bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Use of Composite Functions in the Simple Equations Method to Obtain Exact Solutions of Nonlinear Differential Equations
Authors: Vitanov Nikolay K. , Dimitrova Zlatinka I. , Vitanov Kaloyan N.
Published: September 2021

We discuss the Simple Equations Method (SEsM) for obtaining exact solutions of a class of nonlinear differential equations containing polynomial nonlinearities. We present an amended version of the methodology, which is based on the use of composite functions. The number of steps of the SEsM was reduced from seven to four in the amended version of the methodology. For the case of nonlinear differential equations with polynomial nonlinearities, SEsM can reduce the solved equations to a system of nonlinear algebraic equations. Each nontrivial solution of this algebraic system leads to an exact solution of the solved nonlinear differential equations. We prove the theorems and present examples for the use of composite functions in the methodology of the SEsM for the following three kinds of composite functions: (i) a composite function of one function of one independent variable; (ii) a composite function of two functions of two independent variables; (iii) a composite function of three functions of two independent variables.
Risk Factors of Duodenobiliary Reflux-Related Dysfunction of Covered Biliary Metal Stents after Treatment of Duodenal Stricture in Patients with Malignant Biliary and Duodenal Obstruction
Authors: Wu Chi-Huan , Lee Mu-Hsien , Tsou Yung-Kuan , Lin Cheng-Hui , Sung Kai-Feng , Pan Kuang-Tse , Liu Nai-Jen
Published: September 2021

Duodenal obstruction is often accompanied with unresectable malignant distal biliary obstruction in patients who have undergone biliary self-expandable metal stent (SEMS) placement. Duodenobiliary reflux (DBR) is a major cause of recurrent biliary obstruction (RBO) after covered biliary SEMS placement. We analyzed the risk factors for DBR-related SEMS dysfunction following treatment for malignant duodenal obstruction. Sixty-one patients with covered SEMS who underwent treatment for duodenal obstruction were included. We excluded patients with tumor-related stent dysfunction (n = 6) or metal stent migration (n = 1). Fifty-four patients who underwent covered biliary SEMS placement followed by duodenal metal stenting or surgical gastrojejunostomy were included. Eleven patients had DBR-related biliary SEMS dysfunction after treatment of duodenal obstruction. There was no difference between the duodenal metal stenting group and the surgical gastrojejunostomy group. Duodenal obstruction below the papilla of Vater and a score of ≤2 on the Gastric Outlet Obstruction Scoring System after treatment for duodenal obstruction were associated with DBR-related covered biliary SEMS dysfunction. Thus, creating a reliable route for ensuring good oral intake and avoiding DBR in patients with duodenal obstruction below the papilla of Vater are both important factors in preventing DBR-related covered biliary SEMS dysfunction.
Origin of the Chordate Notochord
Authors: Sui Zihao , Zhao Zhihan , Dong Bo
Published: September 2021

The phylum of Chordata is defined based on the discovery of a coelom-like dorsal notochord in ascidian and amphioxus embryos. Chordata can be classified into three subphylums, Cephalochordata, Urochordata, and Vertebrata, united by the presence of a notochord at some point during development. The origin of the notochord, the signature anatomical structure of chordates, has been under debate since the publication of Alexander Kovalevsky’s work in the mid-19th century that placed ascidians close to the vertebrates on the phylogenetic tree. During the late 20th century, the development of molecular and genetic tools in biology brought about a revival of studies on the evolutionary path of notochord development. Two main hypotheses for the origin of the notochord were proposed, the de novo theory and the axochord theory. The former states that notochord has developed de novo from the mid-dorsal archenteron of a chordate ancestor with simple morphology and no central nervous system nor notochord homolog. The putative notochord along the dorsal side of the animal is proposed to take on the signal functions later from the endoderm and ectoderm. An alternative hypothesis, the axochord theory, proposes that notochord has evolved from the mid-line muscle tissue, the so-called axochord, in annelids. Structural and molecular evidence point to the midline muscle of annelids as a distant homolog of the notochord. This hypothesis thus suggests a notochord-like structure in the urbilaterian ancestor, opposed to the consensus that notochord is a chordate-specific feature. In this review, we introduce the history of the formation of these views and summarize the current understandings of embryonic development, molecular profile, and gene regulatory networks of notochord and notochord-like structures.
A Novel Factor Graph and Cubature Kalman Filter Integrated Algorithm for Single-Transponder-Aided Cooperative Localization
Authors: Zhao Wanlong , Zhao Huifeng , Zou Deyue , Liu Lu
Published: September 2021

Cooperative localization (CL) of underwater multi-AUVs is vital for numerous underwater operations. Single-transponder-aided cooperative localization (STCL) is regarded as a promising scheme for multi-AUVs CL, benefiting from the fact that an accurate reference is adopted. To improve the positioning accuracy and robustness of STCL, a novel Factor Graph and Cubature Kalman Filter (FGCKF)-integrated algorithm is proposed in this paper. In the proposed FGCKF, historical information can be efficiently used in measurement updating to overcome uncertain observation environments, which greatly helps to improve the performance of filtering progress. Furthermore, Adaptive CKF, sum product, and Maximum Correntropy Criterion (MCC) methods are designed to deal with outliers of acoustic transmission delay, sound velocity, and motion velocity, respectively. Simulations and experiments are conducted, and it is verified that the proposed FGCKF algorithm can improve positioning accuracy and robustness greatly than traditional filtering methods.
Dynamic Effects in Nucleation of Receptor Clusters
Authors: Prikhodko Ivan V. , Guria Georgy Th.
Published: September 2021

Nucleation theory has been widely applied for the interpretation of critical phenomena in nonequilibrium systems. Ligand-induced receptor clustering is a critical step of cellular activation. Receptor clusters on the cell surface are treated from the nucleation theory point of view. The authors propose that the redistribution of energy over the degrees of freedom is crucial for forming each new bond in the growing cluster. The expression for a kinetic barrier for new bond formation in a cluster was obtained. The shape of critical receptor clusters seems to be very important for the clustering on the cell surface. The von Neumann entropy of the graph of bonds is used to determine the influence of the cluster shape on the kinetic barrier. Numerical studies were carried out to assess the dependence of the barrier on the size of the cluster. The asymptotic expression, reflecting the conditions necessary for the formation of receptor clusters, was obtained. Several dynamic effects were found. A slight increase of the ligand mass has been shown to significantly accelerate the nucleation of receptor clusters. The possible meaning of the obtained results for medical applications is discussed.
Speech Watermarking Method Using McAdams Coefficient Based on Random Forest Learning
Authors: Mawalim Candy Olivia , Unoki Masashi
Published: September 2021

Speech watermarking has become a promising solution for protecting the security of speech communication systems. We propose a speech watermarking method that uses the McAdams coefficient, which is commonly used for frequency harmonics adjustment. The embedding process was conducted, using bit-inverse shifting. We also developed a random forest classifier, using features related to frequency harmonics for blind detection. An objective evaluation was conducted to analyze the performance of our method in terms of the inaudibility and robustness requirements. The results indicate that our method satisfies the speech watermarking requirements with a 16 bps payload under normal conditions and numerous non-malicious signal processing operations, e.g., conversion to Ogg or MP4 format.
Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine
Authors: Liu Mingyang , Yang Jin , Zheng Wei
Published: September 2021

Numerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assigns the same classification weights to leak samples, including outliers that affect classification, these outliers are often situated away from the main leak samples. To overcome this shortcoming, the maximum entropy (MaxEnt) version of the LST-KSVC is proposed in this paper, called the MLT-KSVC algorithm. In this classification approach, classification weights of leak samples are calculated based on the MaxEnt model. Different sample points are assigned different weights: large weights are assigned to primary leak samples and outliers are assigned small weights, hence the outliers can be ignored in the classification process. Leak recognition experiments prove that the proposed MLT-KSVC algorithm can reduce the impact of outliers on the classification process and avoid the misclassification color block drawback in linear LST-KSVC. MLT-KSVC is more accurate compared with LST-KSVC, TwinSVC, TwinKSVC, and classic Multi-SVM.
A Novel Method for Colorectal Cancer Screening Based on Circulating Tumor Cells and Machine Learning
Authors: Hatzidaki Eleana , Iliopoulos Aggelos , Papasotiriou Ioannis
Published: September 2021

Colorectal cancer is one of the most common types of cancer, and it can have a high mortality rate if left untreated or undiagnosed. The fact that CRC becomes symptomatic at advanced stages highlights the importance of early screening. The reference screening method for CRC is colonoscopy, an invasive, time-consuming procedure that requires sedation or anesthesia and is recommended from a certain age and above. The aim of this study was to build a machine learning classifier that can distinguish cancer from non-cancer samples. For this, circulating tumor cells were enumerated using flow cytometry. Their numbers were used as a training set for building an optimized SVM classifier that was subsequently used on a blind set. The SVM classifier’s accuracy on the blind samples was found to be 90.0%, sensitivity was 80.0%, specificity was 100.0%, precision was 100.0% and AUC was 0.98. Finally, in order to test the generalizability of our method, we also compared the performances of different classifiers developed by various machine learning models, using over-sampling datasets generated by the SMOTE algorithm. The results showed that SVM achieved the best performances according to the validation accuracy metric. Overall, our results demonstrate that CTCs enumerated by flow cytometry can provide significant information, which can be used in machine learning algorithms to successfully discriminate between healthy and colorectal cancer patients. The clinical significance of this method could be the development of a simple, fast, non-invasive cancer screening tool based on blood CTC enumeration by flow cytometry and machine learning algorithms.
A Penalized Matrix Normal Mixture Model for Clustering Matrix Data
Authors: Heo Jinwon , Baek Jangsun
Published: September 2021

Along with advances in technology, matrix data, such as medical/industrial images, have emerged in many practical fields. These data usually have high dimensions and are not easy to cluster due to their intrinsic correlated structure among rows and columns. Most approaches convert matrix data to multi dimensional vectors and apply conventional clustering methods to them, and thus, suffer from an extreme high-dimensionality problem as well as a lack of interpretability of the correlated structure among row/column variables. Recently, a regularized model was proposed for clustering matrix-valued data by imposing a sparsity structure for the mean signal of each cluster. We extend their approach by regularizing further on the covariance to cope better with the curse of dimensionality for large size images. A penalized matrix normal mixture model with lasso-type penalty terms in both mean and covariance matrices is proposed, and then an expectation maximization algorithm is developed to estimate the parameters. The proposed method has the competence of both parsimonious modeling and reflecting the proper conditional correlation structure. The estimators are consistent, and their limiting distributions are derived. We applied the proposed method to simulated data as well as real datasets and measured its clustering performance with the clustering accuracy (ACC) and the adjusted rand index (ARI). The experiment results show that the proposed method performed better with higher ACC and ARI than those of conventional methods.
Microstructural Characteristics of the Weighted and Directed International Crop Trade Networks
Authors: Zhang Yin-Ting , Zhou Wei-Xing
Published: September 2021

With increasing global demand for food, international food trade is playing a critical role in balancing the food supply and demand across different regions. Here, using trade datasets of four crops that provide more than 50% of the calories consumed globally, we constructed four international crop trade networks (iCTNs). We observed the increasing globalization in the international crop trade and different trade patterns in different iCTNs. The distributions of node degrees deviate from power laws, and the distributions of link weights follow power laws. We also found that the in-degree is positively correlated with the out-degree, but negatively correlated with the clustering coefficient. This indicates that the numbers of trade partners affect the tendency of economies to form clusters. In addition, each iCTN exhibits a unique topology which is different from the whole food network studied by many researchers. Our analysis on the microstructural characteristics of different iCTNs provides highly valuable insights into distinctive features of specific crop trades and has potential implications for model construction and food security.
DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis
Authors: Atteia Ghada , Abdel Samee Nagwan , Zohair Hassan Hassan
Published: September 2021

Diabetic macular edema (DME) is the most common cause of irreversible vision loss in diabetes patients. Early diagnosis of DME is necessary for effective treatment of the disease. Visual detection of DME in retinal screening images by ophthalmologists is a time-consuming process. Recently, many computer-aided diagnosis systems have been developed to assist doctors by detecting DME automatically. In this paper, a new deep feature transfer-based stacked autoencoder neural network system is proposed for the automatic diagnosis of DME in fundus images. The proposed system integrates the power of pretrained convolutional neural networks as automatic feature extractors with the power of stacked autoencoders in feature selection and classification. Moreover, the system enables extracting a large set of features from a small input dataset using four standard pretrained deep networks: ResNet-50, SqueezeNet, Inception-v3, and GoogLeNet. The most informative features are then selected by a stacked autoencoder neural network. The stacked network is trained in a semi-supervised manner and is used for the classification of DME. It is found that the introduced system achieves a maximum classification accuracy of 96.8%, sensitivity of 97.5%, and specificity of 95.5%. The proposed system shows a superior performance over the original pretrained network classifiers and state-of-the-art findings.
Population Risk Improvement with Model Compression: An Information-Theoretic Approach
Authors: Bu Yuheng , Gao Weihao , Zou Shaofeng , Veeravalli Venugopal V.
Published: September 2021

It has been reported in many recent works on deep model compression that the population risk of a compressed model can be even better than that of the original model. In this paper, an information-theoretic explanation for this population risk improvement phenomenon is provided by jointly studying the decrease in the generalization error and the increase in the empirical risk that results from model compression. It is first shown that model compression reduces an information-theoretic bound on the generalization error, which suggests that model compression can be interpreted as a regularization technique to avoid overfitting. The increase in empirical risk caused by model compression is then characterized using rate distortion theory. These results imply that the overall population risk could be improved by model compression if the decrease in generalization error exceeds the increase in empirical risk. A linear regression example is presented to demonstrate that such a decrease in population risk due to model compression is indeed possible. Our theoretical results further suggest a way to improve a widely used model compression algorithm, i.e., Hessian-weighted K-means clustering, by regularizing the distance between the clustering centers. Experiments with neural networks are provided to validate our theoretical assertions.
Music as a Tool for Promoting Environmental Awareness. Experiences of Undergraduate Education Students on the Production of Video Tales in the COVID-19 Pandemic
Authors: de Moya Martínez María del Valle , Syroyid Syroyid Bohdan
Published: September 2021

This paper elaborates on the didactic implementation of musical theater with undergraduate education students. Students had to select, prepare, justify, design, elaborate, rehearse, stage, and record an educational tale that targets ecological and environmental values in its main plot or storyline. Due to the COVID-19 pandemic, the implementation of this didactic proposal went online, and the interactions between students took place through video calls. This study surveyed a sample of 86 participants (n = 86) from a population of 116 students (N = 116) from the Faculty of Education of Albacete (UCLM) to investigate the advantages and disadvantages of carrying out this activity in an online format. The main challenges exposed the complexity of coordinating a team in a completely online format, problems related to physical isolation, and the limited access to a high-quality internet connection. The reported benefits included the development of digital competences, the interactions through social media, and creative thinking. Most students viewed this activity as didactically innovative and 98.84% agreed that musical tales and musical theater can be useful for promoting environmental awareness.
Artificial Intelligence, Social Media and Supply Chain Management: The Way Forward
Authors: Khatua Apalak , Khatua Aparup , Chi Xu , Cambria Erik
Published: September 2021

Supply chain management (SCM) is a complex network of multiple entities ranging from business partners to end consumers. These stakeholders frequently use social media platforms, such as Twitter and Facebook, to voice their opinions and concerns. AI-based applications, such as sentiment analysis, allow us to extract relevant information from these deliberations. We argue that the context-specific application of AI, compared to generic approaches, is more efficient in retrieving meaningful insights from social media data for SCM. We present a conceptual overview of prevalent techniques and available resources for information extraction. Subsequently, we have identified specific areas of SCM where context-aware sentiment analysis can enhance the overall efficiency.
Hydrogen Infrastructure Project Risks in The Netherlands
Authors: Vasbinder Pieter W. M. , de Vries Antoine W. G. , Westerman Wim
Published: September 2021

This study aims to assess the potential risks of setting up a hydrogen infrastructure in the Netherlands. An integrated risk assessment framework, capable of analyzing projects, identifying risks and comparing projects, is used to identify and analyze the main risks in the upcoming Dutch hydrogen infrastructure project. A time multiplier is added to the framework to develop parameters. The impact of the different risk categories provided by the integrated framework is calculated using the discounted cash flow (DCF) model. Despite resource risks having the highest impact, scope risks are shown to be the most prominent in the hydrogen infrastructure project. To present the DCF model results, a risk assessment matrix is constructed. Compared to the conventional Risk Assessment Matrix (RAM) used to present project risks, this matrix presents additional information in terms of the internal rate of return and risk specifics.
An Integrated Device of a Lithium-Ion Battery Combined with Silicon Solar Cells
Authors: Lim Hyeonsu , Na Dan , Lee Cheul-Ro , Seo Hyung-Kee , Kwon O-Hyeon , Kim Jae-Kwang , Seo Inseok
Published: September 2021

This study reports an integrated device of a lithium-ion battery (LIB) connected with Si solar cells. A Li(Ni0.65Co0.15Mn0.20)O2 (NCM) cathode and a graphite (G) anode were used to fabricate the lithium-ion battery (LIB). The surface and shape morphologies of NCM and graphite powder were characterized by field emission scanning electron microscopy (FE-SEM). The structural properties of NCM and graphite powder were determined by X-ray diffraction (XRD) analysis. XRD patterns of powders were well matched with those of JCPDS data. To investigate the electrochemical characteristics of NCM and graphite, cycling tests were performed after assembling the NCM-Li, the G-Li half-cell, and the NCM-G full-cell. The discharge capacity of the NCM cathode at 0.1C was 189.82 mAh/g−1. The NCM-graphite full-cell showed 98.25% cycle retention at 1C after 50 cycles. To obtain enough charging voltage for the LIB connected with solar cells in an integrated device, eight single Si solar cells were connected in a series. The short-circuit photocurrent density for Si solar cells was 4.124 mA/cm2. The fill factor and the open circuit voltage were 0.78 and 4.5 V, respectively. These Si solar cells showed a power conversion efficiency of 14.45%. The power conversion andstorage efficiency of the integrated device of the NCM battery and Si solar cells was 7.74%. Charging of the integrated device could be as effective as charging with a battery cycler.
Recent Progress and Trends in the Development of Microbial Biofuels from Solid Waste—A Review
Authors: Azimov Ulugbek , Okoro Victor , Hernandez Hector H.
Published: September 2021

This review covers the recent progress in the design and application of microbial biofuels, assessing the advancement of genetic engineering undertakings and their marketability, and lignocellulosic biomass pretreatment issues. Municipal solid waste (MSW) is a promising sustainable biofuel feedstock due to its high content of lignocellulosic fiber. In this review, we compared the production of fatty alcohols, alkanes, and n-butanol from residual biogenic waste and the environmental/economic parameters to that of conventional biofuels. New synthetic biology tools can be used to engineer fermentation pathways within micro-organisms to produce long-chain alcohols, isoprenoids, long-chain fatty acids, and esters, along with alkanes, as substitutes to petroleum-derived fuels. Biotechnological advances have struggled to address problems with bioethanol, such as lower energy density compared to gasoline and high corrosive and hygroscopic qualities that restrict its application in present infrastructure. Biofuels derived from the organic fraction of municipal solid waste (OFMSW) may have less environmental impacts compared to traditional fuel production, with the added benefit of lower production costs. Unfortunately, current advanced biofuel production suffers low production rates, which hinders commercial scaling-up efforts. Microbial-produced biofuels can address low productivity while increasing the spectrum of produced bioenergy molecules.
A Digital Information Model Framework for UAS-Enabled Bridge Inspection
Authors: Achuthan Kamal , Hay Nick , Aliyari Mostafa , Ayele Yonas Zewdu
Published: September 2021

Unmanned aerial systems (UAS) provide two main functions with regards to bridge inspections: (1) high-quality digital imaging to detect element defects; (2) spatial point cloud data for the reconstruction of 3D asset models. With UAS being a relatively new inspection method, there is little in the way of existing framework for storing, processing and managing the resulting inspection data. This study has proposed a novel methodology for a digital information model covering data acquisition through to a 3D GIS visualisation environment, also capable of integrating within a bridge management system (BMS). Previous efforts focusing on visualisation functionality have focused on BIM and GIS as separate entities, which has a number of problems associated with it. This methodology has a core focus on the integration of BIM and GIS, providing an effective and efficient information model, which provides vital visual context to inspectors and users of the BMS. Three-dimensional GIS visualisation allows the user to navigate through a fully interactive environment, where element level inspection information can be obtained through point-and-click operations on the 3D structural model. Two visualisation environments were created: a web-based GIS application and a desktop solution. Both environments develop a fully interactive, user-friendly model which have fulfilled the aims of coordinating and streamlining the BMS process.
Proposed Commutation Method for Performance Improvement of Brushless DC Motor
Authors: Jin Chang-Sung , Kim Chang-Min , Kim In-Jin , Jang Iksang
Published: September 2021

This study focused on the efficiency improvement and acoustic noise reduction of brushless DC (BLDC) motors by reducing current harmonics using a novel BLDC commutation method. To achieve these goals, we designed an improved 150° commutation method for a three-phase permanent magnet BLDC motor that can improve the current waveform. Although the 120° commutation method is generally employed for BLDC motors, an improved 150° commutation method is introduced to operate the BLDC with increased efficiency and acoustic noise similar to a brushless AC motor. This study investigated the attributes of various commutation methods, both theoretically and experimentally, to determine the optimal commutation method. The results of this study indicate that the improved 150° commutation method is optimal in terms of harmonic attributes and reduced torque ripple, allowing it to improve motor efficiency and reduce acoustic noise.
Thermal Protection Technology for Acoustic–Magnetic Device in a Geothermal Water Anti-Scaling System
Authors: Korzhakov Alexey , Oskin Sergei
Published: September 2021

This article presents the results of the design of acoustic–magnetic device thermal protection technology based on simulation. The acoustic–magnetic device (AMD) was installed in the heat supply system of a greenhouse complex with a geothermal heat source, developed and patented by the authors of this paper. Simulation was performed to investigate the possibility of maintaining the acoustic transmitter temperature of the acoustic–magnetic device in its operating range. The QuickField Student Edition v 6.4 simulation environment was used for this purpose. Based on the results of the simulation, the optimum thermal mode of the acoustic–magnetic device was developed and implemented. The optimum temporal operating mode of the acoustic–magnetic device is necessary for the optimization of the non-reagent treatment of geothermal water in a heat supply system of a greenhouse complex. It allows for a considerable reduction in the intensity of scale formation in the heat exchanger and equipment of a geothermal heating system. As demonstrated by the simulation thermal modes, the acoustic–magnetic device provides conditions for the work maintenance of the AMD acoustic transmitter at the resonance frequency, reduces the power expenses, and increases the efficiency of the acoustic influence on the scale formed in the heat supply system of a greenhouse complex. The results of the simulation were implemented in the greenhouse complex of JSC “Raduga”. The thermal protection technology was realized by installing two acoustic–magnetic devices and automation systems in the geothermal heating system a greenhouse complex.
High Power Normally-OFF GaN/AlGaN HEMT with Regrown p Type GaN
Authors: Rolland Gwen , Rodriguez Christophe , Gommé Guillaume , Boucherif Abderrahim , Chakroun Ahmed , Bouchilaoun Meriem , Pepin Marie Clara , El Hamidi Faissal , Maher Soundos , Arès Richard , MacElwee Tom , Maher Hassan
Published: September 2021

In this paper is presented a Normally-OFF GaN HEMT (High Electron Mobility Transistor) device using p-doped GaN barrier layer regrown by CBE (Chemical Beam Epitaxy). The impact of the p doping on the device performance is investigated using TCAD simulator (Silvaco/Atlas). With 4E17 cm−3 p doping, a Vth of 1.5 V is achieved. Four terminal breakdowns of the fabricated device are investigated, and the origin of the device failure is identified.
A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities
Authors: Chen James Ming , Rehman Mobeen Ur
Published: September 2021

The identification of critical periods and business cycles contributes significantly to the analysis of financial markets and the macroeconomy. Financialization and cointegration place a premium on the accurate recognition of time-varying volatility in commodity markets, especially those for crude oil and refined fuels. This article seeks to identify critical periods in the trading of energy-related commodities as a step toward understanding the temporal dynamics of those markets. This article proposes a novel application of unsupervised machine learning. A suite of clustering methods, applied to conditional volatility forecasts by trading days and individual assets or asset classes, can identify critical periods in energy-related commodity markets. Unsupervised machine learning achieves this task without rules-based or subjective definitions of crises. Five clustering methods—affinity propagation, mean-shift, spectral, k-means, and hierarchical agglomerative clustering—can identify anomalous periods in commodities trading. These methods identified the financial crisis of 2008–2009 and the initial stages of the COVID-19 pandemic. Applied to four energy-related markets—Brent, West Texas intermediate, gasoil, and gasoline—the same methods identified additional periods connected to events such as the September 11 terrorist attacks and the 2003 Persian Gulf war. t-distributed stochastic neighbor embedding facilitates the visualization of trading regimes. Temporal clustering of conditional volatility forecasts reveals unusual financial properties that distinguish the trading of energy-related commodities during critical periods from trading during normal periods and from trade in other commodities in all periods. Whereas critical periods for all commodities appear to coincide with broader disruptions in demand for energy, critical periods unique to crude oil and refined fuels appear to arise from acute disruptions in supply. Extensions of these methods include the definition of bull and bear markets and the identification of recessions and recoveries in the real economy.
Reliability-Based Design Optimization for Reducing the Performance Failure and Maximizing the Specific Energy of Lithium-Ion Batteries Considering Manufacturing Uncertainty of Porous Electrodes
Authors: Yoo Donghyeon , Park Jinhwan , Moon Jaemin , Kim Changwan
Published: September 2021

Uncertainty quantification in LIB manufacturing has received interest in order to improve the reliability of LIB. The uncertainty generated during the manufacturing causes variations in the performance of LIBs, thereby increasing capacity degradation and leading to failure. In this study, a reliability-based design optimization (RBDO) of LIBs is conducted to reduce performance failure while maximizing the specific energy. The design variables with uncertainty are the thickness, porosity, and particle size of the anode and cathode. The specific energy is defined as the objective function in the optimization design problem. To maintain the specific power in the initial design of the LIB, it is defined as the constraint function. Reliability is evaluated as the probability that the battery satisfies the performance of the required design. The results indicate that the design optimized through RBDO increases the specific energy by 42.4% in comparison with that of the initial design while reducing the failure rate to 1.53%. Unlike the conventional deterministic design optimization method (DDO), which exhibits 55.09% reliability, the proposed RBDO method ensures 98.47% reliability. It is shown that the proposed RBDO approach is an effective design method to reduce the failure rate while maximizing the specific energy.
Mapping Research Trends in Residential Construction Retrofitting: A Scientometric Literature Review
Authors: Amirkhani Mehdi , Martek Igor , Luther Mark B.
Published: September 2021

The world is looking to reduce carbon emissions, prevent global warming, and become more energy sustainable. Despite the various strategies for mitigating climate change, the fact remains that 80% of greenhouse gas emissions are attributable to activities associated with the built environment, and this is where a concentrated focus is needed. Moreover, most buildings are residential, not commercial or industrial. In essence, ways must be found to reduce energy consumption and CO2 emissions from existing houses and apartments globally if sustainability is to be realised. The recognised way to achieve this is through the retrofitting of existing residential buildings. Studies in this area have increased in recent times, but the extent of the work remains unmapped and undescribed. If further progress is to be made in this field, researchers’ knowledge domain so far must be documented. This literature review delivers that goal. A scientometric evaluation of research on residential retrofitting is here presented. VOSviewer, Gephi, and CiteSpace are the software packages used. Findings identify retrofitting as an emerging theme, taking off only as recently as 2017. The breadth of research is very limited, primarily concerned with calibrating trade-offs between energy costs and thermal comfort. Emerging and new opportunities to expand retrofitting research are identified. Finally, while several journals accommodate publications on this topic, analysis reveals Energy and Buildings to be the significant citation source.
The Impact of Information and Communication Technologies (ICT) on Energy Poverty and Unemployment in Selected European Union Countries
Authors: Postuła Marta , Chmielewski Wojciech , Puczyński Piotr , Cieślik Rafał
Published: September 2021

The accelerated development of information and communication technologies (ICT) over the last three decades has encouraged researchers to analyse the impact of this phenomenon on the labour market. The potential decline in employment resulting from the proliferation of ICT may reduce access to basic energy services and even lead to energy poverty in the form of inability to heat the apartment as needed, allocating a significant part of revenues to expenses related to heating or problems with the timely payment of energy bills. Because access to energy is of fundamental importance for improving the quality of life and is crucial from the point of view of economic development, it is justified to verify the hypothesis that the accelerated development of ICT in EU countries may contribute to an increase in unemployment and, consequently, translate into a higher level of energy poverty. The described research results were obtained thanks to a comparative factor analysis based on secondary data. The analysis showed that in the period 2009–2019, the use of ICT had a limited impact on the unemployment rate in the EU and had a significant impact on reducing the level of energy poverty in EU Member States. As regards the impact of ICT factors on the level of energy poverty, only IP traffic showed a significant impact in this area. When it comes to the labour market, it was found that employment is chiefly influenced by economic factors such as labour costs.
On the Emergence of Sociotechnical Regimes of Electric Urban Water Transit Systems
Authors: Tarkowski Maciej
Published: September 2021

Urban activities, including urban mobility, play a crucial role in climate change mitigation. Urban mobility is currently at a crossroads. In a business as usual scenario, CO2 emissions from urban transportation will grow by one fourth by 2050. Nevertheless, during this period, it may drop by about one third. To make the drop happen, we need to introduce comprehensive policies and measures. Electrifying urban transit is one feasible solution. This study investigates whether and how urban water transit systems have been electrified—a means of transport which has not been well researched in this respect. A multilevel perspective and the comparative case study method were employed to answer the research questions. The comprehensive study focussed on 24 cities representing the current experience in planning and operating water transport, based mainly on secondary, primarily qualitative, data, such as industry reports, feasibility studies, urban policies, and scientific papers. The primary outcome is that urban electric passenger ferries left their market niches and triggered a radical innovation, diffusing into mainstream markets. However, urban diversity results in various paths to electrification, due to the system’s physical characteristics, local climate and transport policies, manufacturing capacity, green city branding, and the innovativeness of international ferry operators. Three dominant transition pathways were identified—a comprehensive carbon neutral policy, a transport sector policy, and a research and development policy. From a multilevel perspective, cities can be considered a bridge between niches and regimes that provide the actual conditions for implementing sociotechnical configurations.
Vortex Patterns Investigation and Enstrophy Analysis in a Small Scale S-CO2 Axial Turbine
Authors: Ying Qiyu , Zhuge Weilin , Zhang Yangjun , Ma Can , Gou Jinlan , Wang Wei
Published: September 2021

Supercritical carbon dioxide (S-CO2) Brayton cycle system is a promising closed-loop energy conversion system frequently mentioned in the automotive and power generation field in recent years. To develop a suitable design methodology for S-CO2 turbines with better performance, an understanding of the vortex flow patterns and associated aerodynamic loss inside a S-CO2 turbine is essential. In this paper, a hundred-kilowatt level S-CO2 axial turbine is designed and investigated using a three-dimensional transient viscous flow simulation. The NIST Span and Wagner equation of state model that considers the real gas effects is utilized to estimate the thermodynamic properties of the supercritical fluid. The numerical methods are experimentally validated. The results indicates that the aspect ratio and tip-to-hub ratio are different in the S-CO2 turbine from that in the gas turbine, and the vortex flow patterns are influenced notably by these geometrical parameters. Both the vortex structure and moving tracks of passage vortices are changed as a result of large centrifugal force. An interaction between tip leakage vortex and hub passage vortex is observed in the impeller passage and its formation and development mechanism are revealed. To further explore the aerodynamic loss mechanism caused by vortex interaction, the energy loss in the impeller passage is analyzed with the enstrophy dissipation method, which can not only accurately calculate the energy loss but also estimate how the vortical motions occur. It is found that the enstrophy and energy loss can be effectively reduced by vortex interaction between tip leakage vortex and hub passage vortex. The results in this study would increase the knowledge of vortex flow patterns in S-CO2 turbine and the proposed enstrophy production method can be used intuitively to provide a reference for flow vortical motion study in turbines.
Influences of Control Parameters on Reduction of Energy Losses in Electrohydraulic Valve with Stepping Motors
Authors: Milecki Andrzej , Ortmann Jarosław
Published: September 2021

In many heavy machines, the use of high force drives is required. For such tasks, electrohydraulic servo drives with proportional valves are used most often. In these valves, the proportional electromagnets are applied. If high precise control is additionally required, it is necessary to use expensive servo valves or precise stepping motors. In this paper, the application of a valve with one (or with two) stepping motors in the electrohydraulic servo drive is described. Such motors may work in a micro-step mode, which enables the precise positioning of the valve spool with low energy consumption. The control system structure that was used for positioning, consisting of such an electrohydraulic servo drive with a valve having stepping motors, is described. In the investigations, the following control parameters are considered: the number of stepping motors used, proportional gain coefficients, supply pressure, and desired step distance. The simulation model of the servo drive is proposed, enabling the investigations of energy consumption during the positioning process. In the investigations, the drive step responses are recorded and compared, taking into account the rise time and energy consumption. The overshot-free algorithm is used in the following step and tested in positioning tasks. The collected results of energy consumption of the drive during the positioning process are compared with other solutions.
Optical Diagnostic Characterization of the Local Arc on Contaminated Insulation Surface at Low Pressure
Authors: Yang Hao , Zhang Haotian , Cao Wen , Zhao Xuanxiang , Wen Ran , Zhao Junping , Tan Shengwu , Wang Pengchao
Published: September 2021

Flashover of contaminated insulators is a major problem for power systems at high altitude. Laboratory experiments have shown that the optical diagnostic method can provide extensive information on the physical process of contamination flashover. In this paper, a study of the local arc on a wet polluted surface under low pressure by using the optical diagnostic method is presented. The thickness of the continuous spectrum, spectral line intensity and the spectral composition varies significantly in different stages of the local arc development. Thermodynamic parameters of the local arc (including electron temperature, electron density and conductivity) are obtained by analyzing the spectra. Both the electron temperature and the conductivity increase with the increase in leakage current and air pressure. Although the electron density does not change significantly with an increase in leakage current, it increases significantly with an increase in air pressure. The findings of this work could be used as supplementary information for the investigation of local arc parameters, thus providing a reliable reference for the calculation of contamination flashover at high altitude.
Optimizing the Life Cycle of Physical Assets through an Integrated Life Cycle Assessment Method
Authors: de Almeida Pais José Edmundo , Raposo Hugo D. N. , Farinha José Torres , Cardoso Antonio J. Marques , Marques Pedro Alexandre
Published: September 2021

The purpose of this study was to apply new methods of econometric models to the Life Cycle Assessment (LCA) of physical assets, by integrating investments such as maintenance, technology, sustainability, and technological upgrades, and to propose a means to evaluate the Life Cycle Investment (LCI), with emphasis on sustainability. Sustainability is a recurrent theme of existing studies and will be a concern in coming decades. As a result, equipment with a smaller environmental footprint is being continually developed. This paper presents a method to evaluate asset depreciation with an emphasis on the maintenance investment, technology depreciation, sustainability depreciation, and technological upgrade investment. To demonstrate the value added of the proposed model, it was compared with existing models that do not take the previously mentioned aspects into consideration. The econometric model is consistent with asset life cycle plans as part of the Strategic Asset Management Plan of the Asset Management System. It is clearly demonstrated that the proposed approach is new and the results are conclusive, as demonstrated by the presented models and their results. This research aims to introduce new methods that integrate the factors of technology upgrades and sustainability for the evaluation of assets’ LCA and replacement time. Despite the increase in investment in technology upgrades and sustainability, the results of the Integrated Life Cycle Assessment First Method (ILCAM1), which represents an improved approach for the analyzed data, show that the asset life is extended, thus increasing sustainability and promoting the circular economy. By comparison, the Integrated Life Cycle Investment Assessment Method (ILCIAM) shows improved results due to the investment in technology upgrades and sustainability. Therefore, this study presents an integrated approach that may offer a valid tool for decision makers.
Catalytic Temperature Effects on Conversion Efficiency of PM2.5 and Gaseous Emissions from Rice Husk Combustion
Authors: Abah Emmanuel Owoicho , Ahamed Tofael , Noguchi Ryozo
Published: September 2021

Most studies on honeycomb catalysts have been conducted using simulation models and exhaust experiments from automobiles. Very few monolithic catalyst studies have been applied to the agricultural sector, especially the catalyst exhaust system for flue purification from the biomass industry. The importance of exhaust gas purification and particulate removal from biomass power plants has become critical for evaluating the performance and environmental sustainability of biomass combustion. This is one of the first studies to investigate the performance of honeycomb catalysts for the oxidation of flue (PM2.5), (CO), and (SO2) from a rice husk briquette combustion system. The experimental setup comprised a fixed-bed electric furnace, the catalyst, an aerosol sampler, and a flue gas analyzer. Rice husk (0.1 g/mL density) and rice husk briquettes (0.8 g/mL density), were burned at 600–1000 °C for 3 min. From the results, the catalyst CO conversion rate was 100% at the optimum heated temperatures of 427.4–490.3 °C. At these temperatures, the inhibition effect of the chemisorbed CO was significantly minimized, enhancing the adsorption of oxygen, which reacted with CO to form CO2. However, SO2 oxidation was lower than that of CO because platinum-based catalysts are generally more attracted to CO in the presence of oxygen. The emission of PM2.5 decreased from its uncatalyzed-value (1169.9 mg/m3 and 1572.2 mg/m3) to its catalyzed values (18.9 mg/m3 and 170.1 mg/m3). This is a significant result in ensuring cleaner production of energy from rice husk.