bluuebunny/crossref_metadata_2025 huggingface dataset:
Three-Dimensional Flower-like Fe, C-Doped-MoS2/Ni3S2 Heterostructures Spheres for Accelerating Electrocatalytic Oxygen and Hydrogen Evolution
Authors: Lv Xuefeng , Liu Guangsheng , Liu Song , Chen Wenting , Cao Dehua , Song Taize , Wang Nannan , Zhu Yanqiu
Published: March 2021

The exploration of high-efficiency bifunctional electrocatalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) has long been challenging. The rational design of a catalyst by constructing heterostructures and a doping element are possibly expected to achieve it. Herein, the utilization of flower-like Fe/C-doped-MoS2/Ni3S2-450 spherical structural materials for electrocatalytic HER and OER is introduced in this study. The carboxyferrocene-incorporated molybdenum sulfide/nickel sulfide (MoySx/NiS) nanostructures were prepared by solvothermal method. After annealing, the iron and carbon elements derived from ferrocenecarboxylic acid enhanced the electrical transport performance and provided rich electronic sites for HER and OER in alkaline media. Specifically, the optimized flower-like Fe/C-doped-MoS2/Ni3S2-450 exhibited efficient bifunctional performance in alkaline electrolyte, with low overpotentials of 188 and 270 mV required to deliver a current density of 10 mA cm−2 for HER and OER, respectively. This work provides valuable insights for the rational design of energy storage and conversion materials by the incorporation of transition metal and carbon elements into metal sulfide structures utilizing metallocene.
Dopamine-Grafted Hyaluronic Acid Coated Hyperbranched Poly(β-Amino Esters)/DNA Nano-Complexes for Enhanced Gene Delivery and Biosafety
Authors: Guo Man , Meng Yingcai , Qin Xiaoqun , Zhou Wenhu
Published: March 2021

Gene therapy has attracted particular attention for the treatment of various genetic diseases, and the development of gene delivery vectors is of utmost importance for in vivo applications of gene drugs. Various cationic polymers with high nucleic acid loading and intracellular transfection efficiency have been reported, however, their biological applications are limited by potential toxicity. Surface modification is a robust solution to detoxify the cationic vectors, but this can inevitably weaken the transfection efficiency. To address this dilemma, we reported the ability of a dopamine (DA)-grafted hyaluronic acid (HA) to modify gene vectors for enhanced gene delivery and biosafety. The nano-vector was formed by using branched poly(β-amino esters) (PAEs), and surface coating with HA-DA to form a core-shell nano-structure via electrostatic attraction. Upon HA-DA modification, the biosafety of the gene delivery vehicle was improved, as demonstrated by the cell cytotoxicity assay and hemolysis test. Notably, the nano-system displayed a DA-dependent transfection efficiency, in which a higher DA grafting degree resulted in better efficacy. This can be explained by the adhesive nature of DA, facilitating cell membrane interaction, as well as DA receptor mediated active targeting. At the optimal DA grafting ratio, the nano-system achieved a transfection efficiency even better than that of commonly used polyethylenimine (PEI) vectors. Together with its excellent biocompatibility, the vector presented here holds great promise for gene delivery applications.
Effects of High-Volume Ground Slag Powder on the Properties of High-Strength Concrete under Different Curing Conditions
Authors: Zhou Yuqi , Sun Jianwei , Zhang Zengqi
Published: March 2021

Massive high-strength concrete structures tend to have a high risk of cracking. Ground slag powder (GSP), a sustainable and green industrial waste, is suitable for high-strength concrete. We carried out an experimental study of the effects of GSP with a specific surface area of 659 m2/kg on the hydration, pore structure, compressive strength and chloride ion penetrability resistance of high-strength concrete. Results show that adding 25% GSP increases the adiabatic temperature rise of high-strength concrete, whereas adding 45% GSP decreases the initial temperature rise. Incorporating GSP refines the pore structure to the greatest extent and improves the compressive strength and chloride ion penetrability resistance of high-strength concrete, which is more obvious under early temperature-matching curing conditions. Increasing curing temperature has a more obvious impact on the pozzolanic reaction of GSP than cement hydration. From a comprehensive perspective, GSP has potential applications in the cleaner production of green high-strength concrete.
Method for Analyzing the Measurement Error with Respect to Azimuth and Incident Angle for the Rotating Polarizer Analyzer Ellipsometer
Authors: Tu Huatian , Zheng Yuxiang , Shan Yao , Chen Yao , Zhang Haotian , Zhang Rongjun , Wang Songyou , Li Jing , Lee YoungPak , Chen Liangyao
Published: March 2021

We proposed a method to study the effects of azimuth and the incident angle on the accuracy and stability of rotating polarizer analyzer ellipsometer (RPAE) with bulk Au. The dielectric function was obtained at various incident angles in a range of 55°–80° and analyzed with the spectrum of the principal angle. The initial orientations of rotating polarizing elements were deviated by a series of angles to act as the azimuthal errors in various modes. The spectroscopic measurements were performed in a wavelength range of 300–800 nm with an interval of 10 nm. The repeatedly-measured ellipsometric parameters and determined dielectric constants were recorded monochromatically at wavelengths of 350, 550, and 750 nm. The mean absolute relative error was employed to evaluate quantitatively the performance of instrument. Apart from the RPAE, the experimental error analysis implemented in this work is also applicable to other rotating element ellipsometers.
Temperature-Dependent Optical Properties of Graphene on Si and SiO2/Si Substrates
Authors: Wu Sisi , Wan Lingyu , Wei Liangmin , Talwar Devki N. , He Kaiyan , Feng Zhechuan
Published: March 2021

Systematic investigations are performed to understand the temperature-dependent optical properties of graphene on Si and SiO2/Si substrates by using a variable angle spectroscopic ellipsometry. The optical constants of graphene have revealed changes with the substrate and temperature. While the optical refractive index (n) of monolayer graphene on Si exhibited clear anomalous dispersions in the visible and near-infrared region (400–1200 nm), the modification is moderate for graphene on SiO2/Si substrate. Two graphene sheets have shown a pronounced absorption in the ultraviolet region with peak position related to the Van Hove singularity in the density of states. By increasing the temperature from 300 K to 500 K, for monolayer graphene on Si, the n value is gradually increased while k decreased. However, the optical constants [n, k] of monolayer graphene on SiO2/Si exhibited unpredictable wave variations. In the wavelength range of 400–1200 nm, an experiential formula of a like-Sellmeier equation is found well suited for describing the dispersions of graphene on Si and SiO2/Si substrates.
COVID-19 Pneumonia on Post-Operative Day 2 after Esophagectomy: Performing Esophago-Gastric Junction Cancer Surgery during the SARS-Cov-2 Second Wave
Authors: Nurczyk Kamil , Chan Chia-En , Nowak Norbert , Skoczylas Tomasz
Published: March 2021

The coronavirus disease 2019 (COVID-19) pandemic has had a substantial impact on the provision of medical healthcare. Due to an increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) transmission, elective surgical treatment has been suspended in many centers. The effects of COVID-19 in the early post-operative period after esophagectomy remains unknown. In this report, we present three cases of patients diagnosed with esophago-gastric junction cancer who were scheduled for elective esophagectomy with a curative intention during second wave of COVID-19 pandemic in a single high-volume tertiary center. Despite all available safety measures, one of the patients developed COVID-19 pneumonia on post-operative day two, leading to an impaired respiratory function and increased pleural fluid collection from the chest tube, resulting in a prolonged time of hospital stay. Finding a good balance between the COVID-19-related perioperative risks and consequences of delaying surgical treatment in patients diagnosed with esophago-gastric cancer is a challenge. In order to achieve the best possible outcome, care must be taken to ensure availability of necessary treatment options and to reduce the risk of SARS-Cov-2 transmission perioperatively.
National Variations in the Work-Up, Investigation, and Surgical Management of Ductal Carcinoma In Situ of the Breast across Canadian Surgeons
Authors: Seguin Ryerson , Peiris Lashan
Published: March 2021

Variation in the management of Ductal Carcinoma In Situ (DCIS) of the breast occur at both national and international levels. The aim of this study is to determine the degree of, and reasons behind, this variation in the workup and treatment of DCIS among Canadian surgeons. We developed a 35-question survey involving the pre-, peri, and post-operative management of DCIS using SurveyMonkey®. The survey was sent out via email and responses were analyzed using SurveyMonkey® and Microsoft Excel. 51/119 (43%) of the Canadian General Surgeons contacted participated in this study. Some variation was observed in the utilization of pre-operative imaging with 29/48 (60%) surgeons routinely using ultrasound. Perceived contraindications to breast conserving therapy also varied with multicentricity (54%) and the presence of diffuse microcalcifications (13%). Nearly all respondent’s (98%) patients had access to immediate breast reconstruction following a mastectomy but 14/48 (29%) of respondents’ patients were required to travel a mean distance of 300 km to undergo the procedure. Substantial variation was also seen during follow-up with half (52%) of surgeons following up patients for >1 month in their surgical clinic. There is considerable variation in the management of DCIS among Canadian Surgeons. The present study indicates the need for pan-Canadian, evidence-based guidelines to ensure a standardized management strategy for patients with DCIS.
Proteomic Expression Profile in Human Temporomandibular Joint Dysfunction
Authors: Doetzer Andrea Duarte , Herai Roberto Hirochi , Buzalaf Marília Afonso Rabelo , Trevilatto Paula Cristina
Published: March 2021

Temporomandibular joint dysfunction (TMD) is a multifactorial condition that impairs human’s health and quality of life. Its etiology is still a challenge due to its complex development and the great number of different conditions it comprises. One of the most common forms of TMD is anterior disc displacement without reduction (DDWoR) and other TMDs with distinct origins are condylar hyperplasia (CH) and mandibular dislocation (MD). Thus, the aim of this study is to identify the protein expression profile of synovial fluid and the temporomandibular joint disc of patients diagnosed with DDWoR, CH and MD. Synovial fluid and a fraction of the temporomandibular joint disc were collected from nine patients diagnosed with DDWoR (n = 3), CH (n = 4) and MD (n = 2). Samples were subjected to label-free nLC-MS/MS for proteomic data extraction, and then bioinformatics analysis were conducted for protein identification and functional annotation. The three TMD conditions showed different protein expression profiles, and novel proteins were identified in both synovial fluid and disc sample. TMD is a complex condition and the identification of the proteins expressed in the three different types of TMD may contribute to a better comprehension of how each pathology develops and evolutes, benefitting the patient with a focus–target treatment.
Vascular Abnormalities Detected with Chest CT in COVID-19: Spectrum, Association with Parenchymal Lesions, Cardiac Changes, and Correlation with Clinical Severity (COVID-CAVA Study)
Authors: Qanadli Salah D. , Sauter Alexander W. , Alkadhi Hatem , Christe Andreas , Poletti Pierre-Alexandre , Ebner Lukas , Rotzinger David C.
Published: March 2021

Although vascular abnormalities are thought to affect coronavirus disease 2019 (COVID-19) patients’ outcomes, they have not been thoroughly characterized in large series of unselected patients. The Swiss national registry coronavirus-associated vascular abnormalities (CAVA) is a multicentric cohort of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection who underwent a clinically indicated chest computed tomography (CT) aiming to assess the prevalence, severity, distribution, and prognostic value of vascular and non-vascular-related CT findings. Clinical outcomes, stratified as outpatient treatment, inpatient without mechanical ventilation, inpatient with mechanical ventilation, or death, will be correlated with CT and biological markers. The main objective is to assess the prevalence of cardiovascular abnormalities–including pulmonary embolism (PE), cardiac morphology, and vascular congestion. Secondary objectives include the predictive value of cardiovascular abnormalities in terms of disease severity and fatal outcome and the association of lung inflammation with vascular abnormalities at the segmental level. New quantitative approaches derived from CT imaging are developed and evaluated in this study. Patients with and without vascular abnormalities will be compared, which is supposed to provide insights into the prognostic role and potential impact of such signs on treatment strategy. Results are expected to enable the development of an integrative score combining both clinical data and imaging findings to predict outcomes.
Biomimetic Drones Inspired by Dragonflies Will Require a Systems Based Approach and Insights from Biology
Authors: Chahl Javaan , Chitsaz Nasim , McIvor Blake , Ogunwa Titilayo , Kok Jia-Ming , McIntyre Timothy , Abdullah Ermira
Published: March 2021

Many drone platforms have matured to become nearly optimal flying machines with only modest improvements in efficiency possible. “Chimera” craft combine fixed wing and rotary wing characteristics while being substantially less efficient than both. The increasing presence of chimeras suggests that their mix of vertical takeoff, hover, and more efficient cruise is invaluable to many end users. We discuss the opportunity for flapping wing drones inspired by large insects to perform these mixed missions. Dragonflies particularly are capable of efficiency in all modes of flight. We will explore the fundamental principles of dragonfly flight to allow for a comparison between proposed flapping wing technological solutions and a flapping wing organism. We chart one approach to achieving the next step in drone technology through systems theory and an appreciation of how biomimetics can be applied. New findings in dynamics of flapping, practical actuation technology, wing design, and flight control are presented and connected. We show that a theoretical understanding of flight systems and an appreciation of the detail of biological implementations may be key to achieving an outcome that matches the performance of natural systems. We assert that an optimal flapping wing drone, capable of efficiency in all modes of flight with high performance upon demand, might look somewhat like an abstract dragonfly.
New Estimators of the Bayes Factor for Models with High-Dimensional Parameter and/or Latent Variable Spaces
Authors: Pajor Anna
Published: March 2021

Formal Bayesian comparison of two competing models, based on the posterior odds ratio, amounts to estimation of the Bayes factor, which is equal to the ratio of respective two marginal data density values. In models with a large number of parameters and/or latent variables, they are expressed by high-dimensional integrals, which are often computationally infeasible. Therefore, other methods of evaluation of the Bayes factor are needed. In this paper, a new method of estimation of the Bayes factor is proposed. Simulation examples confirm good performance of the proposed estimators. Finally, these new estimators are used to formally compare different hybrid Multivariate Stochastic Volatility–Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MSV-MGARCH) models which have a large number of latent variables. The empirical results show, among other things, that the validity of reduction of the hybrid MSV-MGARCH model to the MGARCH specification depends on the analyzed data set as well as on prior assumptions about model parameters.
Phase Transitions in Transfer Learning for High-Dimensional Perceptrons
Authors: Dhifallah Oussama , Lu Yue M.
Published: March 2021

Transfer learning seeks to improve the generalization performance of a target task by exploiting the knowledge learned from a related source task. Central questions include deciding what information one should transfer and when transfer can be beneficial. The latter question is related to the so-called negative transfer phenomenon, where the transferred source information actually reduces the generalization performance of the target task. This happens when the two tasks are sufficiently dissimilar. In this paper, we present a theoretical analysis of transfer learning by studying a pair of related perceptron learning tasks. Despite the simplicity of our model, it reproduces several key phenomena observed in practice. Specifically, our asymptotic analysis reveals a phase transition from negative transfer to positive transfer as the similarity of the two tasks moves past a well-defined threshold.
A Novel Method of Vein Detection with the Use of Digital Image Correlation
Authors: Lutowski Zbigniew , Bujnowski Sławomir , Marciniak Beata , Kloska Sylwester , Marciniak Anna , Lech Piotr
Published: March 2021

Digital image correlation may be useful in many different fields of science, one of which is medicine. In this paper, the authors present the results of research aimed at detecting skin micro-shifts caused by pulsation of the veins. A novel technique using digital image correlation (DIC) and filtering the resulting shifts map to detect pulsating veins was proposed. After applying the proposed method, the veins in the forearm were visualized. The proposed technique may be used in the diagnosis of venous stenosis and may also contribute to reducing the number of adverse events during blood collection. The great advantage of the proposed method is the lack of the need to have specialized equipment, only a typical mobile phone camera is needed to perform the test.
Measuring the Uncertainty in the Original and Negation of Evidence Using Belief Entropy for Conflict Data Fusion
Authors: Chen Yutong , Tang Yongchuan
Published: March 2021

Dempster-Shafer (DS) evidence theory is widely used in various fields of uncertain information processing, but it may produce counterintuitive results when dealing with conflicting data. Therefore, this paper proposes a new data fusion method which combines the Deng entropy and the negation of basic probability assignment (BPA). In this method, the uncertain degree in the original BPA and the negation of BPA are considered simultaneously. The degree of uncertainty of BPA and negation of BPA is measured by the Deng entropy, and the two uncertain measurement results are integrated as the final uncertainty degree of the evidence. This new method can not only deal with the data fusion of conflicting evidence, but it can also obtain more uncertain information through the negation of BPA, which is of great help to improve the accuracy of information processing and to reduce the loss of information. We apply it to numerical examples and fault diagnosis experiments to verify the effectiveness and superiority of the method. In addition, some open issues existing in current work, such as the limitations of the Dempster-Shafer theory (DST) under the open world assumption and the necessary properties of uncertainty measurement methods, are also discussed in this paper.
Multi-Scale Aggregation Graph Neural Networks Based on Feature Similarity for Semi-Supervised Learning
Authors: Zhang Xun , Yang Lanyan , Zhang Bin , Liu Ying , Jiang Dong , Qin Xiaohai , Hao Mengmeng
Published: March 2021

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as social networks, knowledge graphs, citation networks, the World Wide Web, and so on. As increasingly structured data become available, the importance of being able to effectively mine and learn from such data continues to grow. In this paper, we propose the multi-scale aggregation graph neural network based on feature similarity (MAGN), a novel graph neural network defined in the vertex domain. Our model provides a simple and general semi-supervised learning method for graph-structured data, in which only a very small part of the data is labeled as the training set. We first construct a similarity matrix by calculating the similarity of original features between all adjacent node pairs, and then generate a set of feature extractors utilizing the similarity matrix to perform multi-scale feature propagation on graphs. The output of multi-scale feature propagation is finally aggregated by using the mean-pooling operation. Our method aims to improve the model representation ability via multi-scale neighborhood aggregation based on feature similarity. Extensive experimental evaluation on various open benchmarks shows the competitive performance of our method compared to a variety of popular architectures.
Genomic Intelligence as Über Bio-Cybersecurity: The Gödel Sentence in Immuno-Cognitive Systems
Authors: Markose Sheri M.
Published: March 2021

This paper gives formal foundations and evidence from gene science in the post Barbara McClintock era that the Gödel Sentence, far from being an esoteric construction in mathematical logic, is ubiquitous in genomic intelligence that evolved with multi-cellular life. Conditions uniquely found in the Adaptive Immune System (AIS) and Mirror Neuron System (MNS), termed the genomic immuno-cognitive system, coincide with three building blocks in computation theory of Gödel, Turing and Post (G-T-P). (i) Biotic elements have unique digital identifiers with gene codes executing 3D self-assembly for morphology and regulation of the organism using the recursive operation of Self-Ref (Self-Reference) with the other being a self-referential projection of self. (ii) A parallel offline simulation meta/mirror environment in 1–1 relation to online machine executions of self-codes gives G-T-P Self-Rep (Self-Representation). (iii) This permits a digital biotic entity to self-report that it is under attack by a biotic malware or non-self antigen in the format of the Gödel sentence, resulting in the “smarts” for contextual novelty production. The proposed unitary G-T-P recursive machinery in AIS and in MNS for social cognition yields a new explanation that the Interferon Gamma factor, known for friend-foe identification in AIS, is also integral to social behaviors. New G-T-P bio-informatics of AIS and novel anti-body production is given with interesting testable implications for COVID-19 pathology.
A Robust Quadruplet and Faster Region-Based CNN for UAV Video-Based Multiple Object Tracking in Crowded Environment
Authors: Dike Happiness Ugochi , Zhou Yimin
Published: March 2021

Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) videos has faced several challenges such as motion capture and appearance, clustering, object variation, high altitudes, and abrupt motion. Consequently, the volume of objects captured by the UAV is usually quite small, and the target object appearance information is not always reliable. To solve these issues, a new technique is presented to track objects based on a deep learning technique that attains state-of-the-art performance on standard datasets, such as Stanford Drone and Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking (UAVDT) datasets. The proposed faster RCNN (region-based convolutional neural network) framework was enhanced by integrating a series of activities, including the proper calibration of key parameters, multi-scale training, hard negative mining, and feature collection to improve the region-based CNN baseline. Furthermore, a deep quadruplet network (DQN) was applied to track the movement of the captured objects from the crowded environment, and it was modelled to utilize new quadruplet loss function in order to study the feature space. A deep 6 Rectified linear units (ReLU) convolution was used in the faster RCNN to mine spatial–spectral features. The experimental results on the standard datasets demonstrated a high performance accuracy. Thus, the proposed method can be used to detect multiple objects and track their trajectories with a high accuracy.
Design and Development of a Near Isotropic Printed Arc Antenna for Direction of Arrival (DoA) Applications
Authors: Ali Hafiz T. , Amin Saifullah , Amin Muhammad , Maqsood Moazam , Maud Abdur Rahman , Yusuf Mohammad
Published: March 2021

This research presents an easy to fabricate isotropic printed arc antenna element to be used for direction of arrival (DoA) arrays. The proposed antenna exhibits a total gain variation of 0.5 dB over the entire sphere for 40 MHz impedance bandwidth at 1 GHz, which is the best design isotropy reported in literature so far. In addition, the isotropic bandwidth of the antenna for total gain variation of ≤3 dB is 225 MHz with 86% efficiency. The isotropic wire antenna is first designed and simulated in Numerical Electromagnetic code (NEC). An equivalent printed antenna is then simulated in CST, where single (short circuited) stub is integrated with the antenna for input matching and the results of NEC simulations are verified. The planar antenna is then manufactured using FR4 substrate for measurements. Good agreement between the measured and simulated results is observed, however the total gain variation is increased to 2 dB for the fabricated antenna. This is because of the unavoidable field scattering from the antenna substrate, the feed cables, and the antenna testing platform.
Correspondence Learning for Deep Multi-Modal Recognition and Fraud Detection
Authors: Park Jongchan , Kim Min-Hyun , Choi Dong-Geol
Published: March 2021

Deep learning-based methods have achieved good performance in various recognition benchmarks mostly by utilizing single modalities. As different modalities contain complementary information to each other, multi-modal based methods are proposed to implicitly utilize them. In this paper, we propose a simple technique, called correspondence learning (CL), which explicitly learns the relationship among multiple modalities. The multiple modalities in the data samples are randomly mixed among different samples. If the modalities are from the same sample (not mixed), then they have positive correspondence, and vice versa. CL is an auxiliary task for the model to predict the correspondence among modalities. The model is expected to extract information from each modality to check correspondence and achieve better representations in multi-modal recognition tasks. In this work, we first validate the proposed method in various multi-modal benchmarks including CMU Multimodal Opinion-Level Sentiment Intensity (CMU-MOSI) and CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) sentiment analysis datasets. In addition, we propose a fraud detection method using the learned correspondence among modalities. To validate this additional usage, we collect a multi-modal dataset for fraud detection using real-world samples for reverse vending machines.
The Yellow Active Queue Management Algorithm in ICN Routers Based on the Monitoring of Bandwidth Competition
Authors: Zeng Li , Ni Hong , Han Rui
Published: March 2021

Deploying the active queue management (AQM) algorithm on a router is an effective way to avoid packet loss caused by congestion. In an information-centric network (ICN), routers not only play a role of packets forwarding but are also content service providers. Congestion in ICN routers can be further summarized as the competition between the external forwarding traffic and the internal cache response traffic for limited bandwidth resources. This indicates that the traditional AQM needs to be redesigned to adapt to ICN. In this paper, we first demonstrated mathematically that allocating more bandwidth for the upstream forwarding flow could improve the quality of service (QoS) of the whole network. Secondly, we propose a novel AQM algorithm, YELLOW, which predicts the bandwidth competition event and adjusts the input rate of request and the marking probability adaptively. Afterwards, we model YELLOW through the totally asymmetric simple exclusion process (TASEP) and deduce the approximate solution of the existence condition for each stationary phase. Finally, we evaluated the performance of YELLOW by NS-3 simulator, and verified the accuracy of modeling results by Monte Carlo. The simulation results showed that the queue of YELLOW could converge to the expected value, and the significant gains of the router with low packet loss rate, robustness and high throughput.
Simulation of Low Inertia Power Systems Based on Shifted Frequency Analysis
Authors: Dinkelbach Jan , Nakti Ghassen , Mirz Markus , Monti Antonello
Published: March 2021

New types of power system transients with lower time constants are emerging due to the replacement of synchronous generation with converter interfaced generation and are challenging the modeling approaches conventionally applied in power system simulation. Quasi-stationary simulations are based on classical phasor models, whereas EMT simulations calculate the instantaneous values of models in the time domain. In addition to these conventional modeling approaches, this paper investigates simulation based on dynamic phasor models, as has been proposed by the Shifted Frequency Analysis. The simulation accuracy of the three modeling approaches was analyzed for characteristic transients from the electromagnetic to the electromechanical phenomena range, including converter control as well as low inertia transients. The analysis was carried out for systems with converter interfaced and synchronous generation whilst considering the simulation step size as a crucial influence parameter. The results show that simulations based on dynamic phasors allow for larger step sizes than simulations that calculate the instantaneous values in the time domain. This can facilitate the simulation of more complex component models and larger grid sizes. In addition, with dynamic phasors, more accurate simulation results were obtained than with classical phasors, in particular—but not exclusively—in a low inertia case. Overall, the presented work demonstrates that dynamic phasors can enable fast and accurate simulations during the transition to low inertia power systems.
Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands
Authors: Kausika Bala , van Sark Wilfried
Published: March 2021

Geographic information system (GIS) based tools have become popular for solar photovoltaic (PV) potential estimations, especially in urban areas. There are readily available tools for the mapping and estimation of solar irradiation that give results with the click of a button. Although these tools capture the complexities of the urban environment, they often miss the more important atmospheric parameters that determine the irradiation and potential estimations. Therefore, validation of these models is necessary for accurate potential energy yield and capacity estimations. This paper demonstrates the calibration and validation of the solar radiation model developed by Fu and Rich, employed within ArcGIS, with a focus on the input atmospheric parameters, diffusivity and transmissivity for the Netherlands. In addition, factors affecting the model’s performance with respect to the resolution of the input data were studied. Data were calibrated using ground measurements from Royal Netherlands Meteorological Institute (KNMI) stations in the Netherlands and validated with the station data from Cabauw. The results show that the default model values of diffusivity and transmissivity lead to substantial underestimation or overestimation of solar insolation. In addition, this paper also shows that calibration can be performed at different time scales depending on the purpose and spatial resolution of the input data.
Laplacian Matrix-Based Power Flow Formulation for LVDC Grids with Radial and Meshed Configurations
Authors: Javid Zahid , Karaagac Ulas , Kocar Ilhan , Chan Ka Wing
Published: March 2021

There is an increasing interest in low voltage direct current (LVDC) distribution grids due to advancements in power electronics enabling efficient and economical electrical networks in the DC paradigm. Power flow equations in LVDC grids are non-linear and non-convex due to the presence of constant power nodes. Depending on the implementation, power flow equations may lead to more than one solution and unrealistic solutions; therefore, the uniqueness of the solution should not be taken for granted. This paper proposes a new power flow solver based on a graph theory for LVDC grids having radial or meshed configurations. The solver provides a unique solution. Two test feeders composed of 33 nodes and 69 nodes are considered to validate the effectiveness of the proposed method. The proposed method is compared with a fixed-point methodology called direct load flow (DLF) having a mathematical formulation equivalent to a backward forward sweep (BFS) class of solvers in the case of radial distribution networks but that can handle meshed networks more easily thanks to the use of connectivity matrices. In addition, the convergence and uniqueness of the solution is demonstrated using a Banach fixed-point theorem. The performance of the proposed method is tested for different loading conditions. The results show that the proposed method is robust and has fast convergence characteristics even with high loading conditions. All simulations are carried out in MATLAB 2020b software.
Economic Emission Dispatch for Wind Power Integrated System with Carbon Trading Mechanism
Authors: Jin Jingliang , Wen Qinglan , Zhang Xianyue , Cheng Siqi , Guo Xiaojun
Published: March 2021

Nowadays, the power system is faced with some new changes from low-carbon approaches, though these approaches have proved to be effective in developing low-carbon electricity. Specifically, wind power integration and carbon trading influence the traditional economic emission dispatch (EED) mode, allowing for the disturbance of wind power uncertainties and the fluctuation of carbon trading price. Aiming at the above problems, this study firstly builds a stochastic EED model in the form of chance-constrained programming associated with wind power reliability. Next, wind power features are deduced from the statistic characteristics of wind speed, and thus the established model is converted to a deterministic form. After that, an auxiliary decision-making method based on the technique for order preference by similarity to an ideal solution (TOPSIS) is designed to draw the optimal solution based upon the specific requirements of carbon emission control. The simulation results eventually indicate that the minimization of fuel costs and carbon emissions comes at the expense of wind power reliability. Meanwhile, carbon emission reduction can be effectively realized by carbon trading rather than a substantial increase in fuel costs, and carbon trading may help to improve power generation efficiency. Furthermore, carbon trading prices could be determined by the demands of carbon emission reduction and power generation efficiency improvement.
Determinants of Enterprises’ Capital Structure in Energy Industry: Evidence from European Union
Authors: Jaworski Jacek , Czerwonka Leszek
Published: March 2021

The aim of the study is to identify the main determinants of the capital structure of energy industry companies in the European Union. The study was based on a panel of 6122 companies from 25 EU countries, operating between 2011 and 2018. The study used multiple regression analysis. We have obtained strong evidence for a positive relationship between corporate debt and tangibility and size, and a negative relationship for profitability and liquidity. The factors that also affect the share of debt in capital have turned out to be growth (positive relationship) and non-debt tax shield (negative relationship), but the statistical significance of these relationships is ambiguous. We have shown that growth of industry business risk is accompanied by an increase in corporate debt and this is a distinguishing feature of the energy industry. For country-specific capital structure determinants, we have obtained strong evidence for the negative relationship between GDP growth, the level of stakeholder rights protection, the degree of capital markets development, and indebtedness of the companies studied. There has been moderate support for the hypotheses of a positive effect of inflation, taxation, and the degree of financial institutions development. Our study has also shown a negative impact of the volume of energy consumption and the share of renewable sources in its production and a positive impact of market monopolization on the indebtedness of companies from the energy industry in the EU.
Evaluating Variability of Ground Thermal Conductivity within a Steep Site by History Matching Underground Distributed Temperatures from Thermal Response Tests
Authors: Sakata Yoshitaka , Katsura Takao , Serageldin Ahmed A. , Nagano Katsunori , Ooe Motoaki
Published: March 2021

The variability of ground thermal conductivity, based on underground conditions, is often ignored during the design of ground-source heat pump systems. This study shows a field evidence of such site-scale variations through thermal response tests in eight borehole heat exchangers aligned at a site on a terrace along the foothills of mountains in northern Japan. Conventional analysis of the overall ground thermal conductivity along the total installation length finds that the value at one borehole heat exchanger is 2.5 times that at the other seven boreholes. History matching analysis of underground distributed temperature measurements generates vertical partial ground thermal conductivity data for four depth layers. Based on the moving line heat source theory, the partial values are generally within a narrow range expected for gravel deposits. Darcy velocities of groundwater are estimated to be 74–204 m/y at the borehole with high conductivity, increasing in the shallow layers above a depth of 41 m. In contrast, the velocities at the other seven boreholes are one-to-two orders of magnitude smaller with no trend. These high and low velocity values are considered for the topography and permeability. However, the relatively slow groundwater velocities might not apparently increase the partial conductivity.
The Use of Blockchain Technology in Public Sector Entities Management: An Example of Security and Energy Efficiency in Cloud Computing Data Processing
Authors: Karaszewski Robert , Modrzyński Paweł , Modrzyńska Joanna
Published: March 2021

Blockchain technology is currently one of the trends considered to have a tremendous future ahead. It ensures data security, data sharing protection and automation development—elements that are of colossal importance in the era of cloud solutions, big data and Internet of Things (IoT) reality. Additionally, blockchain technology allows one to create new programmable ecosystems on an unprecedented scale. The implementation of blockchain technology leads not only to improving the flow of documents and data storage, as is the case with the creation of shared service centers (SSCs), but—as this paper shows—allows one to reduce the carbon footprint when servicing SSCs at a considerably higher organizational level at the same time. The example of an SSC in Elbląg, Poland, proves that cloud solutions enabling electronic documents flow and data storage combined with blockchain technology are tools essential for further SSCs development. Furthermore, such tools allow us not only to obtain economic effects (i.e., cost reduction), but also to achieve positive ecological effects (i.e., carbon footprint reduction).
A Combined CFD-FEM Approach to Predict Fluid-Borne Vibrations and Noise Radiation of a Rotary Vane Pump
Authors: Hieronymus Timm , Lobsinger Thomas , Brenner Gunther
Published: March 2021

The rising demand for lower noise emissions of car ancillary units due to electrification and higher customer expectations regarding driving comfort results in the need for more silent car components. Hydraulic driven car components in particular are often identified as a major source of noise in the system. Therefore, it is mandatory to investigate the noise sources inside the hydraulic system. In this work, a combined CFD-FEM approach is applied to estimate the flow-induced noise radiation of a mechanically driven transmission pump. To achieve this goal, the mapping procedure to hand over the pressure field from the CFD to the FEM mesh must be valid. For this purpose, the error during the mapping process is evaluated and different parameters, which influence the mapping results, are analyzed. Additionally, the impact of the time step size and the length of the time signal on the frequency resolution of the force signal is investigated to get an appropriate excitation force for the vibroacoustic simulation. Subsequently, a force analysis and a structural FEM simulation are performed to identify which flow phenomenon contributes most to the excitation of the pump housing. Specific locations in the pump with high loads are pointed out. In a final step, the results of the vibroacoustic model are compared to acceleration and sound pressure level measurements of the pump performed in a hemi-anechoic room.
Assessing the Role of Carbon Capture and Storage in Mitigation Pathways of Developing Economies
Authors: Fragkos Panagiotis
Published: March 2021

The Paris Agreement has set out ambitious climate goals aiming to keep global warming well-below 2 °C by 2100. This requires a large-scale transformation of the global energy system based on the uptake of several technological options to reduce drastically emissions, including expansion of renewable energy, energy efficiency improvements, and fuel switch towards low-carbon energy carriers. The current study explores the role of Carbon Capture and Storage (CCS) as a mitigation option, which provides a dispatchable source for carbon-free production of electricity and can also be used to decarbonise industrial processes. In the last decade, limited technology progress and slow deployment of CCS technologies worldwide have increased the concerns about the feasibility and potential for massive scale-up of CCS required for deep decarbonisation. The current study uses the state-of-the-art PROMETHEUS global energy demand and supply system model to examine the role and impacts of CCS deployment in a global decarbonisation context. By developing contrasted decarbonisation scenarios, the analysis illustrates that CCS deployment might bring about various economic and climate benefits for developing economies, in the form of reduced emissions, lower mitigation costs, ensuring the cost efficient integration of renewables, limiting stranded fossil fuel assets, and alleviating the negative distributional impacts of cost-optimal policies for developing economies.
A Design Method for the Cogging Torque Minimization of Permanent Magnet Machines with a Segmented Stator Core Based on ANN Surrogate Models
Authors: Brescia Elia , Costantino Donatello , Massenio Paolo Roberto , Monopoli Vito Giuseppe , Cupertino Francesco , Cascella Giuseppe Leonardo
Published: March 2021

Permanent magnet machines with segmented stator cores are affected by additional harmonic components of the cogging torque which cannot be minimized by conventional methods adopted for one-piece stator machines. In this study, a novel approach is proposed to minimize the cogging torque of such machines. This approach is based on the design of multiple independent shapes of the tooth tips through a topological optimization. Theoretical studies define a design formula that allows to choose the number of independent shapes to be designed, based on the number of stator core segments. Moreover, a computationally-efficient heuristic approach based on genetic algorithms and artificial neural network-based surrogate models solves the topological optimization and finds the optimal tooth tips shapes. Simulation studies with the finite element method validates the design formula and the effectiveness of the proposed method in suppressing the additional harmonic components. Moreover, a comparison with a conventional heuristic approach based on a genetic algorithm directly coupled to finite element analysis assesses the superiority of the proposed approach. Finally, a sensitivity analysis on assembling and manufacturing tolerances proves the robustness of the proposed design method.
Blockchain Technology Applied to Energy Demand Response Service Tracking and Data Sharing
Authors: Lucas Alexandre , Geneiatakis Dimitrios , Soupionis Yannis , Nai-Fovino Igor , Kotsakis Evangelos
Published: March 2021

Demand response (DR) services have the potential to enable large penetration of renewable energy by adjusting load consumption, thus providing balancing support to the grid. The success of such load flexibility provided by industry, communities, or prosumers and its integration in electricity markets, will depend on a redesign and adaptation of the current interactions between participants. New challenges are, however, bound to appear with the large scale contribution of smaller assets to flexibility, including, among others, the dispatch coordination, the validation of delivery of the DR provision, and the corresponding settlement of contracts, while assuring secured data access among interested parties. In this study we applied distributed ledger (DLT)/blockchain technology to securely track DR provision, focusing on the validation aspect, assuring data integrity, origin, fast registry, and sharing within a permissioned system, between all relevant parties (including transmission system operators (TSOs), aggregators, distribution system operators (DSOs), balance responsible parties (BRP), and prosumers). We propose a framework for DR registry and implemented it as a proof of concept on Hyperledger Fabric, using real assets in a laboratory environment, in order to study its feasibility and performance. The lab set up includes a 450 kW energy storage system, scheduled to provide DR services, upon a system operator request and the corresponding validations and verifications are done, followed by the publication on a blockchain. Results show the end to end execution time remained below 1 s, when below 32 requests/sec. The smart contract memory utilization did not surpass 1% for both active and passive nodes and the peer CPU utilization, remained below 5% in all cases simulated (3, 10, and 28 nodes). Smart Contract CPU utilization remained stable, below 1% in all cases. The performance of the implementation showed scalable results, which enables real world adoption of DLT in supporting the development of flexibility markets, with the advantages of blockchain technology.