bluuebunny/crossref_metadata_2025 huggingface dataset:
Is Methionyl-tRNA Synthetase Applicable as a Diagnostic Marker for Lung Cancer in Bronchial Ultrasound-Guided Brushing Cells?
Authors: Lee Kyungjong , Oh Mijung , Lee Kyo-Sun , Cha Yoon Jin , Chang Yoon Soo
Published: October 2021

Background and objective: Methionyl-tRNA synthetase (MARS) and A variant of Aminoacyl-tRNA synthetase interacting multifunctional protein 2 (AIMP2) with an exon 2 deletion (AIMP2-DX2) are known to be overexpressed in lung cancer. However, their role as diagnostic markers in lung cancer has not been well established. Thus, we evaluated their diagnostic performance in brushed cells obtained from nodular lung lesions suspected of lung cancer. Methods: Samples obtained by radial endobronchial ultrasound-guided brushing were processed for cytological examination with Papanicolaou (Pap) staining. Then, double IF staining with MARS and AIMP2-DX2 antibodies was measured in the cytology samples for peripheral lung nodules. The diagnostic performance was compared against biomarkers. Results: MARS IF staining was the only independent staining method used for the prediction of malignant cells. The area under the curve (AUC) of conventional cytology, MARS IF, and MARS IF plus cytology was 0.64, 0.68, and 0.69, respectively. The diagnostic accuracy was increased in MARS IF plus conventional cytology compared with cytology alone (71% vs. 47%). Conclusions: The combination of MARS staining with conventional cytology showed increases in the diagnostic accuracy for diagnosing lung nodules suspected of lung cancer on chest-computed tomography scans.
Characterizing the Typical Information Curves of Diverse Languages
Authors: Klafka Josef , Yurovsky Daniel
Published: October 2021

Optimal coding theories of language predict that speakers will keep the amount of information in their utterances relatively uniform under the constraints imposed by their language, but how much do these constraints influence information structure, and how does this influence vary across languages? We present a novel method for characterizing the information structure of sentences across a diverse set of languages. While the structure of English is broadly consistent with the shape predicted by optimal coding, many languages are not consistent with this prediction. We proceed to show that the characteristic information curves of languages are partly related to a variety of typological features from phonology to word order. These results present an important step in the direction of exploring upper bounds for the extent to which linguistic codes can be optimal for communication.
Preprocessing Pipelines including Block-Matching Convolutional Neural Network for Image Denoising to Robustify Deep Reidentification against Evasion Attacks
Authors: Pawlicki Marek , Choraś Ryszard S.
Published: October 2021

Artificial neural networks have become the go-to solution for computer vision tasks, including problems of the security domain. One such example comes in the form of reidentification, where deep learning can be part of the surveillance pipeline. The use case necessitates considering an adversarial setting—and neural networks have been shown to be vulnerable to a range of attacks. In this paper, the preprocessing defences against adversarial attacks are evaluated, including block-matching convolutional neural network for image denoising used as an adversarial defence. The benefit of using preprocessing defences comes from the fact that it does not require the effort of retraining the classifier, which, in computer vision problems, is a computationally heavy task. The defences are tested in a real-life-like scenario of using a pre-trained, widely available neural network architecture adapted to a specific task with the use of transfer learning. Multiple preprocessing pipelines are tested and the results are promising.
Travel Characteristics Analysis and Traffic Prediction Modeling Based on Online Car-Hailing Operational Data Sets
Authors: Zhou Shenghan , Chen Bang , Liu Houxiang , Ji Xinpeng , Wei Chaofan , Chang Wenbing , Xiao Yiyong
Published: October 2021

Smart transportation is an important part of smart urban areas, and travel characteristics analysis and traffic prediction modeling are the two key technical measures of building smart transportation systems. Although online car-hailing has developed rapidly and has a large number of users, most of the studies on travel characteristics do not focus on online car-hailing, but instead on taxis, buses, metros, and other traditional means of transportation. The traditional univariate variable hybrid time series traffic prediction model based on the autoregressive integrated moving average (ARIMA) ignores other explanatory variables. To fill the research gap on online car-hailing travel characteristics analysis and overcome the shortcomings of the univariate variable hybrid time series traffic prediction model based on ARIMA, based on online car-hailing operational data sets, we analyzed the online car-hailing travel characteristics from multiple dimensions, such as district, time, traffic jams, weather, air quality, and temperature. A traffic prediction method suitable for multivariate variables hybrid time series modeling is proposed in this paper, which uses the maximal information coefficient (MIC) to perform feature selection, and fuses autoregressive integrated moving average with explanatory variable (ARIMAX) and long short-term memory (LSTM) for data regression. The effectiveness of the proposed multivariate variables hybrid time series traffic prediction model was verified on the online car-hailing operational data sets.
Management of Waste Batteries and Accumulators: Quest of European Union Goals
Authors: Dobrowolski Zbysław , Sułkowski Łukasz , Danielak Wiesław
Published: October 2021

Energy issues are multifaceted and are not limited to power plants, biogas plants or transmission lines. They also include the production, usage and utilisation of batteries and accumulators, which are increasingly valuable due to, among other things, the decision to develop the production of electric cars. This article creates new ground by analysing the European Union management system of batteries and accumulators in the cause–effect context. This paper’s insights have emerged iteratively based on the theory reviewed and the empirical case—a deep analysis of the Polish management system of batteries and accumulators. The findings show that the public institutions in the analysed European Union Member State—Poland—were not ready to create a fully coherent and effective oversight system on managing batteries and accumulators. It may limit the reliability of the European Union’s reporting on battery and accumulator management, which is a part of the European energy policy. The findings make two main contributions: first, they contribute to developing a theory of energy resource management; second, this article contributes to a further contextual diagnosis of the comprehensive management system of waste batteries and accumulators, which is an important part of the European Battery Alliance. Moreover, the avenues for further research emerged from the present study.
Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine
Authors: Ishak Sanuri , Yaw Chong Tak , Koh Siaw Paw , Tiong Sieh Kiong , Chen Chai Phing , Yusaf Talal
Published: October 2021

Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works.
Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF
Authors: Tang Mingzhu , Liang Zixin , Wu Huawei , Wang Zimin
Published: October 2021

A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed for the problem of high false negative and false positive rates in wind turbine gearboxes. Normal samples of raw data were subjected to undersampling first, and various features and data labels in the raw data were provided with importance analysis by XGBoost feature selection to select features with higher label correlation. Two parameters of random forest algorithm were optimized via the whale optimization algorithm to create a fitness function with the false negative rate (FNR) and false positive rate (FPR) as evaluation indexes. Then, the minimum fitness function value within the given scope of parameters was found. The WOA was controlled by the hyper-parameter α to optimize the step size. This article uses the variant form of the sigmoid function to alter the change trend of the WOA hyper-parameter α from a linear decline to a rapid decline first and then a slow decline to allow the WOA to be optimized. In the initial stage, a larger step size and step size change rate can make the model progress to the optimization target faster, while in the later stage of optimization, a smaller step size and step size change rate allows the model to more accurately find the minimum value of the fitness function. Finally, two hyper-parameters, corresponding to the minimum fitness function value, were substituted into a random forest algorithm for model training. The results showed that the method proposed in this paper can significantly reduce the false negative and false positive rates compared with other optimization classification methods.
Effect of the Heat Dissipation System on Hard-Switching GaN-Based Power Converters for Energy Conversion
Authors: Lumbreras David , Vilella Manel , Zaragoza Jordi , Berbel Néstor , Jordà Josep , Collado Alfonso
Published: October 2021

The design of a cooling system is critical in power converters based on wide-bandgap (WBG) semiconductors. The use of gallium nitride enhancement-mode high-electron-mobility transistors (GaN e-HEMTs) is particularly challenging due to their small size and high power capability. In this paper, we model, study and compare the different heat dissipation systems proposed for high power density GaN-based power converters. Two dissipation systems are analysed in detail: bottom-side dissipation using thermal vias and top-side dissipation using different thermal interface materials. The effectiveness of both dissipation techniques is analysed using MATLAB/Simulink and PLECS. Furthermore, the impact of the dissipation system on the parasitic elements of the converter is studied using advanced design systems (ADS). The experimental results of the GaN-based converters show the effectiveness of the analysed heat dissipation systems and how top-side cooled converters have the lowest parasitic inductance among the studied power converters.
Endogenous Approach of a Frequency-Constrained Unit Commitment in Islanded Microgrid Systems
Authors: Rebollal David , Chinchilla Mónica , Santos-Martín David , Guerrero Josep M.
Published: October 2021

Power reserves are usually scheduled in day-ahead unit commitment (UC) to minimize operating costs while maintaining system security. In applying basic UC (bUC) after a contingency, the system frequency may fall upon the activation of the load-shedding global control (under-frequency load-shedding or UFLS) limits. Small isolated microgrids are more sensitive to this issue due to their lack of inertia. Including dynamic considerations into the bUC problem can minimize UFLS activation and also avoid the need for the operator to later check the short-term feasibility of a bUC solution. These proposals are known as Frequency-Constrained UC (FCUC), although the implementation are very time-consuming. FCUC implementation will increase the system’s operational costs, which should be calculated to estimate remuneration to the safety service based on the additional reserve provision. The electrical system of Gran Canaria island has suffered several episodes of greater blackouts in recent years. Shortly, there will be 242 MW of wind generation installed (26% of the thermal power installed on Gran Canaria). The main objective of this work is to improve the island system reliability by means of an FCUC formulation applied by the system operator in practice, including renewable sources. The results show that the frequency values remained within the admissible boundaries, but the system’s operational costs increased by around 13%.
Evolution of Countermeasures against Atmospheric Icing of Power Lines over the Past Four Decades and Their Applications into Field Operations
Authors: Brettschneider Stephan , Fofana Issouf
Published: October 2021

The reliability and efficiency of power grids directly contribute to the economic well-being and quality of life of citizens in any country. This reliability depends, among other things, on the power lines that are exposed to different kinds of factors such as lightning, pollution, ice storm, wind, etc. In particular, ice and snow are serious threats in various areas of the world. Under certain conditions, outdoor equipment and hardware may experience various problems: cracking, fatigue, wear, flashover, etc. In actual fact, a variety of countermeasures has been proposed over the past decades and a certain number have been applied by utilities in various countries. This contribution presents the status and current trends of different techniques against atmospheric icing of power lines. A snapshot look at some significant development on this topic over the last four decades is addressed. Engineering problems in utilizing these techniques, their applications, and perspectives are also foreseen. The latest up-to-date review papers on the applications and challenges in terms of PhD thesis, journal articles, conference proceedings, technical reports, and web materials are reported.
Novel Deperming Protocols to Reduce Demagnetizing Time and Improve the Performance for the Magnetic Silence of Warships
Authors: Im Sang-Hyeon , Lee Ho-Yeong , Park Gwan-Soo
Published: October 2021

Magnetic silence of warships is necessary to prevent damage caused by the magnetic mines detecting the magnetic field of the warship. Anhysteretic and Deperm-ME protocols are used to reduce permanent magnetization among magnetic signals. However, they have some disadvantages. Therefore, this paper proposes an effective deperming protocol that is easily controlled and reduces the demagnetization time. A protocol composed of two Anhysteretic protocols is presented using the Preisach model to easily manage and ensure excellent performance. Each stage has its own advantages by considering the Preisach density distribution. In Stage 1, the existing magnetic history is erased, and the demagnetization time is reduced. In Stage 2, the demagnetization performance is improved. The effectiveness of the protocol was verified via simulations using the Preisach model and experiments using a specimen. When the proposed protocol was applied, the results were excellent when applying Anhysteretic and Deperm-ME. In addition, even if the number of magnetic fields was reduced by 4 and 8 in the proposed protocol, the demagnetization result was maintained. Therefore, if the proposed protocol is applied, excellent demagnetization results can be obtained and the time required to perform demagnetization can be reduced, thereby improving the operational capability of the warship.
Evaluation of Supervised Learning Models in Predicting Greenhouse Energy Demand and Production for Intelligent and Sustainable Operations
Authors: Ouazzani Chahidi Laila , Fossa Marco , Priarone Antonella , Mechaqrane Abdellah
Published: October 2021

Plants need a specific environment to grow and reproduce in fine fettle. Nevertheless, climatic conditions are not stable and can impact their well-being and, consequently, harvest quality. Thus, greenhouse cultivation is one of the suitable agricultural techniques for creating and controlling the inside microclimate to be adequate for plant growth. The relevance of greenhouse control is widely recognized. The prediction of greenhouse variables using artificial intelligence methods is of great interest for intelligent control and the potential reduction in energetic and financial losses. However, the studies carried out in this context are still more or less limited and several machine learning methods have not been sufficiently exploited. The aim of this study is to predict the air conditioning electrical consumption and photovoltaic module electrical production at the smart Agro-Manufacturing Laboratory (SamLab) greenhouse, located in Albenga, north-western Italy. Different supervised machine learning methods were compared, namely, Artificial Neural Networks (ANNs), Gaussian Process Regression (GPR), Support Vector Machine (SVM) and Boosting trees. We evaluated the performance of the models based on three statistical indicators: the coefficient of correlation (R), the normalized root mean square error (nRMSE) and the normalized mean absolute error (nMAE). The results show good agreement between the measured and predicted values for all models, with a correlation coefficient R > 0.9, considering the validation set. The good performance of the models affirms the importance of this approach and that it can be used to further improve greenhouse efficiency through its intelligent control.
Study of the Permeation Flowrate of an Innovative Way to Store Hydrogen in Vehicles
Authors: Pinto Gustavo , Monteiro Joaquim , Baptista Andresa , Ribeiro Leonardo , Leite José
Published: October 2021

With the global warming of the planet, new forms of energy are being sought as an alternative to fossil fuels. Currently, hydrogen (H2) is seen as a strong alternative for fueling vehicles. However, the major challenge in the use of H2 arises from its physical properties. An earlier study was conducted on the storage of H2, used as fuel in road vehicles powered by spark ignition engines or stacks of fuel cells stored under high pressure inside small spheres randomly packed in an envelope tank. Additionally, the study evaluated the performance of this new storage system and compared it with other storage systems already applied by automakers in their vehicles. The current study aims to evaluate the H2 leaks from the same storage system, when inserted in any road vehicle parked in conventional garages, and to show the compliance of these leaks with European Standards, provided that an appropriate choice of materials is made. The system’s compliance with safety standards was proved. Regarding the materials of each component of the storage system, the best option from the pool of materials chosen consists of aluminum for the liner of the spheres and the envelope tank, CFEP for the structural layer of the spheres, and Si for the microchip.
Multiple Regression and Modified Faust Equation on Well Logging Data in Application to Seismic Procedures: Polish Outer Carpathians Case Study
Authors: Wawrzyniak-Guz Kamila , Jarzyna Jadwiga A. , Pieniądz Krzysztof , Starzec Krzysztof
Published: October 2021

An appropriate velocity model from well logs is a key issue in the processing and interpretation of seismic data. In a deep borehole located in the central part of the Polish Outer Carpathians, the sonic measurements were inadequate for seismic purposes due to the poor quality of data and gaps in the logging. Multiple regression (MR) and a modified Faust equation were proposed to model the velocity log. MR estimated the P-wave slowness as a dependent variable on the basis of sets of various logs as independent variables. The solutions were verified by the interval velocity from Check Shots (CS) and by the convergence of synthetic seismograms and the real seismic traces. MR proved to be an effective method when a set of other logs was available. The modified Faust method allowed computation of P-wave velocity based on the shallow resistivity logs, depth, and compaction factor. Faust coefficients were determined according to the lithology and stratigraphy divisions and were calibrated with the use of the velocity previously determined in the MR analysis. The modified Faust equation may be applied in nearby old wells with limited logging data, particularly with no sonic logs, where MR could not be successfully applied.
Thermodynamic Analysis of Negative CO2 Emission Power Plant Using Aspen Plus, Aspen Hysys, and Ebsilon Software
Authors: Ziółkowski Paweł , Madejski Paweł , Amiri Milad , Kuś Tomasz , Stasiak Kamil , Subramanian Navaneethan , Pawlak-Kruczek Halina , Badur Janusz , Niedźwiecki Łukasz , Mikielewicz Dariusz
Published: October 2021

The article presents results of thermodynamic analysis using a zero-dimensional mathematical models of a negative CO2 emission power plant. The developed cycle of a negative CO2 emission power plant allows the production of electricity using gasified sewage sludge as a main fuel. The negative emission can be achieved by the use this type of fuel which is already a “zero-emissive” energy source. Together with carbon capture installation, there is a possibility to decrease CO2 emission below the “zero” level. Developed models of a novel gas cycle which use selected codes allow the prediction of basic parameters of thermodynamic cycles such as output power, efficiency, combustion composition, exhaust temperature, etc. The paper presents results of thermodynamic analysis of two novel cycles, called PDF0 and PFD1, by using different thermodynamic codes. A comparison of results obtained by three different codes offered the chance to verify results because the experimental data are currently not available. The comparison of predictions between three different software in the literature is something new, according to studies made by authors. For gross efficiency (54.74%, 55.18%, and 52.00%), there is a similar relationship for turbine power output (155.9 kW, 157.19 kW, and 148.16 kW). Additionally, the chemical energy rate of the fuel is taken into account, which ultimately results in higher efficiencies for flue gases with increased steam production. A similar trend is assessed for increased CO2 in the flue gas. The developed precise models are particularly important for a carbon capture and storage (CCS) energy system, where relatively new devices mutually cooperate and their thermodynamic parameters affect those devices. Proposed software employs extended a gas–steam turbine cycle to determine the effect of cycle into environment. First of all, it should be stated that there is a slight influence of the software used on the results obtained, but the basic tendencies are the same, which makes it possible to analyze various types of thermodynamic cycles. Secondly, the possibility of a negative CO2 emission power plant and the positive environmental impact of the proposed solution has been demonstrated, which is also a novelty in the area of thermodynamic cycles.
Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model
Authors: Su Lin , Zhou Guangxu , Hu Dairong , Liu Yuan , Zhu Yunhai
Published: October 2021

Accurate estimation of the state of charge (SOC) of lithium batteries is paramount to ensuring consistent battery pack operation. To improve SOC estimation accuracy and suppress colored noise in the system, a fractional order model based on an unscented Kalman filter and an H-infinity filter (FOUHIF) estimation algorithm was proposed. Firstly, the discrete state equation of a lithium battery was derived, as per the theory of fractional calculus. Then, the HPPC experiment and the PSO algorithm were used to identify the internal parameters of the second order RC and fractional order models, respectively. As discovered during working tests, the parameters identified via the fractional order model proved to be more accurate. Furthermore, the feasibility of using the FOUHIF algorithm was evaluated under the conditions of NEDC and UDDS, with obvious colored noise. Compared with the fractional order unscented Kalman filter (FOUKF) and integer order unscented Kalman filter (UKF) algorithms, the FOUHIF algorithm showed significant improvement in both the accuracy and robustness of the estimation, with maximum errors of 1.86% and 1.61% under the two working conditions, and a terminal voltage prediction error of no more than 5.29 mV.
Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems
Authors: Peyman Mohammad , Copado Pedro J. , Tordecilla Rafael D. , Martins Leandro do C. , Xhafa Fatos , Juan Angel A.
Published: October 2021

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.
Enhancing the Fault Ride-through Capability of a DFIG-WECS Using a High-Temperature Superconducting Coil
Authors: Mosaad Mohamed I. , Abu-Siada Ahmed , Ismaiel Mohamed M. , Albalawi Hani , Fahmy Ahmed
Published: October 2021

With the increase in doubly fed induction generator-based wind energy conversion systems (DFIG-WECS) worldwide, improving the fault ride-through (FRT) capability of the entire system has been given much attention. Enhancement of the FRT capability of a DFIG-WECS is conventionally realized by employing a flexible AC transmission system device with a proper control system. This paper presents a non-conventional method for the improvement of the FRT of DFIG-WECS, using a high-temperature superconducting coil interfaced with the DC-link of the rotor and stator side converters through a DC-chopper. A fractional-order proportional-integral (FOPI) controller is utilized to regulate the DC-chopper duty cycle in order to properly manage the power flow between the DC-link and the coil. Two optimization techniques, Harmony Search and Grey Wolf Optimizer, are employed to determine the optimum size of the superconducting coil along with the optimum parameters of the FOPI controller. The effectiveness of the two proposed optimization techniques is highlighted through comparing their performance with the well-known particle swarm optimization technique.
Low Power Modular Battery Management System with a Wireless Communication Interface
Authors: Gozdur Roman , Przerywacz Tomasz , Bogdański Dariusz
Published: October 2021

The paper concerns the design and development of large electric energy storage systems made of lithium cells. Most research advances in the development of lithium-ion battery management systems focus solely on safety, functionality, and improvement of the procedures for assessing the performance of systems without considering their energy efficiency. The paper presents an alternative approach to the design and analysis of large modular battery management systems. A modular battery management system and the dedicated wireless communication system were designed to analyze and optimize energy consumption. The algorithms for assembly, reporting, management, and communication procedures described in the paper are a robust design tool for further developing large and scalable battery systems. The conducted analysis of energy efficiency for the exemplary 100S15P system shows that the energy used to power the developed battery management system is comparable to the energy dissipated due to the intrinsic self-discharge of lithium-ion cells.
The Effect of OPV Module Size on Stability and Diurnal Performance: Outdoor Tests and Application of a Computer Model
Authors: David Tudur Wyn , Bristow Noel , Stoichkov Vasil , Huang Han , Todeschini Grazia , Kettle Jeff
Published: October 2021

The outdoor performance of large area Organic Photovoltaics (OPVs) is investigated in this work. Initially, the diurnal performance of the three modules is determined and found to be similar. Subsequently module degradation is monitored, and it is found that the larger area module displays a significantly greater stability as compared to the smallest area module; in fact the larger module displays a T50% (time to fall to 50% of its original value) of 191 days whilst the smallest module displays a T50% of 57 days. This is attributed to an increased level of water infiltration due to a larger perimeter-to-area ratio. These findings are then used to verify a computer simulation model which allows the model parameters, series and shunt resistances, to be calculated. It is determined that the series resistance is not an obvious obstruction at these module sizes. The findings of this work provide great promise for the application of OPV technology on a larger scale.
Evaporation Heat Transfer and Pressure Drop of Low-Global Warming Potential Refrigerant HFO-1234yf in 6.95-mm Horizontal Smooth Tube
Authors: Son Chang-Hyo , Kim Nam-Wook , Yoon Jung-In , Seol Sung-Hoon , Lee Joon-Hyuk
Published: October 2021

This study investigated the evaporative heat transfer coefficient and pressure drop characteristics of R-1234yf in a horizontal tube with an inner diameter of 6.95 mm under various experimental conditions. The heat transfer coefficient increased with an increase in quality but showed a sharp decrease in the high-quality area. In addition, the heat transfer coefficient increased as the mass flux, heat flux, and saturation temperature increased. Although R-1234yf and R-134a presented similar heat transfer coefficients, that of R-134a was higher. The pressure drop increased with an increase in the quality and mass flux but decreased with an increase in the saturation temperature. The pressure drop of R-134a was larger than that of R-1234yf. In light of the flow pattern diagram by Taitel and Dukler, most of the experiments were included in the annular flow region, and some regions showed intermittent and stratified corrugated flow regions. Kandlikar’s heat transfer coefficient correlation provided the best prediction for the experimental database, with approximately 84% of the predicted data within ±30%. Moreno Quibén and Thome’s equation for pressure drop predicted approximately 88.71% of the data within ±30%.
Bottom-Up Initiatives Undertaken by Local Governments to Support Entrepreneurship
Authors: Derlukiewicz Niki , Mempel-Śnieżyk Anna , Pilewicz Tomasz , Godlewska Małgorzata
Published: October 2021

The present paper deals with issues related to activities undertaken by local government. The article focuses on the identification and assessment of the significance of local government’s different bottom-up initiatives that support development of local entrepreneurship. The article also indirectly discusses the importance of activities referring to energy efficiency. Through the digitization process and by using digital tools, LGs realize projects, improve local actors’ awareness, and finally achieve new challenges such as a higher proportion of gross final energy consumption being from renewable sources. The aim of the paper is to present the relationship between the LGs’ entrepreneurial activities, which we call bottom-up initiatives, and the local entrepreneurship level, understood as the number of entrepreneurs active on the territory of a LG. This paper presents the results of research carried out on the bottom-up initiatives undertaken by Polish local governments to support entrepreneurship. The research was based on survey and statistical tools and stepwise regression analysis. In the paper, bottom-up initiatives undertaken by local governments (such as organizing events at the national or regional scale that enable networking among entrepreneurs or establishing departments for supporting local entrepreneurship that offer cooperation with local entrepreneurs) were found to influence the number of entrepreneurs active in local government territories. The paper concludes that local governments and their bottom-up activities are an important factor influencing local entrepreneurship development. The presented research results have implications for policy makers and may be useful for local governments in Poland and in other countries in the context of supporting entrepreneurship by undertaking bottom-up initiatives at the local level.
Integrated Sustainable Energy for Sub-Saharan Africa: A Case Study of Machinga Boma in Malawi
Authors: Eko Juliana O. , Paul Manosh C.
Published: October 2021

Nearly 60% of the population of sub-Saharan Africa still live without access to electricity. Comparing the access rate of the countries in the region, Malawi ranks as one of the least electrified, with electricity available to only 14.6% of its population, as of 2018. This issue makes Malawi the case study of this research and poses the research question, “How can the low electricity access rate in Malawi be addressed?”. To address this research question, possible off grid, integrated, sustainable energy systems based on locally available energy resources—solar, wind, and diesel—are proposed. The multiyear and sensitivity analysis function of HOMER Pro microgrid simulation software is used to analyze the off grid performance of the proposed combinations of diesel generators, wind turbines, solar Photovoltaics, and battery storage, in providing power for an estimate of 400 households and nonresidential outlets in Machinga Boma, a community in the Southern region of Malawi. Based on the analysis, the Solar Photovoltaic/Diesel Genset/battery system combination consisting of 750 kWp solar Photovoltaic array, 460 kW (575 kVA) diesel generator and 3000 kWh nominal capacity battery bank is shown to be the most optimal system, with an overall energy cost of $0.339/kWh. Under the imposed design constraints and the sensitivity analysis performed to analyze the impact of changing the base fuel price, varying load growth, changing solar irradiation, and wind levels on the system performance, the most optimal system remained the preferred system choice.
Modern Trends in Design of Catalysts for Transformation of Biofuels into Syngas and Hydrogen: From Fundamental Bases to Performance in Real Feeds
Authors: Sadykov Vladislav , Simonov Mikhail , Eremeev Nikita , Mezentseva Natalia
Published: October 2021

This review considers problems related to design of efficient structured catalysts for natural gas and biofuels transformation into syngas. Their active components are comprised of fluorite, perovskite and spinel oxides or their nanocomposites (both bulk and supported on high surface area Mg-doped alumina or MgAl2O4) promoted by platinum group metals, nickel and their alloys. A complex of modern structural, spectroscopic and kinetic methods was applied to elucidate atomic-scale factors controlling their performance and stability to coking, such as dispersion of metals/alloys, strong metal-support interaction and oxygen mobility/reactivity as dependent upon their composition and synthesis procedures. Monolithic catalysts comprised of optimized active components loaded on structured substrates with a high thermal conductivity demonstrated high activity and stability to coking in processes of natural gas and biofuels reforming into syngas. A pilot-scale axial reactor equipped with the internal heat exchanger and such catalysts allowed to efficiently convert into syngas the mixture of natural gas, air and liquid biofuels in the autothermal reforming mode at low (~50–100 °C) inlet temperatures and GHSV up to 40,000 h−1.
Forecasting the CO2 Emissions at the Global Level: A Multilayer Artificial Neural Network Modelling
Authors: Jena Pradyot Ranjan , Managi Shunsuke , Majhi Babita
Published: October 2021

Better accuracy in short-term forecasting is required for intermediate planning for the national target to reduce CO2 emissions. High stake climate change conventions need accurate predictions of the future emission growth path of the participating countries to make informed decisions. The current study forecasts the CO2 emissions of the 17 key emitting countries. Unlike previous studies where linear statistical modeling is used to forecast the emissions, we develop a multilayer artificial neural network model to forecast the emissions. This model is a dynamic nonlinear model that helps to obtain optimal weights for the predictors with a high level of prediction accuracy. The model uses the gross domestic product (GDP), urban population ratio, and trade openness, as predictors for CO2 emissions. We observe an average of 96% prediction accuracy among the 17 countries which is much higher than the accuracy of the previous models. Using the optimal weights and available input data the forecasting of CO2 emissions is undertaken. The results show that high emitting countries, such as China, India, Iran, Indonesia, and Saudi Arabia are expected to increase their emissions in the near future. Currently, low emitting countries, such as Brazil, South Africa, Turkey, and South Korea will also tread on a high emission growth path. On the other hand, the USA, Japan, UK, France, Italy, Australia, and Canada will continuously reduce their emissions. These findings will help the countries to engage in climate mitigation and adaptation negotiations.
Study on the Relationship between Thermal Comfort and Learning Efficiency of Different Classroom-Types in Transitional Seasons in the Hot Summer and Cold Winter Zone of China
Authors: Liu Haiqiang , Ma Xidong , Zhang Zhihao , Cheng Xiaoling , Chen Yanmi , Kojima Shoichi
Published: October 2021

The physical environment of classrooms has a strong relationship with student learning performance and health. Since the outbreak of COVID-19 in 2019, almost all universities have begun implementing closed instructional management, which has forced students to spend a much longer amount of time inside the classroom. This has also led to an increasing problem of thermal comfort in classroom indoor environments. In this paper, classrooms evolved from three dominant teaching modes at Zhejiang Sci-Tech University (ZSTU), located in the Hot Summer and Cold Winter (HSCW) zone of China, were selected as experimental spaces. Meanwhile, 12 learning groups with 60 students (30 of each sex) were selected as the tested samples. The relationship between thermal comfort and learning efficiency of the tested students was established through thermal comfort questionnaires and learning efficiency tests under the typical natural conditions in transition seasons. Based on this, improvement strategies were proposed for the current state of the classroom environment, providing a database for optimizing the environmental conditions of university classrooms in HSCW zone on the basis of improving students’ learning efficiency.
Analytical Solution of Problems about the Radiative and Radiative–Conductive Stationary Heat Transfer in a Medium with an Arbitrary Dependence of the Scattering and Absorption on Frequency Boundary Conditions
Authors: Shamparov Eugene , Rode Sergey , Bugrimov Anatoly , Zhagrina Inna
Published: October 2021

We defined a method for the analytical solution of problems on stationary radiative and radiative–conductive heat transfer in a medium with an arbitrary frequency dependence of absorption and scattering near its boundary. We obtained formulas for the heat conductance of the remote surface and the thickness of the radiative–conductive relaxation of the medium. We determined characteristics of radiant heat transfer from the medium to free space such as the radiation spectrum, the radiation temperature and the medium outer boundary temperature. In addition, we solved the problem on the radiative–conductive heat transfer from one of two parallel surfaces to another with a medium between them.
Investigation on Mechanical Properties of a Carbon Paper Gas Diffusion Layer through a 3-D Nonlinear and Orthotropic Constitutive Model
Authors: Chen Yanqin , Ke Yuchao , Xia Yingsong , Cho Chongdu
Published: October 2021

The mechanical loads that gas diffusion layers (GDLs) withstand in polymer electrolyte membrane fuel cell (PEMFC) stacks are sensitive to the assembly and working conditions. The mechanical properties of GDLs mostly depend on their composition materials, microstructural characteristics, operation conditions, etc. An accurate and comprehensive understanding of the mechanical performance of GDLs is significant for predicting the stress distribution and improving the assembly technology of PEMFC stacks. This study presented a novel 3-D nonlinear and orthotropic constitutive model of a carbon paper GDL to represent the material stiffness matrix with its compressive, tensile, and shear properties. Numerical simulations were performed based on the 3-D constitutive model, and the proposed 3-D model was validated against the experimental data reported previously. It is found that the simulation results of the 3-D constitutive model show a good agreement with the experimental results. Besides, the novel 3-D nonlinear and orthotropic model was applied in the overall stress simulation of a simplified PEMFC unit cell, compared to a conventional 3-D linear and isotropic model, and the simulation results of the two models show a significant difference.
Forest Regeneration Patterns Differ Considerably between Sites with and without Windthrow Wood Logging in the High Tatra Mountains
Authors: Konôpka Bohdan , Šebeň Vladimír , Merganičová Katarína
Published: October 2021

Our research focused on the impact of post-disturbance management on the subsequent forest stands in the territory of the High Tatra Mts. situated in the northern part of Slovakia. The field work was carried out within the post-disturbance area in 2019, i.e., 15 years after the windstorm. In total, we used data from 80 monitoring plots (MP): 40 plots situated inside the managed part and 40 in the unmanaged areas. Post-disturbance management specifically consisted of logging of wind-thrown wood; therefore, the main difference between the two areas (salvaged versus unsalvaged) was the amount of coarse woody debris (CWD) left on site. We focused on the characteristics of newly regenerated forest stands: the number of trees and tree species per MP, tree height and browsing (mostly by red deer, Cervus elaphus) were recorded as was their growth substrate, i.e., soil or CWD. Higher tree densities, larger trees as well as higher tree species richness were found at salvaged plots. In addition, more evident dominance of Norway spruce (Picea abies (L) Karst.) was recorded at unsalvaged plots. Common rowans (Sorbus aucuparia L.) were frequent at both plot types. Birch trees (Betula sp.) were very frequent at salvaged plots, while only a few birch individuals were recorded at unsalvaged ones. The proportion of trees growing on CWD was 15% at unsalvaged and 3% at salvaged plots. Trees growing on the soil contained nearly double the aboveground biomass than those on CWD. Red deer browsing was approximately two times more frequent at salvaged than unsalvaged plots. While rowan was extremely prone to browsing, spruce was damaged negligibly. These main findings could have two contradictory conclusions, the positive one being that differentiated post-disturbance management within a certain area can combine both forestry and nature conservation interests. Moreover, it can generate parallel forests with different properties that might positively influence the future stability of forest ecosystems as a whole. The negative side might be that contrasting post-disturbance management can cause an uneven distribution of red deer population and intensive browsing in areas favourable for game.
Epigenetic Regulation of CDH1 Is Altered after HOXB7-Silencing in MDA-MB-468 Triple-Negative Breast Cancer Cells
Authors: Paço Ana , Leitão-Castro Joana , Freitas Renata
Published: October 2021

HOXB7 is often overexpressed in breast cancer cells and found to relate to poor prognosis. The search for the HOXB7 targets, as a transcription factor, has led to molecules involved in regulating cell proliferation, migration, invasion, and processes such as angiogenesis and therapy resistance. However, the specific targets affected by the deregulation of HOXB7 in breast cancer remain largely unknown in most molecular sub-types, such as triple-negative breast cancers (TNBC). To unveil the molecular basis behind these aggressive and often untreatable cancers, here we explored the contribution of HOXB7 deregulation for their aggressiveness. To this end, HOXB7 was silenced in TNBC Basal A cells MDA-MB-468, and the phenotype, gene/protein expression, and methylation profile of putative targets were analyzed. Lower migration and invasion rates were detected in HOXB7-silenced cells in comparison with the controls. In addition, these cells expressed more CDH1 and less DNMT3B, and the promoter methylation status of CDH1 diminished. Our data suggest that the HOXB7 transcription factor may act on TNBC Basal A cells by controlling CDH1 epigenetic regulation. This may occur indirectly through the up-regulation of DNMT3B, which then controls DNA methylation of the CDH1 promoter. Thus, future approaches interfering with HOXB7 regulation may be promising therapeutic strategies in TNBC treatment.