bluuebunny/crossref_metadata_2025 huggingface dataset:
Simultaneous Determination of δ18O and δ37Cl Isotopic Compositions and the Concentrations of F, Cl, and S in Apatite Using SIMS
Authors: Liu Yu , Li Qiuli , Tang Guoqiang , Li Xianhua
Published: March 2022

Due to its ubiquity in rock types and ability to accommodate a wide range of trace elements, apatite is widely used to interpret magmatic evolution and track volatile elements in hydrothermal systems. Volatile elements and Cl–O isotopes make apatite an ideal mineral with which to record the chemical and textural signatures of geological processes. This study describes a new analytical procedure to obtain the F, Cl, and S contents and Cl and O isotopes simultaneously using secondary ion mass spectrometry (SIMS) within ~10 min of analysis time for each spot. The analytical precision of δ18O (2 SD (standard deviation) ~0.30‰) and δ37Cl (2 SD ~0.20‰) has no obvious trade-off compared with ordinary SIMS analysis. The new protocol is time-efficient and, more importantly, ensures the consistency of data sources. A set of apatite standards with different uses were determined using this new method to provide a complete set of recommended values.
A Molten Salt Electrochemical Process for the Preparation of Cost-Effective p-Block (Coating) Materials
Authors: Tripathy Prabhat Kumar , Mondal Kunal
Published: March 2022

Solar energy applications rely heavily on p-block elements and transition metals. Silicon is, by far, the most commonly used material in photovoltaic cells and accounts for about 85% of modules sold presently. Of late, thin film photovoltaic cells have gained momentum because of their higher efficiencies. Most of these thin film devices are made out of just five elements, namely, cadmium, tellurium, selenium, indium, gallium and copper. The present manuscript describes an elegant and inexpensive molten salt-based electrolytic process for fabricating a tellurium-coated metallic substrate. A three-electrode set up was employed to coat iridium with tellurium from a molten bath containing lithium chloride, lithium oxide and tellurium tetrachloride (LiCl-Li2O-TeCl4) at 650 °C for a duration ranging from 30 to 120 min under a galvanostatic mode. The tellurium coating was observed to be thick, uniform, smooth and homogeneous. Additionally, the deposited tellurium did not chemically react with the iridium substrate to form intermetallic compounds, which is a good feature from the standpoint of the device’s performance characteristics. The present process, being generic in nature, shows the potential for the manufacture of both the coated substates and high-purity elements not just for tellurium but also for other p-block elements.
Study of the Effect of Grain-Boundary Misorientation on Slip Transfer in Magnesium Alloy Using a Misorientation Distribution Map
Authors: Sun Jie , Liu Jianhua , Chen Qingqiang , Lu Laixiao , Zhao Yanhua
Published: March 2022

The microstructure evolution of a Mg–Gd–Y alloy was studied using uniaxial tension combined with an electron backscatter diffraction technique. The results show that large amounts of slip transfer phenomena can be observed around the grain–boundary area after tension, and the activation of these slips depends largely on the misorientation of grain boundaries. The Mg–Gd–Y alloy shows almost randomized grain–boundary misorientation, but transferred slip traces were preferred at boundaries with misorientation around the [0001] axis between 0–30°. Theoretically, materials with a higher fraction of slip transfer at the grain–boundary area would improve the ductility. Upon comparing the two groups of magnesium alloy with different grain–boundary misorientation distributions, the one with more grain boundaries favored for slip transfer achieved higher elongation during a tension test. Therefore, in addition to weakening the texture, adjusting the misorientation of the grain boundaries appears to be a new method to improve the ductility of magnesium alloys.
Living Donor Liver Transplantation for Intrahepatic Cholangiocarcinoma
Authors: Rauchfuß Falk , Ali-Deeb Aladdin , Rohland Oliver , Dondorf Felix , Ardelt Michael , Settmacher Utz
Published: March 2022

Intrahepatic cholangiocarcinoma is in most transplant regions a contraindication for liver transplantation, even ruling out an active waiting list registration. However, recent studies showed that well-selected patients after a neo-adjuvant treatment benefit from liver transplantation with good long-term outcomes. The role of living donor liver transplantation is unclear for this indication. The current study focuses on LDLT for intrahepatic cholangiocarcinoma.
FDG PET/CT to Predict Recurrence of Early Breast Invasive Ductal Carcinoma
Authors: Jo Joon-Hyung , Chung Hyun Woo , So Young , Yoo Young Bum , Park Kyoung Sik , Nam Sang Eun , Lee Eun Jeong , Noh Woo Chul
Published: March 2022

This study investigated the prognostic value of FDG PET/CT radiomic features for predicting recurrence in patients with early breast invasive ductal carcinoma (IDC). The medical records of consecutive patients who were newly diagnosed with primary breast IDC after curative surgery were reviewed. Patients who received any neoadjuvant treatment before surgery were not included. FDG PET/CT radiomic features, such as a maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), skewness, kurtosis, entropy, and uniformity, were measured for the primary breast tumor using LIFEx software to evaluate recurrence-free survival (RFS). A total of 124 patients with early breast IDC were evaluated. Eleven patients had a recurrence (8.9%). Univariate survival analysis identified large tumor size (>2 cm, p = 0.045), high Ki-67 expression (≥30%, p = 0.017), high AJCC prognostic stage (≥II, p = 0.044), high SUVmax (≥5.0, p = 0.002), high MTV (≥3.25 mL, p = 0.044), high TLG (≥10.5, p = 0.004), and high entropy (≥3.15, p = 0.003) as significant predictors of poor RFS. After multivariate survival analysis, only high MTV (p = 0.045) was an independent prognostic predictor. Evaluation of the MTV of the primary tumor by FDG PET/CT in patients with early breast IDC provides useful prognostic information regarding recurrence.
Serum Pepsinogens Combined with New Biomarkers Testing Using Chemiluminescent Enzyme Immunoassay for Non-Invasive Diagnosis of Atrophic Gastritis: A Prospective, Multicenter Study
Authors: Chapelle Nicolas , Osmola Malgorzata , Martin Jérôme , Blin Justine , Leroy Maxime , Jirka Iva , Moussata Driffa , Lamarque Dominique , Olivier Raphael , Tougeron David , Hay-Lombardie Anne , Bigot-Corbel Edith , Masson Damien , Mosnier Jean-François , Matysiak-Budnik Tamara
Published: March 2022

Background: Analysis of serum biomarkers for the assessment of atrophic gastritis (AG), a gastric precancerous lesion, is of growing interest for identification of patients at increased risk of gastric cancer. The aim was to analyze the diagnostic performance of serum pepsinogen testing using another method, chemiluminescent enzyme immunoassay (CLEIA), as well as of other new potential biomarkers. Material and Methods: The sera of patients considered at increased risk of gastric cancer and undergoing upper endoscopy collected in our previous prospective, multicenter study were tested for pepsinogen I (PGI) and II (PGII), interleukin-6 (IL-6), human epididymal protein 4 (HE-4), adiponectin, ferritin and Krebs von den Lungen (KL-6) using the CLEIA. The diagnostic performance for the detection of AG was calculated by taking histology as the reference. Results: In total, 356 patients (162 men (46%); mean age 58.6 (±14.2) years), including 152 with AG, were included. For the detection of moderate to severe corpus AG, sensitivity and specificity of the pepsinogen I/II ratio were of 75.0% (95%CI 57.8–87.9) and 92.6% (88.2–95.8), respectively. For the detection of moderate to severe antrum AG, sensitivity of IL-6 was of 72.2% (95%CI 46.5–90.3). Combination of pepsinogen I/II ratio or HE-4 showed a sensitivity of 85.2% (95%CI 72.9–93.4) for the detection of moderate to severe AG at any location. Conclusion: This study shows that PG testing by CLEIA represents an accurate assay for the detection of corpus AG. Additionally, IL-6 and HE-4 may be of interest for the detection of antrum AG. Mini-abstract: Pepsinogens testing by chemiluminescent enzyme immunoassay is accurate for the detection of corpus atrophic gastritis. IL-6 and HE-4 maybe of interest for the detection of antrum atrophic gastritis.
Fully Automated Thrombus Segmentation on CT Images of Patients with Acute Ischemic Stroke
Authors: Mojtahedi Mahsa , Kappelhof Manon , Ponomareva Elena , Tolhuisen Manon , Jansen Ivo , Bruggeman Agnetha A. E. , Dutra Bruna G. , Yo Lonneke , LeCouffe Natalie , Hoving Jan W. , van Voorst Henk , Brouwer Josje , Terreros Nerea Arrarte , Konduri Praneeta , Meijer Frederick J. A. , Appelman Auke , Treurniet Kilian M. , Coutinho Jonathan M. , Roos Yvo , van Zwam Wim , Dippel Diederik , Gavves Efstratios , Emmer Bart J. , Majoie Charles , Marquering Henk
Published: March 2022

Thrombus imaging characteristics are associated with treatment success and functional outcomes in stroke patients. However, assessing these characteristics based on manual annotations is labor intensive and subject to observer bias. Therefore, we aimed to create an automated pipeline for consistent and fast full thrombus segmentation. We used multi-center, multi-scanner datasets of anterior circulation stroke patients with baseline NCCT and CTA for training (n = 228) and testing (n = 100). We first found the occlusion location using StrokeViewer LVO and created a bounding box around it. Subsequently, we trained dual modality U-Net based convolutional neural networks (CNNs) to segment the thrombus inside this bounding box. We experimented with: (1) U-Net with two input channels for NCCT and CTA, and U-Nets with two encoders where (2) concatenate, (3) add, and (4) weighted-sum operators were used for feature fusion. Furthermore, we proposed a dynamic bounding box algorithm to adjust the bounding box. The dynamic bounding box algorithm reduces the missed cases but does not improve Dice. The two-encoder U-Net with a weighted-sum feature fusion shows the best performance (surface Dice 0.78, Dice 0.62, and 4% missed cases). Final segmentation results have high spatial accuracies and can therefore be used to determine thrombus characteristics and potentially benefit radiologists in clinical practice.
Memory Effects in High-Dimensional Systems Faithfully Identified by Hilbert–Schmidt Speed-Based Witness
Authors: Mahdavipour Kobra , Khazaei Shadfar Mahshid , Rangani Jahromi Hossein , Morandotti Roberto , Lo Franco Rosario
Published: March 2022

A witness of non-Markovianity based on the Hilbert–Schmidt speed (HSS), a special type of quantum statistical speed, has been recently introduced for low-dimensional quantum systems. Such a non-Markovianity witness is particularly useful, being easily computable since no diagonalization of the system density matrix is required. We investigate the sensitivity of this HSS-based witness to detect non-Markovianity in various high-dimensional and multipartite open quantum systems with finite Hilbert spaces. We find that the time behaviors of the HSS-based witness are always in agreement with those of quantum negativity or quantum correlation measure. These results show that the HSS-based witness is a faithful identifier of the memory effects appearing in the quantum evolution of a high-dimensional system with a finite Hilbert space.
Minimum Adversarial Examples
Authors: Du Zhenyu , Liu Fangzheng , Yan Xuehu
Published: March 2022

Deep neural networks in the area of information security are facing a severe threat from adversarial examples (AEs). Existing methods of AE generation use two optimization models: (1) taking the successful attack as the objective function and limiting perturbations as the constraint; (2) taking the minimum of adversarial perturbations as the target and the successful attack as the constraint. These all involve two fundamental problems of AEs: the minimum boundary of constructing the AEs and whether that boundary is reachable. The reachability means whether the AEs of successful attack models exist equal to that boundary. Previous optimization models have no complete answer to the problems. Therefore, in this paper, for the first problem, we propose the definition of the minimum AEs and give the theoretical lower bound of the amplitude of the minimum AEs. For the second problem, we prove that solving the generation of the minimum AEs is an NPC problem, and then based on its computational inaccessibility, we establish a new third optimization model. This model is general and can adapt to any constraint. To verify the model, we devise two specific methods for generating controllable AEs under the widely used distance evaluation standard of adversarial perturbations, namely Lp constraint and SSIM constraint (structural similarity). This model limits the amplitude of the AEs, reduces the solution space’s search cost, and is further improved in efficiency. In theory, those AEs generated by the new model which are closer to the actual minimum adversarial boundary overcome the blindness of the adversarial amplitude setting of the existing methods and further improve the attack success rate. In addition, this model can generate accurate AEs with controllable amplitude under different constraints, which is suitable for different application scenarios. In addition, through extensive experiments, they demonstrate a better attack ability under the same constraints as other baseline attacks. For all the datasets we test in the experiment, compared with other baseline methods, the attack success rate of our method is improved by approximately 10%.
The Retentivity of Four Kinds of Shadowing Properties in Non-Autonomous Discrete Dynamical Systems
Authors: Jiang Yongxi , Lu Tianxiu , Pi Jingmin , Anwar Waseem
Published: March 2022

In this paper, four kinds of shadowing properties in non-autonomous discrete dynamical systems (NDDSs) are discussed. It is pointed out that if an NDDS has a F-shadowing property (resp. ergodic shadowing property, d¯ shadowing property, d̲ shadowing property), then the compound systems, conjugate systems, and product systems all have accordant shadowing properties. Moreover, the set-valued system (K(X),f¯1,∞) induced by the NDDS (X,f1,∞) has the above four shadowing properties, implying that the NDDS (X,f1,∞) has these properties.
Prediction Method of Soft Fault and Service Life of DC-DC-Converter Circuit Based on Improved Support Vector Machine
Authors: Hou Yuntao , Wu Zequan , Cai Xiaohua , Dong Zhongge
Published: March 2022

A data-driven prediction method is proposed to predict the soft fault and estimate the service life of a DC–DC-converter circuit. First, based on adaptive online non-bias least-square support-vector machine (AONBLSSVM) and the double-population particle-swarm optimization (DP-PSO), the prediction model of the soft fault is established. After analyzing the degradation-failure mechanisms of multiple key components and considering the influence of the co-degradation of these components over time on the performance of the circuit, the output ripple voltage is chosen as the fault-characteristic parameter. Finally, relying on historical output ripple voltages, the prediction model is utilized to gradually deduce the predicted values of the fault-characteristic parameter; further, in conjunction with the circuit-failure threshold, the soft fault and the service life of the circuit can be predicted. In the simulation experiment, (1) a time-series prediction is made for the output ripple voltage using the model proposed herein and the online least-square support-vector machine (OLS-SVM). Comparative analyses of fitting-assessment indicators of the predicted and experimental curves confirm that our model is superior to OLS-SVM in both modeling efficiency and prediction accuracy. (2) The effectiveness of the service life prediction method of the circuit is verified.
A Novel Approach to the Partial Information Decomposition
Authors: Kolchinsky Artemy
Published: March 2022

We consider the “partial information decomposition” (PID) problem, which aims to decompose the information that a set of source random variables provide about a target random variable into separate redundant, synergistic, union, and unique components. In the first part of this paper, we propose a general framework for constructing a multivariate PID. Our framework is defined in terms of a formal analogy with intersection and union from set theory, along with an ordering relation which specifies when one information source is more informative than another. Our definitions are algebraically and axiomatically motivated, and can be generalized to domains beyond Shannon information theory (such as algorithmic information theory and quantum information theory). In the second part of this paper, we use our general framework to define a PID in terms of the well-known Blackwell order, which has a fundamental operational interpretation. We demonstrate our approach on numerous examples and show that it overcomes many drawbacks associated with previous proposals.
Meaningful Encounters with the Built Environment as the Basis for Urban Environmental Education
Authors: Hilander Markus , Tani Sirpa
Published: March 2022

As the majority of people in the world now live in cities, it makes sense to question the state of urban environmental education and how it could be developed going forward. In this article, we suggest a way forward based on the essays written by Finnish university students. The students reflected on their relationship with an environment that is meaningful to them. In the essays selected for this case study (n = 25), the built environment of Helsinki—the capital of Finland—and its characteristics are in focus. In this qualitative research, inductive content analysis was used for processing the essays. Many students described the connections between urban nature and built environments, but the results also show that the built environment is especially significant because of its social and cultural aspects. Students described the importance of social bonding and how meeting different sorts of people supports their acceptance of diverse perspectives. Personal experiences and meanings attached to the city, as well as their lack, were mentioned; that is, without these personal bonds, meaningful relations to urban environments cannot be developed. Architecture, aesthetics, and soundscapes were also mentioned. In addition, it was expressed that feeling safe in the built environment is important. In urban environmental education, it is therefore important to pay attention to the social and cultural aspects, too, and not just to the ecological dimension. It is crucial that children and young people have access to the city in order to construct their personal relationships with the built environment.
An Analysis of Hardware Design of MLWE-Based Public-Key Encryption and Key-Establishment Algorithms
Authors: Nguyen Tuy Tan , Nguyen Tram Thi Bao , Lee Hanho
Published: March 2022

This paper presents a review of module ring learning with errors-based (MLWE-based) public-key encryption and key-establishment algorithms. In particular, we introduce the preliminaries of public key cryptography, MLWE-based algorithms, and arithmetic operations in post-quantum cryptography. We then focus on analyzing the state-of-the-art hardware architecture designs of CRYSTALS-Kyber at different security levels, including hardware architectures for Kyber-512, Kyber-768, and Kyber-1024. This analysis is dedicated to providing complete guidelines for selecting the most suitable CRYSTALS-Kyber hardware architecture to apply in post-quantum cryptography-based security systems in reality, with different requirements of security levels and hardware efficiency.
Class-GE2E: Speaker Verification Using Self-Attention and Transfer Learning with Loss Combination
Authors: Bae Ara , Kim Wooil
Published: March 2022

Recent studies prove that speaker verification performance improves by employing an attention mechanism compared to using temporal and statistical pooling techniques. This paper proposes an advanced multi-head attention method, which utilizes a sorted vector of the frame-level features to consider a higher correlation. In this study, we also propose a transfer learning scheme to maximize the effectiveness of the two loss functions, which are the classifier-based cross entropy loss function and metric-based GE2E loss function, to learn the distance between embeddings. The sorted multi-head attention (SMHA) method outperforms the conventional attention methods showing 4.55% in equal error rate (EER). The proposed transfer learning scheme with Class-GE2E loss function significantly improved our attention-based systems. In particular, the EER of the SMHA decreased to 4.39% by employing transfer learning with Class-GE2E loss. The experimental results demonstrate that our effort to include a greater correlation between frame-level features for multi-head attention processing, and the combining of two different loss functions through transfer learning, is highly effective for improving speaker verification performance.
Volatile Memristor in Leaky Integrate-and-Fire Neurons: Circuit Simulation and Experimental Study
Authors: Samardzic Natasa M. , Bajic Jovan S. , Sekulic Dalibor L. , Dautovic Stanisa
Published: March 2022

In this paper, circuit implementation of a leaky integrate-and-fire neuron model with a volatile memristor was proposed and simulated in the SPICE simulation environment. We demonstrate that simple leaky integrate-and-fire (LIF) neuron models composed of: volatile memristor, membrane capacitance and neuron resistance can mimic spatial and temporal integration, firing function and signal decay. The existing leaky term originates from the recovery of the initial resistive state in the memristor in the spontaneous reset cycle, which is essential for emulating the forgetting process in all-memristive neural networks (MNNs). Furthermore, a diffusive perovskite memristor was used to validate the model where intrinsic memristors’ capacitance acts as neuron membrane capacitance. Good agreement with experimental and simulation results was observed. Volatility, as an inherent property of specific memristors, eliminates the need for usage of an additional peripheral circuit which will reinitialize device state, thus allowing the development of energy-efficient, large scale complex memristive neural networks. The presented circuit level model of LIF neurons can facilitate the design of MNNs.
Low Threshold Voltage Shift in AlGaN/GaN MIS-HEMTs on Si Substrate Using SiNx/SiON as Composite Gate Dielectric
Authors: Zhang Xiaodong , Wei Xing , Zhang Peipei , Zhang Hui , Zhang Li , Deng Xuguang , Fan Yaming , Yu Guohao , Dong Zhihua , Fu Houqiang , Cai Yong , Fu Kai , Zhang Baoshun
Published: March 2022

This study has demonstrated AlGaN/GaN metal-insulator-semiconductor high electron mobility transistors (MIS-HEMTs) on Si substrates with a SiNx/SiON composite gate dielectric. The threshold voltage shift in the devices was investigated. The MIS-HEMTs with the SiNx/SiON composite gate dielectric exhibited superior threshold voltage uniformity and small threshold voltage hysteresis than the reference device with SiNx only gate dielectric. The variation of the device threshold voltage was mainly related to trapping process by the interface states, as confirmed by band diagrams of MIS-HEMTs at different gate biases. Based on frequency-dependent capacitance measurements, interface state densities of the devices with the composite and single gate dielectrics were extracted, where the former showed much smaller interface state density. These results indicate that the SiNx/SiON composite gate dielectric can effectively improve the device performance of GaN-based MIS-HEMTs and contribute to the development of high-performance GaN electronic devices.
Scalable Room Occupancy Prediction with Deep Transfer Learning Using Indoor Climate Sensor
Authors: Stjelja Davor , Jokisalo Juha , Kosonen Risto
Published: March 2022

An important instrument for achieving smart and high-performance buildings is Machine Learning (ML). A lot of research has been done in exploring the ML models for various applications in the built environment such as occupancy prediction. Nevertheless, the research focused mostly on analyzing the feasibility and performance of different supervised ML models but has rarely focused on practical applications and the scalability of those models. In this study, a transfer learning method is proposed as a solution to typical problems in the practical application of ML in buildings. Such problems are scaling a model to a different building, collecting ground truth data necessary for training the supervised model, and assuring the model is robust when conditions change. The practical application examined in this work is a deep learning model used for predicting room occupancy using indoor climate IoT sensors. This work proved that it is possible to significantly reduce the length of ground truth data collection to only two days. The robustness of the transferred model was tested as well, where performance stayed on a similar level if a suitable normalization technique was used. In addition, the proposed methodology was tested with room occupancy level prediction, showing slightly lower performance. Finally, the importance of understanding the performance metrics is crucial for market adoption of ML-based solutions in the built environment. Therefore, in this study, additional analysis was done by presenting the occupancy prediction model performance in understandable ways from the practical perspective.
Features, Mechanisms and Optimization of Embodied Carbon Emissions for Energy Supply Bases: Case Study of Shanxi, China
Authors: Liu Qian , Dong Suocheng , Li Fujia , Cheng Hao , Li Shantong , Yang Yang
Published: March 2022

Energy supply bases (ESBs) are vital regions, intended to satisfy global energy demands and secure global energy supplies, which provide large amounts of energy products to their host countries (and even the world through trade). However, due to long-term dependency on energy trade, ESBs are facing the dual pressure of reaching emission reduction targets and securing energy supplies and have become one of the main obstacles for host countries trying to reach emission reduction targets. (1) Methods: We used the EEBT model, SDA model, and CR model to explore the spatio-temporal features and mechanisms of embodied carbon emissions in inter-provincial trade (ECEs-PT) in Shanxi. (2) Results: The spatio-temporal development characteristic of net ECEs-PT outflow in Shanxi is “from expanded coverage to enhanced agglomeration”. A total of 98% of the net ECEs-PT is highly concentrated in coal mining and washing (Coalmin), metal smelting and rolling processing (MetalSmelt) and petroleum processing, coking, and nuclear fuel processing (RefPetraol). Moreover, the ECEs-PT driving forces were technology, structure, and scale. While trade expands, the pressure of CEs reduction is increasing. We discussed optimization for different types of sectors. The results could provide scientific support for similar ESBs to reduce carbon emissions more efficiently with less disturbance to energy supply stability.
Numerical Simulation of Heat Removal from a Window Slab Partition of a Radiative Coil Coating Oven
Authors: Neno Rodrigo J. F. , Dias Beatriz S. , Navalho Jorge E. P. , Pereira José C. F.
Published: March 2022

In this work, fluid flow and heat transfer performance of a radiative coil coating oven is numerically investigated. In the coil coating oven concept under consideration, porous radiant burners provide the required energy to evaporate the volatile species (solvents) from the applied coating and to promote curing reactions. To avoid the mixing between burners flue gas (with a non-negligible oxygen content) and evaporated (combustible) solvents in the oven (which could lead to a catastrophic oven failure), a semi-transparent window in between both atmospheres is applied. To ensure the window thermal stability during the oven operation, window cooling by wall jets is considered. Different turbulence models were compared against available wall jet heat transfer correlations to select the most suitable for three-dimensional (3D) numerical simulations. Convective heat transfer correlations purposefully developed were embedded in a one-dimensional (1D) window energy model for fast performance characterization, analysing the most influencing parameters—window radiative properties, thickness, inlet temperature and velocity of wall jets, and cooling strategy. The 1D window thermal performance is compared with literature and 3D results considering the full coil coating oven, providing satisfactory confidence on the developed strategy. The 1D model is used for an optimisation study to find the minimum energy consumption while ensuring the safety requirements (maximum window temperature and thermal gradient) are met.
An Update on the Electronic Connection Issues of Low Power SWTs in AC-Coupled Systems: A Review and Case Study
Authors: Bufanio Rubén , Arribas Luis , de la Cruz Javier , Karlsson Timo , Amadío Mariano , Zappa Andrés Enrique , Marasco Damián
Published: March 2022

Small wind turbines (SWTs) have been a common option for rural electrification during recent decades. Recently, an application for connecting these devices to the grid was proposed. This requires an AC-coupled configuration, which brings the need for a power converter necessary for connection to the AC bus. This publication analyzes the requirements and commercial availability of such power converters, coming first to a conclusion on the lack of existing electronic power converters for SWTs, mainly in the lowest power range (<1 kW). As a result of this need, PV power converters were proposed, as they are both economically competitive and commercially available. The use of PV power converters for SWT AC-coupled applications is therefore analyzed as well, bringing a second conclusion: their use is possible but not straightforward, as some adaptations have to be taken into account. Finally, a suggested adaptation is proposed, in terms of hardware and software; the first field results of a prototype are presented, and they look very promising.
A Novel Solution Method for the Distribution Network Reconfiguration Problem Based on a Search Mechanism Enhancement of the Improved Harmony Search Algorithm
Authors: Dias Santos Josephy , Marques Frederico , Garcés Negrete Lina Paola , Andrêa Brigatto Gelson A. , López-Lezama Jesús M. , Muñoz-Galeano Nicolás
Published: March 2022

This paper addresses the problem of Distribution Systems Reconfiguration (DSR), which consists of finding the state of switching devices (open or closed) in a given distribution network, aiming to minimize active power loses. DSR is modeled as a mixed-integer non-linear optimization problem, in which the integer variables represent the state of the switches, and the continuous variables represent the power flowing through the branches. Given the multi-modal and non-convex nature of the problem, an improved harmony search (IHS) algorithm is proposed to solve the DSR problem. The main novelty of this approach is the inclusion of a Path Relinking phase which accelerates convergence of the DSR problem. Several tests were carried out in four benchmark distribution systems, evidencing the effectiveness and applicability of the proposed approach.
Early Warning Weather Hazard System for Power System Control
Authors: Božiček Amalija , Franc Bojan , Filipović-Grčić Božidar
Published: March 2022

Power systems and their primary components, mostly the transmission and distribution of overhead lines, substations, and other power facilities, are distributed in space and are exposed to various atmospheric and meteorological conditions. These conditions carry a certain level of risk for reliable electrical power delivery. Various atmospheric hazards endanger the operation of power systems, where the most significant are thunderstorms, wildfire events, and floods which can cause various ranges of disturbances, faults, and damages to the power grid, or even negatively affect the quality of life. By utilizing a weather monitoring and early warning system, it is possible to ensure a faster reaction against different weather-caused fault detections and elimination, to ensure a faster and more adequate preparation for fighting extreme weather events, while maintaining overhead line protection and fault elimination. Moreso, it is possible to bypass overhead lines that have the highest risk of unfavorable meteorological events and hazards, and reroute the energy, thus providing electricity to endangered areas in times of need while minimizing blackouts, and consequently, improving the quality of human life. This paper will present an analysis of the various risks of atmospheric phenomena, in the meteorological and climate context, and discuss various power system components, the power system control, operations, planning, and power quality. A concept with the main functionalities and data sources needed for the establishment of an early warning weather hazard system will be proposed. The proposed solution can be used as a utility function in power system control to mitigate risks to the power system due to atmospheric influences and ongoing climate change.
The Effect of Sample’s Dimension and Cutting Technology on Magnetization and Specific Iron Losses of FeSi Laminations
Authors: Dems Maria , Komeza Krzysztof , Gmyrek Zbigniew , Szulakowski Jacek
Published: March 2022

The process of cutting laminations from which the cores of electric machines are built causes a change in their magnetic properties and losses, which can significantly affect machines’ parameters, mainly losses of power and efficiency. Electric motors are a significant consumer of electricity; therefore, the problem of increasing their efficiency is fundamental from the point of view of environmental impact. The subject of the work is the study of the influence of punching and laser cutting on the magnetization and loss characteristics of sheets, taking into account the phenomenon of magnetic anisotropy. For this purpose, samples cut in different directions were tested. As the direction of the field action in the motor core varies in different parts of the machine and time moments, it was investigated how to obtain average characteristics for different directions of magnetization. Then, a simplified method for determining the characteristics of punched sheets of various widths based on small-width samples and water cut samples is presented. The proposed solutions allow for refinement of the calculations of magnetic circuits with a simplified consideration of the influence of punching.
Vertical Dynamic Impedance of a Viscoelastic Pile in Arbitrarily Layered Soil Based on the Fictitious Soil Pile Model
Authors: Yang Xiaoyan , Wang Lixing , Wu Wenbing , Liu Hao , Jiang Guosheng , Wang Kuihua , Mei Guoxiong
Published: March 2022

The vertical vibration of a viscoelastic pile immersed in arbitrarily layered soil is investigated by taking the interaction among pile, pile surrounding soil (PSS) and pile end soil (PES) into account. Firstly, considering both the stratification and stress wave effect of soil, a mathematical model of the pile–soil system is established based on the fictitious soil pile (FSP) model. Then, utilizing the impedance function transfer method and Laplace transform technique, the analytical solutions of the vertical dynamic impedance of pile are derived in the frequency domain. The analytical solutions are validated by comparing them with other existing solutions. Finally, a parametric study is put forward to investigate the properties of PES on the vertical dynamic impedance of pile. The results reveal that the properties of PES have a significant effect on the vertical dynamic impedance of pile, but there is a critical influence thickness for this effect. For the cases of the PES thickness exceeding the critical influence thickness, further increase of PES thickness will not affect the dynamic behavior of the pile–soil system.
Evaluation of NVIDIA Xavier NX Platform for Real-Time Image Processing for Plasma Diagnostics
Authors: Jabłoński Bartłomiej , Makowski Dariusz , Perek Piotr , Nowak vel Nowakowski Patryk , Sitjes Aleix Puig , Jakubowski Marcin , Gao Yu , Winter Axel ,   The W-X Team
Published: March 2022

Machine protection is a core task of real-time image diagnostics aiming for steady-state operation in nuclear fusion devices. The paper evaluates the applicability of the newest low-power NVIDIA Jetson Xavier NX platform for image plasma diagnostics. This embedded NVIDIA Tegra System-on-a-Chip (SoC) integrates a Graphics Processing Unit (GPU) and Central Processing Unit (CPU) on a single chip. The hardware differences and features compared to the previous NVIDIA Jetson TX2 are signified. Implemented algorithms detect thermal events in real-time, utilising the high parallelism provided by the embedded General-Purpose computing on Graphics Processing Units (GPGPU). The performance and accuracy are evaluated on the experimental data from the Wendelstein 7-X (W7-X) stellarator. Strike-line and reflection events are primarily investigated, yet benchmarks for overload hotspots, surface layers and visualisation algorithms are also included. Their detection might allow for automating real-time risk evaluation incorporated in the divertor protection system in W7-X. For the first time, the paper demonstrates the feasibility of complex real-time image processing in nuclear fusion applications on low-power embedded devices. Moreover, GPU-accelerated reference processing pipelines yielding higher accuracy compared to the literature results are proposed, and remarkable performance improvement resulting from the upgrade to the Xavier NX platform is attained.
Studying the Wake of a Tidal Turbine with an IBM-LBM Approach Using Realistic Inflow Conditions
Authors: Grondeau Mickael , Guillou Sylvain , Poirier Jean , Mercier Philippe , Poizot Emmnuel , Méar Yann
Published: March 2022

The lattice Boltzmann method is used to model a horizontal axis tidal turbine. Because tidal turbines generally operate in highly turbulent flows, a synthetic eddy method is implemented to generate realistic turbulent inflow condition. The approach makes use of the open-source code Palabos. Large eddy simulation is employed. A coupling between an immersed boundary method and a wall model is realized to model the turbine. Calculations are performed at two different turbulence rates. The upstream flow condition is first set up to match with experimental results. Numerical simulations of a tidal turbine with realistic turbulent inflow conditions are then realized with the lattice Boltzmann method. The approach is found to be in good agreement with experimental data. Cases with three different inflow turbulence rates are simulated. An almost linear evolution with the turbulence rate is observed for the axial velocity deficit. An analysis of the propagation of tip-vortices in the close wake is carried out. It is found that turbulence has a great impact on the tip-vortices propagation envelope.
A Novel Algorithm for Hydrostatic-Mechanical Mobile Machines with a Dual-Clutch Transmission
Authors: Xiang Yusheng , Li Ruoyu , Brach Christine , Liu Xiaole , Geimer Marcus
Published: March 2022

Mobile machines using a hydrostatic transmission is highly efficient under lower working-speed condition but less capable at higher transport velocities. To enhance overall efficiency, we have improved the powertrain design by combining a hydrostatic transmission with a dual-clutch transmission (DCT). Compared with other mechanical gearboxes, the DCT avoids the interruption of torque transmission in the process of shifting without sacrificing more transmission efficiency. However, there are some problems of unstable torque transmission during the shifting process, and an excessive torque drop occurring at the end of the gear shift, which result in a poor drive comfort. To enhance the performance of the novel structural possibility of powertrain design, we designed a novel control strategy, which maintains the sliding in the torque phase and reduces the difference before and after the engagement, for the motor torque and the clutch torques during the shifting process, and then validated the control effect with model-based simulation. As a result, the control strategy employing clutch and motor torque control achieve a smooth shifting process since the drive torque is well tracked, and highly dynamical actuators are not required. As another benefit, only two calibration parameters are designed and actually needed to adjust the control performance systematically, even for any different sizes machines. Our research indicates the possibility to adopt dual-clutch in the field of construction machines.
Extended Digital Programmable Low-Pass Filter for Direct Noise Filtering of Three-Phase Variables in Low-Cost AC Drives
Authors: Choi Chinchul , Lee Wootaik
Published: March 2022

This paper proposes an extended structure of a programmable low-pass filter (PLPF) for direct filtering of three-phase variables in automotive AC drives. The PLPF adjusts its cut-off frequency according to the synchronous frequency of the AC motors. Moreover, it compensates gain attenuation and phase delay of the fundamental component signal. The conventional PLPF can only be applied to variables in the stationary reference frame. In order to obtain filtered three-phase variables, an inverse reference frame transformation is required. However, the extended structure, which includes reference frame transformations, can be directly applied to three-phase variables and remove the noise in the earlier stage before transformation. The extended PLPF is more useful for making signal flows clear and intuitive when organizing an algorithm that directly requires three-phase variables. The extended PLPF requires a moderate computational load corresponding with that of the conventional PLPF because its formula is simplified by use of Kirchhoff’s law. The reduction in the execution time is critical for implementing the filter based on low-cost microcontrollers. Using case studies, dead-time compensations and open-phase fault detection for AC drives show the effectiveness of the extended PLPF.
IoT-Based PV Array Fault Detection and Classification Using Embedded Supervised Learning Methods
Authors: Hojabri Mojgan , Kellerhals Samuel , Upadhyay Govinda , Bowler Benjamin
Published: March 2022

Faults on individual modules within a photovoltaic (PV) array can have a significant detrimental effect on the power efficiency and reliability of the entire PV system. In addition, PV module faults can create risks to personnel safety and fire hazards if they are not detected quickly. As IoT hardware capabilities increase and machine learning frameworks mature, better fault detection performance may be possible using low-cost sensors running machine learning (ML) models that monitor electrical and thermal parameters at an individual module level. In this paper, to evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation. The faults are emulated and applied to a real PV system, allowing the collection and labelling of panel-level measurement data. Then, different ML methods are used to classify these faults in comparison to the normal condition. Results confirm that NN obtain 93% classification accuracy for seven selected classes.
A Conceptual Transdisciplinary Framework to Overcome Energy Efficiency Barriers in Ship Operation Cycles to Meet IMO’s Initial Green House Gas Strategy Goals: Case Study for an Iranian Shipping Company
Authors: Vakili Seyed Vahid , Ballini Fabio , Dalaklis Dimitrios , Ölçer Aykut I.
Published: March 2022

Through a systematic, holistic and transdisciplinary approach and by proposing five phases of “goal information”, “system analyzing”, “scenario construction”, “multi-criteria assessment” and “strategy building”, the study offers a process for recognizing and prioritizing energy-efficient barriers in the ship’s operational cycle according to decision-makers’ concerns. The study utilized the proposed conceptual transdisciplinary framework for overcoming energy efficiency barriers in ship operating cycles. The framework categorizes the barriers in the operational cycle into five disciplines, i.e., operations, policy and regulations, technology and innovation, human element and economics, and applies the framework to an Iranian shipping company. The results show that the economic discipline has the highest priority, and the human discipline has the least importance for the company’s decision makers. In addition, “adverse selection” (operational discipline), “policy implementation” (policy and regulatory discipline), “split incentives” (economic discipline), “limited access to capital” (economic discipline) and “imperfect budgeting” were the main barriers to energy efficiency in the company.
Employee Financial Wellness Programs (EFWPs) as an Innovation in Incentive Systems of Energy Sector Enterprises in Poland during the COVID-19 Pandemic—Current Status and Development Prospects
Authors: Malkowska Agnieszka , Tokarz-Kocik Anna , Drela Karolina , Bera Anna
Published: March 2022

The COVID-19 pandemic has caused a loss of the sense of financial security among employees. The consequence is decreased productivity, affecting employers who also suffer related costs. Actions to support employees’ financial wellness are therefore important and necessary, especially in the case of those working in the strategic sector ensuring national energy security. The main aim of the article is to present, from the theoretical and practical point of view, the essence and role of Employee Financial Wellness Programs (EFWPs) in the creation of an effective incentive system in energy sector companies in Poland during the crisis caused by the COVID-19 pandemic. The following research methods were used: critical analysis of source literature, comparative analysis of existing data and the descriptive method with elements of deductive reasoning. In order to collect primary data, the indirect survey method was used, namely, the CAWI Internet survey technique, which was addressed to the main energy generation sector entities in the country. The research has shown that financial wellness instruments are new to the Polish market, where they have been in limited operation since 2021. It was confirmed that some companies in the energy sector are using such innovative tools, but they also show the need and interest to implement more extensive and comprehensive Employee Financial Wellness Programs that will reduce the financial stress of employees while motivating them to work more efficiently during the COVID-19 pandemic.