bluuebunny/crossref_metadata_2025 huggingface dataset:
The miR-200 Family of microRNAs: Fine Tuners of Epithelial-Mesenchymal Transition and Circulating Cancer Biomarkers
Authors: Cavallari Ilaria , Ciccarese Francesco , Sharova Evgeniya , Urso Loredana , Raimondi Vittoria , Silic-Benussi Micol , D’Agostino Donna M. , Ciminale Vincenzo
Published: November 2021

The miR-200 family of microRNAs (miRNAs) includes miR-200a, miR-200b, miR-200c, miR-141 and miR-429, five evolutionarily conserved miRNAs that are encoded in two clusters of hairpin precursors located on human chromosome 1 (miR-200b, miR-200a and miR-429) and chromosome 12 (miR-200c and miR-141). The mature -3p products of the precursors are abundantly expressed in epithelial cells, where they contribute to maintaining the epithelial phenotype by repressing expression of factors that favor the process of epithelial-to-mesenchymal transition (EMT), a key hallmark of oncogenic transformation. Extensive studies of the expression and interactions of these miRNAs with cell signaling pathways indicate that they can exert both tumor suppressor- and pro-metastatic functions, and may serve as biomarkers of epithelial cancers. This review provides a summary of the role of miR-200 family members in EMT, factors that regulate their expression, and important targets for miR-200-mediated repression that are involved in EMT. The second part of the review discusses the potential utility of circulating miR-200 family members as diagnostic/prognostic biomarkers for breast, colorectal, lung, ovarian, prostate and bladder cancers.
CD73-Mediated Immunosuppression Is Linked to a Specific Fibroblast Population That Paves the Way for New Therapy in Breast Cancer
Authors: Magagna Ilaria , Gourdin Nicolas , Kieffer Yann , Licaj Monika , Mhaidly Rana , Andre Pascale , Morel Ariane , Vincent-Salomon Anne , Paturel Carine , Mechta-Grigoriou Fatima
Published: November 2021

Background: Cancer-associated fibroblasts (CAF) are heterogeneous with multiple functions in breast cancer. Recently, we identified a specific CAF subpopulation (referred to as CAF-S1), which promotes immunosuppression and immunotherapy resistance. Methods and Results: Here, by studying a large collection of human samples, we highlight the key function of CD73/NT5E in CAF-S1-mediated immunosuppression in breast cancer. We first reveal that CD73 protein level specifically accumulates in CAF-S1 in breast cancer patients. Interestingly, infiltration of regulatory T lymphocytes (Tregs) is significantly correlated with CD73 expression in stroma but not in epithelium, indicating that CD73 contributes to immunosuppression when expressed in CAF-S1 and not in tumor cells. By performing functional assays based on relevant systems using primary CAF-S1 isolated from patients, we demonstrate that CAF-S1 increase the content in both PD-1+ and CTLA-4+ Tregs. Importantly, the use of a blocking anti-CD73 antibody on CAF-S1 reduces CAF-S1-mediated immunosuppression by preventing expression of these immune checkpoints on Tregs. Conclusions: Our data support the potential clinical benefit of using both anti-CD73 and immune-checkpoint inhibitors in breast cancer patients for inhibiting CAF-S1-mediated immunosuppression and enhancing anti-tumor immune response.
The Impact of Core Tissues on Successful Next-Generation Sequencing Analysis of Specimens Obtained through Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration
Authors: Uchimura Keigo , Yanase Komei , Imabayashi Tatsuya , Takeyasu Yuki , Furuse Hideaki , Tanaka Midori , Matsumoto Yuji , Sasada Shinji , Tsuchida Takaaki
Published: November 2021

The success rate of next-generation sequencing (NGS) with specimens obtained through endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) among patients with lung cancer as well as the related clinical factors remain unclear. We aimed to determine the optimal number of punctures and core tissues during EBUS-TBNA for NGS in patients with non-small-cell lung cancer (NSCLC) as well as the association of chest computed tomography (CT) and EBUS findings with successful NGS. We retrospectively reviewed 156 consecutive patients with NSCLC who underwent EBUS-TBNA for NGS (OncomineTM Dx Target Test). Using the receiver operating characteristic curve, we calculated the optimal numbers of punctures and core tissues for NGS and evaluated CT and EBUS findings suggestive of necrosis and vascular pattern within the lesion. The success rate of NGS was 83.3%. The cut-off value for the number of core tissues was 4, and the sensitivity and specificity of successful NGS were 73.8% and 61.5%, respectively. Logistic regression analysis revealed that the number of core tissues (≥4) was the sole predictor of successful NGS. CT and EBUS findings were not associated with successful NGS. Bronchoscopists should obtain sufficient core tissues for successful NGS using EBUS-TBNA specimens.
Detection of VAR2CSA-Captured Colorectal Cancer Cells from Blood Samples by Real-Time Reverse Transcription PCR
Authors: Bang-Christensen Sara R. , Katerov Viatcheslav , Jørgensen Amalie M. , Gustavsson Tobias , Choudhary Swati , Theander Thor G. , Salanti Ali , Allawi Hatim T. , Agerbæk Mette Ø.
Published: November 2021

Analysis of circulating tumor cells (CTCs) from blood samples provides a non-invasive approach for early cancer detection. However, the rarity of CTCs makes it challenging to establish assays with the required sensitivity and specificity. We combine a highly sensitive CTC capture assay exploiting the cancer cell binding recombinant malaria VAR2CSA protein (rVAR2) with the detection of colon-related mRNA transcripts (USH1C and CKMT1A). Cancer cell transcripts are detected by RT-qPCR using proprietary Target Enrichment Long-probe Quantitative Amplified Signal (TELQAS) technology. We validate each step of the workflow using colorectal cancer (CRC) cell lines spiked into blood and compare this with antibody-based cell detection. USH1C and CKMT1A are expressed in healthy colon tissue and CRC cell lines, while only low-level expression can be detected in healthy white blood cells (WBCs). The qPCR reaction shows a near-perfect amplification efficiency for all primer targets with minimal interference of WBC cDNA. Spike-in of 10 cancer cells in 3 mL blood can be detected and statistically separated from control blood using the RT-qPCR assay after rVAR2 capture (p < 0.01 for both primer targets, Mann-Whitney test). Our results provide a validated workflow for highly sensitive detection of magnetically enriched cancer cells.
The HROC-Xenobank—A High Quality Assured PDX Biobank of >100 Individual Colorectal Cancer Models
Authors: Matschos Stephanie , Bürtin Florian , Kdimati Said , Radefeldt Mandy , Krake Susann , Prall Friedrich , Engel Nadja , Krohn Mathias , Micheel Bianca , Kreutzer Michael , Mullins Christina Susanne , Linnebacher Michael
Published: November 2021

Based on our research group’s large biobank of colorectal cancers (CRC), we here describe the ongoing activity of establishing a high quality assured PDX biobank for more than 100 individual CRC cases. This includes sufficient numbers of vitally frozen (n > 30 aliquots) and snap frozen (n > 5) backups, “ready to use”. Additionally, PDX tumor pieces were paraffin embedded. At the current time, we have completed 125 cases. This resource allows histopathological examinations, molecular characterizations, and gene expression analysis. Due to its size, different issues of interest can be addressed. Most importantly, the application of low-passage, cryopreserved, and well-characterized PDX for in vivo studies guarantees the reliability of results due to the largely preserved tumor microenvironment. All cases described were molecularly subtyped and genetic identity, in comparison to the original tumor tissue, was confirmed by fingerprint analysis. The latter excludes ambiguity errors between the PDX and the original patient tumor. A cancer hot spot mutation analysis was performed for n = 113 of the 125 cases entities. All relevant CRC molecular subtypes identified so far are represented in the Hansestadt Rostock CRC (HROC)-Xenobank. Notably, all models are available for cooperative research approaches.
Comment on Geoffroy, M.-C.; de Thé, H. Classic and Variants APLs, as Viewed from a Therapy Response. Cancers 2020, 12, 967
Authors: Su Zhan , Liu Xin
Published: November 2021

Acute promyelocytic leukemia (APL) is a unique and very deeply studied acute myeloid leukemia [...]
Leucine-Rich Diet Improved Muscle Function in Cachectic Walker 256 Tumour-Bearing Wistar Rats
Authors: Viana Laís Rosa , Chiocchetti Gabriela de Matuoka e , Oroy Lucas , Vieira Willians Fernando , Busanello Estela Natacha Brandt , Marques Ana Carolina , Salgado Carla de Moraes , de Oliveira Alexandre Leite Rodrigues , Vieira André Schwambach , Suarez Paula Saenz , de Sousa Lizandra Maia , Castelucci Bianca Gazieri , Vercesi Anibal Eugenio , Consonni Sílvio Roberto , Gomes-Marcondes Maria Cristina Cintra
Published: November 2021

Skeletal muscle atrophy occurs in several pathological conditions, such as cancer, especially during cancer-induced cachexia. This condition is associated with increased morbidity and poor treatment response, decreased quality of life, and increased mortality in cancer patients. A leucine-rich diet could be used as a coadjutant therapy to prevent muscle atrophy in patients suffering from cancer cachexia. Besides muscle atrophy, muscle function loss is even more important to patient quality of life. Therefore, this study aimed to investigate the potential beneficial effects of leucine supplementation on whole-body functional/movement properties, as well as some markers of muscle breakdown and inflammatory status. Adult Wistar rats were randomly distributed into four experimental groups. Two groups were fed with a control diet (18% protein): Control (C) and Walker 256 tumour-bearing (W), and two other groups were fed with a leucine-rich diet (18% protein + 3% leucine): Leucine Control (L) and Leucine Walker 256 tumour-bearing (LW). A functional analysis (walking, behaviour, and strength tests) was performed before and after tumour inoculation. Cachexia parameters such as body weight loss, muscle and fat mass, pro-inflammatory cytokine profile, and molecular and morphological aspects of skeletal muscle were also determined. As expected, Walker 256 tumour growth led to muscle function decline, cachexia manifestation symptoms, muscle fibre cross-section area reduction, and classical muscle protein degradation pathway activation, with upregulation of FoxO1, MuRF-1, and 20S proteins. On the other hand, despite having no effect on the walking test, inflammation status or muscle oxidative capacity, the leucine-rich diet improved muscle strength and behaviour performance, maintained body weight, fat and muscle mass and decreased some protein degradation markers in Walker 256 tumour-bearing rats. Indeed, a leucine-rich diet alone could not completely revert cachexia but could potentially diminish muscle protein degradation, leading to better muscle functional performance in cancer cachexia.
Assessing Farmers’ Typologies of Perception for Adopting Sustainable Adaptation Strategies in Bangladesh
Authors: Islam Abu Reza Md. Towfiqul , Hasanuzzaman Md. , Jaman Mahmud , Alam Edris , Mallick Javed , Alam G. M. Monirul , Sattar Md. Abdus , Techato Kuaanan
Published: November 2021

The implementation of sustainable adaptation strategies (SASs) is crucial to mitigate climate change impact as well as reduce the loss of natural disasters and increase agricultural crop production. However, current policies and programs based on agricultural incentives are mostly inadequate to increase SASs practices at the farm level. Hence, a deeper understanding of farmers’ ‘perceived typologies to the environmental issue and climate change’ is necessary for implementing SASs to enhance farmers’ ability to adapt at the farm level. This research intends to demarcate farmers in various categories, according to their perceptions on environmental and climate change issues in the northern part of Bangladesh. Principal component analysis (PCA) and cluster analysis (CA) were employed to analyze the survey data collected from 501 households in the study area. Farmers were clustered into three types, ‘Ecocentric’, ‘Worried’, and ‘Anthropocentric’, based on their perceived knowledge regarding environmental issues and climate change, which guides the adoption of SASs. The ‘Worried’ cluster showed a high sense of perceived risk of climate change and a significant positive association with the adoption of SASs. By contrast, ‘Ecocentric’ and ‘Anthropocentric’ groups showed a low sense of awareness of climate change and a significant negative association with the adoption of SASs. The findings can assist policymakers in promoting the adoption of SASs based on the farmers’ cluster and thus enhance their resilience.
Smart Master Production Schedule for the Supply Chain: A Conceptual Framework
Authors: Serrano-Ruiz Julio C. , Mula Josefa , Poler Raúl
Published: November 2021

Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments.
Predictors of CRC Stage at Diagnosis among Male and Female Adults Participating in a Prospective Cohort Study: Findings from Alberta’s Tomorrow Project
Authors: Ghebrial Monica , Aktary Michelle L. , Wang Qinggang , Spinelli John J. , Shack Lorraine , Robson Paula J. , Kopciuk Karen A.
Published: November 2021

Colorectal cancer (CRC) is a leading cause of morbidity and mortality in Canada. CRC screening and other factors associated with early-stage disease can improve CRC treatment efficacy and survival. This study examined factors associated with CRC stage at diagnosis among male and female adults using data from a large prospective cohort study in Alberta, Canada. Baseline data were obtained from healthy adults aged 35–69 years participating in Alberta’s Tomorrow Project. Factors associated with CRC stage at diagnosis were evaluated using Partial Proportional Odds models. Analyses were stratified to examine sex-specific associations. A total of 267 participants (128 males and 139 females) developed CRC over the study period. Among participants, 43.0% of males and 43.2% of females were diagnosed with late-stage CRC. Social support, having children, and caffeine intake were predictors of CRC stage at diagnosis among males, while family history of CRC, pregnancy, hysterectomy, menopausal hormone therapy, lifetime number of Pap tests, and household physical activity were predictive of CRC stage at diagnosis among females. These findings highlight the importance of sex differences in susceptibility to advanced CRC diagnosis and can help inform targets for cancer prevention programs to effectively reduce advanced CRC and thus improve survival.
Thermodynamic Properties of the First-Generation Hybrid Dendrimer with “Carbosilane Core/Phenylene Shell” Structure
Authors: Sologubov Semen S. , Markin Alexey V. , Smirnova Natalia N. , Chamkina Elena S. , Krasnova Irina Yu. , Milenin Sergey A. , Serenko Olga A. , Shifrina Zinaida B. , Muzafarov Aziz M.
Published: November 2021

The molar heat capacity of the first-generation hybrid dendrimer with a “carbosilane core/phenylene shell” structure was measured for the first time in the temperature range T = 6–600 K using a precise adiabatic vacuum calorimeter and DSC. In the above temperature interval, the glass transition of the studied compound was observed, and its thermodynamic characteristics were determined. The standard thermodynamic functions (the enthalpy, the entropy, and the Gibbs energy) of the hybrid dendrimer were calculated over the range from T = 0 to 600 K using the experimentally determined heat capacity. The standard entropy of formation of the investigated dendrimer was evaluated at T = 298.15 K. The obtained thermodynamic properties of the studied hybrid dendrimer were compared and discussed with the literature data for some of the first-generation organosilicon and pyridylphenylene dendrimers.
Numerical Algorithms for Estimating Probability Density Function Based on the Maximum Entropy Principle and Fup Basis Functions
Authors: Brajčić Kurbaša Nives , Gotovac Blaž , Kozulić Vedrana , Gotovac Hrvoje
Published: November 2021

Estimation of the probability density function from the statistical power moments presents a challenging nonlinear numerical problem posed by unbalanced nonlinearities, numerical instability and a lack of convergence, especially for larger numbers of moments. Despite many numerical improvements over the past two decades, the classical moment problem of maximum entropy (MaxEnt) is still a very demanding numerical and statistical task. Among others, it was presented how Fup basis functions with compact support can significantly improve the convergence properties of the mentioned nonlinear algorithm, but still, there is a lot of obstacles to an efficient pdf solution in different applied examples. Therefore, besides the mentioned classical nonlinear Algorithm 1, in this paper, we present a linear approximation of the MaxEnt moment problem as Algorithm 2 using exponential Fup basis functions. Algorithm 2 solves the linear problem, satisfying only the proposed moments, using an optimal exponential tension parameter that maximizes Shannon entropy. Algorithm 2 is very efficient for larger numbers of moments and especially for skewed pdfs. Since both Algorithms have pros and cons, a hybrid strategy is proposed to combine their best approximation properties.
Output-Based Tracking Control for a Class of Car-Like Mobile Robot Subject to Slipping and Skidding Using Event-Triggered Mechanism
Authors: Wang Changshun , Wang Dan , Pan Weigang , Zhang Huang
Published: November 2021

This paper presents an output-based tracking controller for a class of car-like mobile robot (CLMR) subject to slipping and skidding. The slipping and skidding are regarded as external disturbances, and an event-triggered extended state observer (ET-ESO) is utilized to recover the velocities as well as to estimate the uncertainties and disturbances. The constrained longitudinal velocity is established, conforming to the traffic flow theory on the kinematic level. The velocity control law and heading angle control law are developed on the dynamic level, respectively. The input to state stability (ISS) of the closed-loop system is analyzed via cascade theory. Simulation results are given to demonstrate the effectiveness of the proposed tracking controller for CLMR subject to slipping and skidding.
Intelligent Fault Detection and Identification Approach for Analog Electronic Circuits Based on Fuzzy Logic Classifier
Authors: Nasser Ahmed R. , Azar Ahmad Taher , Humaidi Amjad J. , Al-Mhdawi Ammar K. , Ibraheem Ibraheem Kasim
Published: November 2021

Analog electronic circuits play an essential role in many industrial applications and control systems. The traditional way of diagnosing failures in such circuits can be an inaccurate and time-consuming process; therefore, it can affect the industrial outcome negatively. In this paper, an intelligent fault diagnosis and identification approach for analog electronic circuits is proposed and investigated. The proposed method relies on a simple statistical analysis approach of the frequency response of the analog circuit and a simple rule-based fuzzy logic classification model to detect and identify the faulty component in the circuit. The proposed approach is tested and evaluated using a commonly used low-pass filter circuit. The test result of the presented approach shows that it can identify the fault and detect the faulty component in the circuit with an average of 98% F-score accuracy. The proposed approach shows comparable performance to more intricate related works.
Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review
Authors: Deb Sanchari
Published: November 2021

As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is an intricate process involving various activities, such as charging station placement, charging demand prediction, and charging scheduling. This planning process involves interactions between power distribution and the road network. The advent of machine learning has made data-driven approaches a viable means for solving charging infrastructure planning problems. Consequently, researchers have started using machine learning techniques to solve the aforementioned problems associated with charging infrastructure planning. This work aims to provide a comprehensive review of the machine learning applications used to solve charging infrastructure planning problems. Furthermore, three case studies on charging station placement and charging demand prediction are presented. This paper is an extension of: Deb, S. (2021, June). Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. In the 2021 5th International Conference on Smart Grid and Smart Cities (ICSGSC) (pp. 16–22). IEEE. I would like to confirm that the paper has been extended by more than 50%.
Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning
Authors: Hecht Christopher , Figgener Jan , Sauer Dirk Uwe
Published: November 2021

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.
Numerical Optimization of the β-Type Stirling Engine Performance Using the Variable-Step Simplified Conjugate Gradient Method
Authors: Cheng Chin-Hsiang , Phung Duc-Thuan
Published: November 2021

This study focuses on optimizing a 100-W-class β-Type Stirling engine by combining the modified thermodynamic model and the variable-step simplified conjugate gradient (VSCGM) method. For the modified thermodynamic model, non-uniform pressure is directly introduced into the energy equation, so the indicated power and heat transfer rates can reach energy balance while the VSCGM is an updated version of the simplified conjugate gradient method (SCGM) with adaptive increments and step lengths to the optimization process; thus, it requires fewer iterations to reach the optimal solution than the SCGM. For the baseline case, the indicated power progressively raises from 88.2 to 210.2 W and the thermal efficiency increases from 34.8 to 46.4% before and after optimization, respectively. The study shows the VSCGM possesses robust property. All optimal results from the VSCGM are well-matched with those of the computational fluid dynamics (CFD) model. Heating temperature and rotation speed have positive effects on optimal engine performance. The optimal indicated power rises linearly with the charged pressure, whereas the optimal thermal efficiency tends to decrease. The study also points out that results of the modified thermodynamic model with fixed values of unknowns agree well with the CFD results at points far from the baseline case.
Legal Aspects of Cybersecurity in the Energy Sector—Current State and Latest Proposals of Legislative Changes by the EU
Authors: Krzykowski Michał
Published: November 2021

Due to the strategic nature of the energy sector, legal solutions to protect cross-border electricity and gas connections will be of particular importance. The author realizes that at the present stage of development, the cross-border impact may also be manifested by generating units (e.g., wind farms) or even end users themselves. The lack of harmonized regulations in this area may not only lead to limitations in the physical supply of electricity and gas, but also affect future investment decisions regarding, for example, new generation capacities. In a broader aspect, it will delay, and in the extreme case prevent, the achievement of the objectives resulting from the EU energy policy, in particular integration within the single energy market. In this article, the author identifies devices and entities responsible for energy infrastructure that should be classified as necessary for the functioning of the single energy market. The research includes the analysis and evaluation of regulations governing cybersecurity in the energy sector, taking into account the interdependencies within, intersectoral and cross-border. In addition, the author refers to the need to introduce individual legal solutions regarding the protection of energy infrastructure.
Elastic Reverse-Time Migration with Complex Topography
Authors: Zhong Yu , Gu Hanming , Liu Yangting , Mao Qinghui
Published: November 2021

Migration is an important step in seismic data processing for oil and gas exploration. The accuracy of migration directly affects the accuracy of subsequent oil and gas reservoir characterization. Reverse-time migration is one of the most accurate migration methods at present. Multi-wave and multicomponent seismic data contain more P- and S-wave information. Making full use of multi-wave and multicomponent seismic data can offer more information about underground structure and lithology, as well as improve the accuracy of seismic exploration. Elastic reverse-time migration (ERTM) has no dip restriction and can be applied to image multi-wave and multicomponent seismic data in complex structural areas and some special lithology structures. However, the surface topography of complex regions has an influence on wavefield and seriously degrades the quality of ERTM’s migration results. We developed a new ERTM method to migrate multi-wave and multicomponent seismic data in the region with complex surface topography. We first fill the layers between the highest and lowest undulating surface with near-surface elastic parameters in a complex topography model to obtain a new model with a horizontal surface. This allows the finite difference (FD) method based on the regular rectangular grid to be used to numerically solve elastic wave equations in the model with complex topography. The decoupled wave equations are used to generate source P- and S-waves and receiver P- and S-waves to reduce crosstalk artefacts in ERTM. A topography-related filter is further used to remove the influence of surface topography on migration results. The scalar imaging condition is also applied to generate PP and PS migration images. Some numerical examples with different complex topographies demonstrate that our proposed ERTM method can remove the influence of complex topography on ERTM’s images and effectively generate high-quality ERTM images.
New Methods for Assessing Sustainability of Wood-Burning Energy Facilities: Combining Historical and Spatial Approaches
Authors: Mittlefehldt Sarah , Bunting Erin , Huff Emily , Welsh Joseph , Goodwin Robert
Published: November 2021

Methods to assess wood-based bioenergy projects have tended to focus on technological and physical constraints. Less is known about how longer-term environmental, economic, and social systems—the three pillars of sustainable development—have influenced technological development in the context of woody biomass energy. This research offers new methods for assessing the sustainability of wood-based energy projects by combining spatial analysis, semi-structured interviews, and archival data analysis. By integrating quantitative and qualitative methods, this project offers ways to understand how social and environmental dynamics from the past shape technological development in the future. A propensity analysis of biomass energy plants in Michigan, USA was performed using US Census data grouped by social, economic, and environmental categories. This quantitative analysis helped to characterize community and landscape types in which woody biomass plants were developed in Michigan in the late-twentieth century. To help illustrate some of the often-hidden social and political dimensions of energy development, such as access to decision-making and attitudes toward bioenergy projects, transcripts of public hearings, media coverage, and other archival sources were examined, and 30 stakeholder interviews were conducted. By integrating these qualitative and quantitative approaches, this paper aims to provide a more comprehensive approach to assessing the sustainability of wood-based biofuel technologies.
The Role of BECCS in Achieving Climate Neutrality in the European Union
Authors: Tatarewicz Igor , Lewarski Michał , Skwierz Sławomir , Krupin Vitaliy , Jeszke Robert , Pyrka Maciej , Szczepański Krystian , Sekuła Monika
Published: November 2021

The achievement of climate neutrality in the European Union by 2050 will not be possible solely through a reduction in fossil fuels and the development of energy generation from renewable sources. Large-scale implementation of various technologies is necessary, including bioenergy with carbon capture and storage (BECCS), carbon capture and storage (CCS), and carbon capture and utilisation (CCU), as well as industrial electrification, the use of hydrogen, the expansion of electromobility, low-emission agricultural practices, and afforestation. This research is devoted to an analysis of BECCS as a negative emissions technology (NET) and the assessment of its implementation impact upon the possibility of achieving climate neutrality in the EU. The modelling approach utilises tools developed within the LIFE Climate CAKE PL project and includes the MEESA energy model and the d-PLACE CGE economic model. This article identifies the scope of the required investment in generation capacity and the amount of electricity production from BECCS necessary to meet the greenhouse gas (GHG) emission reduction targets in the EU, examining the technology’s impact on the overall system costs and marginal abatement costs (MACs). The modelling results confirm the key role of BECCS technology in achieving EU climate goals by 2050.
Capacity Market and (the Lack of) New Investments: Evidence from Poland
Authors: Kaszyński Przemysław , Komorowska Aleksandra , Zamasz Krzysztof , Kinelski Grzegorz , Kamiński Jacek
Published: November 2021

Capacity remuneration mechanisms operate in many European countries. In 2018, Poland implemented a centralized capacity market to ensure appropriate funding for the existing and new power generation units to improve long-term energy security. One of the declarations made while the mechanism was deployed was its beneficial influence on incentives for investments in new units. In this context, this paper aims to analyze the effects of the capacity mechanism adopted for investments in new power generation units that may be financed under the capacity market mechanism in Poland. The analysis is conducted for four types of capacity market units, the existing, refurbishing, planned, and demand-side response types, and includes the final results of capacity auctions. The results prove that the primary beneficiaries of the capacity market in Poland have been the existing units (including the refurbishing ones) responsible for more than 80% of capacity obligation volumes contracted for 2021–2025. Moreover, during the implementation of the capacity market in Poland, the planned units that signed long-term capacity contracts with a total share of 12% of the whole market were already at the advanced phases of construction, and the investment decisions were made long before the implementation of the capacity market mechanism. Therefore, they were not associated with the financial support from the capacity market. The study indicates that the capacity market did not bring incentives for investments in new power generation units in the investigated period.
Development of Four-Channel Buck-Type LED Driver with Automatic Current Sharing
Authors: Yau Yeu-Torng , Hwu Kuo-Ing , Tsai Yao-De
Published: November 2021

A buck-type light-emitting diode (LED) driver is proposed herein. The proposed LED driver automatically possesses current sharing and high step-down voltage gain. Without complex control, the proposed LED driver, with a single input and multiple outputs, can achieve automatic current sharing of four-channel LED strings, even under the different number of LEDs of each LED string. Furthermore, as compared with the traditional four-phase interleaved buck converter with a single input and a single output having current sharing required, the proposed circuit has the duty cycle up to 0.5, not 0.25, meaning that under the same input voltage the latter has a wider output voltage range than that of the former. Above all, if the proposed circuit with N outputs, then it still has the duty cycle up to 0.5, not one over N as shown traditionally. Moreover, as compared with the current sharing based on the differential-mode transformer, the proposed circuit has no magnetic resetting loop required. In this paper, the operating principles and design considerations of the proposed converter are discussed. Finally, the theoretical analyses and performances of the proposed LED driver are verified by simulation and experiment.
Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study
Authors: Jaramillo-Morán Miguel A. , Fernández-Martínez Daniel , García-García Agustín , Carmona-Fernández Diego
Published: November 2021

European Union Allowances (EUAs) are rights to emit CO2 that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and energetic variables; two of them are used in this work, the Multilayer Preceptron (MLP) and the Long Short-Term Memories (LSTM), along with another artificial intelligence algorithm (XGBoost). They are combined with two preprocessing tools, decomposition of the time series into its trend and fluctuation and decomposition into Intrinsic Mode Functions (IMF) by the Empirical Mode Decomposition (EMD). The price prediction is obtained by adding those from each subseries. These two tools are combined with the three forecasting tools to provide 20 future predictions of EUA prices. The best results are provided by MLP-EMD, which is able to achieve a Mean Absolute Percentage Error (MAPE) of 2.91% for the first predicted datum and 5.65% for the twentieth, with a mean value of 4.44%.
Resiliency Improvement of an AC/DC Power Grid with Embedded LCC-HVDC Using Robust Power System State Estimation
Authors: Aljabrine Abdulwahab A. , Smadi Abdallah A. , Chakhchoukh Yacine , Johnson Brian K. , Lei Hangtian
Published: November 2021

The growth of renewable energy generation in the power grid brings attention to high-voltage direct current (HVDC) transmission as a valuable solution for stabilizing the system. Robust hybrid power system state estimation could enhance the resilience of the control of these systems. This paper proposes a two-stage, highly robust least-trimmed squares (LTS)-based estimator. The first step combines the supervisory control and data acquisition (SCADA) measurements using the robust LTS-based estimator. The second step merges the obtained state results with the available phasor measurement units (PMUs) measurements using a robust Huber M-estimator. The proposed robust LTS-based estimator shows good performance in the presence of Gaussian measurement noise. The proposed estimator is shown to resist and correct the effect of false data injection (FDI) attacks and random errors on the measurement vector and the Jacobian matrix. The state estimation (SE) is executed on a modified version of the CIGRE bipole LCC-HVDC benchmark model integrated into the IEEE 12-bus AC dynamic test system. The obtained simulation results confirm the effectiveness and robustness of the proposed two-stage LTS-based SE.
Characterization and Modeling of LV Cables Considering External Parameters for Distribution Networks
Authors: Binot Ferréol , Le Trung Dung , Petit Marc
Published: November 2021

In response to the climate emergency, new uses are plugged to low voltage (LV) electrical networks. The development of self-consumption complicates the LV grid operation, and force distribution system operators (DSOs) to better model and characterize their networks. DSOs mainly use a three-conductor model (3 CM) to compute power flows, and consider error margins of 2% for voltage profiles to reflect their model inaccuracy. The characteristics of the future LV grids call into question these margins, and the models used. In this paper, a four-conductor model (4 CM), and an additional model named 4 CMext, that considers external parameters (i.e., cable temperature, ground electrical resistivity, and value/number of the earthing resistances) are proposed. The best model for cable characterization and voltage profile calculation is chosen; the 4 CMext is more adapted for the characterization, and corresponds with the finite element model, with an error margin of 4%, experimental measurements of 15%, and French cable manufacturer data of 0.5%. For the voltage profile, the 4 CMext provides a more detailed view of the critical cases that could lead to a violation of the limits of the EN 50160 standard than 3 CM and 4 CM. Violations of high or low voltages are underestimated by two to six times by the 3 CM and 4 CM. Not considering external parameters can lead to a voltage profile error of above 3%. In this paper, we recommend that DSOs use the 4 CMext to represent LV networks, which would allow LV networks to be used closer to their physical limits, and avoid or postpone network reinforcements.
Signal Analysis in Power Systems
Authors: Leonowicz Zbigniew , Jasiński Michał
Published: November 2021

The idea of the call for the Special Issue “Signal Analysis in Power Systems” came from scholarly discussions about ever increasing complexity of the management and operation of today’s power system [...]
Acceleration of Chemical Kinetics Computation with the Learned Intelligent Tabulation (LIT) Method
Authors: Haghshenas Majid , Mitra Peetak , Santo Niccolò Dal , Schmidt David P.
Published: November 2021

In this work, a data-driven methodology for modeling combustion kinetics, Learned Intelligent Tabulation (LIT), is presented. LIT aims to accelerate the tabulation of combustion mechanisms via machine learning algorithms such as Deep Neural Networks (DNNs). The high-dimensional composition space is sampled from high-fidelity simulations covering a wide range of initial conditions to train these DNNs. The input data are clustered into subspaces, while each subspace is trained with a DNN regression model targeted to a particular part of the high-dimensional composition space. This localized approach has proven to be more tractable than having a global ANN regression model, which fails to generalize across various composition spaces. The clustering is performed using an unsupervised method, Self-Organizing Map (SOM), which automatically subdivides the space. A dense network comprised of fully connected layers is considered for the regression model, while the network hyper parameters are optimized using Bayesian optimization. A nonlinear transformation of the parameters is used to improve sensitivity to minor species and enhance the prediction of ignition delay. The LIT method is employed to model the chemistry kinetics of zero-dimensional H2–O2 and CH4-air combustion. The data-driven method achieves good agreement with the benchmark method while being cheaper in terms of computational cost. LIT is naturally extensible to different combustion models such as flamelet and PDF transport models.
Enhanced Hydrogen Storage Properties of Li-RHC System with In-House Synthesized AlTi3 Nanoparticles
Authors: Le Thi-Thu , Pistidda Claudio , Puszkiel Julián , Castro Riglos María Victoria , Dreistadt David Michael , Klassen Thomas , Dornheim Martin
Published: November 2021

In recent years, the use of selected additives for improving the kinetic behavior of the system 2LiH + MgB2 (Li-RHC) has been investigated. As a result, it has been reported that some additives (e.g., 3TiCl3·AlCl3), by reacting with the Li-RHC components, form nanostructured phases (e.g., AlTi3) possessing peculiar microstructural properties capable of enhancing the system’s kinetic behavior. The effect of in-house-produced AlTi3 nanoparticles on the hydrogenation/dehydrogenation kinetics of the 2LiH + MgB2 (Li-RHC) system is explored in this work, with the aim of reaching high hydrogen storage performance. Experimental results show that the AlTi3 nanoparticles significantly improve the reaction rate of the Li-RHC system, mainly for the dehydrogenation process. The observed improvement is most likely due to the similar structural properties between AlTi3 and MgB2 phases which provide an energetically favored path for the nucleation of MgB2. In comparison with the pristine material, the Li-RHC doped with AlTi3 nanoparticles has about a nine times faster dehydrogenation rate. The results obtained from the kinetic modeling indicate a change in the Li-RHC hydrogenation reaction mechanism in the presence of AlTi3 nanoparticles.
Design of Passive Building Foundations in the Polish Climatic Conditions
Authors: Godlewski Tomasz , Mazur Łukasz , Szlachetka Olga , Witowski Marcin , Łukasik Stanisław , Koda Eugeniusz
Published: November 2021

A Passive House (PH) system is not only an opportunity but also a necessity for the further development of sustainable eco-buildings. Construction of the foundation in energy-efficient houses is the key to maintaining low energy losses. The appropriate selection of building materials requires considering the thermal conditions of the environment, including its location and the zero isotherms in the ground. The main objective of this work is to analyze the possibilities of designing foundations for PHs in Poland, according to the current methodological data. In order to realize the basic aims, the work was divided into the following materials and methods: (I) literature review; (II) database of PH in Central Europe; (III) method of depth of ground freezing determination; (IV) selection of the joint of slab-on-ground foundation and external wall to analysis; (V) description and validation of the heat-transfer model. The result of the research work is: (i) analysis of the foundation under the conditions of freezing of the ground in Poland; (ii) description and validation of the heat-transfer model. The research has revealed that in the Polish climate zone, the most efficient solution for passive buildings is to build them on a foundation slab. The foundation of a building below the latest specified ground frost depths in Poland is inefficient in terms of, for example, thermal insulation, economics, and the idea of PH.
Research on Economic and Operating Characteristics of Hydrogen Fuel Cell Cars Based on Real Vehicle Tests
Authors: Duan Zhijie , Zhang Luo , Feng Lili , Yu Shuguang , Jiang Zengyou , Xu Xiaoming , Hong Jichao
Published: November 2021

With the increase of the requirement for the economy of vehicles and the strengthening of the concept of environmental protection, the development of future vehicles will develop in the direction of high efficiency and cleanliness, and the current power system of vehicles based on traditional fossil fuels will gradually transition to hybrid power. As an essential technological direction for new energy vehicles, the development of fuel cell passenger vehicles is of great significance in reducing transportation carbon emissions, stabilizing energy supply, and maintaining the sustainable development of the automotive industry. To study the fuel economy of a passenger car with the proton exchange membrane fuel cell (PEMFC) during the operating phase, two typical PEMFC passenger cars, test vehicles A and B, were compared and analyzed. The hydrogen consumption and hydrogen emission under two operating conditions, namely the different steady-state power and the Chinese Vehicle Driving Conditions-Passenger Car cycle, were tested. The test results show the actual hydrogen consumption rates of vehicle A and vehicle B are 9.77 g/kM and 8.28 g/kM, respectively. The average hydrogen emission rates for vehicle A and vehicle B are 1.56 g/(kW·h) and 5.40 g/(kW·h), respectively. By comparing the hydrogen purge valve opening time ratio, the differences between test vehicles A and B in control strategy, hydrogen consumption, and emission rate are analyzed. This study will provide reference data for China to study the economics of the operational phase of PEMFC vehicles.
Long-Term Changes in Cork Oak and Holm Oak Patches Connectivity. The Algarve, Portugal, a Mediterranean Landscape Case Study
Authors: Loureiro Nuno de Santos , Fernandes Maria Jacinta
Published: November 2021

Structural connectivity can be inferred by several landscape metrics that appear to be relevant for characterizing how landscapes constrain or favor the presence and movement of animal species at the level of the regional landscape. Trends of change can be estimated trough spatial time-series analysis. The use of historical maps increases the time span of analysis of the landscape dynamic, relative to the use of remote sensing-related information. Supported by GIS, in this study, a framework for the analyses of the long-term trends of change in the connectivity of the Algarve regional landscape was used to seize the possibility of expanding the span of the spatial time series by integrating an unpublished agricultural and forest map from the turn of the 19th to the 20th century with another historical map and two recent maps. The total area covered by cork and holm oak-related community patches and their connectivity increased over the 20th century and stabilized in the 21st century. A reflection on Portuguese contemporary land-cover policies is urged, to face the sustainable planning and management challenges concerning biodiversity.