and marking estimation. First, we define GMECs on some so-called critical places, such that the satisfaction of the GMECs can be determined by only monitoring the number of tokens in their corresponding critical places during runtime. Then, an efficient and effective control strategy is developed such that the controllers forbid all those transition firings that lead to the violated GMECs based on the estimated markings of critical places derived by observers from a resource perspective rather than exploring the markings of the entire system. Finally, we present procedures to deal with decision deadlocks, which are induced by one-sided decisions made by some controllers and may prevent all enabled transitions from firing. Global GMECs are always implemented through the local observation and control of processes without knowing an extra information.

Structure design and performance study on filtration-adsorption bifunctional blood purification membrane

Plasma perfusion is one of the primary therapies for hepatic failure. However, biocompatibility and toxins removal of plasma perfusion adsorbents are mutual constraints. Herein, a novel bifunctional blood purification membrane structure is proposed, which can remove toxins via “filtration-adsorption”. The internal surface of membrane has excellent biocompatibility. Meanwhile, the external surface can adsorb toxins. When blood flows in internal surface, toxins in plasma can be filtrated through membrane and adsorbed on the external surface. Herein, plasma separation membrane with excellent biocompatibility was prepared firstly. Then, the external surface was modified by β-cyclodextrin to construct bilirubin adsorption functional layer. XPS, ATR-FTIR and FE-SEM demonstrated that β-cyclodextrin was grafted on the external surface without modifying the internal surface. The bovine serum albumin sieving coefficient of membrane reached (97.8 ± 1.9)%. The water flux reached (1084 ± 30)L.m2.h−1. Bilirubin adsorption capacity of the external surface reached 12.8 mg/g via “filtration-adsorption”. Bilirubin competitive adsorption coefficient was 9.1. The internal surface was similar to original plasma separation membrane, which had excellent biocompatibility due to low hemolysis and blood cells adsorption. Thus, the bifunctional blood purification membrane can remove bilirubin safely via one-step “filtration-adsorption”. The structure design of membrane provides a new blood purification treatment scheme for clinical hepatic failure.

The multi-trip vehicle routing problem with time windows and unloading queue at depot

Motivated by the practice of urban waste collection, we introduce a new variant of the multi-trip vehicle routing problem with time windows, where vehicles unload cargos collected from customers at a depot that owns a limited unloading capacity, and therefore some vehicles need to wait at a queue once the unloading capacity is fully occupied. Unloading queue at depot significantly compicates this problem since it makes a trip involve traveling, waiting, and unloading stages. We first formulate this problem into a trip-based set partitioning model, which has two sets of mutual exclusion constraints. To solve the model, we propose a branch-and-price-and-cut algorithm (BPC), in which the linear relaxation of the model is solved by column generation, and moreover, the relaxation gap is tightened by incorporating rounded capacity inequalities. Particularly, the mutual exclusion constraints induce a new and challening pricing problem, and we design a novel label-setting algorithm with tailored label structure to solve it. We conduct compuational experiments on a set of random instances generated from the Solomon’s benchmark, and the results demonstrate the effectiveness of the proposed model and algorithm.

Thermal transport in pristine and defective two-dimensional polyaniline ([formula omitted

Two-dimensional polyaniline (2D-PANI) with semiconductor properties, a single crystalline carbon nitride with a stoichiometry of C3N, has attracted a lot of interest after its successful synthesis. In this study, the thermal transport properties in pristine and defective 2D-PANI were explored by extensive molecular dynamics (MD) simulations. Results based on three different versions of the MD method consistently showed that the lattice thermal conductivity of the pristine 2D-PANI is up to around 2000Wm−1K−1. It decreases significantly after the introduction of structural defects and is essentially in a low-power law with the defects concentration. In addition, the difference in the weakening of thermal conductivity between vacancy and topological defects stems mainly from their respective differential effects on the low-frequency out-of-plane phonons. Remarkably, it also reveals the potential mutual constraints between anharmonic phonon-phonon scattering and phonon-defect scattering. These findings provide guidance for the thermal management of 2D-PANI-based electronic devices and are also expected to advance their application in the field of thermal design of nanomaterials.

Coral Image Segmentation with Point-Supervision via Latent Dirichlet Allocation with Spatial Coherence

Deep neural networks provide remarkable performances on supervised learning tasks with extensive collections of labeled data. However, creating such large well-annotated data sets requires a considerable amount of resources, time and effort, especially for underwater images data sets such as corals and marine animals. Therefore, the overreliance on labels is one of the main obstacles for widespread applications of deep learning methods. In order to overcome this need for large annotated dataset, this paper proposes a label-efficient deep learning framework for image segmentation using only very sparse point-supervision. Our approach employs a latent Dirichlet allocation (LDA) with spatial coherence on feature space to iteratively generate pseudo labels. The method requires, as an initial condition, a Wide Residual Network (WRN) trained with sparse labels and mutual information constraints. The proposed method is evaluated on the sparsely labeled coral image data set collected from the Pulley Ridge region in the Gulf of Mexico. Experiments show that our method can improve image segmentation performance against sparsely labeled samples and achieves better results compared with other semi-supervised approaches.

CT Image Segmentation of Liver Tumor with Deep Convolutional Neural Network

The liver is the largest substantial organ in the abdominal cavity of the human body. Its structure is complex, the incidence of vascular abundance is high, and it has been seriously ribbed, human health and life. In this study, an automatic segmentation method based on deep convolutional neural network is proposed. Image data blocks of different sizes are extracted as training data and different network structures are designed, and features are automatically learned to obtain a segmentation structure of the tumor. Secondly, in order to further refine the segmentation boundary, we establish a multi-region segmentation model with region mutual exclusion constraints. The model combines the image grayscale, gradient and prior probability information, and overcomes the problem that the boundary point attribution area caused by boundary blur and regional adhesion is difficult to determine. Finally, the model is solved quickly using the time-invisible multi-phase level set. Compared with the traditional multi-organ segmentation method, this method does not require registration or model initialization. The experimental results show that the model can segment the liver, kidney and spleen quickly and effectively, and the segmentation accuracy reaches the advanced level of current methods.

高寒植物叶片性状对模拟降水变化的响应

降水变化对高寒草甸生态系统产生了显著影响，植物叶片性状特别是叶脉特征对降水变化非常敏感，然而高寒植物叶片性状特征如何响应降水变化还知之较少。采用集雨棚模拟增减50%降水的条件，以高寒草甸8种主要植物叶片为研究对象，研究了降水变化对叶片的叶脉率、叶脉密度、叶片大小、比叶质量、叶片总有机碳含量、叶片全氮含量、叶片碳同位素相对含量和碳氮比等叶片性状的影响。发现增水显著增加了植物的叶片大小、稳定碳同位素千分值、总有机碳含量、全氮含量，但显著降低了叶脉密度；而减水显著降低了叶片大小、稳定碳同位素千分值。植物叶片性状各指标对降水变化的响应存在协同变化和相互制约。不同水分生态类型的植物对降水变化的响应存在差异，中生植物通过增加叶片大小和减少叶脉密度积极应对降水的增加，矮生嵩草的叶片大小分别增加了200.3%，叶脉密度减小了17.5%，而旱中生植物通过减少叶片大小和增加叶脉密度应对降水的减少，垂穗披碱草和异针茅的叶片大小分别减少54.9%和30.7%，其叶脉密度分别增加25%和22.4%。羽状叶脉植物增加叶脉密度和稳定碳同位素千分值以适应增水条件，花苜蓿、异叶米口袋的叶脉密度的增加了7.8%和4.0%，稳定碳同位素千分值增加2.5%和3.3%，但增水条件下平行叶脉植物的叶脉密度不变或降低和稳定碳同位素千分值保持不变；减水增加了平行叶脉植物叶脉密度并减低了稳定碳同位素千分值，异针茅的叶脉密度增加了22.4%，稳定碳同位素千分值减小2.9%，而对羽状叶脉植物的叶脉密度和稳定碳同位素千分值减少或不变。植物叶片性状对增水的敏感性显著大于对减水的敏感性，增水的效应约为减水的2倍；叶片大小的敏感性显著大于其它叶片性状，约为其它叶片性状的10倍。因此，植物在应对短期降水变化时，植物形态可塑性的作用凸显，放大或缩小叶片大小是植物应对降水变化的最有效的途径，但是不同水分生态类型和叶脉类型植物可塑性的方向存在显著差异。;Variations in precipitation had a significant impact on the alpine meadow ecosystem. Among plant leaf traits, leaf vein properties are very sensitive to the changed precipitation. However, how the leaf traits of alpine plants respond to precipitation variations was still little known. The precipitation in the study area was artificially regulated increase and decrease of rain using rainwater shed, and the responses of leaf properties from 8 main plants in the alpine meadow were systematically analyzed under 50%, 100%, and 150% rainfall treatments. This study investigated the effects of altered precipitation on the vein mass per leaf mass (VMM), vein length per area (VLA), leaf size (LS), leaf mass per area (LMA), total organic carbon concentration (TOC), total nitrogen concentration (TN), carbon isotope discrimination (δ¹³C) and the ratio of carbon to nitrogen (C/N). The results indicated that water increasing significantly enhanced LS, δ¹³C, TOC, and TN, but significantly reduced for VLA. While water decreasing significantly reduced the LS and δ¹³C. There were synergistic changes and mutual constraints in the response of plant leaf traits to precipitation alteration. Plants belonging to different water ecological groups had different responses to precipitation alteration. Mesophytes actively responded to the increase in precipitation by increasing LS and reducing VLA. The LS of Kobresia humilis was increased by 200.3%, and the VLA was reduced by 17.5%. While dry mesophytes responded to the decrease in precipitation by reducing LS and increasing VLA. The LS of Elymus nutans and Stipa purpure decreased by 54.9% and 30.7%, and their VLA increased by 25% and 22.4%, respectively. Pinnate vein plants could enhance VLA and δ¹³C in order to adapt to water increasing condition. The VLA of Medicago ruthenica and Gueldenstaedtia diversifolia increased by 7.8% and 4.0%, and the δ¹³C increased by 2.5% and 3.3%. While for parallel vein plants, they remained unchang or decrease. Water reducing enhanced VLA of parallel veins plant and reduced δ¹³C, while had no impact on pinnate vein plants. The VLA of S. purpure increased by 22.4% and the δ¹³C decreased by 2.9%. The plant leaf traits were much more significantly sensible to water increasing than water reducing, and the effect of water increasing was about 2 times than that of water reduction. LS was the most significantly sensitive among all leaf traits, about 10 times that of other leaf traits. Therefore, plant morphology plasticity played a prominent role when responding to short-term precipitation variation. Enlarging or reducing leaf size was the most effective way to respond to precipitation alteration. However, plant plasticity was significantly different among the water ecological types and leaf vein types.

Learning Disentangled Representation for Mixed- Reality Human Activity Recognition With a Single IMU Sensor

Together with the rapid development of the sensors technology in recent years, sensor-based human activity recognition (HAR) has shown promising performance using well-known supervised deep learning methods. However, it remains challenging in a realistic scenario, i.e., limited number of labeled samples and sensors. This article proposes a novel deep learning method to achieve accurate and robust HAR with only a single inertial measurement unit (IMU) sensor. Our contributions are twofold. First, based on the skinned multiperson linear (SMPL) model, we build a large synthetic HAR dataset containing multimodal measurements: acceleration and angular velocity, which were generated according to the forward kinematics. Second, We propose a multiple-level domain adaptive learning model with information-theoretically stimulated constraints to simultaneously align the distributions of low- and high-level representations of virtual and real HAR data. The proposed mutual information constraints encourage the neural network to learn a disentangled representation for the multimodal sensing data. Comprehensive experimental results on three publicly available datasets demonstrate that the proposed method compares favorably with competing ones and has robust performance with variable labeled samples.

Walk the Talk: ESG Mutual Fund Voting on Shareholder Proposals

We document evidence that mutual funds designated as “Sustainable Investment Overall” by Morningstar (which we classify as ESG mutual funds) are no more likely than other mutual funds to support shareholder proposals. We find, however, that ESG mutual funds are more likely than non-ESG mutual funds to support environmental and social (ES) shareholder proposals. We also find that ESG mutual fund support for ES proposals is more pronounced in ESG index funds than in ESG active funds and in the ESG funds of small rather than large fund families. These results imply that mutual fund investment constraints and access to investor demand may shape how ESG mutual funds influence portfolio firms’ environmental and social action choices.

The Physical Behavior of Interphase Chromosomes: Polymer Theory and Coarse-Grain Computer Simulations.

Fluorescence in situ hybridization and chromosome conformation capture methods point to the same conclusion: that chromosomes appear to the external observer as compact structures with a highly nonrandom three-dimensional organization. In this work, we recapitulate the efforts made by us and other groups to rationalize this behavior in terms of the mathematical language and tools of polymer physics. After a brief introduction dedicated to some crucial experiments dissecting the structure of interphase chromosomes, we discuss at a nonspecialistic level some fundamental aspects of theoretical and numerical polymer physics. Then, we inglobe biological and polymer aspects into a polymer model for interphase chromosomes which moves from the observation that mutual topological constraints, such as those typically present between polymer chains in ordinary melts, induce slow chain dynamics and "constraint" chromosomes to resemble double-folded randomly branched polymer conformations. By explicitly turning these ideas into a multi-scale numerical algorithm which is described here in full details, we can design accurate model polymer conformations for interphase chromosomes and offer them for systematic comparison to experiments. The review is concluded by discussing the limitations of our approach and pointing to promising perspectives for future work.

Closed-Loop Deadlock-Free Supervision for GMECs in Time Petri Net Systems

This article investigates the enforcement of generalized mutual exclusion constraints (GMECs) and deadlock-freeness on a time Petri net (TPN) system with uncontrollable transitions, motivated by the fact that the existing methods enforcing GMECs may degrade the performance of a closed-loop system and lead to deadlock states. A supervisor enforcing a set of GMECs and deadlock-freeness on an underlying untimed Petri net system is assumed to be available. By exploiting timing information and mathematical programming, a control function is designed to restrict the firing intervals of transitions such that a TPN system can avoid entering forbidden states. The key idea behind the proposed approach is the online computation of a graph, called reduced modified state class graph (RMSCG), that is an extension of the partial modified state class graph recently introduced by the authors. Based on the RMSCG, an online control synthesis procedure is developed, which can enforce the originally given GMECs and deadlock-freeness in a maximally permissive way.

Mechanical equilibrium operation integrated modelling of recuperative solid oxide fuel cell – gas turbine hybrid systems: Design conditions and off-design analysis

A novel model integrating mechanical equilibrium operation, electrochemical reactions and other thermodynamic processes has been built in this research to gain energetic and operational insights into recuperative solid oxide fuel cell – gas turbine hybrid systems. Determination mechanism for equilibrium running is proposed for both design stage analysis and off-design operation through reanalysis of classical speed and flow compatibility requirements. The coupling with solid oxide fuel cells and inclusion of a recuperator substantively introduces several independent variables to the related equation, while the number of independent parameters decreases in modelling when matching issues of turbomachinery are taken into account. Compared to simple hybridization, there are increasingly complex mutual influence and constraints between upstream and downstream parameters that are linked by the heat recovery process. Quantitative analysis of factors in turbine design shows 14–16% higher system efficiency with component pressure losses taken into consideration. When the turbine is appropriately designed, an electric efficiency of 68% can be achieved with recuperator effectiveness of 0.9. Results also highlight the significantly expanded operating ranges of turbomachinery under lower recuperator effectiveness of 0.8 as compensation for a 3% lower electrical efficiency at design conditions. A performance map for the proposed system is developed based on off-design operation analysis with stable running state ensured throughout.