Operational Features Research Articles

Dual-Centre Harmonised Multimodal Positron Emission Tomography/Computed Tomography Image Radiomic Features and Machine Learning Algorithms for Non-small Cell Lung Cancer Histopathological Subtype Phenotype Decoding

AimsWe aimed to build radiomic models for classifying non-small cell lung cancer (NSCLC) histopathological subtypes through a dual-centre dataset and comprehensively evaluate the effect of ComBat harmonisation on the performance of single- and multimodality radiomic models. Materials and methodsA public dataset of NSCLC patients from two independent centres was used. Two image fusion methods, namely guided filtering-based fusion and image fusion based on visual saliency map and weighted least square optimisation, were used. Radiomic features were extracted from each scan, including first-order, texture and moment-invariant features. Subsequently, ComBat harmonisation was applied to the extracted features from computed tomography (CT), positron emission tomography (PET) and fused images to correct the centre effect. For feature selection, least absolute shrinkage and selection operator (Lasso) and recursive feature elimination (RFE) were investigated. For machine learning, logistic regression (LR), support vector machine (SVM) and AdaBoost were evaluated for classifying NSCLC subtypes. Training and evaluation of the models were carried out in a robust framework to offset plausible errors and performance was reported using area under the curve, balanced accuracy, sensitivity and specificity before and after harmonisation. N-way ANOVA was used to assess the effect of different factors on the performance of the models. ResultsSupport vector machine fed with selected features by recursive feature elimination from a harmonised PET feature set achieved the highest performance (area under the curve = 0.82) in classifying NSCLC histopathological subtypes. Although the performance of the models did not significantly improve for CT images after harmonisation, the performance of PET and guided filtering-based fusion feature signatures significantly improved for almost all models. Although the selection of the image modality and feature selection methods was effective on the performance of the model (ANOVA P-values <0.001), machine learning and harmonisation did not change the performance significantly (ANOVA P-values = 0.839 and 0.292, respectively). ConclusionThis study confirmed the potential of radiomic analysis on PET, CT and hybrid images for histopathological classification of NSCLC subtypes.

Contemporary Strategies for Immobilizing Metallophthalocyanines for Electrochemical Transformations of Carbon Dioxide.

Metallophthalocyanine (PcM) coordination complexes are well-known mediators of the electrochemical reduction of carbon dioxide (CO2). They have many properties that show promise for practical applications in the energy sector. Such properties include synthetic flexibility, a high stability, and good efficiencies for the reduction of CO2 to useful feedstocks, such as carbon monoxide (CO). One of the ongoing challenges that needs to be met is the incorporation of PcM into the heterogeneous materials that are used in a great many CO2-reduction devices. Much progress has been made in the last decade and there are now several promising approaches to incorporate PcM into a range of materials, from simple carbon-adsorbed preparations to extended polymer networks. These approaches all have important advantages and drawbacks. In addition, investigations have led to new proposals regarding CO2 reduction catalytic cycles and other operational features that are crucial to function. Here, we describe developments in the immobilization of PcM CO2 reduction catalysts in the last decade (2013 to 2023) and propose promising avenues and strategies for future research.

Green Hyperbolic Complexes on Lorentzian Manifolds

We develop a homological generalization of Green hyperbolic operators, called Green hyperbolic complexes, which cover many examples of derived critical loci for gauge-theoretic quadratic action functionals in Lorentzian signature. We define Green hyperbolic complexes through a generalization of retarded and advanced Green’s operators, called retarded and advanced Green’s homotopies, which are shown to be unique up to a contractible space of choices. We prove homological generalizations of the most relevant features of Green hyperbolic operators, namely that (1) the retarded-minus-advanced cochain map is a quasi-isomorphism, (2) a differential pairing (generalizing the usual fiber-wise metric) on a Green hyperbolic complex leads to covariant and fixed-time Poisson structures and (3) the retarded-minus-advanced cochain map is compatible with these Poisson structures up to homotopy.

Predicting urban rail transit safety via artificial neural networks

This paper studies the operational safety of urban rail transit (URT) systems through Artificial Neural Networks. While recent safety literature adopting systematic models of analysis consider the complexity of URT operations, they focus on single systems or single components of the operational process. Our study contributes to the URT safety literature by having a macro perspective, while considering that such complex socio-technical systems involve multiple non-linear interactions among their components. To our knowledge, we present the first cross-country analysis of URT safety through machine learning models in the literature, using a unique international dataset from 31 URT systems which comprises annual system-level data. Two models are estimated to predict the annual URT injuries. The first model includes safety-related incidents as inputs, while the second includes operational characteristics of the system. Additionally, a closed-form formula is presented to predict the annual number of injuries based on operational features of the URT system along with an illustrative example to demonstrate benchmarking applications. The results are promising and indicate good generalizability. The models proposed in this study could be useful for operators and policy makers as they aid in prioritizing improvements, predicting future safety performance based on changes in operational features, and as a benchmarking tool.

Reliability-Guaranteed Uplink Resource Management in Proactive Mobile Network for Minimal Latency Communications

Proactive Mobile Network (PMN) has been proposed to support extremely low latency communications with multi-tier computing architectures, machine-centricity, and data-driven operation features. Nevertheless, the communication reliability of the PMN introduces new substantial technological challenges. As PMN employs unique proactive open-loop communication, any feedback-based control is avoided to enhance end-to-end latency. This paper focuses on machine-initiated uplink transmission in PMN and proposes a reliability-guaranteed resource management scheme. Without requiring feedback control information, our scheme uniquely decomposes conventional resource management into two collaborative decision processes: predictive resource allocation suggested by network anchor nodes (ANs) and proactive smart resource utilization by smart equipment (SE). These two decision-making processes are constructed as independent reinforcement learning (RL) problems, but implicitly share the states of radio resources according to operating environments. Different algorithms for different operating scenarios have been investigated for this dual-decision solution. Simulation results in various scenarios show that our scheme enables PMN’s reliability close to the theoretical optimal value and successfully serves radio resource utilization in the PMNs.

Predicting Li-Ion Battery Remaining Useful Life: An XDFM-Driven Approach with Explainable AI

The accurate prediction of the remaining useful life (RUL) of Li-ion batteries holds significant importance in the field of predictive maintenance, as it ensures the reliability and long-term viability of these batteries. In this study, we undertake a comprehensive analysis and comparison of three distinct machine learning models—XDFM, A-LSTM, and GBM—with the objective of assessing their predictive capabilities for RUL estimation. The performance evaluation of these models involves the utilization of root-mean-square error and mean absolute error metrics, which are derived after the training and testing stages of the models. Additionally, we employ the Shapley-based Explainable AI technique to identify and select the most relevant features for the prediction task. Among the evaluated models, XDFM consistently demonstrates superior performance, consistently achieving the lowest RMSE and MAE values across different operational cycles and feature selections. However, it is worth noting that both the A-LSTM and GBM models exhibit competitive results, showcasing their potential for accurate RUL prediction of Li-ion batteries. The findings of this study offer valuable insights into the efficacy of these machine learning models, highlighting their capacity to make precise RUL predictions across diverse operational cycles for batteries.

Brushless direct current motors with permanent magnets for marine electric drive systems: state and prospects

The main trends in the development of designs, power semiconductor circuits and control systems of promising valve electromechanical converters — brushless DC motors with permanent magnets for marine electric drive and automation equipment, are discussed in the paper. A variant of their implementation in an adjustable electric drive is presented. The object of the study is a brushless direct current electromechanical system, including a synchronous machine with magnetoelectric excitation, covered by positional feedback through a dependent controlled valve switch, and the subject is structures, the circuit of the power part, control systems, the algorithm of functioning and characteristics. The purpose of the research is to evaluate the operational features, properties and possibilities of introducing brushless direct current motors with permanent magnets as marine electric drive systems of low and medium power. The results of research and development are embodied in experimental industrial samples. A variant with microprocessor control has been developed for marine electric drive systems. The switch is assembled on transistor switches that provide high reliability and enhance the functionality of the control system. The minimum necessary system of logical variables, ensuring the implementation of rational laws of switching and speed control, has been formed. Operational characteristics at various leading angles of switching on the switch valves are presented. The presence of pole shoes at the poles introduces significant features in the patterns of their change. The main advantages and features of the proposed design and circuit solutions are formulated in the conclusions. It is indicated that valve motors with excitation from permanent magnets are the most promising solution in terms of integrating an electromechanical converter into an adjustable marine electric drive of a range of small and medium capacities.Engines of this type are structurally simple and reliable, have rigid natural mechanical characteristics, do not require energy for excitation, have a large overload capacity and high speed in transients. Their distinctive feature is the high specific moment.

Mathematical Modelling of Resonant Electric Power Transmission System

The most significant feature of the power supply to agricultural consumers is the uneven distribution of the electrical load across three phases of the 0.4 kilovolt four-wire power grid and low load density (515 kilowatts per square kilometer) and coverage of large areas. Based on modern requirements for the quality of electricity and reliability of power supply to agricultural consumers, there is a need to modernize existing and create a new generation of rural electric grid. One of the variants of such grid is a resonant power transmission system. (Research purpose) The research object is a physical and mathematical model of a resonant system, taking into account a wide selection of the necessary physical parameters for the transfer function, the research purpose is determining the parameters of the resonant system. (Materials and methods) Used Mathematica version 4.2.0.0. It was established that all physical parameters can be set in accordance with the experiment. (Results and discussion) A new type of electrical systems for electric power transmission with the use of a resonant method of electric power transmission was considered. A mathematical model of a high-frequency line based on a single-wire line with a return through the ground was given. (Conclusions) We investigated the general principle of operation, the main components, and also calculated electrical characteristics, including power, frequency response, efficiency, features of operation, including wireless communication. The calculation results were compared with the existing equipment with a transmission power of up to 8000 watts. It was shown that on the basis of the obtained equations, it is possible to correctly select the parameters of the resonant power transmission line and voltage converters. It was determined that the obtained physical and mathematical model of a resonant single-conductor system, taking into account a wide selection of the necessary physical parameters, makes it possible to understand the operation of a resonant power transmission line and build optimized receiving and transmitting equipment complexes.

An approach to railway network sections modeling based on queuing networks

The paper is devoted to the study and long-term forecasting of railway network sections operating. We call a railway network section a set of several railway stations interacting with each other and lines between them. We propose a methodology for mathematical modeling of train traffic on the railway network based on the queuing theory. The resulting models are a set of mathematical descriptions of the incoming train traffic and the train running within the system. Several Markovian Arrival Process are used to describe train arrivals from different directions. Such a description makes it possible to take into account the parameters of separate train flows, which depend on the category of trains and their directions. A queuing network in which all nodes have a finite capacity simulates the movement of trains through the system. This mathematical apparatus allows us to consider the nonlinear structure of the railway network, the operation features of stations, the capacity of railway lines, and the influence of random factors. To apply the methodology, we have chosen two objects that differ in infrastructure and properties of train flows. The first is located in the east of Russia and focused on servicing freight trains. The second is located in Germany, close to Belgium and The Netherlands borders. It is characterized by the predominance of passenger traffic. We construct mathematical models and perform numerical simulations. Based on the results obtained, the maximum allowable load is estimated, and bottlenecks in the structure of transport systems are found. Besides, we give some recommendations on how to increase capacity in the long term.

Analysis of structures and application of different types of engines in cars

Problem. The publication analyzes the structures and application of various types of engines in a car, which allows to determine the structural aspects, features of placement and operation of engines, their future application prospects. Purpose. The purpose of the work is to analyze the design and operation of engines used in modern cars, to determine their advantages and disadvantages, and the prospects for the development and application of various types of engines. Methodology. The approaches adopted in the work to achieve the set goal consist in the analysis of designs of various types of engines of modern cars, their layout and features of their use. Results. The paper includes a comparative analysis of various types of engines used in modern cars. The peculiarity of the design and structure of internal combustion engines, electric cars and hybrid cars is considered. The scheme of the general configuration of electric vehicles, possible configurations of electric vehicles, layout schemes of hybrid cars are given. The advantages and disadvantages of each type of engine, the main prospects for their development and application are shown. Originality. The conducted analysis of the design features of various types of engines that are installed in modern cars allows us to formulate a general idea about the peculiarities of the operation of cars with the considered types of engines. Practical meaning. The obtained results of the analysis and the given advantages and disadvantages in the use of different types of engines allow us to formulate the main directions in the development of structures and the prospects of their application in different types of cars.

Optimal planning and allocation of Plug-in Hybrid Electric Vehicles charging stations using a novel hybrid optimization technique.

India's expanding population has necessitated the development of alternate transportation methods with electric vehicles (EVs) being the most indigenous and need for the current scenario. The major hindrance is the undue influence on the power distribution system caused by incorrect charging station setup. Renewable Energy Sources (RES) have a lower environmental impact than the non-renewable sources of energy and due to which Plug-in Hybrid Electric Vehicles (PHEV) charging stations are installed in the highest-ranking buses to facilitate their effective placements. Based on meta-heuristic optimization, this study offers an effective PHEV charging stations allocation approach for RES applications. The primary objective of the developed system is to create a charging network at a reasonable cost while maintaining the operational features of the distribution network. These troublesare handled by applying meta-heuristic algorithms and optimum planning based on renewable energy systems to satisfy the outcomes of the variables. As a result, by adding charging station parameters, this research proposes to conceptualize the distribution of optimal charging stationsas multiple-objectives of the problem. Furthermore, the PHEV RES and charging station location problem is handled in this study by deploying a novel hybrid algorithm termed as Atom Search Woven Aquila Optimization Algorithm (AT-AQ) that includes the ideas of both Aquila Optimizer (AO) and Atom Search Optimization (ASO) Algorithms. In reality, Aquila Optimizer is a unique population-based optimization approach energized by Aquila's behaviour when seeking prey and it solves the problems of slow convergence and local optimum trapping. According to the findings of the experiments, the proposed model outperformed the other methods in terms of minimized cost function.

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein–protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

Numerical Comparison of Active Shielding Techniques for Magnetic Fluid Hyperthermia Application

This study aims to eliminate magnetic field leakage at 100 kHz with active shielding in Magnetic Fluid Hyperthermia (MFH) system. First of all, the structural and operational features of the MFH system were analyzed. Then, the active regions of the coils were determined, graphical results were obtained and the designs were compared with each other. We first simulated the magnetic field distribution of a circular coil active shielding system by the commercial software tool COMSOL Multiphysics. Secondly, a single D-shape shielding was configured in the MFH system and the size and location of the shielding were examined. The magnetic field attenuation percentages of the methods in the x, y, z axes and the target region selected as the representative tumor region were compared. The simulation results show that the shielding ability of the double D-shape shielding type is better in the x and y axes than the other coil types (51.43% for the x-axis and 95.77% for the y-axis). The fact that the attenuation percentage of this shielding type in the target region is 0.0287% is a secondary advantage. It has been observed that the attenuation in the z-axis is higher in other shielding types, but the attenuation percentage in the target region also increases.

Improving the performance of diesel engines fueled with water-fuel emulsion

Due to unique properties, production and operation features, water-fuel emulsion (WFE) could be considered as one of the most promising type of alternative fuels for diesel engines. Experimental research showed that comparing to traditional diesel fuel, application of water-fuel emulsion allows to reduce nitrogen oxides and soot emissions, which is due primarily to a decrease in the level of maximum temperatures in the engine cylinder, as well as a more uniform distribution of fuel over the combustion chamber volume thanks to its secondary dispersion (micro-explosion phenomena). To control the stability of water-fuel emulsion properties during engine operation it is recommended to install water content sensor in the fuel supply system.

Rethinking Representation Learning-Based Hyperspectral Target Detection: A Hierarchical Representation Residual Feature-Based Method

Representation learning-based hyperspectral target detection (HTD) methods generally follow a learning paradigm of single-layer or one-step representation residual learning and the target detection on original full spectral bands, which, in some cases, cannot accurately distinguish the target pixels from variable background pixels via one-round of the detection process. To alleviate the problem and make full use of the latent discriminate characteristics in different spectral bands and the representation residual, this paper proposes a level-wise band-partition-based hierarchical representation residual feature (LBHRF) learning method for HTD with a parallel and cascaded hybrid structure. Specifically, the LBHRR method proposes to partition and fuse different levels of sub-band spectra combinations, and take full advantages of the discriminate information in representation residuals from different levels of band-partition. The highlights of this work include three aspects. First, the original full spectral bands are partitioned in a parallel level-wise manner to obtain the augmented representation residual feature through level-wise band-partition-based representation residual learning, such that the global spectral integrity and contextual information of local adjacent sub-bands are flexibly fused. Second, the SoftMax transformation, pooling operation, and augmented representation residual feature reuse among different layers are equipped in cascade to enhance the learning ability of the nonlinear and discriminant hierarchical representation residual features of the method. Third, a hierarchical representation residual feature-based HTD method is developed in an efficient stepwise learning manner instead of back-propagation optimization. Experimental results on several HSI datasets demonstrate that the proposed model can yield promising detection performance in comparison to some state-of-the-art counterparts.

Design of Off-Grid Lighting Business Models to Serve the Poor: Field Experiments and Structural Analysis

A significant proportion of the world’s population has no access to grid-based electricity and so relies on off-grid lighting solutions. Rechargeable lamp technology is gaining popularity as an alternative off-grid lighting model in developing countries. In this paper, we explore consumer behavior and the operational inefficiencies that result under this model. Specifically, we are interested in (i) measuring the impact of inconvenience (of traveling to recharge the lamp) along with the impact of liquidity constraints (because of poverty) on lamp usage and (ii) evaluating the efficacy of strategies that address these factors. We build a structural model of consumers’ recharge decisions that incorporates several operational features of the low-income regions. We conducted large-scale field experiments in Rwanda in partnership with a local rechargeable lamp operator and use the resultant data to estimate and test our model. We find that the complete removal of inconvenience and liquidity constraints from the current business model results in 73% and 126% increases in both recharges and revenue, thereby suggesting that these constraints are major sources of inefficiency. By implementing simple operations-based strategies—such as starting more recharge centers, visiting consumers periodically to collect their lamps for recharge, and allowing consumers to partially recharge their lamps and pay flexibly for the recharge—more than half the benefit of completely eliminating the inefficiencies can be attained. By contrast, the price- and capacity-based strategies that vary the economic variables (i.e., the amount paid per recharge and the amount of light obtained in return) but not the operational model perform far worse than the aforementioned strategies. Overall, our analysis emphasizes the importance of managing operations effectively even in markets with cash-constrained consumers, in which firms may have a natural tendency to focus more on reducing prices. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported by the International Growth Centre; Wharton School, University of Pennsylvania; and Institut Européen d’Administration des Affaires; The Environment and Energy Partnership; Grand Challenges Canada (Stars in Global Health); INSEAD’s Emerging Markets Institute; INSEAD’s Randomized Controlled Trials Laboratory; the INSEAD–Wharton Alliance, and International Growth Centre. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4844 .

Operation features of PEMFCs with De-alloyed PtCu/C catalysts

This work is aimed at studying the electrochemical behavior of the MEA containing the bimetallic catalysts with an alloy structure. The relevance of the study is associated with application of a simple method to synthesize the bimetallic catalysts using the acid post-treatment to obtain de-alloyed nanoparticles with an increased activity in the oxygen reduction reaction, a performance stability during the long-term stress testing, and a composition stability during the operation. The novelty of the work consists in a detailed consideration of the mechanisms of poisoning the MEA's components by copper cations formed during a selective dissolution of the catalysts' components as well as the mechanisms of the proton-exchange membrane degradation.

Effective ratiometric optical thermometry based on inverse temperature dependent dual near infrared emission of Cr3+ doped BaAl4Sb2O12 phosphor

The device of reliable non-contact ratiometric optical thermometry technique gradually became a cutting-edge research pot over decades due to its intriguing features of rapid response, high accuracy and simple operation. In which, Boltzmann-based optical thermometry has attracted widespread attention for its exceptional reliability. Here in, we put forward an effective ratiometric optical thermometry based on the highly sensitive and effective Boltzmann-based 2E→4A2/4T2→4A2 emission ratio of BaAl3.9Sb2O12:0.1Cr3+. The phosphor shows dual near-infrared luminescence lines at 698 nm and 710 nm (2E→4A2) and the luminescence band near 750 nm (4T2→4A2). With the temperature elevating from 170 to 470 K, the emission intensity of luminescence lines undergoes continuous enhancement and possess excellent anti-thermal quenching property (3.5-fold at 470 K, compared to 170 K). In contrast, the emission intensity of luminescence band shows a monotonic decline trend. The luminescence intensity ratio shows a good linear relationship in the Arrhenius diagram, and the relative sensitivity reaches 2.08 % K−1 at 170 K, which corroborates the BaAl3.9Sb2O12:0.1Cr3+ has the potential as the optical cryogenic thermometer. In the end, the work provides some insights into the development of novel Cr3+-doped thermometry materials based on the 2E and 4T2 energy level transitions in intermediate crystal field environments.

The SLC38A5/SNAT5 amino acid transporter: from pathophysiology to pro-cancer roles in the tumor microenvironment.

SLC38A5/SNAT5 is a system N transporter that can mediate net inward or outward transmembrane fluxes of neutral amino acids coupled with Na+ (symport) and H+ (antiport). Its preferential substrates are not only amino acids with side chains containing amide (glutamine and asparagine) or imidazole (histidine) groups, but also serine, glycine, and alanine are transported by the carrier. Expressed in the pancreas, intestinal tract, brain, liver, bone marrow, and placenta, it is regulated at mRNA and protein levels by mTORC1 and WNT/β-catenin pathways, and it is sensitive to pH, nutritional stress, inflammation, and hypoxia. SNAT5 expression has been found to be altered in pathological conditions such as chronic inflammatory diseases, gestational complications, chronic metabolic acidosis, and malnutrition. Growing experimental evidence shows that SNAT5 is overexpressed in several types of cancer cells. Moreover, recently published results indicate that SNAT5 expression in stromal cells can support the metabolic exchanges occurring in the tumor microenvironment of asparagine-auxotroph tumors. We review the functional role of the SNAT5 transporter in pathophysiology and propose that, due to its peculiar operational and regulatory features, SNAT5 may play important pro-cancer roles when expressed either in neoplastic or in stromal cells of glutamine-auxotroph tumors.NEW & NOTEWORTHY The transporter SLC38A5/SNAT5 provides net influx or efflux of glutamine, asparagine, and serine. These amino acids are of particular metabolic relevance in several conditions. Changes in transporter expression or activity have been described in selected types of human cancers, where SNAT5 can mediate amino acid exchanges between tumor and stromal cells, thus providing a potential therapeutic target. This is the first review that recapitulates the characteristics and roles of the transporter in physiology and pathology.

Stability evaluation of a grid-tied hybrid wind/PV farm joined with a hybrid energy-storage system

This paper presents the stability-evaluation outcomes of a multimachine power system (MMPS) connected with a large-scale hybrid wind farm (WF) and photovoltaic (PV) farm or hybrid wind/PV farm (HWPF) and a hybrid energy-storage system (HESS) consisting of a vanadium redox flow battery (VRFB) and a supercapacitor (SC). A probability scheme is used to determine the rated power of the proposed HESS, where the capacities of the VRFB-ESS and the SC-ESS are designed to effectively utilize their operating features. The control strategy of the HESS is proposed to reduce the pressure of the VRFB-ESS and smooth the output power fluctuations of the HWPF. The steady-state stability, small-signal stability, dynamic performances, and transient simulations of the studied grid-tied HWPF fed to the MMPS with and without the HESS are achieved. The simulation outcomes show that the proposed HESS can enhance the stability and power-smoothing performance of the HWPF fed to the MMPS.