bluuebunny/crossref_metadata_2025 huggingface dataset:
Pretreatment Radiologically Enlarged Lymph Nodes as a Significant Prognostic Factor in Clinical Stage IIB Cervical Cancer: Evidence from a Taiwanese Tertiary Care Center in Reaching Consensus
Authors: Liu Chia-Hao , Yang Szu-Ting , Chao Wei-Ting , Lin Jeff Chien-Fu , Lee Na-Rong , Chang Wen-Hsun , Chen Yi-Jen , Wang Peng-Hui
Published: May 2022

The incidence of lymph node (LN) involvement and its prognostic value based on radiological imaging in stage IIB cervical cancer (CC) remains unclear, and evidence regarding oncological outcomes of patients with stage IIB CC with LN metastases is limited. In this study we retrospectively reviewed the incidence and prognostic significance of pretreatment radiologic LN status in 72 patients with clinical stage IIB CC (FIGO 2009), with or without radiologic evidence of LN enlargement. An enlarged LN was defined as a diameter > 10 mm on CT/MRI. Progression-free survival (PFS) and overall survival (OS) were assessed. Radiologic LN enlargement of >10 mm was observed in 45.8% of patients with stage IIB CC. PFS (p = 0.0088) and OS rates (p = 0.0032) were significantly poorer in the LN group (n = 33) than in the non-LN group (n = 39). Univariate Cox analysis revealed that LN > 10 mm contributed to a higher rate of recurrence and mortality. In conclusion, nearly half of the patients with clinical stage IIB CC had enlarged LNs (>10 mm) identified during pretreatment radiologic evaluation, which negatively impacted prognosis. Our findings highlight the need to incorporate CT- or MRI-based LN assessment before treatment for stage IIB CC.
Optimal Conspicuity of Liver Metastases in Virtual Monochromatic Imaging Reconstructions on a Novel Photon-Counting Detector CT—Effect of keV Settings and BMI
Authors: Bette Stefanie , Decker Josua A. , Braun Franziska M. , Becker Judith , Haerting Mark , Haeckel Thomas , Gebhard Michael , Risch Franka , Woźnicki Piotr , Scheurig-Muenkler Christian , Kroencke Thomas J. , Schwarz Florian
Published: May 2022

In dual-energy CT datasets, the conspicuity of liver metastases can be enhanced by virtual monoenergetic imaging (VMI) reconstructions at low keV levels. Our study investigated whether this effect can be reproduced in photon-counting detector CT (PCD-CT) datasets. We analyzed 100 patients with liver metastases who had undergone contrast-enhanced CT of the abdomen on a PCD-CT (n = 50) or energy-integrating detector CT (EID-CT, single-energy mode, n = 50). PCD-VMI-reconstructions were performed at various keV levels. Identical regions of interest were positioned in metastases, normal liver, and other defined locations assessing image noise, tumor-to-liver ratio (TLR), and contrast-to-noise ratio (CNR). Patients were compared inter-individually. Subgroup analyses were performed according to BMI. On the PCD-CT, noise and CNR peaked at the low end of the keV spectrum. In comparison with the EID-CT, PCD-VMI-reconstructions exhibited lower image noise (at 70 keV) but higher CNR (for ≤70 keV), despite similar CTDIs. Comparing high- and low-BMI patients, CTDI-upregulation was more modest for the PCD-CT but still resulted in similar noise levels and preserved CNR, unlike the EID-CT. In conclusion, PCD-CT VMIs in oncologic patients demonstrated reduced image noise–compared to a standard EID-CT–and improved conspicuity of hypovascularized liver metastases at low keV values. Patients with higher BMIs especially benefited from constant image noise and preservation of lesion conspicuity, despite a more moderate upregulation of CTDI.
A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing
Authors: Sajjadi Sina , Mehrandezh Mehran , Janabi-Sharifi Farrokh
Published: May 2022

In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.
Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm
Authors: Yan Yan , Jiang Feng , Zhang Xinan , Tian Tianhai
Published: May 2022

One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random samples, and then infers a network by using the path-consistent algorithm based on each variable order. We propose measures to determine the edge weights using the corresponding edge weights in the inferred networks, and choose the edges with the largest weights as the putative regulations between genes or proteins. The developed method is rigorously assessed by the six benchmark networks in DREAM challenges, the mitogen-activated protein (MAP) kinase pathway, and a cancer-specific gene regulatory network. The inferred networks are compared with those obtained by using two up-to-date inference methods. The accuracy of the inferred networks shows that the developed method is effective for discovering molecular regulatory systems.
Statistical Modeling of the Seismic Moments via Mathai Distribution
Authors: Vega-Jorquera Pedro , De la Barra Erick , Torres Héctor , Vásquez Yerko
Published: May 2022

Mathai’s pathway model is playing an increasingly prominent role in statistical distributions. As a generalization of a great variety of distributions, the pathway model allows the studying of several non-linear dynamics of complex systems. Here, we construct a model, called the Pareto–Mathai distribution, using the fact that the earthquakes’ magnitudes of full catalogues are well-modeled by a Mathai distribution. The Pareto–Mathai distribution is used to study artificially induced microseisms in the mining industry. The fitting of a distribution for entire range of magnitudes allow us to calculate the completeness magnitude (Mc). Mathematical properties of the new distribution are studied. In addition, applying this model to data recorded at a Chilean mine, the magnitude Mc is estimated for several mine sectors and also the entire mine.
Fiscal Policy and Income Inequality: The Critical Role of Institutional Capacity
Authors: Malla Manwar Hossein , Pathranarakul Pairote
Published: May 2022

Rising income inequality has become a defining global challenge that hinders the achievement of the United Nations Sustainable Development Goals. The paper investigates the effect of fiscal policy and institutional capacity on income inequality among developed and developing countries. Applying the system Generalized Method of Moments (GMM) to control potential endogeneity for countries from 2000 to 2019, the following results have been established. The dynamic effect captured by the first lag of inequality suggests that the widening income gap is persistent in both developed and developing countries. We also find evidence that income tax is more progressive and may abate income inequality in developing countries and not in developed countries. However, taxes on goods and services were found not to impact income equalization globally. Furthermore, the findings reveal that government size, education expenditure, and health expenditure are negatively associated with income inequality in developed countries only. Public debt was observed not to influence income distribution across the world. We observed that corruption and government effectiveness do not significantly impact income distribution in developed and developing countries for institutional capacity. However, in most cases, the coefficients of the interactions between fiscal policy and institutional capacity bear the expected signs, albeit insignificant. Some policy recommendations have been offered.
Active Blended Learning Engineering Students: A Case Study
Authors: Vodovozov Valery , Raud Zoja , Petlenkov Eduard
Published: May 2022

An ongoing technological, economic, and societal change forces a new understanding of engineering and modifies the requirements for higher engineering education. Consequently, an educational approach based on the combination of blended learning and active learning promises great prospects for the enhancement of the knowledge and skills acquisition thanks to the flexibility it opens up. In this research, an attitude to study is first ever analysed from the standpoint of students enrolled in three various degree levels, namely, a Bachelor of Science program, a Master of Science program, and a new EuroTeQ program. The strengths and weaknesses of the blended and active learning combination are evaluated from didactic and methodological sides. It shows a fundamentally different approach to learning from students belonging to various degree programs. Most of the bachelor degree learners volunteered to participate in active learning, although a significant proportion of them was unable to succeed. In contrast, the learning of master students looks more circumspect and selective; they devote less time to study, but spend it more wisely, being more highly responsible for the outcomes than bachelors. The EuroTeQ participants did not push on many active learning activities, but demonstrated quite high motivation in quizzes, labs, and online consultations. As a result, the outcomes of the first academic year satisfied mainly the most strong of the EuroTeQ students.
Impact Analysis of Emerging Semantic Communication Systems on Network Performance
Authors: Lee Harim , Ahn Hyeongtae , Park Young Deok
Published: May 2022

With the paradigm shift from Shannon’s legacy, semantic communication (SC) is emerging as one of the promising next-generation communication technologies. The new paradigm in communication technology allows the meaning of transmitted messages to be successfully delivered to a receiver. Hence, the semantic communication focuses on the successful delivery of transmitted messages such as human language communication. In order to realize such new communication, both transmitter and receiver should share the same background knowledge with each other. Recently, several researchers have developed task-specific SC systems by exploiting astonishing achievements in deep learning, which can allow the same knowledge to be shared between them. However, since such SC systems are specialized to handle specific applications, not all users can be serviced by the SC systems. Therefore, a network will face a coexistence of an SC system and a traditional communication (TC) system. In this paper, we investigate how introducing emerging SC systems affects the performance of the TC system from a network perspective. For analysis, we consider the signal-to-noise ratio (SNR) differently for the user served by an SC system and the user served by a TC system. Then, by using two different SNR equations, we formulate a max-min fairness problem in the coexistence of SC and TC systems. Via extensive numerical results, we compare the network performance of TC and SC users with and without SC systems, and then confirm that SC systems are indeed a promising next-generation communication alternative.
Machine-Learning-Based Multi-Corner Timing Prediction for Faster Timing Closure
Authors: Zhao Zhenyu , Zhang Shuzheng , Liu Guoqiang , Feng Chaochao , Yang Tianhao , Han Ao , Wang Lei
Published: May 2022

For the purpose of fixing timing violations, static timing analysis (STA) of full-corners is repeatedly executed, which is time-consuming. Given a timing path, timing results at some corners (“dominant corners”) are utilized to predict timing at other corners (“non-dominant corners”), which can greatly shorten the runtime of STA. However, the huge number of combinations of the dominant corners and the wide difference in prediction accuracy make it difficult to apply multi-corner timing prediction to chip industrial design. In this paper, we propose a dominant corner selection strategy to quickly determine the dominant corner combination with high prediction accuracy, along with which a new multi-corner timing prediction process is established to speed up STA. Experimental results show that our method can not only effectively accelerate STA, but also ensure the high prediction accuracy of the prediction timing. On the public ITC’99 benchmark, the prediction accuracy of the dominant corner combination selected by the proposed method is up to 98.2%, which is an improvement of 15% compared to the state-of-the-art method. For industrial application, we apply our method by using timing results on only 2 dominant corners to predict the other 12 non-dominant corners, which accelerates the runtime of the timing closure process by more than 2×.
Global Maximum Power Point Tracking of Photovoltaic Module Arrays Based on Improved Artificial Bee Colony Algorithm
Authors: Chao Kuei-Hsiang , Li Jia-Yan
Published: May 2022

In this paper, an improved artificial bee colony (I-ABC) algorithm for the maximum power point tracking (MPPT) of a photovoltaic module array (PVMA) is presented. Even though the P-V output characteristic curve with multi-peak was generated due to any damages or shading discovered on the PVMA, the I-ABC algorithm could get rid of stuck on tracking the local maximum power point (LMPP), but quickly and stably track the global maximum power point (GMPP), thereby improving the power generation efficiency. This proposed I-ABC algorithm could search for the higher power point of a PVMA by a small bee colony, determine the next tracking direction through the perturb and observe (P&O) method, and keep tracking until the GMPP is obtained. This method could prevent tracking the GMPP for too long due to applying a small bee colony. First, in this study, the photovoltaic modules produced by Sunworld Co., Ltd. were used and were configured as a PVMA with four series and three parallel connections under different numbers of shaded modules and different shading ratios, so that corresponding P-V output characteristic curves with multi-peak values were generated. Then, the GMPP was tracked by the proposed MPPT method. The simulation and experimental results showed that the proposed method performed better both in dynamic response and steady-state performance than the traditional artificial bee colony (ABC) algorithm. According to the experimental results, it showed that the tracking accuracy for the GMPP based on the proposed MPPT with 100 iterations under 5 different shading ratios was about 100%; on the other hand, that of the traditional ABC algorithm was 70%, and that of the P&O method was lower at about 30%.
A Semi-Unsupervised Segmentation Methodology Based on Texture Recognition for Radiomics: A Preliminary Study on Brain Tumours
Authors: Donelli Massimo , Espa Giuseppe , Feraco Paola
Published: May 2022

Because of the intrinsic anatomic complexity of the brain structures, brain tumors have a high mortality and disability rate, and an early diagnosis is mandatory to contain damages. The commonly used biopsy is the diagnostic gold standard method, but it is invasive and, due to intratumoral heterogeneity, biopsies may lead to an incorrect result. Moreover, some tumors cannot be resectable if located in critical eloquent areas. On the other hand, medical imaging procedures can evaluate the entire tumor in a non-invasive and reproducible way. Radiomics is an emerging diagnosis technique based on quantitative medical image analyses, which makes use of data provided by non-invasive diagnosis techniques such as X-ray, computer-tomography (CT), magnetic resonance (MR), and proton emission tomography (PET). Radiomics techniques require the comprehensive analysis of huge numbers of medical images to extract a large and useful number of phenotypic features (usually called radiomics biomarkers). The goal is to explore and obtain the associations between features of tumors, diagnosis and patients’ prognoses to choose the best treatments and maximize the patient’s survival rate. Current radiomics techniques are not standardized in term of segmentation, feature extraction, and selection, moreover, the decision on suitable therapies still requires the supervision of an expert doctor. In this paper, we propose a semi-automatic methodology aimed to help the identification and segmentation of malignant tissues by using the combination of binary texture recognition, growing area algorithm, and machine learning techniques. In particular, the proposed method not only helps to better identify pathologic tissues but also permits to analyze in a fast way the huge amount of data, in Dicom format, provided by non-invasive diagnostic techniques. A preliminary experimental assessment has been conducted, considering a real MRI database of brain tumors. The method has been compared with the segmentation software’s tools “slicer 3D”. The obtained results are quite promising and demonstrate the potentialities of the proposed semi-unsupervised segmentation methodology.
Developing a Cyber Incident Exercises Model to Educate Security Teams
Authors: Alothman Basil , Alhajraf Aldanah , Alajmi Reem , Farraj Rawan Al , Alshareef Nourah , Khan Murad
Published: May 2022

Since cyber attacks are increasing and evolving rapidly, the need to enhance cyber-security defense is crucial. A cyber incident exercise model is a learning technique to provide knowledge about cyber security to enhance a security team’s incident response. In this research work, we proposed a cyber incident model to handle real-time security attacks in various scenarios. The proposed model consisted of three teams: (1) the black team, (2) the red team, and (3) the blue team. The black team was a group of instructors responsible for setting up the environment. They had to educate the red and blue teams about cyber security and train them on facing cyber attacks. Once the training period was completed, the members were divided into two teams to conduct a cyber-security competition in a cyber game scenario. Each of the two teams performed a different task. The red team was the offensive team that was responsible for launching cyber-security attacks. The blue team was the defensive team that was responsible for countering attacks and minimizing the damage caused by attackers; they had to conduct both cyber-security configuration and incident handling. During the scenario, the black team was responsible for guiding and monitoring both the red and the blue teams, ensuring the rules were applied throughout the competition. At the end of the competition, the members of each team changed with each other to make sure every team member was using the knowledge they gained from the training period and every participant was evaluated impartially. Finally, we showed the security team’s offensive and defensive skills via the red team and the blue team, respectively.
The Joint Phantom Track Deception and TDOA/FDOA Localization Using UAV Swarm without Prior Knowledge of Radars’ Precise Locations
Authors: Wang Yubing , Wang Weijia , Zhang Xudong , Wu Lirong , Yin Hang
Published: May 2022

This paper develops the model of the joint phantom track deception and the joint techniques of time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) localization to deceive air defense radar networks under the condition that an unmanned aerial vehicle (UAV) swarm has no prior knowledge of the radars’ precise locations, and related performance experiment and analysis are presented to demonstrate the effectiveness of the proposed method and to clarify the influence factors of phantom track deception. The main contributions of this paper are as follows. Firstly, the model of phantom track deception against a radar network by UAV swarm without prior knowledge of the radars’ positions are established. Secondly, TDOA/FDOA are adapted to locate networked enemy radars using UAV swarm, where the Fisher information matrix (FIM) is derived to evaluate the estimation accuracy. Thirdly, the uncertainty analysis consisting of radar location error and UAV position error is deduced. With these efforts, the integrated capability of sensing and jamming is realized. Moreover, the same source testing using space resolution cell (SRC) from the perspective of a radar network is executed to provide guidance for phantom track design. Finally, performance experiment and analysis are given to verify the theoretical analysis with simulation results.
Reliability Testing of Wind Power Plant Devices with the Use of an Intelligent Diagnostic System
Authors: Duer Stanislaw , Paś Jacek , Stawowy Marek , Hapka Aneta , Duer Radosław , Ostrowski Arkadiusz , Woźniak Marek
Published: May 2022

This paper introduces the issue of reliability simulation studies of wind farm equipment in the process of an operation. By the improvement, retrofitting and insertion of new (optimal) solutions to change the quality and terms of the use of wind farm equipment, an evaluation of their impact on reliability under real conditions can be carried out over a long period of time. Over a brief period, testing the reliability of a technical facility is only possible in a simulation. The aspect of evaluating the reliability of wind farm equipment after the application of intelligent systems, including the Wind Power Plant Expert System (WPPES), can be tested in the manner of a simulation. It was accepted in this article that the operation of the wind farm equipment is detailed based on Markov processes. The results of such research activities are the development of reliable and appropriate strategies and an exploitation policy of PE facilities. The above-mentioned issues in such a comprehensive approach have not been fully presented in the literature. The process of exploitation of complex technical objects such as PE devices is a complex random technical and technological process.
Electrified Hydrogen Production from Methane for PEM Fuel Cells Feeding: A Review
Authors: Meloni Eugenio , Iervolino Giuseppina , Ruocco Concetta , Renda Simona , Festa Giovanni , Martino Marco , Palma Vincenzo
Published: May 2022

The greatest challenge of our times is to identify low cost and environmentally friendly alternative energy sources to fossil fuels. From this point of view, the decarbonization of industrial chemical processes is fundamental and the use of hydrogen as an energy vector, usable by fuel cells, is strategic. It is possible to tackle the decarbonization of industrial chemical processes with the electrification of systems. The purpose of this review is to provide an overview of the latest research on the electrification of endothermic industrial chemical processes aimed at the production of H2 from methane and its use for energy production through proton exchange membrane fuel cells (PEMFC). In particular, two main electrification methods are examined, microwave heating (MW) and resistive heating (Joule), aimed at transferring heat directly on the surface of the catalyst. For cases, the catalyst formulation and reactor configuration were analyzed and compared. The key aspects of the use of H2 through PEM were also analyzed, highlighting the most used catalysts and their performance. With the information contained in this review, we want to give scientists and researchers the opportunity to compare, both in terms of reactor and energy efficiency, the different solutions proposed for the electrification of chemical processes available in the recent literature. In particular, through this review it is possible to identify the solutions that allow a possible scale-up of the electrified chemical process, imagining a distributed production of hydrogen and its consequent use with PEMs. As for PEMs, in the review it is possible to find interesting alternative solutions to platinum with the PGM (Platinum Group Metal) free-based catalysts, proposing the use of Fe or Co for PEM application.
A Study on Swirling Jets Flow Controlled by Two Tandem Bodies
Authors: Le Minh Duc , Jufar Shiferaw Regassa , Duong Dinh Nghia , Nguyen Thi Phuong Quyen
Published: May 2022

This study presents the effects of using two centerbodies arranged in tandem on the flow and velocity properties of swirling jets. The centerbodies are installed concentrically downstream of the jets to modify the wake. Smoke flow visualization was employed to illustrate the flow behaviors, while the particle image velocimetry technique was utilized to investigate the velocity fields, turbulent properties, vorticities, and Lagrangian time and length scales. The toroidal recirculation and vortex shedding are found near the field using one centerbody. The recirculation zone is evolved from the edge of the centerbody, while the vortex shedding is formed by the interaction between the central and the annular jets. Since two centerbodies are installed, two four-way saddles are located near the rim of the upstream centerbody to associate two pairs of vortices. A large momentum of the annular jet dominates the flow field; therefore, the central jet forms dual-rotating vortices at downstream centerbody. The turbulent intensity and vorticity along the central axis in the case of two centerbodies are extremely lower than those in the case of one centerbody. Large turbulent intensity and vorticity are located around the shear layers of jets. This promotes a better mixing efficiency of the swirling jets.
An Input–Output Analysis of the Water–Energy–Food Nexus Based on the Intensity and Quantity Index System—A Case Study of 30 Provinces in China
Authors: Zhang Ke , Shen Zihao , Sun Chengshuang
Published: May 2022

In the study of the water–energy–food nexus (WEF nexus), the importance of the intensity and quantity index system has been widely recognized. In order to study the impact of WEF on the economy, this paper establishes an intensity index system and a quantity index system, taking account of the impact of environmental pollution. Using a DEA model and China’s provincial data from 2019, this paper calculated the efficiency of the WEF nexus with the developed intensity and quantity index systems. The results show that the efficiency is not high in areas with a high economic development level, and efficiency is not the lowest in areas with a relatively low economic development level. When considering environmental pollution, the efficiency of some provinces has increased significantly, indicating that the WEF nexus has not caused environmental damage and is conducive to sustainable economic development. In the two intensity index systems, the efficiency of the production system is significantly lower than that of the consumption system, indicating that there is a serious waste of cultivated land per capita. Compared with the intensity index system, the efficiency of the quantity index system is low, and the polarization is obvious. A high level of GDP does not mean a high level of economic development. There may be a low level of resource utilization technology or environmental pollution underlying it. It is unscientific to evaluate local economic development only by GDP. When evaluating the urban economy and national economy, we should conduct an overall study of WEF and reasonably allocate WEF resources, which will not only help to alleviate the current situation of resource shortage in various countries but also effectively promote the coordinated development of national and regional economies. At the same time, environmental protection should also be taken into account. Compared with the economic development model of developing the economy first and then solving environmental problems, developing and solving at the same time is more conducive to the sustainable development of the national economy.
A Mid/Long-Term Optimization Model of Power System Considering Cross-Regional Power Trade and Renewable Energy Absorption Interval
Authors: Ma Xiaowei , Zhang Zhiren , Bai Hewen , Ren Jing , Cheng Song , Kang Xiaoning
Published: May 2022

With the integration of large-scale renewable energy into the power grids, cross-regional power trade can play a major role in promoting renewable energy consumption, as it can effectively achieve the optimal allocation of interconnected power grid resources and ensure the safe and economic operation of the power grid. An optimization model on a mid/long-term scale is established, considering the relationship between the renewable energy absorption interval and the regulation of resources in the system. The model is based on the load block curve and the renewable energy power model, considering the maintenance constraints of conventional units, the operation constraints of conventional units and renewable energy units, cross-regional power trade constraints and system operation constraints. By analyzing the results of the adapted IEEE RELIABILITY TEST SYSTEM (IEEE-RTS), the validity of the model and method proposed in this paper is proven. The results show that the coordinated optimization of conventional energy and renewable energy in the system can be achieved, and the complementarity of power supply and load can be promoted.
The Review of Electromagnetic Field Modeling Methods for Permanent-Magnet Linear Motors
Authors: Wang Xinmei , Wang Yifei , Wu Tao
Published: May 2022

Permanent-magnet linear motors (PMLMs) are widely used in various fields of industrial production, and the optimization design of the PMLM is increasingly attracting attention in order to improve the comprehensive performance of the motor. The primary problem of PMLM optimization design is the establishment of a motor model, and this paper summarizes the modeling of the PMLM electromagnetic field. First, PMLM parametric modeling methods (model-driven methods) such as the equivalent circuit method, analytical method, and finite element method, are introduced, and then non-parametric modeling methods (data-driven methods) such as the surrogate model and machine learning are introduced. Non-parametric modeling methods have the characteristics of higher accuracy and faster computation, and are the mainstream approach to motor modeling at present. However, surrogate models and traditional machine learning models such as support vector machine (SVM) and extreme learning machine (ELM) approaches have shortcomings in dealing with the high-dimensional data of motors, and some machine learning methods such as random forest (RF) require a large number of samples to obtain better modeling accuracy. Considering the modeling problem in the case of the high-dimensional electromagnetic field of the motor under the condition of a limited number of samples, this paper introduces the generative adversarial network (GAN) model and the application of the GAN in the electromagnetic field modeling of PMLM, and compares it with the mainstream machine learning models. Finally, the development of motor modeling that combines model-driven and data-driven methods is proposed.
A Proper-Orthogonal-Decomposition (POD) Study of the Wake Characteristics behind a Wind Turbine Model
Authors: Premaratne Pavithra , Tian Wei , Hu Hui
Published: May 2022

A comprehensive study was performed to analyze turbine wake characteristics by using a Proper-Orthogonal-Decomposition (POD) method to identify the dominant flow features from a comprehensive experimental database. The wake flow characteristics behind a typical three-bladed horizontal-axis wind turbine (HAWT) were measured in a large-scale wind tunnel with a scaled turbine model placed in a typical offshore Atmospheric Boundary Layer (ABL) wind under a neutral stability condition. A high-resolution Particle Image Velocimetry (PIV) system was used to achieve detailed flow field measurements to characterize the turbulent flows and wake vortex structures behind the turbine model. Statistically averaged measurements revealed the presence of the characteristic helical-tip vortex filament along with a unique secondary vortex filament emanating from 60% of the blade span measured from the hub. Both filaments breakup in the near-wake region (~0.6 rotor diameter downstream) to form shear layers, contrary to previous computational and experimental observations in which vortex filaments break up in the far wake. A Proper-Orthogonal-Decomposition (POD) analysis, based on both velocity and vorticity-based formulations, was used to extract the coherent flow structures, predominantly comprised of tip and midspan vortex elements. The reconstructions showed coherence in the flow field prior to the vortex breakup which subsequently degraded in the turbulent shear layer. The accuracy of the POD reconstructions was validated qualitatively by comparing the prediction results between the velocity and vorticity-based formulations as well as the phase-averaged PIV measurement results. This early vortex breakup was attributed to the reduced pitch between consecutive helical turns, the proximity between midspan filaments and blade tips as well as the turbulence intensity of the incoming boundary layer wind.
Benchmarking of Single-Stage and Two-Stage Approaches for an MMC-Based BESS
Authors: Pinto Jonathan Hunder Dutra Gherard , Amorim William Caires Silva , Cupertino Allan Fagner , Pereira Heverton Augusto , Junior Seleme Isaac Seleme
Published: May 2022

Modular multilevel converter-based battery energy storage systems (MMC-based BESS) can play an important role when applied to power systems, for example, stabilizing and improving power quality. The integration of batteries in an MMC is usually performed in two ways: single-stage (SS) and two-stage (TS) (i.e., with dc/dc converter). Different references discuss the control strategies, sizing methodologies, and the advantages/drawbacks of these approaches. However, a deep comparison of these topologies is still missing in the literature. Thus, benchmarking SS and TS approaches is provided in this work. The battery current spectrum, the battery lifetime, the converter power losses, and the total costs are evaluated for both approaches. In addition, energy oversizing due to rounding is an important figure of merit since batteries account for a large amount of the costs. The case study is evaluated considering commercial battery racks (standard solution) and battery cells (customized solution). For the case studies, different insulated gate bipolar transistor (IGBT) models and states of charge (SOC) ranges are considered. The system under review is a 10.9 MVA/5.76 MWh connected to a 13.8 kV power system. This system aims to perform a time-shift for an industry. In an analysis to optimize the costs of a project that evaluates several variables, the best configuration option is found in the most balanced option. In this sense, when balancing costs in project sizing, power losses, and battery replacement, the optimal design is the SS approach in the customized solution.
Dispatch Optimization, System Design and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage
Authors: Soto Gabriel J. , Lindley Ben , Neises Ty , Stansbury Cory , Wagner Michael J.
Published: May 2022

Variable renewable energy availability has increased the volatility in energy prices in most markets. Nuclear power plants, with a large ratio of capital to variable costs, have historically operated as base load energy suppliers but the need for more flexible operation is increasing. We simulate the techno-economic performance of a 950 MWt nuclear power plant, based on the Westinghouse lead-cooled fast reactor, coupled with molten salt thermal storage as a method for flexible energy dispatch. We use the System Advisor Model to model the nuclear reactor thermal power input and power cycle operating modes. We combine this robust engineering model with a mixed-integer linear program model for optimized dispatch scheduling. We then simulate the coupled nuclear and thermal storage system under different market scenarios with varying price volatility. We find that the coupled plant outperforms the base plant under markets where energy price peaks fluctuate by a factor of two or more about the mean price. We show that a calculated power purchase agreement price for the plant improves by up to 10% when operating under California energy market conditions. Sensitivity analysis on the thermal storage cost shows that the optimal design remains unchanged even when doubling costs.
Hydrological and Environmental Conditions and Implications of the Operation of a Thermal Power Plant with an Open Cooling System—An Example from Poland
Authors: Walczykiewicz Tomasz , Żelazny Mateusz
Published: May 2022

In Poland, coal-based thermal energy for cooling power plant installations uses a large amount of surface water. Historically, there have been cases of limitations in electricity supply due to low water levels and high temperature of water in rivers. Moreover, environmental requirements limit the possibility of using water resources for cooling purposes, pointing to the necessity to leave inviolable flows in rivers. This raises questions about the future of the operation of thermal power plants with open cooling systems and hence the research undertaken by the authors. The research consisted of a questionnaire survey, hydrological analyses, the impact assessment of climate change on the operation of power plants, and a discussion of technical solutions for water abstraction and power loss analysis in a particular power plant. The results indicate that there are power plants that are more sensitive to hydrological and environmental conditions and the temperature of the water required for cooling. In one case, keeping a power plant in operation requires the maintenance of periodic artificial damming of water. The conclusions from the research indicate that in Poland, regardless of the source of thermal energy, it is necessary to implement only closed cooling circuits.
Inter-Provincial Electricity Trading and Its Effects on Carbon Emissions from the Power Industry
Authors: Li Yanfeng , Li Yongping , Huang Guohe , Zheng Rubing
Published: May 2022

Electricity trading is an effective measure to minimize carbon emissions and alleviate the imbalance between reverse distribution of regional energy resources and power load. However, the effects of China’s electricity trading on carbon emissions have not been fully explored due to lack of complete and balanced inter-provincial power transmission data. Therefore, the electricity generation–consumption downscaling model, logarithmic mean Divisia index (LMDI) model, and random forest clustering algorithm within a general framework were used in the present study to explore the effect of electricity trading on level of carbon emissions. Comprehensive inter-provincial electricity transmission data were generated, driving factors including electricity imports and exports were decomposed at the national and provincial scales, and clustered provincial policy implications were evaluated. The results revealed that: (i) although economic activities were the main driving factor for increase in carbon emissions at the national level, 382.95 million tons carbon emissions were offset from 2005 to 2019 due to inter-provincial electricity importation, whereas electricity export increased carbon emission by 230.30 million tons; (ii) analysis at the provincial level showed that electricity exports from Sichuan and Yunnan provinces accounted for more than 20% of the nation’s total electricity flow. Notably, this high level of exports did not significantly increase carbon emissions in these provinces owing to the abundant hydropower resources; (iii) emission reductions were only observed at the national level if the carbon intensity of the exporting provinces was lower compared with that of importing provinces, or if the electricity trading was generated from renewable sources; (iv) the effect of electricity import on emissions reduction was markedly higher relative to the effect of electricity export in most provinces, which reflected the actual situation of sustaining optimization of electricity generation structure in provincial grids of China. These findings provide a basis for decision makers to understand the contributions of electricity trading to the changes in carbon emissions from electricity generation, as well as form a foundation to explore practicable carbon emission mitigation strategies in the power industry.
Novel Adaptive Extended State Observer for Dynamic Parameter Identification with Asymptotic Convergence
Authors: Patelski Radosław , Pazderski Dariusz
Published: May 2022

In this paper, a novel method of parameter identification of linear in parameter dynamic systems is presented. The proposed scheme employs an Extended State Observer to online estimate a state of the plant and momentary value of total disturbance present in the system. A notion is made that for properly redefined dynamics of the system, this estimate can be interpreted as a measure of modeling error caused by the parameter uncertainty. Under this notion, a disturbance estimate is used as a basis for classic gradient identification. A global convergence of both state and parameter estimates to their true values is proved using the Lyapunov approach under an assumption of a persistent excitation. Finally, results of simulation and experiments are presented to support the theoretical analysis. The experiments were conducted using a compliant manipulator joint and obtained results show the usefulness of the proposed method in drive control systems and robotics.
Dimensionless Impedance Method for General Design of Surge Tank in Simple Pipeline Systems
Authors: Kim Sanghyun , Choi Dooyong
Published: May 2022

The design of surge protection devices is a practical issue for the management of pressurized pipeline systems. Depending on the flow status, dimension, material, and fluid properties of a particular pipeline, the generation of hydraulic transients and their interactions with surge protection devices have been explored considering different conditions for various pipeline systems. The resonance between the pipeline elements and surge energy absorption function of the hydraulic structure should be adaptively considered for each pipeline system. To comprehensively address surge generation and surge alleviating process, this study introduced dimensionless equations of fluid motion and continuity, and their solutions were developed in the dimensionless frequency domain. The impact of the surge tank, pressure accumulator, and its connector were also developed in terms of dimensionless operators. The impact of distinct flow conditions and pipeline properties was successfully addressed by an integrated parameter, dimensionless resistance, which also provided a unified condition for water hammer similarity in reservoir pipeline surge tank pipeline valve (RPSPV) systems. The development of dimensionless hydraulic impedance expressions along a pipeline system and its conversion into a response function provides a normalized pressure response in the dimensionless time domain. Excellent agreement was found between transient simulations using the developed method and those obtained using existing methods. The integration of a dimensionless approach into a metaheuristic engine provides a general platform for surge tank (ST) design in the comprehensive bounds of flow and pipeline conditions.
Study on the Uniformity of Secondary Air of a 660 MW Ultra-Supercritical CFB Boiler
Authors: Nie Li , Lu Jiayi , Deng Qigang , Gong Liming , Xue Dayong , Yang Zhongzhi , Lu Xiaofeng
Published: May 2022

Based on the field test of a 600 MW supercritical circulating-fluidized bed boiler, this paper optimizes the secondary air pipe layout scheme of a 660 MW ultra-supercritical circulating fluidized bed boiler with a similar furnace structure and carried out a numerical simulation on its air distribution uniformity. The secondary air box of the 660 MW ultra-supercritical circulating-fluidized bed boiler adopts a variable section design, and the secondary air branch pipe adopts a separate air inlet mode. The experimental results showed that the oxygen concentration was uniform near the rear wall, but all exhibited a “decrease–increase–decrease” profile along the horizontal line, which indicated that the trajectory of the secondary air jet was first in the shape of bending downward and then upward. To achieve a more uniform secondary air distribution for supercritical CFB boilers, further optimization of the layout of the bellows and branch pipes should be considered. Numerical results showed that the deviation rate of the internal and lower secondary air reached 17%, and there was optimization space for the secondary air branch pipe layout of the boiler. Its uniformity can be increased by adding valves and other measures. The velocity deviation of the secondary air of the 660 MW ultra-supercritical circulating-fluidized bed boiler can be within 3% by means of separate air supply and pipe diameter change, and better uniform air supply can be achieved to reduce the speed deviation by adding valves and adjusting the combustion.
Carbon, Nitrogen and Phosphorus Stoichiometry in Natural and Plantation Forests in China
Authors: Li Lin , Liu Lei , Yu Zhen , Peñuelas Josep , Sardans Jordi , Chen Qifei , Xu Jiangbing , Zhou Guoyi
Published: May 2022

Ecological stoichiometry is essential for understanding the biogeochemical cycle in forest ecosystems. However, previous studies of ecological stoichiometry have rarely considered the impacts of forest origins, which could help explain why to date so much uncertainty has been reported on this subject. In this study, we tried to reduce this uncertainty by examining carbon (C), nitrogen (N) and phosphorus (P) in roots, litter and soil in both natural and plantation forests throughout China. The sampled forest sites were divided into three groups according to the identified succession stages: early (ES), middle (MS) and late (LS) stages. Our results show that soil C, N and P concentrations were significantly higher in natural (NF) than in plantation (PL) forests. As succession/growth proceeded, P concentrations significantly increased in litter, roots and soil in NF, while the opposite occurred in PL. These results indicate that NF are able to use P more efficiently than PL, especially in the LS. Furthermore, the higher root N:P ratio indicates that the growth of PL was limited by P in both MS and LS. Our results also suggest that geographical and climatic factors are not the dominant factors in the differences in P between NF and PL, and, even more clearly and importantly, that native forests with native species are more capable of conserving P than planted forests, which are frequently less diverse and dominated by fast-growing non-site native species. These results will help improve biogeochemical models and forest management throughout the world.