bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Objective Optimization of Process Parameters of 45 Steel Laser Cladding Ni60PTA Alloy Powder
Authors: Li Tiankai , Long Haiyang , Qiu Changming , Wang Mingming , Li Dongdong , Dong Zhen , Gui Yongliang
Published: July 2022

When laser cladding is used to repair parts, the interaction of parameters has a significant influence on equipment performance. In order to explore the process parameters and quality of Ni60PTA coating, the statistical relationship between the process parameters (laser power, scanning speed, and powder feeding rate) and the responses (dilution ratio, ratio of layer width to height, and contact angle) was established by experiments using the response surface method (RSM) and variance analysis. The results show that the laser power is the dominant factor affecting the dilution ratio. However, the scanning speed has the greatest influence on the ratio of layer width to height and contact angle. These experimental results show that the proposed model can predict the actual data. In addition, the microstructure and microhardness of the samples prepared with the optimum process parameters were characterized. The results show that the quality of the cladding layer is good without cracks, deformation, and pores. The microstructure of the cladding zone is mainly composed of γ (Ni), FeNi3, M (M = Fe, Ni, Cr)23C6, M7C3, and CrB. The average microhardness of the coating is about 620 HV0.2, which is about 3.1 times that of 45 steel substrate.
Design and Optimization of Interface Morphology of Thermal Barrier Coatings Based on Regulation of Residual Stress: A Finite Element Simulation Study
Authors: Wang Dali , Wang Liang , Wang Weize , Zhang Xiancheng , Tu Shantung
Published: July 2022

Laser surface texture (LST) technology can be used to increase the adherence of thermal barrier coating (TBC). The primary research method is to conduct a large number of laser experiments to determine the optimal texture parameters. To minimize costs and enhance efficiency, in the current work, five types of circular pit textures were summarized; the plane strain model was established using the transient thermomechanical coupling finite element method; the residual stress field after spraying was used as the prestress field; the influence of different textures on the distribution of the residual stress field after a thermal cycling was analyzed; and the propagation law of cracks in the coating was predicted. The current work focuses on: (1) The two-dimensional cross-sectional morphology of texture; (2) the principal stress s22 perpendicular to the interface (resulting in mode I interface crack) and the shear stress s12 parallel to the interface (resulting in mode II interface crack); (3) texture variables—diameter, depth, and spacing. The results revealed that after thermal cycling, the texture in the ceramic top coat (TC) bore tensile stress of around 350 MPa. Both sides of the pit in the metallic bond coat (BC) bore tensile stress, while the bottom bore compressive stress. Among them, the positive tensile stress of the texture with a sinusoidal section was the greatest, whereas the shear stress was the least. The maximum stress in texture increased as the diameter and depth increased, while the minimum principal stress was obtained by adjusting the spacing among the adjacent textures. The stress level in the coating was reduced by selecting the appropriate texture morphology, and the crack propagation was more complex, that is, it took a longer time before reaching failure, which is expected to improve the life.
Study on Corrosion Resistance and Biological Properties of the Double Glow Plasma Nb-Zr Biological Implantation Alloying Layers
Authors: Zhao Ke , Wu Hongyan , Xiao Changle , Dong Jieyang , Ren Junzhao , Peng Zhaoxiang
Published: July 2022

In order to improve the corrosion resistance of implant materials and understand the corrosion mechanisms, we prepared a biomedical Nb-Zr alloying layer on 316L stainless steel using double-layer glow plasma surface-alloying technology and investigated the effects of gas pressures on its surface structure, mechanical properties, and corrosion behavior. In particular, the surface states of the substrate and alloying layers were investigated using 3D confocal micrographs, the water contact angle, and UV reflectance, which aims to study the effect of the surface quality on corrosion resistance and discuss the corrosion mechanisms. The results show that the working pressure has an effect on the current density, the sputtering amount of the alloying elements, and the diffusion process of the alloying elements during glow discharge. The Nb-Zr alloying layer prepared under a pressure of 40 Pa had a uniform and dense surface structure, and the distribution was island-like. A Nb-Zr alloying layer with a thickness of 15 μm was successfully obtained, including the diffusion layer and the deposition layer. Simultaneously, the elements Nb and Zr were gradually distributed along the depth, and a high Nb concentration formed in the Nb-Zr alloying layer. The solid solution formed by Zr in the Nb layer significantly improved the microhardness and corrosion resistance of the substrate. The Nb-Zr alloying layer prepared under a pressure of 40 Pa had the lowest corrosion current density and excellent corrosion resistance, which originated from the passive film formed by the Nb-Zr alloying layer that could inhibit the invasion of corrosive ions and improve the corrosion resistance. In addition, the Nb-Zr alloying layer could promote cell proliferation during long-term use and had good biocompatibility. Our study provides an efficient, high-quality processing method for the surface modification of biomedical metallic materials to form thicker Nb-Zr alloying layers as a cost-effective alternative to bulk Nb-based alloys.
Enhancing GAN-LCS Performance Using an Abbreviations Checker in Automatic Short Answer Scoring
Authors: Muhammad Ar-Razy , Permanasari Adhistya Erna , Hidayah Indriana
Published: July 2022

Automatic short answer scoring methods have been developed with various algorithms over the decades. In the Indonesian language, the string-based similarity is more commonly used. This method is difficult to accurately measure the similarity of two sentences with significantly different word lengths. This problem has been handled by the Geometric Average Normalized-Longest Common Subsequence (GAN-LCS) method by eliminating non-contributive words utilizing the Longest Common Subsequence method. However, students’ answers may vary not only in character length but also in the words they choose. For instance, some students tend only to write the abbreviations or acronyms of the phrase instead of writing meaningful words. As a result, it will reduce the intersection character between the reference answer and the student answer. Moreover, it can change the sentence structure even though it has the same meaning by definition. Therefore, this study aims to improve GAN-LCS method performance by incorporating the abbreviation checker to handle the abbreviations or acronyms found in the reference answer or student answer. The dataset used in this study consisted of 10 questions with 1 reference answer for each question and 585 student answers. The experimental results show an improvement in GAN-LCS performance that could run 34.43% faster. Meanwhile, the Root Mean Square Error (RSME) value became lower by 7.65% and the correlation value was increased by 8%. Looking forward, future studies may continue to investigate a method for automatically generate the abbreviations dictionary.
A New Development of FDOSM Based on a 2-Tuple Fuzzy Environment: Evaluation and Benchmark of Network Protocols as a Case Study
Authors: Maher Rand M. , Salih Mahmood M. , Hussein Harith A. , Ahmed Mohamed A.
Published: July 2022

Multicriteria decision-making (MCDM) is one of the most common methods used to select the best alternative from a set of available alternatives. Many methods in MCDM are presented in the academic literature, with the latest being the Fuzzy Decision by Opinion Score Method (FDOSM). The FDOSM can solve many challenges that are present in other MCDM methods. However, several problems still exist in the FDOSM and its extensions, such as uncertainty. One of the most significant problems in the use of the FDOSM is the loss of information during the conversion of a decision matrix into an opinion decision matrix. In this paper, the authors expanded the FDOSM into the 2-tuple-FDOSM to solve this problem. The methodology behind the development of the 2-tuple-FDOSM was presented. Within the methodology, definitions of the 2-tuple linguistic fuzzy method, which was used to solve the loss-of-information problem that is present in the FDSOM method, are presented. A network case study was used in the application of the 2-tuple-FDOSM. The final results show that the 2-tuple-FDOSM can be used to address the problem of loss of information. Finally, a comparison between the basic FDOSM, TOPSIS, and 2-tuple-FDOSM was presented.
Routine Screening for Central and Primary Adrenal Insufficiency during Immune-Checkpoint Inhibitor Therapy: An Endocrinology Perspective for Oncologists
Authors: Druce Irena , Tawagi Karine , Shaw Julie L. V. , Ibrahim Andrea , Lochnan Heather , Ong Michael
Published: July 2022

Background: Immune checkpoint inhibitor (ICI)-associated hypothalamic–pituitary–adrenal axis disruption can lead to hypocortisolism. This is a life-threatening but difficult to diagnose condition, due to its non-specific symptoms that overlap with symptoms of malignancy. Currently, there is no consensus on how to best screen asymptomatic patients on ICI therapy for hypophysitis with serum cortisol. Methods: A retrospective chart review of patients treated with ICI in a tertiary care centre was conducted to assess the rate of screening with cortisol and whether this had an impact on diagnosis of ICI-hypophysitis in the preclinical stage. Patients were identified as having hypophysitis with an adrenocorticotropin hormone (ACTH) deficiency based on chart review of patients with cortisol values ≤ 140 nmol/L (≤5 mcg/dL). We also assessed what proportion of cortisol values were drawn at the correct time for interpretation (between 6 AM and 10 AM). Results: Two hundred and sixty-five patients had 1301 cortisol levels drawn, only 40% of which were drawn correctly (between 6 and 10 AM). Twenty-two cases of hypophysitis manifesting with ACTH deficiency were identified. Eight of these patients were being screened with cortisol following treatment and were detected in the outpatient setting. The remaining 14 patients were not screened and were diagnosed when symptomatic, after an emergency room visit or hospital admission. Sixty percent of the cortisol tests were uninterpretable as they were not drawn within the appropriate time window. Conclusion: Measuring morning serum cortisol in asymptomatic patients on ICI therapy is a fast and inexpensive way to screen for hypophysitis and should become the standard of care. Random serum cortisol measurement has no clinical value. Education needs to be provided on when to correctly perform the test and how to interpret it and we provide an algorithm for this purpose. The adoption and validation of such an algorithm as part of routine practice could significantly reduce morbidity and mortality in patients, especially as ICI therapy is becoming increasingly commonplace.
Safety of Drugs Used during the First Wave of COVID-19: A Hospital-Registry-Based Study
Authors: Aguilera Cristina , Danés Immaculada , Guillén Elena , Vimes Alba , Bosch Montserrat , Cereza Gloria , Sánchez-Montalvá Adrián , Campos-Varela Isabel , Miarons Marta , Mestre-Torres Jaume , Agustí Antònia
Published: July 2022

The emergency of the coronavirus disease 2019 (COVID-19) pandemic led to the off-label use of drugs without data on their toxicity profiles in patients with COVID-19, or on their concomitant use. Patients included in the COVID-19 Patient Registry of a tertiary hospital during the first wave were analyzed to evaluate the adverse drug reactions (ADRs) with the selected treatments. Twenty-one percent of patients (197 out of 933) had at least one ADR, with a total of 240 ADRs. Patients with ADRs were more commonly treated with multiple drugs for COVID-19 infection than patients without ADRs (p < 0.001). They were younger (median 62 years vs. 70.1 years; p < 0.001) and took less medication regularly (69.5% vs. 75.7%; p = 0.031). The most frequent ADRs were gastrointestinal (67.1%), hepatobiliary (10.8%), and cardiac disorders (3.3%). Drugs more frequently involved included lopinavir/ritonavir (82.2%), hydroxychloroquine (72.1%), and azithromycin (66.5%). Although most ADRs recovered without sequelae, fatal cases were described, even though the role of the disease could not be completely ruled out. In similar situations, efforts should be made to use the drugs in the context of clinical trials, and to limit off-label use to those drugs with a better benefit/risk profile in specific situations and for patients at high risk of poor disease prognosis.
The Effects of 810 nm Diode Laser and Indocyanine Green on Periodontal Parameters and HbA1c in Patients with Periodontitis and Type II Diabetes Mellitus: A Randomized Controlled Study
Authors: Sufaru Irina-Georgeta , Martu Maria-Alexandra , Luchian Ionut , Stoleriu Simona , Diaconu-Popa Diana , Martu Cristian , Teslaru Silvia , Pasarin Liliana , Solomon Sorina Mihaela
Published: July 2022

The aim of this study was to investigate the effects of adjunctive periodontal therapy of 5 mg/mL indocyanine green irradiation by an 810 nm diode laser (aPDT), supplementary to scaling and root planing (SRP) in patients with periodontitis and type II diabetes mellitus (DM) compared to the SRP alone, on periodontal clinical parameters and glycated hemoglobin A1c (HbA1c). The study was conducted on 49 subjects with type II DM and periodontitis, divided into two groups: the SRP group (n = 25), who followed SRP alone, and the SRP + aPDT group (n = 24), who followed SRP and four weekly sessions of indocyanine green irradiation by an 810 nm diode laser. Plaque Index (PI), Bleeding on Probing Index (BOP), probing depth (PD), clinical attachment loss (CAL) and HbA1c were investigated at baseline and after 6 months. At 6 months, both SRP alone and SRP + aPDT generated significant reductions in all the investigated parameters; SRP + aPDT produced more significant reductions for BOP, PD and CAL (p < 0.001) but not for PI and HbA1c, than SRP alone. aPDT with indocyanine green therapy was well tolerated, with two subjects reporting slight discomfort. Therefore, aPDT with indocyanine green might represent a good adjunctive periodontal treatment option for SRP in patients with type II DM and periodontitis.
Status of North American Graduate Programs in Periodontics Providing Laser Education and Clinical Training: A Cross-Sectional Survey
Authors: Paes Batista da SIlva Andre , Irfan Uma M. , Furman Elena , Jasinevicius T. Roma
Published: July 2022

The aim of this cross-sectional study was to evaluate the status of laser use and training in the U.S. and Canadian graduate periodontology programs. A survey questionnaire was sent electronically to 55 periodontology program directors in North America. The questions focused on laser implementation, types of lasers used, for which procedures lasers were used, and level of education/clinical training provided to residents. Data were analyzed using descriptive statistics and Fisher’s exact test. Twenty-two directors responded (40%). Most programs (86%) used lasers and 89% used a diode laser. Laser treatment was the most used for periimplantitis (84%). Fifteen programs (79%) provided didactic and clinical training, with 47% programs giving 4–12 h of didactic training. In 53% of programs, residents completed 4 to10 procedures. Only 29% of programs had residents who had received a certification in lasers, with most (80%) programs requiring between 1–9 cases for certification. Of the participants not providing laser training, the major barrier was indicated as being “expense”, with 68.7% reporting plans to implement laser education. Conclusions: Most graduate periodontics programs were providing laser training and treatment. There was great variability regarding the training methods, specifically in number of dedicated laser courses, time allocated for laser training, and prerequisites for laser certification.
Regularity in Stock Market Indices within Turbulence Periods: The Sample Entropy Approach
Authors: Olbryś Joanna , Majewska Elżbieta
Published: July 2022

The aim of this study is to assess and compare changes in regularity in the 36 European and the U.S. stock market indices within major turbulence periods. Two periods are investigated: the Global Financial Crisis in 2007–2009 and the COVID-19 pandemic outbreak in 2020–2021. The proposed research hypothesis states that entropy of an equity market index decreases during turbulence periods, which implies that regularity and predictability of a stock market index returns increase in such cases. To capture sequential regularity in daily time series of stock market indices, the Sample Entropy algorithm (SampEn) is used. Changes in the SampEn values before and during the particular turbulence period are estimated. The empirical findings are unambiguous and confirm no reason to reject the research hypothesis. Moreover, additional formal statistical analyses indicate that the SampEn results are similar both for developed and emerging European economies. Furthermore, the rolling-window procedure is utilized to assess the evolution of SampEn over time.
On the Distribution of the Information Density of Gaussian Random Vectors: Explicit Formulas and Tight Approximations
Authors: Huffmann Jonathan E. W. , Mittelbach Martin
Published: July 2022

Based on the canonical correlation analysis, we derive series representations of the probability density function (PDF) and the cumulative distribution function (CDF) of the information density of arbitrary Gaussian random vectors as well as a general formula to calculate the central moments. Using the general results, we give closed-form expressions of the PDF and CDF and explicit formulas of the central moments for important special cases. Furthermore, we derive recurrence formulas and tight approximations of the general series representations, which allow efficient numerical calculations with an arbitrarily high accuracy as demonstrated with an implementation in Python publicly available on GitLab. Finally, we discuss the (in)validity of Gaussian approximations of the information density.
The Impact of the COVID-19 Pandemic on Economic Growth and Monetary Policy: An Analysis from the DSGE Model in Vietnam
Authors: Nguyen Trung Duc , Le Anh Hoang , Thalassinos Eleftherios I. , Trieu Lanh Kim
Published: July 2022

Facing the current complicated situation of the COVID-19 pandemic, in addition to medical efforts on disease prevention and treatment, governments of countries also have to come up with solutions to deal with the negative impacts of the pandemic on the economy. This study aims to provide specific, comprehensive, and scientific estimates of the impact of the COVID-19 pandemic on the Vietnamese economy. By using the Bayesian method to estimate DSGE models, research results show that a shock increase by one standard deviation (about 1.49% increase in the probability of a COVID-19 outbreak) to the Covid status variable immediately reduces the output gap by 0.94%. However, this effect only lasts for one quarter, and the output gap widens again. Meanwhile, refinancing interest rates, inflation, and exchange rate changes also have an immediate decline in response to this shock, but the magnitude of the reduction is relatively small.
The Impact of the National Economic Recovery Program and Digitalization on MSME Resilience during the COVID-19 Pandemic: A Case Study of Bank Rakyat Indonesia
Authors: Supari Supari , Anton Hendranata
Published: July 2022

This study aims to evaluate the impact of the National Economic Recovery Program—Pemulihan Ekonomi Nasional (PEN) and digitalization on micro, small, and medium enterprises’ (MSMEs) resilience during the COVID-19 pandemic. This research is based on primary data from a survey of 6009 Bank Rakyat Indonesia customers conducted from March–June 2021. Using the generalized ordered logistic regression technique, this study found that a combination of new loans, credit restructuring, and/or interest subsidies was the most successful PEN for enhancing MSME resilience. Meanwhile, providing new loans merely improved liquidity, not sales or profitability. However, just providing a restructuring program weakened resiliency. This research also discovered that MSMEs that have been digitalizing for more than a year are more resilient than those that have not. This study highlights the necessity of offering several interventions for MSMEs and assisting MSMEs in going digital to improve MSME resilience during the COVID-19 pandemic.
Survey on Online Learning at Universities of Slovakia, Czech Republic and Kazakhstan during the COVID-19 Pandemic
Authors: Guncaga Jan , Lopuchova Jana , Ferdianova Vera , Zacek Martin , Ashimov Yeskendyr
Published: July 2022

The article points out some of the challenges faced by students at the University of Ostrava, Comenius University in Bratislava and Al-Farabi Kazakh National University, who had almost no previous practice in online teaching during the COVID-19 pandemic situation. The students from the mentioned universities were interviewed to share their experiences with online teaching and learning during the COVID-19 lockdown. In this paper, qualitative pedagogical research in the local conditions of the three mentioned universities is used, with the processing of answers of selected students who were interviewed as an available sample. The students were also asked to describe their impressions of this situation from their social and personal points of view. The focus is on the positive and negative aspects, boundaries, and problems of online university teaching during the pandemic situation, as well as changes in the personal life of the students. The aim of this introductory small-scale study is to provide a basis for future research on the impact that the COVID-19 situation has had on the educational process at the above-mentioned universities, as well as to assist educational providers in foreseeing and eliminating the possible problems of lecturers when establishing an online educational environment. Some conclusions are drawn from the interviews that offer potential for further research in educational science, because many difficulties from the students’ point of view are related to their social status, lack of social contact, technical problems with Internet connections, the carrying out of lectures and the exams of students. The students’ answers are categorized, and each category is described.
Who Teaches Physics in Croatian Elementary Schools?
Authors: Erceg Nataša , Nikolaus Patricija , Nikolaus Vesko , Poljančić Beljan Ivana
Published: July 2022

The shortage of physics teachers is a global and persistent problem, resulting in the employment of nonprofessionals who cannot teach physics in a student-centered, high-quality and effective manner. This situation has implications for the educational policies of governments, universities, and schools. The aim of this study is to identify whether there is a shortage of elementary school physics teachers in Croatia. This type of survey was conducted for the first time in the country. Based on the online survey, we asked elementary school principals how many employees they have working as physics teachers in their schools and the type of education of these teachers. A total of 260 principals responded to the survey, representing 25% of the total population of elementary school principals in Croatia, where physics is taught according to the regular program in the 2020/2021 school year. The respondents did not mention the availability of physics teacher vacancies in their schools. However, they mentioned the engagement of nonprofessional substitute physics teachers accounting for 14% of all physics teaching positions in schools that participated in our study. Among the nonprofessional substitutes, most were from the social-humanities field. Differences in the representation of nonprofessional physics teachers by county were also found, indicating different causes of physics teacher shortages in different geographic areas. The survey results provide a basis for future policy decisions that can lead to better results in resolving the issue of physics teacher shortages in Croatia and beyond.
Image Segmentation Methods for Subpicture Partitioning in the VVC Video Encoder
Authors: Panagou Natalia , Belememis Panagiotis , Koziri Maria
Published: July 2022

The emergence of the new generation video coding standard, Versatile Video Coding (VVC), has brought along novel features rendering the new standard more efficient and flexible than its predecessors. Aside from efficient compression of 8 k or higher camera-captured content, VVC also supports a wide range of applications, including computer-generated content, high dynamic range (HDR) content, multilayer and multi-view coding, video region extraction, as well as 360° video. One of the newly introduced coding tools in VVC, offering extraction and independent coding of rectangular sub-areas within a frame, is called Subpicture. In this work, we turn our attention to frame partitioning using Subpictures in VVC, and more particularly, a content-aware partitioning is considered. To achieve that, we make use of image segmentation algorithms and properly modify them to operate on a per Coding Tree Unit (CTU) basis in order to render them compliant with the standard’s restrictions. Additionally, since subpicture boundaries need to comply with slice boundaries, we propose two methods for properly partitioning a frame using tiles/slices aiming to avoid over-partitioning of a frame. The proposed algorithms are evaluated regarding both compression efficiency and image segmentation effectiveness. Our evaluation results indicate that the proposed partitioning schemes have a negligible impact on compression efficiency and video quality
AI-Based Resource Allocation Techniques in Wireless Sensor Internet of Things Networks in Energy Efficiency with Data Optimization
Authors: Ahmed Quazi Warisha , Garg Shruti , Rai Amrita , Ramachandran Manikandan , Jhanjhi Noor Zaman , Masud Mehedi , Baz Mohammed
Published: July 2022

For the past few years, the IoT (Internet of Things)-based restricted WSN (Wireless sensor network) has sparked a lot of attention and progress in order to attain improved resource utilisation as well as service delivery. For data transfer between heterogeneous devices, IoT requires a stronger communication network and an ideally placed energy-efficient WSN. This study uses deep learning architectures to provide a unique resource allocation method for wireless sensor IoT networks with energy efficiency as well as data optimization. EE (Energy efficiency) and SE (spectral efficiency) are two competing optimization goals in this case. The network’s energy efficiency has been improved because of a deep neural network based on whale optimization. The heuristic-based multi-objective firefly algorithm was used to optimise the data. This proposed method is applied to optimal power allocation and relay selection. The study is for a cooperative multi-hop network topology. The best resource allocation is achieved by reducing overall transmit power, and the best relay selection is accomplished by meeting Quality of Service (QoS) standards. As a result, an energy-efficient protocol has been created. The simulation results demonstrate the suggested model’s competitive performance when compared to traditional models in terms of throughput of 96%, energy efficiency of 95%, QoS of 75%, spectrum efficiency of 85%, and network lifetime of 91 percent.
Improvement of the Coupling of Renewable Sources through Z-Source Converters Based on the Study of Their Dynamic Model
Authors: Lanagran Enrique , Ortega María Victoria , Ortega Manuel , Roa Juan Pedro , García-Triviño Pablo
Published: July 2022

The classical coupling of renewable energy sources greatly limits the coupling power and the output voltage of the coupled sources. Moreover, it does not eliminate the randomness of the sources. In this work a renewable sources coupling with high randomness is obtained by series connection of the output terminals of Z-source converters. To achieve the coupling, the stationary and dynamic models of a Z-source-based converter have been studied. With the results of the stationary model, the converter behavior has been evaluated as a function of its parameters and a method for calculating the Z-network parameters has been implemented. Moreover, with the dynamic model a controller has been designed for all the converters. The main contributions of this work are the coupling of the sources, the stationary and dynamic models obtained and their analysis. The coupling achieves a stable supply avoiding the sources’ randomness reaching the load. A system composed of a wind turbine, a set of photovoltaic panels and two groups of batteries has been modeled. To study the system behavior and the supply quality, several aggressive tests have been forced and experimental evidence has also been provided.
New Architecture of Solid-State High-Voltage Pulse Generators
Authors: Achour Yahia , Starzyński Jacek , Jakubiuk Kazimierz
Published: July 2022

The application of the nanosecond pulsed electric field (nsPEF) for biomedical treatments has gained more interest in recent decades due to the development of pulsed power technologies which provides the ability to control the electric field dose applied during tests. In this context, the proposed paper describes a new architecture of solid-state high-voltage pulse generators (SS-HVPG) designed to generate fully customised sequences of quasi-rectangular pulses. The idea is based on the combination of semiconductor switches (IGBT/MOSFET) known for their flexibility and controllability with special magnetic switches to build compact and modular generators. The proposed structure is inspired by the most known pulse generator of Marx, but mixes its two variants for negative and positive polarities. Thus, the polarity of the generated pulses can be freely selected. In addition to that, the use of IGBTs/MOSFET ensures a tunable repetition rate and pulse width. The capacitors are charged via a series of magnetic switches and a flyback DC–DC converter which provides fast and efficient charging and also an adjustable amplitude of the output pulses. The design can be easily simplified giving two other modified structures, based on the same idea, for mono-polar operating (only positive or only negative pulses) with a reduced number of switches. A SPICE simulation of the generator and results of experimental tests carried out on a three stages generator are presented. The obtained results confirm the operating principle and the claimed performances of the new structure.
Common Transmission Point (CTP) Gathers: A New Domain for Amplitude Variation with Offset
Authors: Surachman Lutfi Mulyadi , Al-Shuhail Abdullatif
Published: July 2022

Analysis of amplitudes of transmitted waves (TAVO) is an extension of the conventional AVO analysis using amplitudes of reflected waves. In this study, we introduce the common transmission point (CTP) gather, which is a new domain that is convenient for TAVO analysis. A CTP gather is formed by binning traces that have the same transmission point across a layer interface. We use the proposed domain to invert the ratios Δαα, Δρρ, Δββ, and βα in a model consisting of a gas channel nestled within an oil reservoir. The TAVO equations are fitted to amplitudes calculated by Zoeppritz equations within CTPs inside and outside the channel. Within each CTP gather, we use all traces with incidence angles less than 90% of the critical angle (if any) as TAVO approximations break down beyond this point. The proposed CTP TAVO analysis method estimated Δαα, Δρρ, Δββ, and βα in the gas channel within 1% of their corresponding true values.
A Novel Fault Diagnosis Approach for the Manufacturing Processes of Permanent Magnet Actuators for Renewable Energy Systems
Authors: Tan Jun , Chen Hao , Ye Xuerong , Lin Yigang
Published: July 2022

A permanent magnet actuator (PMA) is a critical device for transforming, transmitting, and protecting electrical energy in renewable energy systems. The reliability of a PMA exerts a direct effect on the operational safety, stability, and reliability of renewable energy systems. An effective fault diagnosis and adjustments for manufacturing processes (MPs) are vital for improving the reliability of a PMA. However, the state-of-the-art fault diagnosis methods are mainly used for single process parameters, extensive sample data, and automated manufacturing systems under real-time monitoring and are not applicable to a PMA with low levels of automation and high human factor-induced uncertainties. This study proposes a novel fault diagnosis approach based on a surrogate model and machine learning for multiple manufacturing processes of a PMA with insufficient training data due to human factor uncertainties. First, a surrogate model that correlated the MP parameters with the output characteristics (OCs) was constructed by a finite element simulation. Second, the quality performance of the OCs under different fault combinations with the mean or variance of the shift of the MP parameters as typical patterns was calculated by the Monte Carlo method. Finally, using the above computations as the training data, a fault diagnosis model capable of identifying the fault pattern of the manufacturing process parameters according to the OCs was constructed based on machine learning. This approach compensated for the inadequacies of traditional fault diagnosis methods with complex analytical models or numerous processing data. The effectiveness and potential applications of the proposed approach were verified through a case study of a rotary PMA in smart grids.
Autoregressive Reconstruction of Total Water Storage within GRACE and GRACE Follow-On Gap Period
Authors: Lenczuk Artur , Weigelt Matthias , Kosek Wieslaw , Mikocki Jan
Published: July 2022

For 15 years, the Gravity Recovery and Climate Experiment (GRACE) mission have monitored total water storage (TWS) changes. The GRACE mission ended in October 2017, and 11 months later, the GRACE Follow-On (GRACE-FO) mission was launched in May 2018. Bridging the gap between both missions is essential to obtain continuous mass changes. To fill the gap, we propose a new approach based on a remove–restore technique combined with an autoregressive (AR) prediction. We first make use of the Global Land Data Assimilation System (GLDAS) hydrological model to remove climatology from GRACE/GRACE-FO data. Since the GLDAS mis-models real TWS changes for many regions around the world, we further use least-squares estimation (LSE) to remove remaining residual trends and annual and semi-annual oscillations. The missing 11 months of TWS values are then predicted forward and backward with an AR model. For the forward approach, we use the GRACE TWS values before the gap; for the backward approach, we use the GRACE-FO TWS values after the gap. The efficiency of forward–backward AR prediction is examined for the artificial gap of 11 months that we create in the GRACE TWS changes for the July 2008 to May 2009 period. We obtain average differences between predicted and observed GRACE values of at maximum 5 cm for 80% of areas, with the extreme values observed for the Amazon, Alaska, and South and Northern Asia. We demonstrate that forward–backward AR prediction is better than the standalone GLDAS hydrological model for more than 75% of continental areas. For the natural gap (July 2017–May 2018), the misclosures in backward–forward prediction estimated between forward- and backward-predicted values are equal to 10 cm. This represents an amount of 10–20% of the total TWS signal for 60% of areas. The regional analysis shows that the presented method is able to capture the occurrence of droughts or floods, but does not reflect their magnitudes. Results indicate that the presented remove–restore technique combined with AR prediction can be utilized to reliably predict TWS changes for regional analysis, but the removed climatology must be properly matched to the selected region.
Horizontal Shower Heat Exchanger as an Effective Domestic Hot Water Heating Alternative
Authors: Kordana-Obuch Sabina , Starzec Mariusz
Published: July 2022

Wastewater has significant potential as a source of clean energy. This energy can be used both within external sewer networks and on the scale of individual residential buildings, and the use of shower heat exchangers appears to be the most reasonable solution. However, in the case of Poland, the problem is still the unwillingness of society to use this type of solution, caused mainly by the lack of space for the installation of vertical drain water heat recovery (DWHR) units and the low efficiency of horizontal units. In response to this issue, the efficiency of a new compact shower heat exchanger designed to be mounted below the shower tray, as well as its linear counterpart, was investigated under various operating conditions. In addition, the financial efficiency of using the compact DWHR unit with average water consumption for showering was evaluated. For this purpose, discount methods were used to estimate the financial efficiency of investments. The study showed that the compact shower heat exchanger has higher efficiency than its linear counterpart. Depending on the temperature of cold water and the flow rate of both media through the heat exchanger, it achieves efficiencies ranging from 22.43% to 31.82%, while the efficiency of the linear DWHR unit did not exceed 23.03% in the study. The financial analysis showed that its use is particularly beneficial when the building uses an electric hot water heater. The investment’s sensitivity to changes in the independent variables is small in this case, even with low water consumption per shower. The only exceptions are investment outlays. Therefore, the compact DWHR unit is a clean energy device, which in many cases is financially viable.
Spatial Fractional Darcy’s Law on the Diffusion Equation with a Fractional Time Derivative in Single-Porosity Naturally Fractured Reservoirs
Authors: Alcántara-López Fernando , Fuentes Carlos , Camacho-Velázquez Rodolfo G. , Brambila-Paz Fernando , Chávez Carlos
Published: July 2022

Due to the complexity imposed by all the attributes of the fracture network of many naturally fractured reservoirs, it has been observed that fluid flow does not necessarily represent a normal diffusion, i.e., Darcy’s law. Thus, to capture the sub-diffusion process, various tools have been implemented, from fractal geometry to characterize the structure of the porous medium to fractional calculus to include the memory effect in the fluid flow. Considering infinite naturally fractured reservoirs (Type I system of Nelson), a spatial fractional Darcy’s law is proposed, where the spatial derivative is replaced by the Weyl fractional derivative, and the resulting flow model also considers Caputo’s fractional derivative in time. The proposed model maintains its dimensional balance and is solved numerically. The results of analyzing the effect of the spatial fractional Darcy’s law on the pressure drop and its Bourdet derivative are shown, proving that two definitions of fractional derivatives are compatible. Finally, the results of the proposed model are compared with models that consider fractal geometry showing a good agreement. It is shown that modified Darcy’s law, which considers the dependency of the fluid flow path, includes the intrinsic geometry of the porous medium, thus recovering the heterogeneity at the phenomenological level.
A Short-Term and Long-Term Prognostic Method for PEM Fuel Cells Based on Gaussian Process Regression
Authors: Wang Tianxiang , Zhou Hongliang , Zhu Chengwei
Published: July 2022

An accurate prediction of the remaining useful life (RUL) of a proton exchange membrane fuel cell (PEMFC) is of great significance for its large-scale commercialization and life extension. This paper aims to develop a PEMFC degradation prediction method that incorporates short-term and long-term predictions. In the short-term prediction, a long short-term memory (LSTM) neural network is combined with a Gaussian process regression (GPR) probabilistic model to form a hybrid LSTM-GPR model with a deep structure. The model not only can accurately forecast the nonlinear details of PEMFC degradation but also provide a reliable confidence interval for the prediction results. The results showed that the proposed LSTM-GPR model outperforms the single models in both prediction accuracy and confidence interval. For the long-term prediction, a novel RUL prediction model based on an extended Kalman filter (EKF) and GPR is proposed. The GPR model is used to solve the problem that the EKF cannot update the model parameters in the prediction stage. The results showed that the proposed EKF-GPR model can achieve better RUL prediction than the model-based approach and the data-driven approach.
An LBM-Based Investigation on the Mixing Mechanism of Double Rows Film Cooling with the Combination of Forward and Backward Jets
Authors: Shangguan Yanqin , Cao Fei
Published: July 2022

Film cooling has been widely applied to the highly efficient thermal protection of gas turbines. By using the simplified thermal lattice Boltzmann method (STLBM), a series of large-scale simulations of film cooling are performed to dig up the mixing mechanism of double rows film cooling with the combination of forward and backward jets at the first attempt. The combination of an upstream row with forward jet and a downstream row with backward jet is considered. The Reynolds number is 4000. The blowing ratio of the upstream coolant jet is fixed as BR1=0.5. For the downstream coolant jet (BR2), five values ranging from 0.2–0.8 are considered. The inclination angles of forward jet and backward jet are 35° and 145°, respectively. The numerical results reveal that the performance of film cooling is greatly improved by backward downstream jet due to the suppression of counterrotating vortex pair (CVP). Moreover, the flow structure is changed with the blowing ratio of backward jet. An anti-CVP having the opposite rotational direction to CVP appears as the blowing ratio of backward jet is large. The special flow structure weakens the adverse effect of CVP and transports much coolant jet to the cooled wall. Correspondingly, the time-averaged film cooling effectiveness is increased and the fluctuation of film cooling effectiveness is decreased. All of these indicate that a backward downstream jet with a large blowing ratio improves film cooling performance. The results obtained in this work help to the optimization of film cooling scheme, which also benefit the promotion and application of STLBM in gas turbine engineering.
Sizing and Sitting of Static VAR Compensator (SVC) Using Hybrid Optimization of Combined Cuckoo Search (CS) and Antlion Optimization (ALO) Algorithms
Authors: Merah Hana , Gacem Abdelmalek , Ben Attous Djilani , Lashab Abderezak , Jurado Francisco , Sameh Mariam A.
Published: July 2022

Worldwide, due to the abrupt growth of population, the load demand has been rising dramatically in the last few years. This led to an increase in branch overloads, voltage deviations, and power losses. These problems may result in line outages or the occurrence of blackouts. Flexible AC transmission system (FACTS) devices can be installed in the power system to ensure increased power flow capability and flexible voltage control to address these issues. In this paper, one of the most used FACTS is utilised. It is called Static VAR Compensator (SVC). This controller is one of the most commonly used shunt FACTS controllers due to its low cost in comparison to others, ease of operation, and integration into the power grid. Two Optimization algorithms are combined to form a hybrid optimization approach: Cuckoo Search (CS) and Antlion Optimization (ALO). This hybrid approach employs the exploration of ALO to adjust the optimum allocation and size for SVCs in the power system. This study proposes the IEEE 57 bus scheme as a fairly large structure, with the 50 and 41 branch outages considered the worst-case scenarios for line outages in this system. The simulation results show that the proposed methodology balances exploring the research space and exploiting the best existing solutions compared to some of the other introduced approaches in the literature.
Optimal Design of a Dual-Pressure Steam Turbine for Rankine Cycle Based on Constructal Theory
Authors: Feng Huijun , Chen Lingen , Tang Wei , Ge Yanlin
Published: July 2022

A one-dimensional dual-pressure steam turbine (ST) model for the marine Rankine cycle is built in this paper. Based on constructal theory, the optimal design of the dual-pressure ST is performed with a fixed total volume of the high- and low-pressure STs. The total power output (PO) of the dual-pressure ST is maximized. Seventeen parameters, including the dimensionless average diameters (DADs) of the stages, steam inlet angles (SIAs) of the stages, average reaction degrees (ARDs) of the stages, and volume ratio of the high-pressure ST are taken as optimization variables. The optimal structure parameters of the stages are gained. It reveals that the total PO of the dual-pressure ST is increased by 2.59% by optimizing the average diameter of the Curtis stage, and the change in the total PO is not obvious by optimizing the average diameter of the third stage of the low-pressure ST. Both the total PO and the corresponding efficiency of the dual-pressure ST are increased by 10.8% after simultaneously optimizing 17 variables with the help of the Matlab optimization toolbox. The novelty of this paper is introducing constructal theory into turbine performance optimization by varying seventeen structure, thermal and flow parameters, and the result shows that the constructal optimization effect is remarkable. Optimal designs of practical STs can be guided by the optimization results gained in this paper.
Nonlinear Self-Synchronizing Current Control for Grid-Connected Photovoltaic Inverters
Authors: Alqatamin Moath , McIntyre Michael L.
Published: July 2022

Three-phase inverters for photovoltaic grid-connected applications typically require some form of grid voltage phase-angle detection in order to properly synchronize to the grid and control real and reactive power generation. Typically, a phase-locked loop scheme is used to determine this real-time phase angle information. However, in the present work, a novel method is proposed whereby the phase angle of the grid can be accurately identified solely via the grid current feedback. This phase-angle observer is incorporated into a current controller which can manage the real and reactive power of the grid-connected PV inverter system. Moreover, the maximum power point of the photovoltaic arrays is achieved without using a DC–DC converter. The proposed method achieves the grid current and DC-link voltage control objectives without the knowledge of the grid information and without the need for a cascaded control scheme. The design of this combined observer/controller scheme is motivated and validated via a Lyapunov stability analysis. The experimental setup is prototyped utilizing a real-time Typhoon HIL 603 and National Instrument cRIO embedded controller in order to validate the proposed observer/controller scheme under different operation scenarios such as irradiation changes, frequency changes, reactive power injection, and operation with a distorted grid. The results show that the DC-link voltage and the active and reactive powers are well regulated from the proposed control scheme without the measurement of the grid phase and frequency.