bluuebunny/crossref_metadata_2025 huggingface dataset:
Progesterone Signaling and Mammalian Ovarian Follicle Growth Mediated by Progesterone Receptor Membrane Component Family Members
Authors: Peluso John J.
Published: May 2022

How progesterone influences ovarian follicle growth is a difficult question to answer because ovarian cells synthesize progesterone and express not only the classic nuclear progesterone receptor but also members of the progestin and adipoQ receptor family and the progesterone receptor membrane component (PGRMC) family. Which type of progestin receptor is expressed depends on the ovarian cell type as well as the stage of the estrous/menstrual cycle. Given the complex nature of the mammalian ovary, this review will focus on progesterone signaling that is transduced by PGRMC1 and PGRMC2 specifically as it relates to ovarian follicle growth. PGRMC1 was identified as a progesterone binding protein cloned from porcine liver in 1996 and detected in the mammalian ovary in 2005. Subsequent studies focused on PGRMC family members as regulators of granulosa cell proliferation and survival, two physiological processes required for follicle development. This review will present evidence that demonstrates a causal relationship between PGRMC family members and the promotion of ovarian follicle growth. The mechanisms through which PGRMC-dependent signaling regulates granulosa cell proliferation and viability will also be discussed in order to provide a more complete understanding of our current concept of how progesterone regulates ovarian follicle growth.
Murine RAW Macrophages Are a Suitable Model to Study the CD3 Signaling in Myeloid Cells
Authors: Ocaña-Guzmán Ranferi , Ramón-Luing Lucero A. , Rodríguez-Alvarado Michelle , Voss Timo-Daniel , Fuchs Tina , Chavez-Galan Leslie
Published: May 2022

In recent years, a growing body of evidence has shown the presence of a subpopulation of macrophages that express CD3, especially in the context of mycobacterial infections. Despite these findings, the function of these cells has been poorly understood. Furthermore, the low frequency of CD3+ macrophages in humans limits the study of this subpopulation. This work aimed to evaluate the expression of CD3 in a murine macrophage cell line and its potential for the study of CD3 signaling. The murine macrophage cell line RAW was used to evaluate CD3 expression at the transcriptional and protein levels and the effect of in vitro infection with the Mycobacterium bovis Bacillus Calmette-Guérin (BCG) on these. Our data showed that RAW macrophages express CD3, both the ε and ζ chains, and it is further increased at the transcriptional level after BCG infection. Furthermore, our data suggest that CD3 can be found on the cell surface and intracellularly. However, this molecule is internalized constantly, mainly after activation with anti-CD3 stimulus, but interestingly, it is stably maintained at the transcriptional level. Finally, signaling proteins such as NFAT1, c-Jun, and IKK-α are highly expressed in RAW macrophages. They may play a role in the CD3-controlled signaling pathway to deliver inflammatory cytokines such as TNF and IL-6. Our study provides evidence to support that RAW cells are a suitable model to study the function and signaling of the CD3 complex in myeloid cells.
The Effect of Temperature Cycling on the Magnetic Degradation and Microstructure of a Zn-Coated NdFeB Magnet
Authors: Wu Hu-Lin , Long Zhi-Mei , Song Kai-Qiang , Li Chao-Qun , Cong Da-Long , Shao Bin , Liu Xiao-Wei , Ma Yi-Long
Published: May 2022

Temperature cycling tests in various temperature ranges were carried out to investigate the magnetic degradation of the Zn-coated NdFeB magnet. The losses of the surface magnetic field and magnetic flux were well fitted by using an index model. Compared with the lower limit temperature, the upper limit temperature had more obvious effect on the magnetic degradation. Once the upper limit temperature exceeded ≥160 °C, the magnetic degradation mainly occurred during the first cycle, which was different from the gradual decline with an increase in cycle number at a temperature of ≤140 °C. Moreover, the temperature cycling with a maximum upper limit temperature of 180 °C led to a loss of the remanence intensity, while the coercivity remained stable. Microstructure and element distribution analysis revealed that the oxidation of the Zn coating layer during the temperature cycling causes its cracking and an insertion of the oxygen element into the NdFeB substrate. The Nd-, Pr-rich phase at grain boundaries provided diffusion channels for oxygen elements, leading to a surface oxidation of NdFeB grains.
Corrosion Enhancement for FGM Coolant Pipes Subjected to High-Temperature and Hydrostatic Pressure
Authors: Lo Kai-Chien , Lai Hsin-Yi
Published: May 2022

The objective of this paper is to enhance the corrosion resistance of coolant pipes in high temperature and lateral hydrostatic pressure in critical engineering environment, especially for circular coolant pipes under hydrostatic pressure of LBE (lead-bismuth eutectic) applications in nuclear power plants. The resistance against corrosion caused by LBE liquid is mainly formed by Fe-12Cr-2Si solid solutions coatings on the pipe. The silicon concentration in Fe-12Cr-2Si can interact with LBE as an effective oxidized compound such as SiO2 and Fe2SiO4 when the silicon concentration is higher than 1.25 wt.%. The oxide film formed on the coating can resist the LBE corroding in the Fe-12Cr-2Si structure. The primary material of a constructing coolant pipe is T91 ferritic-martensitic alloys, and the surface anti-corrosion coating is Fe-12Cr-2Si solid solution. With a high strength structure, FGC (functionally graded composite material), ensures that the pipe resists the corrosion from LBE liquid. In this study, both the steady-state stress values and silicon concentration are evaluated at 700, 1000, and 1200 °C to know the fatigue problems. The research result indicates the FGM (functionally graded material) structure performs better in promoting the margin of safety on stress distribution and reserving the silicon concentration on the inner surface higher than 1.25 wt.% over 60 years as compared to the FGC structure with 34 μm thickness of Fe-12Cr-2Si coating in a high temperature environment.
Co-Evaluation of Plant Leaf Nutrient Concentrations and Resorption in Response to Fertilization under Different Nutrient-Limited Conditions
Authors: Zhang Meixia , Zhang Leiyi , Yao Xianyu , Li Jianling , Deng Qi
Published: May 2022

Plant leaf nutrient concentrations and resorption are sensitive to fertilization, yet their co-responses under different nutrient-limited conditions have not been well studied. We conducted a meta-analysis from a global dataset of 43 reports, including 130 observations of studies with plant leaf nitrogen (N) or phosphorus (P) concentrations and nitrogen resorption efficiency (NRE) or phosphorus resorption efficiency (PRE), in response to fertilization under different nutrient-limited conditions divided by the thresholds of leaf N:P ratio values of 10 and 20. The results showed that N fertilization generally increased leaf N concentration and decreased NRE, with greater magnitudes under N-limited conditions. P fertilization also generally increased leaf P concentration and decreased PRE, with greater magnitudes under P-limited conditions. N fertilization decreased leaf P concentration and increased PRE only under the N-limited condition. Under the P-limited or N and P co-limited conditions, however, N fertilization increased leaf P concentration and did not change PRE. Moreover, P fertilization did not change leaf N concentration under all nutrient-limited conditions but significantly increased NRE under the N-limited or N and P co-limited conditions. These findings suggest that plants cope with fertilization-induced N limitation vs. P limitation at the leaf level with different nutrient-use strategies.
Variations in Biochemical Values under Stress in Children with SARS-CoV-2 Infection
Authors: Belu Alina , Trandafir Laura Mihaela , Țarcă Elena , Cojocaru Elena , Frăsinariu Otilia , Stârcea Magdalena , Moscalu Mihaela , Tiutiuca Razvan Calin , Luca Alina Costina , Galaction Anca
Published: May 2022

In the case of SARS-CoV-2 infection, children seem to be less affected than adults, but data regarding epidemiologic characteristics and biochemical values are poor and essentially based on limited case series. The aim of our study is to highlight the predictive value of some biochemical markers at hospitalization, for the correct classification of the patient in the form of disease. Methods: We performed an analytical retrospective study on 82 pediatric patients diagnosed with COVID-19 in the emergency department, with moderate or severe form of disease, and treated in our tertiary hospital. We analyzed the epidemiologic characteristics, symptomatology and biochemical values and compare the data according to the form of disease. Results: The mean age at admission was 4.5 years (median 1 year) and the masculine/feminine ratio was 1.5. Comparing the data between the two groups of patients (42 severe/40 moderate), we observed that the severe form presented with a lower pH at admission (p = 0.02), hyperglycemia (p = 0.01), increased values of transaminases (p = 0.01 and 0.02) and hypoproteinemia (p = 0.01). Also, the severe form was statistically significantly associated with comorbidities, acute respiratory distress, rising of the inflammatory markers during hospitalization. Hyperlactatemia (Lactate > 1.5 mmol/L) was significantly associated with the age under one year (p < 0.001). Mortality rate was 9.75% and the median age at death was 3 months. Univariate logistic regression model shows that the presence of anemia increased the probability of death 88 times, comorbidities 23.3 times and ketoacidosis 16.4 times. Conclusions: Metabolic acidosis, hyperlactatemia, hyperglycemia, modified hepatic values and hypoproteinemia are biochemical markers associated with the severe form of disease in SARS-CoV-2 infection in children. Presence of anemia, comorbidities and ketoacidosis are important risk factors for death of pediatric patients with SARS-CoV-2 infection.
Evaluation of Anti-Helicobacter pylori IgG Antibodies for the Detection of Helicobacter pylori Infection in Different Populations
Authors: Yu Jin-Han , Zhao Ying , Wang Xiao-Feng , Xu Ying-Chun
Published: May 2022

Serological testing (immunoassay) for Helicobacter pylori (H. pylori) is widely available and inexpensive, and does not require medication modifications before testing. It can also determine the type of infection, which helps with clinical diagnosis and treatment, and guides the use of medication. However, the performance of immunoblotting for the detection of H. pylori infections in different populations has still not been fully evaluated. We performed a retrospective analysis of patients in the Health Examination Center and Outpatient Department, from November 2017 to September 2020, at Peking Union Medical College Hospital. All the subjects were tested with the 13C-urea breath test (13C-UBT) and for IgG antibodies. A total of 1678 participants, including 1377 individuals who had undergone physical examinations, were recruited. The results of the immunoassay were significantly different from those of the 13C-UBT for all the subjects and outpatients (p < 0.001). For the physical examinations of individuals, the agreement between the immunoassay and the 13C-UBT was 0.64 (95%CI: 0.59–0.68; p < 0.001), and the H. pylori immunoassay demonstrated a sensitivity and specificity of 74.24% and 90.45%, respectively, with a positive predictive value of 71.01% and negative predictive value of 91.76%. In addition, in patients with gastric mucosal atrophy or early gastric cancer, antibody typing tests can also detect infected patients with missed UBT. The prevalence of H. pylori in Beijing was 26.8%, and the serological positivity rate for H. pylori in the population of Beijing was about 31.7% (25.1% in the physical examination population). The rate of H. pylori antibody positivity among patients with allergic diseases was 73.5%, which is significantly higher than that of the non-allergic disease population (29.3%, p < 0.001). In conclusion, H. pylori antibody typing testing can be applied as a specific test in the healthy physical examination population, and the test can be performed with the remaining serum during the physical examination.
Morphological Biomarkers in the Amygdala and Hippocampus of Children and Adults at High Familial Risk for Depression
Authors: Peterson Bradley S. , Kaur Tejal , Baez Maria Andrea , Whiteman Ronald C. , Sawardekar Siddhant , Sanchez-Peña Juan , Hao Xuejun , Klahr Kristin W. , Talati Ardesheer , Wickramaratne Priya , Weissman Myrna M. , Bansal Ravi
Published: May 2022

Major Depressive Disorder (MDD) is highly familial, and the hippocampus and amygdala are important in the pathophysiology of MDD. Whether morphological markers of risk for familial depression are present in the hippocampus or amygdala is unknown. We imaged the brains of 148 individuals, aged 6 to 54 years, who were members of a three-generation family cohort study and who were at either high or low familial risk for MDD. We compared surface morphological features of the hippocampus and amygdala across risk groups and assessed their associations with depression severity. High- compared with low-risk individuals had inward deformations of the head of both hippocampi and the medial surface of the left amygdala. The hippocampus findings persisted in analyses that included only those participants who had never had MDD, suggesting that these are true endophenotypic biomarkers for familial MDD. Posterior extension of the inward deformations was associated with more severe depressive symptoms, suggesting that a greater spatial extent of this biomarker may contribute to the transition from risk to the overt expression of symptoms. Significant associations of these biomarkers with corresponding biomarkers for cortical thickness suggest that these markers are components of a distributed cortico-limbic network of familial vulnerability to MDD.
Endoscopic Management of Biliary Strictures after Orthotopic Liver Transplantation: A Single Center Experience Study
Authors: Sandru Vasile , Stan-Ilie Madalina , Plotogea Oana-Mihaela , Vladut Catalina , Ungureanu Bogdan Silviu , Balan Gheorghe G. , Gheonea Dan Ionut , Constantinescu Gabriel
Published: May 2022

Background and Aim. Endoscopic therapy is the method of choice in the management of biliary strictures after orthotopic liver transplantation (OLT). Even though the mainstay approach for OLT stricture complications is represented by consecutive procedures of multiple plastic stents (MPS) insertion, a valuable alternative is the use of fully covered self-expandable metal stents (FCSEMS). The aim of the study was to compare MPS with FCSEMS used in the management of OLT biliary strictures, in terms of clinical outcomes and complications. Material and Methods. This is a retrospective, single-center study conducted between February 2014 and November 2019 in the Clinical Emergency Hospital of Bucharest, Romania. We enrolled all consecutive patients who developed biliary strictures after OLT and were treated by endoscopic retrograde cholangiopancreatography (ERCP) either with MPS or FCSEMS. Results. Thirty-six patients were included in the study, 27 patients had MPS and nine patients had FCSEMS. 106 ERCP procedures were performed and 159 stents were inserted. The mean number of ERCP procedures required per patient was significantly higher for MPS than for FCSEMS (3.34 ± 1.46 vs. 2.11 ± 0.33, p < 0.001). In the FCSEMS group only one patient had three procedures, due to stent migration. Difficult cannulation was encountered in 22 patients, 18 patients from MPS group and four patients from FCSEMS group. Dilation was performed in 20 (77%) MPS patients and in four FCSEMS patients (44%). Overall, we used 159 stents for stricture resolution, 149 plastic stents and 10 FCSEMS. Mean number of stents inserted was significantly lower in the FCSEMS group compared to MPS group (1.16 ± 0.40 vs. 5.73 ± 2.64, p < 0.001). Conclusions. Stricture resolution using FCSEMS is comparable to MPS and even has some advantages. In line with prior studies, FCSEMS are effective, with fewer complications and similar outcome compared to plastic stents. Other particular aspects should be further assessed, especially long-term follow up of FCSEMS and their cost efficiency.
Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19
Authors: Kockelbergh Hannah , Evans Shelley , Deng Tong , Clyne Ella , Kyriakidou Anna , Economou Andreas , Luu Hoang Kim Ngan , Woodmansey Stephen , Foers Andrew , Fowler Anna , Soilleux Elizabeth J.
Published: May 2022

Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights.
Effectiveness of Biologically Inspired Neural Network Models in Learning and Patterns Memorization
Authors: Squadrani Lorenzo , Curti Nico , Giampieri Enrico , Remondini Daniel , Blais Brian , Castellani Gastone
Published: May 2022

Purpose: In this work, we propose an implementation of the Bienenstock–Cooper–Munro (BCM) model, obtained by a combination of the classical framework and modern deep learning methodologies. The BCM model remains one of the most promising approaches to modeling the synaptic plasticity of neurons, but its application has remained mainly confined to neuroscience simulations and few applications in data science. Methods: To improve the convergence efficiency of the BCM model, we combine the original plasticity rule with the optimization tools of modern deep learning. By numerical simulation on standard benchmark datasets, we prove the efficiency of the BCM model in learning, memorization capacity, and feature extraction. Results: In all the numerical simulations, the visualization of neuronal synaptic weights confirms the memorization of human-interpretable subsets of patterns. We numerically prove that the selectivity obtained by BCM neurons is indicative of an internal feature extraction procedure, useful for patterns clustering and classification. The introduction of competitiveness between neurons in the same BCM network allows the network to modulate the memorization capacity of the model and the consequent model selectivity. Conclusions: The proposed improvements make the BCM model a suitable alternative to standard machine learning techniques for both feature selection and classification tasks.
Information Hiding Based on Statistical Features of Self-Organizing Patterns
Authors: Saunoriene Loreta , Jablonskaite Kamilija , Ragulskiene Jurate , Ragulskis Minvydas
Published: May 2022

A computational technique for the determination of optimal hiding conditions of a digital image in a self-organizing pattern is presented in this paper. Three statistical features of the developing pattern (the Wada index based on the weighted and truncated Shannon entropy, the mean of the brightness of the pattern, and the p-value of the Kolmogorov-Smirnov criterion for the normality testing of the distribution function) are used for that purpose. The transition from the small-scale chaos of the initial conditions to the large-scale chaos of the developed pattern is observed during the evolution of the self-organizing system. Computational experiments are performed with the stripe-type patterns, spot-type patterns, and unstable patterns. It appears that optimal image hiding conditions are secured when the Wada index stabilizes after the initial decline, the mean of the brightness of the pattern remains stable before dropping down significantly below the average, and the p-value indicates that the distribution becomes Gaussian.
Robust Aggregation for Federated Learning by Minimum γ-Divergence Estimation
Authors: Li Cen-Jhih , Huang Pin-Han , Ma Yi-Ting , Hung Hung , Huang Su-Yun
Published: May 2022

Federated learning is a framework for multiple devices or institutions, called local clients, to collaboratively train a global model without sharing their data. For federated learning with a central server, an aggregation algorithm integrates model information sent from local clients to update the parameters for a global model. Sample mean is the simplest and most commonly used aggregation method. However, it is not robust for data with outliers or under the Byzantine problem, where Byzantine clients send malicious messages to interfere with the learning process. Some robust aggregation methods were introduced in literature including marginal median, geometric median and trimmed-mean. In this article, we propose an alternative robust aggregation method, named γ-mean, which is the minimum divergence estimation based on a robust density power divergence. This γ-mean aggregation mitigates the influence of Byzantine clients by assigning fewer weights. This weighting scheme is data-driven and controlled by the γ value. Robustness from the viewpoint of the influence function is discussed and some numerical results are presented.
Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection
Authors: Mendonça Fábio , Mostafa Sheikh Shanawaz , Freitas Diogo , Morgado-Dias Fernando , Ravelo-García Antonio G.
Published: May 2022

Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.
Applying the Free Energy Principle to Complex Adaptive Systems
Authors: Badcock Paul B. , Ramstead Maxwell J. D. , Sheikhbahaee Zahra , Constant Axel
Published: May 2022

The free energy principle (FEP) is a formulation of the adaptive, belief-driven behaviour of self-organizing systems that gained prominence in the early 2000s as a unified model of the brain [...]
Design and Research on High Power Density Motor of Integrated Motor Drive System for Electric Vehicles
Authors: Wu Shaopeng , Zhou Jinyang , Zhang Xinmiao , Yu Jiaqiang
Published: May 2022

Although many PMSMs are used as the driving source for electric vehicle motor drive systems, there is still a gap compared with the power density index in the DOE roadmap. Considering that the motor occupies a large space in the motor drive system, it is of great significance for the system to increase the motor power density and thus reach the system power density index. This article starts with electrical machine basic design theory and finds the motor power density influence factors. Guided by the theory and considering motor driver influence, this article proposes a high power density motor for electric vehicle integrated motor drive system. The motor for the system is a five-phase interior permanent magnet synchronous motor (IPMSM) with a double-layer rotor structure and fractional slot distributed winding. Compared with Ver1.0 motor, Ver2.0 motor power density improves significantly. In order to prevent damage from excessive temperature, a temperature field solution model is established in this article to compare the cooling effect and pressure loss of the spiral, dial, and axial water jackets. The temperature is checked at motor main operating conditions using an optimal cooling structure. Finally, the power density of the designed Ver2.0 motor reaches 3.12 kW/kg in mass and 15.19 kW/L in volume.
Technologies for Deep Biogas Purification and Use in Zero-Emission Fuel Cells Systems
Authors: Paglini Roberto , Gandiglio Marta , Lanzini Andrea
Published: May 2022

A proper exploitation of biogas is key to recovering energy from biowaste in the framework of a circular economy and environmental sustainability of the energy sector. The main obstacle to widespread and efficient utilization of biogas is posed by some trace compounds (mainly sulfides and siloxanes), which can have a detrimental effect on downstream gas users (e.g., combustion engines, fuel cells, upgrading, and grid injection). Several purification technologies have been designed throughout the years. The following work reviews the main commercially available technologies along with the new concepts of cryogenic separation. This analysis aims to define a summary of the main technological aspects of the clean-up and upgrading technologies. Therefore, the work highlights which benefits and criticalities can emerge according to the intended final biogas application, and how they can be mitigated according to boundary conditions specific to the plant site (e.g., freshwater availability in WWTPs or energy recovery).
System Performance Analyses of Supercritical CO2 Brayton Cycle for Sodium-Cooled Fast Reactor
Authors: Xie Min , Cheng Jian , Ren Xiaohan , Wang Shuo , Che Pengcheng , Zhang Chunwei
Published: May 2022

The system performance of the supercritical CO2 Brayton cycle for the Sodium Fast Reactor with a partial-cooling layout was studied, and an economic analysis was carried out. The energetic, exergetic, and exergoeconomic analyses are presented, and the optimized results were compared with the recompression cycle. The sensitivity analyses were conducted by considering the variations in the pressure ratios and inlet temperatures of the main compressor and the turbine. The exergy efficiency of the partial-cooling cycle reached 63.65% with a net power output of 34.39 MW via optimization. The partial-cooling cycle obtained a minimum total cost rate of 2230.36 USD/h and exergy efficiency of 63.65% when the pressure ratio was equal to 3.50. The inlet temperature of the main compressor was equal to 35 °C, and the inlet temperature of the turbine was equal to 480 °C. The total cost of recuperators decreased with the increase in the pressure ratio and the inlet temperatures of the main compressor. In addition, the total cost of recuperator could be reduced by increasing the outlet temperature of the turbine. The change in cost from exergy loss and destruction with the pressure ratio was substantially larger than with the inlet temperature of the turbine or the main compressor. Manipulating the pressure ratio is an essential method to guarantee good economy of the system. Moreover, capital investment, operation, and maintenance costs normally accounted for large proportions of the total cost rate, being almost double the cost from the exergy loss and destruction occurring in each condition.
A Data-Driven Methodology for the Reliability Analysis of the Natural Gas Compressor Unit Considering Multiple Failure Modes
Authors: Yu Weichao , Zheng Xianbin , Huang Weihe , Cai Qingwen , Guo Jie , Xu Jili , Liu Yang , Gong Jing , Yang Hong
Published: May 2022

In this study, a data-driven methodology for the reliability analysis of natural gas compressor units is developed, and both the historical failure data and performance data are employed. In this methodology, firstly, the reliability functions of the catastrophic failure and degradation failure are built. For catastrophic failure, the historical failure data are collected, and the rank regression model is utilized to obtain the reliability function of the catastrophic failure. For degradation failure, a support-vector machine is employed to predict the unit’s performance parameters, and the reliability function of the degradation failure is determined by comparing the performance parameters with the failure threshold. Finally, the reliability of the compressor unit is assessed and predicted by integrating the reliability functions of the catastrophic failure and the degradation failure, and both their correlation and competitiveness are considered. Furthermore, the developed methodology is applied to an actual compressor unit to confirm its feasibility, and the reliability of the compressor unit is predicted. The assessment results indicate the significant impact of the operating conditions on the precise forecasting of the performance parameters. Moreover, the effects of the value of the failure threshold and the correlation of the two failure modes on the reliability are investigated.
How to Reduce Carbon Dioxide Emissions from Power Systems in Gansu Province—Analyze from the Life Cycle Perspective
Authors: Shi Wei , Tang Wenwen , Qiao Fuwei , Sha Zhiquan , Wang Chengyuan , Zhao Sixue
Published: May 2022

To develop effective strategies to reduce CO2 emissions from electricity systems, this study accounted for carbon emissions from power systems (production, transmission, consumption) in Gansu Province based on life cycle theory. We used LMDI and SDA decomposition methods to analyze the driving force of carbon emissions and quantified the influence effect and action intensity of various factors on carbon emissions in corresponding links. Several results were found: (1) Direct carbon emissions during the stage of electricity production had the largest share of the entire electricity life cycle. (2) From the perspective of the cumulative contribution rate, electricity consumption and the electricity trade promoted carbon emissions in the stage of electricity production; the power structure, electricity efficiency, and fuel structure had opposite effects. (3) In the stage of electricity transmission, the higher the voltage level, the lower the net loss rate; high-voltage-level transmission lines effectively reduced the growth of implied carbon emissions. (4) Industrial restructuring and technological advances effectively offset the growth in carbon emissions due to population, economy, and electricity consumption. The results can provide a scientific basis for energy-saving and emission reduction policies in provincial government departments and the electric industry.
Reaction Kinetics Analysis of Treatment Process on Light-Induced Degradation for p-Type Passivated Emitter and Rear Contact Solar Cell Module with Gallium Cz-Si Wafer
Authors: Seok Min-Gwang , Kim Yangdo , Kim Soo Min
Published: May 2022

The light-induced degradation (LID) phenomenon in solar cells reduces power generation output. Previously, a method was developed to prevent LID where a group III impurity that can replace boron is added to the silicon wafer. However, in a subsequent study, performance degradation was observed in gallium-doped solar wafers and cells, and a degradation pattern similar to that occurring in light and elevated temperature-induced degradation (LeTID) was reported. In this study, a 72-cell module was fabricated using gallium-doped PERC cells, and the treatment of the LID process for carrier injection in the range of 1 to 7 A at 130 °C was analyzed using kinetic theory. We selectively heated only the solar cells inside a 72-cell module using a half-bridge resonance circuit for remote heating. To monitor the treatment of LID process in real time, a custom multimeter manufactured using an ACS758 current sensor and a microcomputer was used. Least-squares curve fitting was performed on the measured data using a reaction kinetics model. When the carrier-injection condition was applied to the gallium-doped PERC solar cell module at a temperature of 130 °C, the observed degradation and treatment pattern were similar to LeTID. We assumed that the treatment rate would increase as the size of the injected carrier increased; however, the 5 A condition exhibited the fastest treatment rate. It was deduced that the major factors of change in the overall treatment of the LID process vary depending on the rate of conversion from the LID state to the treatment state. In conclusion, it can be expected that the deterioration state of the gallium-doped solar cell module changes due to the treatment rate that varies depending on the carrier-injection conditions.
Static and Dynamic Simulation of an Induction Motor Using Matlab/Simulink
Authors: Le Roux P. F. , Ngwenyama M. K.
Published: May 2022

Industries are adequately configured with the operational devices that are required to develop induction motors. Engineers should precisely comprehend the kind of equipment that is constructed, as with every other production system, and should start by having the goal in their perspective. An adaptable simulation of an induction motor with a protective scheme is presented. The adaptable simulation assists engineers in accurately designing motors that meet all protective standards for certain purposes. This work achieved simulations of induction motors in stable and unstable conditions. An extensive study was performed to determine the optimum design of an induction motor. This paper attempts to provide engineers with a thorough grasp of the adaptable modelling of an induction motor. In this work, a direct dq0-direct axis algorithm is presented to implement both static and dynamic modelling of a three-phase induction machine due to possible faults and high-performance requirements in induction machines. The proposed algorithm was tested against several conventional methods, and it was observed that under the stable condition of the machinery, the proposed algorithm could remove any developing faults. This conserves time and minimises the labour required of the operator, which makes the proposed algorithm more efficient. Furthermore, the machine is demonstrated in a steady-state performance with respect to the current, active power, efficiency, reactive power, power factor, and speed when the torque loads range from 0 to 125% of its nominal torque. The transient behaviour of the machine was shown through the current, electromagnetic torque, electromagnetic torque versus speed, and speed under no-load, half-load (50%), and full-load (100%) conditions. Finally, the results of the proposed technique were compared to the results of the measured parameters. It was observed that when the load changed from a half load (50%) to a full load (100%), then the supply voltage was suddenly halved with the load at full load (100%). It was observed that the proposed algorithm provides accurate estimates with a deviation of not more than +/−2% from the measured parameters.
Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia
Authors: Aisyah Siti , Simaremare Arionmaro Asi , Adytia Didit , Aditya Indra A. , Alamsyah Andry
Published: May 2022

Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively.
A Practical Review of the Public Policies Used to Promote the Implementation of PV Technology in Smart Grids: The Case of Portugal
Authors: Lage Mágui , Castro Rui
Published: May 2022

Every country has objectives for climate change, and public policies are implemented to achieve those commitments. This paper aims to review the different public policies used to promote the integration of photovoltaic technology into smart grids, taking the case of Portugal as reference. An analysis of previous supporting policies is performed both in Portugal and some European countries; these policies consist of feed-in tariffs, feed-in premiums, green certificates, electricity compensation, direct capital subsidies, and tax credits. The policies currently in force in Portugal no longer aim to support the technology; instead, they intend to promote it. Energy communities, local markets, and solar auctions are examples of public policies that are currently being used, not only to promote PV power but also the development of microgrids. Finally, the Portuguese solar auctions of 2019 and 2020 are analyzed and compared. It is concluded that auctions are a very effective way of expanding the installed capacity of the PV technology in the country and have more weight on promoting the technology than other promoting policies currently being used.
Evaluation of Hierarchical, Multi-Agent, Community-Based, Local Energy Markets Based on Key Performance Indicators
Authors: Okwuibe Godwin C. , Gazafroudi Amin Shokri , Hambridge Sarah , Dietrich Christopher , Trbovich Ana , Shafie-khah Miadreza , Tzscheutschler Peter , Hamacher Thomas
Published: May 2022

In recent years, local energy markets (LEMs) have been introduced to empower end-customers within energy communities at the distribution level of the power system, in order to be able to trade their energy locally in a competitive and fair environment. However, there is still some challenge with regard to the most efficient approach in organising the LEMs for the electricity exchange between consumers and prosumers while ensuring that they are responsible for their electricity-related choices, and concerning which LEM model is suitable for which prosumer or consumer type. This paper presents a hierarchical model for the organisation of agent-based local energy markets. According to the proposed model, prosumers and consumers are enabled to transact electricity within the local energy community and with the grid in a coordinated manner to ensure technical and economic benefits for the LEM’s agents. The model is implemented in a software tool called Grid Singularity Exchange (GSyE), and it is verified in a real German energy community case study. The simulation results demonstrate that trading electricity within the LEM offers economic and technical benefits compared to transacting with the up-stream grid. This can further lead to the decarbonization of the power system sector. Furthermore, we propose two models for LEMs consisting of multi-layer and single-layer hierarchical agent-based structures. According to our study, the multi-layer hierarchical model is more profitable for household prosumers as compared to trading within the single-layer hierarchical LEM. However, the single-layer LEM is more be beneficial for industrial prosumers.
Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria
Authors: Belboul Zakaria , Toual Belgacem , Kouzou Abdellah , Mokrani Lakhdar , Bensalem Abderrahman , Kennel Ralph , Abdelrahem Mohamed
Published: May 2022

Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.
Hydrogen Blending in Gas Pipeline Networks—A Review
Authors: Mahajan Devinder , Tan Kun , Venkatesh T. , Kileti Pradheep , Clayton Clive R.
Published: May 2022

Replacing fossil fuels with non-carbon fuels is an important step towards reaching the ultimate goal of carbon neutrality. Instead of moving directly from the current natural gas energy systems to pure hydrogen, an incremental blending of hydrogen with natural gas could provide a seamless transition and minimize disruptions in power and heating source distribution to the public. Academic institutions, industry, and governments globally, are supporting research, development and deployment of hydrogen blending projects such as HyDeploy, GRHYD, THyGA, HyBlend, and others which are all seeking to develop efficient pathways to meet the carbon reduction goal in coming decades. There is an understanding that successful commercialization of hydrogen blending requires both scientific advances and favorable techno-economic analysis. Ongoing studies are focused on understanding how the properties of methane-hydrogen mixtures such as density, viscosity, phase interactions, and energy densities impact large-scale transportation via pipeline networks and end-use applications such as in modified engines, oven burners, boilers, stoves, and fuel cells. The advantages of hydrogen as a non-carbon energy carrier need to be balanced with safety concerns of blended gas during transport, such as overpressure and leakage in pipelines. While studies on the short-term hydrogen embrittlement effect have shown essentially no degradation in the metal tensile strength of pipelines, the long-term hydrogen embrittlement effect on pipelines is still the focus of research in other studies. Furthermore, pressure reduction is one of the drawbacks that hydrogen blending brings to the cost dynamics of blended gas transport. Hence, techno-economic models are also being developed to understand the energy transportation efficiency and to estimate the true cost of delivery of hydrogen blended natural gas as we move to decarbonize our energy systems. This review captures key large-scale efforts around the world that are designed to increase the confidence for a global transition to methane-hydrogen gas blends as a precursor to the adoption of a hydrogen economy by 2050.