bluuebunny/crossref_metadata_2025 huggingface dataset:
Growth Peculiarities and Properties of KR3F10 (R = Y, Tb) Single Crystals
Authors: Karimov Denis N. , Buchinskaya Irina I. , Arkharova Natalia A. , Ivanova Anna G. , Savelyev Alexander G. , Sorokin Nikolay I. , Popov Pavel A.
Published: March 2021

Cubic KR3F10 (R = Y, Tb) single crystals have been successfully grown using the Bridgman technique. Growth of crystals of this type is complicated due to the hygroscopicity of potassium fluoride and melt overheating. The solution to the problem of oxygen-incorporated impurities has been demonstrated through the utilization of potassium hydrofluoride as a precursor. In this study, the crystal quality, structure features, and optical, thermal and electrophysical properties of KR3F10 were examined. Data on the temperature dependences of conductivity properties of KTb3F10 crystals were obtained for the first time. These crystals indicated thermal conductivity equal to 1.54 ± 0.05 Wm−1K−1 at room temperature caused by strong phonon scattering in the Tb-based crystal lattice. Ionic conductivities of KY3F10 and KTb3F10 single crystals were 4.9 × 10−8 and 1.2 × 10−10 S/cm at 500 K, respectively, and the observed difference was determined by the activation enthalpy of F− ion migration. Comparison of the physical properties of the grown KR3F10 crystals with the closest crystalline analog from the family of Na0.5−xR0.5+xF2+2x (R = Tb, Y) cubic solid solutions is reported.
Effect of Rare Earth Elements on Stability and Sintering Resistance of Tetragonal Zirconia for Advanced Thermal Barrier Coatings
Authors: Yi Hao , Che Junwei , Liang Gongying , Liu Xiangyang
Published: March 2021

The effect of dopant species on the sintering resistance of zirconia-based ceramics remains a huge challenge in terms of both experiment and theory. As one of the most popular materials for high-temperature protective coatings, it is still urgent to obtain rare earth-doped ZrO2 with high sintering resistance and good phase stability. Here, the sintering resistance and phase stability of rare earth oxides (La2O3, Nd2O3, Gd2O3, and Y2O3)-stabilized zirconia (ZrO2) were thoroughly studied by theoretical and experimental methods. According to experimental data, ZrO2 doped with rare earth ions with larger radii (La3+, Nd3+, and Gd3+) exhibited improved sintering resistance at reduced tetragonal phase stability. Molecular dynamics simulation results revealed that rare earth ions with larger ionic radii are prone to segregation at grain boundaries, which can more effectively reduce the grain boundary energy in the materials under consideration. Therefore, the proposed approach involving doping of NdO1.5 (~1 mol%) and YO1.5 (YbO1.5, 6 mol%) in ZrO2 is considered to be a promising route for the effective preparation of sinter-resistant ZrO2-based ceramics for refractory and thermal barrier materials.
Laser-Induced Graphene on a Quartz Crystal Microbalance for Humidity Sensing
Authors: Choi Jihun , Baek Saeyeon , Jeon Sangmin , Yim Changyong
Published: March 2021

In this study, a simple method for synthesizing graphene layer directly on a quartz crystal microbalance (QCM) using a laser was developed. This laser-induced graphene (LIG) was used for sensing surface to simultaneously measure changes in the adsorbed mass, film stiffness, and electrical resistance during water adsorption. The developed LIG-QCM is convenient because its fabrication process is free of any tedious masking and vacuuming steps. A thin layer of polyimide (PI) film was spin-coated on one side of a quartz crystal microresonator, and interdigitated electrodes (IDE) were patterned on the PI surface using a laser engraver. The adsorption of water molecules on the sensing surface induced changes in mass, stiffness, and electrical conductivity, which were measured from the changes in resonance frequency, Q factor of the quartz crystal, and electrical resistance, respectively. The results indicated that the developed sensor could be a humidity sensing platform using LIG.
The Rabbitfish Siganus virgatus as Key Macroalgae Browser in Coral Reefs of the Gulf of Thailand
Authors: Müller Malika , Staab Constanze F. K. , Puk Laura D. , Schoenig Eike M. , Ferse Sebastian C. A. , Wild Christian
Published: March 2021

Coral reef resilience is greatly influenced by herbivory. There is a need to identify key fish species fulfilling this critical function in biogeographically distinct regions. This experimental in situ study investigated fish herbivory in coral reefs of the lower Gulf of Thailand characterized by a considerably low herbivorous fish biomass and diversity, but high live coral and low macroalgal cover. This provided an intriguing situation for macroalgal browsing research. Visual census techniques assessed the abundance of local herbivorous fish species, and filmed single-choice assays using the macroalga Turbinaria evaluated mass-standardized bites (ms-bites) and biomass removal. Multiple-choice assays offering four locally abundant macroalgae identified specific biomass removal and ms-bites to uncover selection and avoidance patterns of observed fish species. The rabbitfish Siganusvirgatus constituted only 39% of herbivore biomass but accounted for 90% of ms-bites. In multiple-choice assays, fishes took most (61%) bites on Sargassum, followed by Padina (28%) and Turbinaria (11%), while Lobophora was avoided. S. virgatus exhibited the most generalized browsing pattern of all species observed. Coinciding with recent studies, our findings suggest that S. virgatus plays a key functional role in reefs characterized by low diversity of herbivores and low functional redundancy.
A High-Accuracy GNSS Dataset of Ground Truth Points Collected within Îles-de-Boucherville National Park, Quebec, Canada
Authors: Elmer Kathryn , Kalacska Margaret
Published: March 2021

A new ground truth dataset generated with high-accuracy Global Navigation Satellite Systems (GNSS) positional data of the invasive reed Phragmites australis subsp. australis within Îles-de-Boucherville National Park (Quebec, Canada) is described. The park is one of five study sites for the Canadian Airborne Biodiversity Observatory (CABO) and has stands of invasive P. australis spread throughout the park. Previously, within the context of CABO, no ground truth data had been collected within the park consolidating the locations of P. australis. This dataset was collected to serve as training and validation data for CABO airborne hyperspectral imagery acquired in 2019 to assist with the detection and mapping of P. australis. The locations of the ground truth points were found to be accurate within one pixel of the hyperspectral imagery. Overall, 320 ground truth points were collected, representing 158 locations where P. australis was present and 162 locations where it was absent. Auxiliary data includes field photographs and digitized field notes that provide context for each point.
Amyotrophic Lateral Sclerosis and Frontotemporal Lobar Degenerations: Similarities in Genetic Background
Authors: Parobkova Eva , Matej Radoslav
Published: March 2021

Amyotrophic lateral sclerosis (ALS) is a devastating, uniformly lethal progressive degenerative disorder of motor neurons that overlaps with frontotemporal lobar degeneration (FTLD) clinically, morphologically, and genetically. Although many distinct mutations in various genes are known to cause amyotrophic lateral sclerosis, it remains poorly understood how they selectively impact motor neuron biology and whether they converge on common pathways to cause neuronal degeneration. Many of the gene mutations are in proteins that share similar functions. They can be grouped into those associated with cell axon dynamics and those associated with cellular phagocytic machinery, namely protein aggregation and metabolism, apoptosis, and intracellular nucleic acid transport. Analysis of pathways implicated by mutant ALS genes has provided new insights into the pathogenesis of both familial forms of ALS (fALS) and sporadic forms (sALS), although, regrettably, this has not yet yielded definitive treatments. Many genes play an important role, with TARDBP, SQSTM1, VCP, FUS, TBK1, CHCHD10, and most importantly, C9orf72 being critical genetic players in these neurological disorders. In this mini-review, we will focus on the molecular mechanisms of these two diseases.
Association of Inflammatory Metabolic Activity of Psoas Muscle and Acute Myocardial Infarction: A Preliminary Observational Study with 18F-FDG PET/CT
Authors: Pahk Kisoo , Kim Eung Ju , Kwon Hyun Woo , Joung Chanmin , Seo Hong Seog , Kim Sungeun
Published: March 2021

Inflamed skeletal muscle promotes chronic inflammation in atherosclerotic plaques, thereby contributing to the increased risk of coronary artery disease (CAD). In this study, we evaluated the metabolic activity of psoas muscle, using 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT), and its association with carotid artery inflammation and acute myocardial infarction (AMI). In total, 90 participants (32 AMI, 33 chronic stable angina (CSA), and 25 control) were enrolled in this prospective study. Metabolic activity of skeletal muscle (SM) was measured by using maximum standardized uptake value (SUVmax) of psoas muscle, and corresponding psoas muscle area (SM area) was also measured. Carotid artery inflammation was evaluated by using the target-to background ratio (TBR) of carotid artery. SM SUVmax was highest in AMI, intermediate in CSA, and lowest in control group. SM SUVmax was significantly correlated with carotid artery TBR and systemic inflammatory surrogate markers. Furthermore, SM SUVmax was independently associated with carotid artery TBR and showed better predictability than SM area for the prediction of AMI. Metabolic activity of psoas muscle assessed by 18F-FDG PET/CT was associated with coronary plaque vulnerability and synchronized with the carotid artery inflammation in the participants with CAD. Furthermore, it may also be useful to predict AMI.
RNA-Based CTC Analysis Provides Prognostic Information in Metastatic Breast Cancer
Authors: Strati Areti , Nikolaou Michail , Georgoulias Vassilis , Lianidou Evi S.
Published: March 2021

In metastatic breast cancer (MBC) the molecular characterization of circulating tumor cells (CTCs) provides a unique tool to understand metastasis-biology and therapy-resistance. We evaluated the prognostic significance of gene expression in EpCAM(+) CTCs in 46 MBC patients based on a long follow-up. We selected a panel consisting of stem cell markers (CD24, CD44, ALDH1), the mesenchymal marker TWIST1, receptors (ESR1, PGR, HER2, EGFR) and the epithelial marker CK-19. Singleplex RT-qPCR was used for TWIST1 and CK-19 and multiplex RT-qPCR for stem cell markers and receptors. A group of 19 healthy donors (HD) was used as control. Univariate (p = 0.001) and multivariate analysis (p = 0.002) revealed the prognostic value of combined gene expression of CK-19(+), CD44high/CD24low, ALDH1high/CD24low and HER2 over-expression for overall survival (OS). The Kaplan–Meier estimates of OS were significantly different in patients positive for CK-19 (p = 0.028), CD44high/CD24low (p = 0.002), ALDH1high/CD24low (p = 0.007) and HER2-positive (p = 0.022). Our results indicate that combined gene expression analysis in EpCAM(+) CTCs provides prognostic information in MBC.
Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach
Authors: Lorenzovici Noémi , Dulf Eva-H. , Mocan Teodora , Mocan Lucian
Published: March 2021

Colorectal cancer is the third most common and second most lethal tumor globally, causing 900,000 deaths annually. In this research, a computer aided diagnosis system was designed that detects colorectal cancer, using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumor position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localization). The intelligent computer aided colorectal cancer diagnosis system was designed using different machine learning techniques, such as classification and shallow and deep neural networks. The maximum accuracy obtained from solving the binary classification problem with traditional machine learning algorithms was 77.8%. However, the regression problem solved with deep neural networks yielded with significantly better performance in terms of mean squared error minimization, reaching the value of 0.0000529.
CEA, EpCAM, αvβ6 and uPAR Expression in Rectal Cancer Patients with a Pathological Complete Response after Neoadjuvant Therapy
Authors: Linders Daan , Deken Marion , van der Valk Maxime , Tummers Willemieke , Bhairosingh Shadhvi , Schaap Dennis , van Lijnschoten Gesina , Zonoobi Elham , Kuppen Peter , van de Velde Cornelis , Vahrmeijer Alexander , Farina Sarasqueta Arantza , Sier Cornelis , Hilling Denise
Published: March 2021

Rectal cancer patients with a complete response after neoadjuvant therapy can be monitored with a watch-and-wait strategy. However, regrowth rates indicate that identification of patients with a pathological complete response (pCR) remains challenging. Targeted near-infrared fluorescence endoscopy is a potential tool to improve response evaluation. Promising tumor targets include carcinoembryonic antigen (CEA), epithelial cell adhesion molecule (EpCAM), integrin αvβ6, and urokinase-type plasminogen activator receptor (uPAR). To investigate the applicability of these targets, we analyzed protein expression by immunohistochemistry and quantified these by a total immunostaining score (TIS) in tissue of rectal cancer patients with a pCR. CEA, EpCAM, αvβ6, and uPAR expression in the diagnostic biopsy was high (TIS > 6) in, respectively, 100%, 100%, 33%, and 46% of cases. CEA and EpCAM expressions were significantly higher in the diagnostic biopsy compared with the corresponding tumor bed (p < 0.01). CEA, EpCAM, αvβ6, and uPAR expressions were low (TIS < 6) in the tumor bed in, respectively, 93%, 95%, 85%, and 62.5% of cases. Immunohistochemical evaluation shows that CEA and EpCAM could be suitable targets for response evaluation after neoadjuvant treatment, since expression of these targets in the primary tumor bed is low compared with the diagnostic biopsy and adjacent pre-existent rectal mucosa in more than 90% of patients with a pCR.
Clinical Significance of Histone Deacetylase (HDAC)-1, -2, -4 and -6 Expression in Salivary Gland Tumors
Authors: Pouloudi Despoina , Manou Maria , Sarantis Panagiotis , Tsoukalas Nikolaos , Tsourouflis Gerasimos , Dana Eougken , Karamouzis Michalis V. , Klijanienko Jerzy , Theocharis Stamatios
Published: March 2021

Salivary gland tumors (SGTs) comprise a group of rare neoplasms. Locally aggressive, recurrent and/or metastatic SGTs are notorious for their resistance to systemic therapy, making the need for carefully designed, prospective and randomized trials with useful predictive markers mandatory to define new effective therapeutic protocols. Histone Deacetylases (HDACs), are thought to play a crucial role in carcinogenesis. They affect the DNA structure, being also able to regulate its transcription, repair, and replication. This study aimed to evaluate—to our knowledge for the first time—the HDAC-1, -2, -4 and -6 immunohistochemical expression in SGTs and their potential use as prognostic biomarkers. Medical records and archival histopathological material of 58 (36 benign and 22 malignant) SGT patients were included in this study. The H-score was statistically correlated with the clinicopathological characteristics for all cases and patients’ survival rate in malignant SGTs. HDAC-2 positivity was significantly associated with more prolonged overall survival (OS) of patients with malignant SGTs (p = 0.028), while HDAC-2 positivity and no HDAC-6 expression were associated with prolonged OS of patients with HG malignant SGT (p = 0.003 and p = 0.043, respectively). Additionally, a high HDAC-2 H-score was significantly associated with longer OS for HG malignant SGT patients (p = 0.027). In our study, HDAC-2 expression is a marker for good prognosis, whereas HDAC-6 expression indicated poor prognosis; thus, an inhibitor of HDAC-6 may be used to improve patients’ survival.
Of Course We Fly Unmanned—We’re Women!
Authors: Joyce Karen E. , Anderson Karen , Bartolo Renee E.
Published: March 2021

Striving to achieve a diverse and inclusive workplace has become a major goal for many organisations around the world [...]
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
Authors: Ahmed Rami , Gogate Mandar , Tahir Ahsen , Dashtipour Kia , Al-tamimi Bassam , Hawalah Ahmad , El-Affendi Mohammed A. , Hussain Amir
Published: March 2021

Offline Arabic Handwriting Recognition (OAHR) has recently become instrumental in the areas of pattern recognition and image processing due to its application in several fields, such as office automation and document processing. However, OAHR continues to face several challenges, including high variability of the Arabic script and its intrinsic characteristics such as cursiveness, ligatures, and diacritics, the unlimited variation in human handwriting, and the lack of large public databases. In this paper, we introduce a novel context-aware model based on deep neural networks to address the challenges of recognizing offline handwritten Arabic text, including isolated digits, characters, and words. Specifically, we propose a supervised Convolutional Neural Network (CNN) model that contextually extracts optimal features and employs batch normalization and dropout regularization parameters. This aims to prevent overfitting and further enhance generalization performance when compared to conventional deep learning models. We employ a number of deep stacked-convolutional layers to design the proposed Deep CNN (DCNN) architecture. The model is extensively evaluated and shown to demonstrate excellent classification accuracy when compared to conventional OAHR approaches on a diverse set of six benchmark databases, including MADBase (Digits), CMATERDB (Digits), HACDB (Characters), SUST-ALT (Digits), SUST-ALT (Characters), and SUST-ALT (Names). A further experimental study is conducted on the benchmark Arabic databases by exploiting transfer learning (TL)-based feature extraction which demonstrates the superiority of our proposed model in relation to state-of-the-art VGGNet-19 and MobileNet pre-trained models. Finally, experiments are conducted to assess comparative generalization capabilities of the models using another language database , specifically the benchmark MNIST English isolated Digits database, which further confirm the superiority of our proposed DCNN model.
Mismatch Negativity and Stimulus-Preceding Negativity in Paradigms of Increasing Auditory Complexity: A Possible Role in Predictive Coding
Authors: Ruiz-Martínez Francisco J. , Arjona Antonio , Gómez Carlos M.
Published: March 2021

The auditory mismatch negativity (MMN) has been considered a preattentive index of auditory processing and/or a signature of prediction error computation. This study tries to demonstrate the presence of an MMN to deviant trials included in complex auditory stimuli sequences, and its possible relationship to predictive coding. Additionally, the transfer of information between trials is expected to be represented by stimulus-preceding negativity (SPN), which would possibly fit the predictive coding framework. To accomplish these objectives, the EEG of 31 subjects was recorded during an auditory paradigm in which trials composed of stimulus sequences with increasing or decreasing frequencies were intermingled with deviant trials presenting an unexpected ending. Our results showed the presence of an MMN in response to deviant trials. An SPN appeared during the intertrial interval and its amplitude was reduced in response to deviant trials. The presence of an MMN in complex sequences of sounds and the generation of an SPN component, with different amplitudes in deviant and standard trials, would support the predictive coding framework.
Designing of Nanomaterials-Based Enzymatic Biosensors: Synthesis, Properties, and Applications
Authors: Cavalcante Francisco T. T. , de A. Falcão Italo R. , da S. Souza José E. , Rocha Thales G. , de Sousa Isamayra G. , Cavalcante Antônio L. G. , de Oliveira André L. B. , de Sousa Maria C. M. , dos Santos José C. S.
Published: March 2021

Among the many biological entities employed in the development of biosensors, enzymes have attracted the most attention. Nanotechnology has been fostering excellent prospects in the development of enzymatic biosensors, since enzyme immobilization onto conductive nanostructures can improve characteristics that are crucial in biosensor transduction, such as surface-to-volume ratio, signal response, selectivity, sensitivity, conductivity, and biocatalytic activity, among others. These and other advantages of nanomaterial-based enzymatic biosensors are discussed in this work via the compilation of several reports on their applications in different industrial segments. To provide detailed insights into the state of the art of this technology, all the relevant concepts around the topic are discussed, including the properties of enzymes, the mechanisms involved in their immobilization, and the application of different enzyme-derived biosensors and nanomaterials. Finally, there is a discussion around the pressing challenges in this technology, which will be useful for guiding the development of future research in the area.
Synthesis of a Path-Planning Algorithm for Autonomous Robots Moving in a Game Environment during Collision Avoidance
Authors: Lisowski Józef
Published: March 2021

This paper describes and illustrates the optimization of a safe mobile robot control process in collision situations using the model of a multistep matrix game of many participants in the form of a dual linear programming problem. The synthesis of non-cooperative and cooperative game control software was performed in Matlab/Simulink software to determine the safe path of the robot when passing a greater number of other robots and obstacles. The operation of the game motion control algorithm of a mobile robot is illustrated by computer simulations made in the Matlab/Simulink program of two real previously recorded navigation situations while passing dozens of other autonomous mobile robots.
Design Topologies of a CMOS Charge Pump Circuit for Low Power Applications
Authors: Rahman Labonnah Farzana , Marufuzzaman Mohammad , Alam Lubna , Mokhtar Mazlin Bin
Published: March 2021

Applications such as non-volatile memories (NVM), radio frequency identification (RFID), high voltage generators, switched capacitor circuits, operational amplifiers, voltage regulators, and DC–DC converters employ charge pump (CP) circuits as they can generate a higher output voltage from the very low supply voltage. Besides, continuous power supply reduction, low implementation cost, and high efficiency can be managed using CP circuits in low-power applications in the complementary metal-oxide-semiconductor (CMOS) process. This study aims to figure out the most widely used CP design topologies for embedded systems on the chip (SoC). Design methods have evolved from diode-connected structures to dynamic clock voltage scaling charge pumps have been discussed in this research. Based on the different architecture, operating principles and optimization techniques with their advantages and disadvantages have compared with the final output. Researchers mainly focused on designing the charge pump topologies based on input/output voltage, pumping efficiency, power dissipation, charge transfer capability, design complexity, pumping capacitor, clock frequencies with a minimum load balance, etc. Finally, this review study summarizes with the discussion on the outline of appropriate schemes and recommendations to future researchers in selecting the most suitable CP design methods for low power applications.
Role of Wide Bandgap Materials in Power Electronics for Smart Grids Applications
Authors: Ballestín-Fuertes Javier , Muñoz-Cruzado-Alba Jesús , Sanz-Osorio José F. , Laporta-Puyal Erika
Published: March 2021

At present, the energy transition is leading to the replacement of large thermal power plants by distributed renewable generation and the introduction of different assets. Consequently, a massive deployment of power electronics is expected. A particular case will be the devices destined for urban environments and smart grids. Indeed, such applications have some features that make wide bandgap (WBG) materials particularly relevant. This paper analyzes the most important features expected by future smart applications from which the characteristics that their power semiconductors must perform can be deduced. Following, not only the characteristics and theoretical limits of wide bandgap materials already available on the market (SiC and GaN) have been analyzed, but also those currently being researched as promising future alternatives (Ga2O3, AlN, etc.). Finally, wide bandgap materials are compared under the needs determined by the smart applications, determining the best suited to them. We conclude that, although SiC and GaN are currently the only WBG materials available on the semiconductor portfolio, they may be displaced by others such as Ga2O3 in the near future.
Simultaneous Voltage and Current Measurement Instrumentation Amplifier for ECG and PPG Monitoring
Authors: Kim Jongpal
Published: March 2021

An instrumentation amplifier (IA) capable of sensing both voltage and current at the same time has been introduced and applied to electrocardiogram (ECG) and photoplethysmogram (PPG) measurements for cardiovascular health monitoring applications. The proposed IA can switch between the voltage and current sensing configurations in a time–division manner faster than the ECG and PPG bandwidths. The application-specific integrated circuit (ASIC) of the proposed circuit design was implemented using 180 nm CMOS fabrication technology. Input-referred voltage noise and current noise were measured as 3.9 µVrms and 172 pArms, respectively, and power consumption was measured as 34.9 µA. In the current sensing configuration, a current noise reduction technique is applied, which was confirmed to be a 25 times improvement over the previous version. Using a single IA, ECG and PPG can be monitored in the form of separated ECG and PPG signals. In addition, for the first time, a merged ECG/PPG signal is acquired, which has features of both ECG and PPG peaks.
Two Decades of Condition Monitoring Methods for Power Devices
Authors: Susinni Giovanni , Rizzo Santi Agatino , Iannuzzo Francesco
Published: March 2021

Condition monitoring (CM) of power semiconductor devices enhances converter reliability and customer service. Many studies have investigated the semiconductor devices failure modes, the sensor technologies, and the signal processing techniques to optimize the CM. Furthermore, the improvement of power devices’ CM thanks to the use of the Internet of Things and artificial intelligence technologies is rising in smart grids, transportation electrification, and so on. These technologies will be widespread in the future, where more and more smart techniques and smart sensors will enable a better estimation of the state of the health (SOH) of the devices. Considering the increasing use of power converters, CM is essential as the analysis of the data obtained from multiple sensors enables the prediction of the SOH, which, in turn, enables to properly schedule the maintenance, i.e., accounting for the trade-off between the maintenance cost and the cost and issues due to the device failure. From this perspective, this review paper summarizes past developments and recent advances of the various methods with the aim of describing the current state-of-the-art in CM research.
Inductance Simulators and Their Application to the 4th Order Elliptic Lowpass Ladder Filter Using CMOS VD-DIBAs
Authors: Jaikla Winai , Bunrueangsak Sirigul , Khateb Fabian , Kulej Tomasz , Suwanjan Peerawut , Supavarasuwat Piya
Published: March 2021

This paper presents inductance simulators using the voltage differencing differential input buffered amplifier (VD-DIBA) as an active building block. Three types of inductance simulators, including floating lossless inductance, series inductance-resistance, and parallel inductance-resistance simulators, are proposed, in addition to their application to the 4th order elliptic lowpass ladder filter. The simple design procedures of these inductance simulators using a circuit block diagram are also given. The proposed inductance simulators employ two VD-DIBAs and two passive elements. The complementary metal oxide semiconductor (CMOS) VD-DIBA used in this design utilizes the multiple-input metal oxide semiconductor (MOS) transistor technique in order to achieve a compact and simple structure with a minimum count of transistors. Thanks to this technique, the VD-DIBA offers high performances compared to the other CMOS structures presented in the literature. The CMOS VD-DIBAs and their applications are designed and simulated in the Cadence environment using a 0.18 µm CMOS process from Taiwan semiconductor manufacturing company (TSMC). Using a supply voltage of ±0.9 V, the linear operation of VD-DIBA is obtained over a differential input range of −0.5 V to 0.5 V. The lowpass (LP) ladder filter realized with the proposed inductance simulators shows a dynamic range (DR) of 80 dB for a total harmonic distortion (THD) of 2% at 1 kHz and a 1.8 V peak-to-peak output. In addition, the experimental results of the floating inductance simulators and their applications are obtained by using VD-DIBA constructed from the available commercial components LM13700 and AD830. The simulation results are in agreement with the experimental ones, confirming the advantages of the inductance simulators and their application.
Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
Authors: Takabayashi Kento , Tanaka Hirokazu , Sakakibara Katsumi
Published: March 2021

This research provides a study on a smart body area network (SmartBAN) physical layer (PHY), as an of the Internet of medical things (IoMT) technology, for an advanced human monitoring system in industrial use. The SmartBAN provides a new PHY and a medium access control (MAC) layer, improving its performance and providing very low-latency emergency information transmission with low energy consumption compared with other wireless body area network (WBAN) standards. On the other hand, IoMT applications are expected to become more advanced with smarter wearable devices, such as augmented reality-based human monitoring and work support in a factory. Therefore, it is possible to develop more advanced human monitoring systems for industrial use by combining the SmartBAN with multimedia devices. However, the SmartBAN PHY is not designed to transmit multimedia information such as audio and video. To address this issue, multilevel phase shift keying (PSK) modulation is applied to the SmartBAN PHY, and the symbol rate is improved by setting the roll-off rate appropriately to realize the system. The numerical results show that a sufficient link budget, receiver sensitivity and fade margin were obtained even when those approaches were applied to the SmartBAN PHY. The results indicate that these techniques are required for high-quality audio or video transmission, as well as vital sign data transmission, in a SmartBAN.
On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures
Authors: Springer Tom , Eiroa-Lledo Elia , Stevens Elizabeth , Linstead Erik
Published: March 2021

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems.
Wind Turbine Generator Controller Signals Supervised Machine Learning for Shaft Misalignment Fault Detection: A Doubly Fed Induction Generator Practical Case Study
Authors: Al-Ajmi Ahmed , Wang Yingzhao , Djurović Siniša
Published: March 2021

With a continued strong increase in wind generator applications, the condition monitoring of wind turbine systems has become ever more important in ensuring the availability and reduced cost of produced power. One of the key turbine conditions requiring constant monitoring is the generator shaft alignment, which if compromised and untreated can lead to catastrophic system failures. This study explores the possibility of employing supervised machine learning methods on the readily available generator controller loop signals to achieve detection of shaft misalignment condition. This could provide a highly noninvasive and low-cost solution for misalignment monitoring in comparison with the current misalignment monitoring field practice that relies on invasive and costly drivetrain vibration analysis. The study utilises signal datasets measured on a dedicated doubly fed induction generator test rig to demonstrate that high consistency and accuracy recognition of shaft angular misalignment can be achieved through the application of supervised machine learning on controller loop signals. The average recognition accuracy rate of up to 98.8% is shown to be attainable through analysis of a key feature subset of the stator flux-oriented controller signals in a range of operating speeds and loads.
Experimental Study on Dynamic Combustion Characteristics in Swirl-Stabilized Combustors
Authors: Hwang Donghyun , Ahn Kyubok
Published: March 2021

An experimental study was performed to investigate the combustion instability characteristics of swirl-stabilized combustors. A premixed gas composed of ethylene and air was burned under various flow and geometric conditions. Experiments were conducted by changing the inlet mean velocity, equivalence ratio, swirler vane angle, and combustor length. Two dynamic pressure sensors, a hot-wire anemometer, and a photomultiplier tube were installed to detect the pressure oscillations, velocity perturbations, and heat release fluctuations in the inlet and combustion chambers, respectively. An ICCD camera was used to capture the time-averaged flame structure. The objective was to understand the relationship between combustion instability and the Rayleigh criterion/the flame structure. When combustion instability occurred, the pressure oscillations were in-phase with the heat release oscillations. Even if the Rayleigh criterion between the pressure and heat release oscillations was satisfied, stable combustion with low pressure fluctuations was possible. This was explained by analyzing the dynamic flow and combustion data. The root-mean-square value of the heat release fluctuations was observed to predict the combustion instability region better than that of the inlet velocity fluctuations. The bifurcation of the flame structure was a necessary condition for combustion instability in this combustor. The results shed new insight into combustion instability in swirl-stabilized combustors.
Distribution Network Hierarchically Partitioned Optimization Considering Electric Vehicle Orderly Charging with Isolated Bidirectional DC-DC Converter Optimal Efficiency Model
Authors: Zhang Qiushi , Zhao Jian , Wang Xiaoyu , Tong Li , Jiang Hang , Zhou Jinhui
Published: March 2021

The access of large-scale electric vehicles (EVs) will increase the network loss of medium voltage distribution network, which can be alleviated by adjusting the network structure and orderly charging for EVs. However, it is difficult to accurately evaluate the charging efficiency in the orderly charging of electric vehicle (EV), which will cause the scheduling model to be insufficiently accurate. Therefore, this paper proposes an EV double-layer scheduling model based on the isolated bidirectional DC–DC (IBDC) converter optimal efficiency model, and establishes the hierarchical and partitioned optimization model with feeder–branch–load layer. Firstly, based on the actual topology of medium voltage distribution network, a dynamic reconfiguration model between switching stations is established with the goal of load balancing. Secondly, with the goal of minimizing the branch layer network loss, a dynamic reconstruction model under the switch station is established, and the chaotic niche particle swarm optimization is proposed to improve the global search capability and iteration speed. Finally, the power transmission loss model of IBDC converter is established, and the optimal phase shift parameter is determined to formulate the double-layer collaborative optimization operation strategy of electric vehicles. The example verifies that the above model can improve the system load balancing degree and reduce the operation loss of medium voltage distribution network.
A Theoretical Model for Voltage-Dependent Photocurrent Collection in CdTe Solar Cells
Authors: Zhao Cindy X. , Chin Ken K.
Published: March 2021

The classic solar cell model assumes that the photo-generated current is a constant, independent of the cell’s output voltage. Experimental data of CdTe solar cells, however, show that the photocurrent collection efficiency decreases with the increase of the cell’s output voltage. In this work, we proposed a theoretical model for the CdTe thin-film cell, which assumes that the loss of photocurrent in the CdTe absorber is primarily due to the minority carrier recombination in the neutral region and at the back contact. By solving the neutral region’s diffusion equation, with proper boundary conditions, we have obtained the analytical expressions of the photocurrent collection efficiency and the cell’s J-V performance. Our theoretical results agree well with the experimental data. According to our theoretical model, the CdTe thin-film solar cell has an optimized p-doping level. A higher doping density may not be always good for a CdTe solar cell due to the reduced depletion width and decreased photocurrent at normal operation voltage, although the higher doping density can improve the open-circuit voltage by increasing built-in voltage.
Special Issue “Numerical Simulation of Wind Turbines”
Authors: Ferrara Giovanni , Bianchini Alessandro
Published: March 2021

To fulfill global needs for a more sustainable energy, a further development of wind energy is fostered [...]
Minimum Leading Edge Protection Application Length to Combat Rain-Induced Erosion of Wind Turbine Blades
Authors: Verma Amrit Shankar , Noi Sandro Di , Ren Zhengru , Jiang Zhiyu , Teuwen Julie J. E.
Published: March 2021

Leading edge erosion (LEE) repairs of wind turbine blades (WTBs) involve infield application of leading edge protection (LEP) solutions. The industry is currently aiming to use factory based LEP coatings that can applied to the WTBs before they are shipped out for installation. However, one of the main challenges related to these solutions is the choice of a minimum LEP application length to be applied in the spanwise direction of the WTBs. Generally, coating suppliers apply 10–20 m of LEP onto the blades starting from the tip of the blade using the “rule of thumb”, and no studies in the literature exist that stipulate how these LEP lengths can be calculated. In this study, we extend the scope of a recently developed long-term probabilistic framework to determine the minimum LEP application length required for WTBs to combat rain-induced erosion. A parametric study is performed where different wind turbines with varying power ratings of 2.1 MW to 15 MW at different Dutch sites ranging from inland to coastal are considered. The results of the study show that the LEP application length is sensitive to the choice of the site, as well as the turbine attributes. Further, LEP lengths for WTBs are found to be the highest for turbines installed at coastal sites and turbines with higher power ratings. A detailed investigation is further performed to check the sensitivity of the LEP application length with the wind turbine parameters. The results of the study are expected to provide guidelines to the industry for efficient repair strategies for WTBs.
Energy Market Prices in Times of COVID-19: The Case of Electricity and Natural Gas in Spain
Authors: Abadie Luis M.
Published: March 2021

The COVID-19 pandemic is having a strong impact on the economies of all countries, negatively affecting almost all sectors. This paper compares Spanish electricity and natural gas prices in the first half-year of 2020 with the prices expected for that period at the end of 2019. The half-year of 2020 selected coincides with the period of greatest impact of COVID-19 on Spanish society. Expected prices and their future probability distributions are calculated using a stochastic model with deterministic and stochastic parts; the stochastic part includes mean-reverting and jumps behaviour. The model is calibrated with 2016–2019 daily spot prices for electricity and with day-ahead prices for natural gas. The results show large monthly differences between the prices expected at the end of the year 2019 and the actual prices for the half-year; in May 2020, wholesale electricity prices are found to be EUR 31.60/MWh lower than expected, i.e., 60% lower. In the case of natural gas, the prices in the same month are EUR 8.96/MWh lower than expected, i.e., 62% lower. The spark spread (SS) is positive but lower than expected and also lower than in the same months of the previous year.
The Investigation of Fracture Networks on Heat Extraction Performance for an Enhanced Geothermal System
Authors: Li Linkai , Guo Xiao , Zhou Ming , Xiang Gang , Zhang Ning , Wang Yue , Wang Shengyuan , Landjobo Pagou Arnold
Published: March 2021

Hydraulic fracturing is usually employed to create a complex fracture network to enhance heat extraction in the development of an enhanced geothermal system. The heat extraction depends on the heat conduction from the rock matrix to the flowing fractures and the heat convection through a complex fracture network. Therefore, the geometries of the fracture network have important influences on the thermal breakthrough. In this paper, a hydro-thermal coupling mathematical model considering a complex fracture network is established. The embedded discrete fracture model is adopted to explicitly model the individual fracture on the mass flow and heat transfer. The model is validated by analytical solutions. Fracture network parameters are changed systematically to investigate the effects of fracture network distribution including regular and complex shape on the thermal production performance. The results show that the increase of producing pressure differential, fracture number, and conductivity will cause an early thermal breakthrough. The strong variation in fracture conductivity, as well as spacing and orientation, will cause thermal flow channeling and decrease the efficiency of heat extraction. A modified connectivity field is proposed to characterize the spatial variation of fracture network connectivity, which can be used to infer the thermal flow path.
Covid-19’s Impact on European Power Sectors: An Econometric Analysis
Authors: Hauser Philipp , Schönheit David , Scharf Hendrik , Anke Carl-Philipp , Möst Dominik
Published: March 2021

Covid-19 affects the personal lives of millions and led to an economic crisis. Changed behavioral patterns and a reduction of industrial activity result in a reduction in power demand, and thus Covid-19 impacts the power systems around the world. Bottom-up mapping of the effect of Covid-19 on the energy demand is challenging, if not impossible. In order to analyze the impact of the pandemic on power demand, we instead propose a simplified approach based on an econometric analysis that quantifies the country-wide load reduction of Covid-19, using the number of active cases as well as the specific lockdown period as proxies. The time span covered is from 1 January 2016 to 31 August 2020. This long time span allows us to investigate the effect of Covid-19 on the power demand. We find that in Germany (DE) and Great Britain (GB) the power demand is reduced by about 1–1.7 MW per case, while in France the demand increased by 1 MW per case during times outside of the lockdown. On the other hand, in France the lockdown itself has a much higher load reduction effect in France than in GB and DE. Based on the elasticity of power demand regarding Covid-19 cases, we calculate the impact of Covid-19 on the power prices through reduced loads. We find that Covid-19 reduced power prices by 3 to 6 EUR per MWh. The effect of Covid-19 on carbon emissions in the power sector is likely to be small. In Germany, the country with the highest absolute level, emissions in the power sector were reduced by approximately 2% (4 Mio. t CO2).
Impact on the Operation of a Forwarder with the Wheeled, Tracked-Wheel or Tracked Chassis on the Soil Surface
Authors: Zemánek Tomáš , Neruda Jindřich
Published: March 2021

The impact of a small forwarder with the wheeled chassis, tracked-wheel chassis, and tracked chassis traveling on the soil profile was studied. The three chassis types were assessed for the influence of the loading of forwarder cargo space and the degree of tire inflation on induced specific and actual pressures of tires on the soil surface. Penetrometric resistances of soil profile and rut depths in the forwarder driving track were measured. The effect of a layer of logging residues in the forwarder driving track on the size of induced actual pressures was determined. The practice of determining the impact of forest machines on the soil surface by means of a specific tire pressure does not have a full informative value. In the forwarder wheeled chassis, maximum values of actual pressures exceeded specific pressures established numerically by up to 203%. Average values of actual pressures could be reduced by 45% by reducing the pressure of tire inflation, by 70% with the use of tracks, or by 49% by traveling on the layer of logging residues. As compared with the wheeled chassis type, the tracked type of the forwarder chassis induced actual pressures to lower by 81% and the rut depth after ten forwarder passes was smaller by 50%.