bluuebunny/crossref_metadata_2025 huggingface dataset:
The Development of Copper Clad Laminate Horn Antennas for Drone Interferometric Synthetic Aperture Radar
Authors: Carpenter Anthony , Lawrence James A. , Ghail Richard , Mason Philippa J.
Published: March 2023

Interferometric synthetic aperture radar (InSAR) is an active remote sensing technique that typically utilises satellite data to quantify Earth surface and structural deformation. Drone InSAR should provide improved spatial-temporal data resolutions and operational flexibility. This necessitates the development of custom radar hardware for drone deployment, including antennas for the transmission and reception of microwave electromagnetic signals. We present the design, simulation, fabrication, and testing of two lightweight and inexpensive copper clad laminate (CCL)/printed circuit board (PCB) horn antennas for C-band radar deployed on the DJI Matrice 600 Pro drone. This is the first demonstration of horn antennas fabricated from CCL, and the first complete overview of antenna development for drone radar applications. The dimensions are optimised for the desired gain and centre frequency of 19 dBi and 5.4 GHz, respectively. The S11, directivity/gain, and half power beam widths (HPBW) are simulated in MATLAB, with the antennas tested in a radio frequency (RF) electromagnetic anechoic chamber using a calibrated vector network analyser (VNA) for comparison. The antennas are highly directive with gains of 15.80 and 16.25 dBi, respectively. The reduction in gain compared to the simulated value is attributed to a resonant frequency shift caused by the brass input feed increasing the electrical dimensions. The measured S11 and azimuth HPBW either meet or exceed the simulated results. A slight performance disparity between the two antennas is attributed to minor artefacts of the manufacturing and testing processes. The incorporation of the antennas into the drone payload is presented. Overall, both antennas satisfy our performance criteria and highlight the potential for CCL/PCB/FR-4 as a lightweight and inexpensive material for custom antenna production in drone radar and other antenna applications.
Inclusive Human Development and Governance Nexus: Causality Analysis of Selected Asian Countries
Authors: Stylianou Tasos , Nasir Rakia , Waqas Muhammad
Published: March 2023

This study investigates the role of governance in inclusive human development in the case of twenty-four selected Asian countries, using panel data for the time period from 2010 to 2017. The inequality-adjusted human development index, developed by the United Nation Development Program, has been used as a proxy for inclusive human development. In addition, six indicators of governance have been used as independent variables in a regression model, along with three control variables. We have tested the stationarity of our data using panel unit root tests such as Leviv-Lin-Chu and Augmented Dickey Fuller. The Pearson correlation matrix helps us find out the correlation among variables. The findings show a mixed level of correlation among the variables (i.e., high, low, and moderate). Furthermore, our results show that a strong causal relationship among the variables exists. One of the most important findings is that there is bi-directional causality between the inclusive human development index (IHDI) and development expenditure. The IHDI causes more government investment in development projects, whereas more development expenditure in the country achieves a higher IHDI. Trade openness and development expenditure have a bi-directional causal relationship. Finally, economic governance causes political governance in the case of our selected Asian countries.
Business Leadership and Corporate Social Responsibility in the Post-COVID Era
Authors: Freze Tatiana , Korneev Artem , Krayneva Raisa , Oruch Tatiana , Kandalov Wadim , Strielkowski Wadim
Published: March 2023

Our paper focuses on the transformation of the concept of sustainable business leadership (especially regarding the Sustainable Development Goals (SDGs)) and corporate social responsibility (CSR) in the post-COVID-19 era. The COVID-19 pandemic is without question posing a generation-defining challenge for public health and the global economy. Moreover, the pandemic has revived some old and introduced many new threats that today’s business leaders will have to face in the years to come. Despite the fact that the COVID-19 crisis was a humanitarian tragedy that continues to ravage millions of lives, it can also be viewed as an excellent opportunity to restart sustainable economic development as well as to help our business and economy to shift towards real business corporate social responsibility and ethical decision-making (thanks to the reduction in carbon emissions as a result of reduced economic activity and travel, increasing investments into healthcare and education, or finding the new ways for working and learning, such as remote work and online education). Our paper examines the effect of small entrepreneurs’ perceptions of CSR on their daily business under the conditions of the COVID-19 pandemic. The paper features an empirical model that assesses the implementation of CSR practices and principles (social responsibility, environmental sustainability, or ecological governance) after the COVID-19 pandemic. The model investigates whether the factors internal to the company might influence the enhancement of the CSR principles. The model is based on our own data obtained from the 450 online questionnaire surveys conducted with managers of small and medium enterprises (SMEs) in the Czech Republic and the Russian Federation. The two countries in question have been selected due to their similarities and, at the same time, differences regarding their geopolitical orientation, pathways of economic transformation, and attitudes to sustainable development policies and embracing SDGs. Our results demonstrate that the business type, as well as the ownership structure, positively impacts the introduction of CSR and sustainable development principles in SMEs in question, while the company’s age has a negative impact. Our results might be of special importance for the stakeholders and business owners wishing to enhance CSR practices and promote sustainable economic development in their enterprises in the post-COVID era.
From Hi-Tech to Hi-Touch: A Global Perspective of Design Education and Practice
Authors: Lin Rungtai , Lin Po-Hsien , Chiang I-Ying , Yen Ching-Chiuan
Published: March 2023

Facing the change in and impact of globalization, the question of how to apply design thinking to improve people’s lives, from hi-tech to hi-touch, is becoming increasingly vital during this disquieting era [...]
Analysis of the Relationship between Creativity in Fermi Problems Measured by Applying Information Theory, Creativity in Psychology, and Mathematical Creativity
Authors: Okamoto Hidemichi , Hartmann Mutfried , Kawasaki Tetsushi
Published: March 2023

Many educational institutions demand the development of creativity. However, it is still insufficient for encouraging creative work or thinking. One reason is the lack of simple tools to measure creativity in schools. This study focused on Fermi problems to solve the reason for this issue. Fermi problems have been suggested to be deeply related to creativity. However, few empirical studies have been conducted on their relationship. Therefore, this study conducted a survey and analyzed them by structural equation modeling. The results showed a moderate correlation between creativity in Fermi problems and creativity in psychology (r = 0.47, p < 0.01). Additionally, it was shown that there is a strong correlation between creativity in Fermi problems and mathematical creativity (r = 0.76, p < 0.01). Furthermore, regression analysis showed that creativity in Fermi problems is an important factor for measuring creativity in psychology and mathematical creativity.
“So, We Can’t Play”: Limitations to Play at School in Periods of Educational Transition in Chile
Authors: Rupin Pablo , Muñoz Carla , Jadue-Roa Daniela
Published: March 2023

Based on a reflection on the tensions between play, education, and learning in early childhood, this article aims to identify and analyze the factors that limit or negatively condition the possibilities for play at school during the transition between early childhood education and primary education in Chile. The study was conducted in four Chilean schools, with the transition levels of Chilean early childhood education (children aged 4 and 5) and the first two levels of primary education (children aged 6 and 7). Based on an ethnographic approach, we combined 22 ethnographic observation days spread over two consecutive years and 59 interviews of various types with children, educators, and parents. The results show structural and material limitations on school play that mainly affect children in primary education. Beyond, factors associated with routines, indications, and statements governing functioning and participation in this level tend to limit or prevent play, often considered a “non-legitimate” activity. Conclusions highlight the potentialities that children’s restricted or resisted play can present in terms of learning.
Learning Science at University in Times of COVID-19 Crises from the Perspective of Lecturers—An Interview Study
Authors: Henne Anna , Möhrke Philipp , Huwer Johannes , Thoms Lars-Jochen
Published: March 2023

The COVID-19 pandemic changed higher education radically and challenged faculties to adapt their teaching to the new circumstances. The aim of this study is to highlight changes, in particular, the advantages and disadvantages associated with them, and to find out what conclusions were drawn for the future in the three experimental natural sciences of biology, chemistry, and physics at the University of Konstanz (Germany). In a guided interview, the majority of the university teachers in the bachelor’s programs were interviewed, and their statements were subsequently categorized. While lectures and tutorials in distance learning were held asynchronously or synchronously online, laboratory courses used a variety of formats. The number of disadvantages cited, as well as the number of university faculty citing the same disadvantage, is greater than for advantages. The most commonly cited drawbacks fall into the areas of workload, communication, feedback, and active student participation. Physical presence and a return to the original learning objectives in the lab courses is wanted by the majority. The results point to commonalities between the science subjects and should encourage science departments to work together on similar problems in similar formats in the future. Furthermore, there is an urgent and ongoing need for the training of natural science teachers in competence-oriented digital teaching.
Separation Process of Biodiesel-Product Mixture from Crude Glycerol and Other Contaminants Using Electrically Driven Separation Technique with AC High Voltage
Authors: Ampairojanawong Rossarin , Boripun Ajalaya , Ruankon Sayan , Suwanasri Thanapong , Cheenkachorn Kraipat , Kangsadan Tawiwan
Published: March 2023

Electrically driven separation (EDS) technology with a high voltage (HV) alternating current source (AC) was used to remove glycerol and other contaminants from biodiesel in order to meet the ASTM D6751 and EN 14214 standards. Biodiesel was produced from a transesterification of refined palm oil and methanol using sodium methylate as a homogeneous catalyst. The effects of an Iron (Fe) electrode, including types of electrode configurations, vertical distance between electrodes, applied voltage, and separation time, were studied. Furthermore, the effects of the remaining catalyst and soap content in biodiesel phase were also investigated to improve the separating performance using the EDS technique. The EDS using HVAC and low amperage with a point-to-point electrode configuration showed the highest separation efficiency of 99.8%. The optimum vertical distance between electrodes was 3 cm, while the optimum applied voltage was 3 kV. The separation time of 240 s yielded the best separating performance, completely eliminating the unreacted catalyst, and the lowest of the normalized remaining soap value content was obtained. Considering all of this, the EDS technique had higher efficiency to remove glycerol and other contaminants than a conventional separation of gravitation settling. The final biodiesel product was produced with the high purity of 98.0 wt% after purification and met all standard specifications.
Extraction of Interconnect Parasitic Capacitance Matrix Based on Deep Neural Network
Authors: Ma Yaoyao , Xu Xiaoyu , Yan Shuai , Zhou Yaxing , Zheng Tianyu , Ren Zhuoxiang , Chen Lan
Published: March 2023

Interconnect parasitic capacitance extraction is crucial in analyzing VLSI circuits’ delay and crosstalk. This paper uses the deep neural network (DNN) to predict the parasitic capacitance matrix of a two-dimensional pattern. To save the DNN training time, the neural network’s output includes only coupling capacitances in the matrix, and total capacitances are obtained by summing corresponding predicted coupling capacitances. In this way, we can obtain coupling and total capacitances simultaneously using a single neural network. Moreover, we introduce a mirror flip method to augment the datasets computed by the finite element method (FEM), which doubles the dataset size and reduces data preparation efforts. Then, we compare the prediction accuracy of DNN with another neural network ResNet. The result shows that DNN performs better in this case. Moreover, to verify our method’s efficiency, the total capacitances calculated from the trained DNN are compared with the network (named DNN-2) that takes the total capacitance as an extra output. The results show that the prediction accuracy of the two methods is very close, indicating that our method is reliable and can save the training workload for the total capacitance. Finally, a solving efficiency comparison shows that the average computation time of the trained DNN for one case is not more than 2% of that of FEM.
An Efficient and Secure Model Using Adaptive Optimal Deep Learning for Task Scheduling in Cloud Computing
Authors: Badri Sahar , Alghazzawi Daniyal M. , Hasan Syed Humaid , Alfayez Fayez , Hasan Syed Hamid , Rahman Monawar , Bhatia Surbhi
Published: March 2023

Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in performance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its performance. In addition to this, the proposed approach is compared with various other task scheduling-based approaches for various performance metrics, namely, resource utilization, response time, as well as energy consumption. The experimental results revealed that the proposed approach achieved minimum energy consumption of 180 kWh, a minimum response time of the 20 s, a minimum execution time of 0.43 s, and maximum utilization of 98% for task size 100.
Traffic Signal Control with Successor Feature-Based Deep Reinforcement Learning Agent
Authors: Szoke Laszlo , Aradi Szilárd , Bécsi Tamás
Published: March 2023

In this paper, we study the problem of traffic signal control in general intersections by applying a recent reinforcement learning technique. Nowadays, traffic congestion and road usage are increasing significantly as more and more vehicles enter the same infrastructures. New solutions are needed to minimize travel times or maximize the network capacity (throughput). Recent studies embrace machine learning approaches that have the power to aid and optimize the increasing demands. However, most reinforcement learning algorithms fail to be adaptive regarding goal functions. To this end, we provide a novel successor feature-based solution to control a single intersection to optimize the traffic flow, reduce the environmental impact, and promote sustainability. Our method allows for flexibility and adaptability to changing circumstances and goals. It supports changes in preferences during inference, so the behavior of the trained agent (traffic signal controller) can be changed rapidly during the inference time. By introducing the successor features to the domain, we define the basics of successor features, the base reward functions, and the goal preferences of the traffic signal control system. As our main direction, we tackle environmental impact reduction and support prioritized vehicles’ commutes. We include an evaluation of how our method achieves a more effective operation considering the environmental impact and how adaptive it is compared to a general Deep-Q-Network solution. Aside from this, standard rule-based and adaptive signal-controlling technologies are compared to our method to show its advances. Furthermore, we perform an ablation analysis on the adaptivity of the agent and demonstrate a consistent level of performance under similar circumstances.
Simulation Study on Heat Generation Characteristics of Lithium-Ion Battery Aging Process
Authors: Huang Rui , Xu Yidan , Wu Qichao , Chen Junxuan , Chen Fenfang , Yu Xiaoli
Published: March 2023

Lithium-ion battery heat generation characteristics during aging are crucial for the creation of thermal management solutions. The heat generation characteristics of 21700 (NCA) cylindrical lithium-ion batteries during aging were investigated using the mathematical model that was created in this study to couple electrochemical mechanisms, heat transfer, and aging loss. These findings indicate that, at the same operating current, the heat generation power of the cell increased significantly with battery aging. This increase was primarily due to the energy loss caused by the growth of the solid–electrolyte interface (SEI) and a reduction in the negative porosity and other physical characteristics of the SEI, such as its ionic conductivity and molar volume, which also had an impact on the heat generation power. By investigating the variations in battery heat generation in different aging modes, the electrochemical mechanisms underlying the effects of aging on battery heat generation can be comprehended in depth.
Intelligent Approaches to Fault Detection and Diagnosis in District Heating: Current Trends, Challenges, and Opportunities
Authors: van Dreven Jonne , Boeva Veselka , Abghari Shahrooz , Grahn Håkan , Al Koussa Jad , Motoasca Emilia
Published: March 2023

This paper presents a comprehensive survey of state-of-the-art intelligent fault detection and diagnosis in district heating systems. Maintaining an efficient district heating system is crucial, as faults can lead to increased heat loss, customer discomfort, and operational cost. Intelligent fault detection and diagnosis can help to identify and diagnose faulty behavior automatically by utilizing artificial intelligence or machine learning. In our survey, we review and discuss 57 papers published in the last 12 years, highlight the recent trends, identify current research gaps, discuss the limitations of current techniques, and provide recommendations for future studies in this area. While there is an increasing interest in the topic, and the past five years have shown much advancement, the absence of open-source high-quality labeled data severely hinders progress. Future research should aim to explore transfer learning, domain adaptation, and semi-supervised learning to improve current performance. Additionally, a researcher should increase knowledge of district heating data using data-centric approaches to establish a solid foundation for future fault detection and diagnosis in district heating.
URNet: An UNet-Based Model with Residual Mechanism for Monocular Depth Estimation
Authors: Duong Hoang-Thanh , Chen Hsi-Min , Chang Che-Cheng
Published: March 2023

Autonomous vehicle systems rely heavily upon depth estimation, which facilitates the improvement of precision and stability in automated decision-making systems. Noteworthily, the technique of monocular depth estimation is critical for one of these feasible implementations. In the area of segmentation of medical images, UNet is a well-known encoder–decoder structure. Moreover, several studies have proven its further potential for monocular depth estimation. Similarly, based on UNet, we aim to propose a novel model of monocular depth estimation, which is constructed from the benefits of classical UNet and residual learning mechanisms and named URNet. Particularly, we employ the KITTI dataset in conjunction with the Eigen split strategy to determine the efficacy of our model. Compared with other studies, our URNet is significantly better, on the basis of higher the precision and lower error rate. Hence, it can deal properly with the depth estimation issue for autonomous driving systems.
Applying Monte Carlo Dropout to Quantify the Uncertainty of Skip Connection-Based Convolutional Neural Networks Optimized by Big Data
Authors: Choubineh Abouzar , Chen Jie , Coenen Frans , Ma Fei
Published: March 2023

Although Deep Learning (DL) models have been introduced in various fields as effective prediction tools, they often do not care about uncertainty. This can be a barrier to their adoption in real-world applications. The current paper aims to apply and evaluate Monte Carlo (MC) dropout, a computationally efficient approach, to investigate the reliability of several skip connection-based Convolutional Neural Network (CNN) models while keeping their high accuracy. To do so, a high-dimensional regression problem is considered in the context of subterranean fluid flow modeling using 376,250 generated samples. The results demonstrate the effectiveness of MC dropout in terms of reliability with a Standard Deviation (SD) of 0.012–0.174, and of accuracy with a coefficient of determination (R2) of 0.7881–0.9584 and Mean Squared Error (MSE) of 0.0113–0.0508, respectively. The findings of this study may contribute to the distribution of pressure in the development of oil/gas fields.
Blockchain Data Scalability and Retrieval Scheme Based on On-Chain Storage Medium for Internet of Things Data
Authors: Yu Caoyi , Mei Niansong , Du Chong , Luo Haotian
Published: March 2023

The combination of blockchain and internet of things (IoT) technology realizes reliable storage of IoT data. However, the data stored on the blockchain (on-chain) face the problem of poor scalability and inefficient retrieval. In this paper, the on-chain data scalability schemes based on transactions and smart contracts are first proposed. Subsequently, on the basis of the above on-chain data scalability scheme based on transactions, an on-chain data index based on skip lists is proposed to improve the retrieval efficiency. The experimental results show that both the on-chain data scalability schemes achieve on-chain data scalability while reducing storage overhead. Meanwhile, the on-chain data index based on skip lists has significantly improved dynamic range retrieval efficiency and reduced the time complexity of single data retrieval to O(log(n)).
Improvement of PBFT Algorithm Based on CART
Authors: Liu Jian , Feng Wenlong , Zhang Yu , He Feiyang
Published: March 2023

In response to the problems of the practical Byzantine fault-tolerant algorithm (PBFT), such as random selection of master nodes and poor scalability, a CART-based PBFT optimization algorithm is proposed, namely the C-PBFT algorithm. First, the introduction of weighted impurity variables improves the CART algorithm, overcomes the mutual influence of attributes between nodes, and improves the classification accuracy. Secondly, through the point grouping mechanism, the nodes are divided into three types: consensus nodes, candidate nodes, and alternate nodes, which are dynamically adjusted based on node behavior to ensure the reliability of consensus nodes. Finally, the voting weight is introduced into the consensus node, and the consensus is reached by using the voting weight exceeding the threshold, which reduces the message transmission in the blockchain network and improves the efficiency of the algorithm operation. The experimental results show that the improved consensus algorithm has better performance in terms of delay, throughput, and fault tolerance and reduces the frequent switching of views caused by the failure of the master node.
Ethical Impacts, Risks and Challenges of Artificial Intelligence Technologies in Business Consulting: A New Modelling Approach Based on Structural Equations
Authors: Gînguță Andrea , Ștefea Petru , Noja Grațiela Georgiana , Munteanu Valentin Partenie
Published: March 2023

Artificial intelligence (AI) affects all aspects of a business, significantly contributing to problem-solving and introducing new operational processes within companies. Interest in AI is growing due to its capacities regarding the efficiency of operations, reduced working time, and quality improvements. However, to ensure proper development, businesses should also address the ethical effects generated by AI technologies. This research aims to identify AI’s ethical impact and associated challenges in the business consulting industry and the consultants’ perspective on AI’s future implementation in their specific sector. The methodology is based on the design of a structural equation modelling using data collected through a survey addressed to business consultants. The results highlight that ethical concerns are positively correlated with the identified harmful consequences of AI, such as high implementation costs, the possibility that this technology will lead to job losses, or a lack of human interaction and creativity. The consultants’ perspective on the future aspects of AI’s use in business consulting is negatively impacted by the following ethical outcomes: discrimination, invasions of privacy, denial of individual autonomy, unjustifiable results, and disintegration of social connection.
Influence of Yttria-Stabilized Zirconium Oxide Thermal Swing Coating on the Flame-Wall Interaction in Spark Ignition Engines
Authors: Fischer Marcus , Nolte Adrian , Wu Xiaochao , Zhou Dapeng , Pischinger Stefan , Heufer Karl Alexander , Simon Ulrich , Vaßen Robert
Published: March 2023

Future vehicle powertrain systems with internal combustion engines must achieve higher efficiencies and further reduced pollutant emissions. This will require the application of new advanced technologies. Against this background, this paper presents a holistic approach to reduce temporally the wall heat losses, and hydrocarbon and carbon monoxide emissions with thermal coatings on the combustion chamber walls. For this purpose, an yttria-stabilized zirconia (YSZ) coating is applied and evaluated by different methods. The thin layer in combination with the low thermal effusivity of the material allows the wall temperature to follow the gas temperature and perform a so-called thermal swing. The interaction between an uncoated and a YSZ-coated wall with the flame front as well as partially burned gas was investigated. First, in terms of the coating’s potential to reduce the flame quenching distance using an optical method in a constant volume combustion chamber. Second, regarding its influence on the near-wall gas composition, which was analyzed with in-situ diffuse reflectance infrared Fourier transform spectroscopy measurements and a fast gas sampling technique on a single-cylinder engine. From this, it could be derived that the quenching distance can be reduced by 10% at ambient conditions and by 5% at an elevated temperature of 200 °C by using the coating. These findings also support the results that have been obtained by the near-wall gas composition measurements, where a reduced total hydrocarbon emission was found with the applied coating.
Salt Hydrate Adsorption Material-Based Thermochemical Energy Storage for Space Heating Application: A Review
Authors: Yang Hui , Wang Chengcheng , Tong Lige , Yin Shaowu , Wang Li , Ding Yulong
Published: March 2023

Recent years have seen increasing attention to TCES technology owing to its potentially high energy density and suitability for long-duration storage with negligible loss, and it benefits the deployment of future net-zero energy systems. This paper provides a review of salt hydrate adsorption material-based TCES for space heating applications at ~150 °C. The incorporation of salt hydrates into a porous matrix to form composite materials provides the best avenue to overcome some challenges such as mass transport limitation and lower thermal conductivity. Therefore, a systematic classification of the host matrix is given, and the most promising host matrix, MIL-101(Cr)(MOFs), which is especially suitable for loading hygroscopic salt, is screened from the perspective of hydrothermal stability, mechanical strength, and water uptake. Higher salt content clogs pores and, conversely, reduces adsorption performance; thus, a balance between salt content and adsorption/desorption performance should be sought. MgCl2/rGOA is obtained with the highest salt loading of 97.3 wt.%, and the optimal adsorption capacity and energy density of 1.6 g·g−1 and 2225.71 kJ·kg−1, respectively. In general, larger pores approximately 8–10 nm inside the matrix are more favorable for salt dispersion. However, for some salts (MgSO4-based composites), a host matrix with smaller pores (2–3 nm) is beneficial for faster reaction kinetics. Water molecule migration behavior, and the phase transition path on the surface or interior of the composite particles, should be identified in the future. Moreover, it is essential to construct a micromechanical experimental model of the interface.
A Comparative Analysis of Two Pricing Mechanisms, MCP and PAB, in the Chinese Frequency Regulation Market
Authors: Huang Xian , Li Zhehan
Published: March 2023

In the face of the new reform of China’s “unified electricity market”, the frequency regulation ancillary service market in each region of China is still divided between using the MCP and PAB pricing mechanisms. The purpose of this paper is to explore the different impact the laws of the above two pricing mechanisms have on the frequency regulation market and to provide relevant suggestions for this electricity market reform. This paper simulates the competitive activities of the frequency regulation market based on the multi-agent simulation model, conducts a comparative experiment by changing the pricing mechanism to a single variable based on the rules of the Sichuan frequency regulation market in China, and concludes that MCP can make the market fast and stable, and that its market settlement price is low, which is suitable for the “unified electricity market”. Although PAB makes the market settlement price high, it can ensure the retention of high-performance units in the market, and the stable settlement price makes this model able to accurately reflect the “price signal”, making it suitable for late adoption in the “unified electricity market”.
Theoretical Investigation of Rate Rules for H-Intermigration Reactions for Cyclic Alkylperoxy Radicals
Authors: Yang Kun , Tian Zemin , Li Jinghua , Yan Yingwen
Published: March 2023

As a starting channel, the H-intermigration reaction of alkylperoxy radicals (ROO radicals) that yields hydroperoxyl alkyl radicals (QOOH radicals) determines the low-temperature chemistry of alkanes. In this work, this type of reaction was investigated for typical cyclic alkanes, which are important fuel components and soot precursors, using theoretical ab initio methods. First, all the molecular geometries and vibrational frequencies were computed using the density functional theory method and the single point energies were refined using the post-Hartree fork method (M062X/6-311G(d,p)//DLPNO-CCSD(T)/CBS). Then, high-pressure limit rate constants were evaluated with tight transition state theory, with which tunneling effects were considered using the Eckart model and low-frequency torsion modes were modeled as hindered rotors. Pressure-dependent rate constants were also calculated for typical reaction channels. Rate expressions in the Arrhenius form for 91 reactions are proposed. All reactions were categorized into seven reaction types and the rate rule for each reaction type was estimated with uncertainty factors of three to six. These rules can be potentially used in the development of low-temperature kinetic mechanisms for cycloalkanes. A comparison between different reaction types was also performed and the favorable channels are discussed.
Influence of Pre-Chamber Volume, Orifice Diameter and Orifice Number on Performance of Pre-Chamber SI Engine—An Experimental and Numerical Study
Authors: Tomić Rudolf , Sjerić Momir , Krajnović Josip , Ugrinić Sara
Published: March 2023

This paper presented an experimental and numerical study of pre-chamber volume, number of orifices and orifice diameter influence on engine performance and emissions. All the measurements were performed on a single cylinder test engine at fixed engine speed of 1600 rpm, while engine load was varied by a change of the excess air ratio in the main chamber from a stochiometric mixture to a lean limit. The total of nine pre-chamber variants comprised three different pre-chamber volumes, two orifice number combinations (six and four orifices) and nine different orifice diameters. It was observed that the pre-chamber volume affects the indicated efficiency in a trend which is mostly independent of excess air ratio, with the efficiency gain between the best and worst results ranging from 1 to 4.4%. While keeping the same pre-chamber volume and the total cross-sectional area of the orifices, the larger number of orifices show better performance on two out of three investigated pre-chamber volumes, with the efficiency gains more pronounced at higher excess air ratios. Finally, on a fixed pre-chamber volume and number of orifices, the variation of orifice diameter leads to a trend in efficiency gains which favor larger orifice diameter. The comparison of the obtained efficiencies between all pre-chamber variants identified two pre-chambers, differing in each of the varied geometrical parameters, that show the best performance depending on excess air ratio range. On the other hand, a single variant which showed the worst performance on each excess ratio was identified. An additional investigation was performed by the application of the cycle-simulation model to quantify the share of emissions which are formed in the pre-chamber. The presented results showed that when PC volume is lowered, PC emission shares of NOX and CO grow larger. The influence of orifice number and size has a minor effect on the pre-chamber emissions shares. The maximum PC emission shares of 54.8% and 80.6% are achieved at lean limit (λ = 2.2) for NOX and CO, respectively. THC emission share, on the other hand, is not affected in a significant manner by either the pre-chamber geometry or operating conditions.
Oscillating-Foil Turbine Performance Improvement by the Addition of Double Gurney Flaps and Kinematics Optimization
Authors: Genest Benoît , Dumas Guy
Published: March 2023

Refinement of the performance of a fully constrained oscillating-foil turbine is carried out via the addition of passive double Gurney flaps. Flaps ranging from hGF=0.005c to 0.075c are added at the trailing edge of the NACA 0015 blade of turbines operating in high-efficiency regimes without leading-edge vortex shedding (LEVS). Performance improvements are determined using 2D numerical simulations with an unsteady Reynolds-averaged Navier–Stokes (URANS) approach. Based on a recent study of the double Gurney flaps on stationary foils, instantaneous power-extraction coefficients are analyzed and modifications of the foil’s kinematics are tested in order to fully benefit from the Gurney flaps’ performance improvements. Modifications to the pivot point location of the foil, to the pitch-heave phase, and to the pitching amplitude of the turbine are considered. Improvements are found for all turbine cases studied, including some of the previously optimal cases reported in the literature. The double Gurney flaps, being a simple and passive device, offer great practical application potential. They represent an efficient refinement to already robust and high-performance oscillating-foil turbines operating without the perceived benefit of leading-edge vortex shedding, an essential characteristic for actual, finite-span applications.
Wind Forecast at Medium Voltage Distribution Networks
Authors: Amezquita Herbert , Carvalho Pedro M. S. , Morais Hugo
Published: March 2023

Due to the intermittent and variable nature of wind, Wind Power Generation Forecast (WPGF) has become an essential task for power system operators who are looking for reliable wind penetration into the electric grid. Since there is a need to forecast wind power generation accurately, the main contribution of this paper is the development, implementation, and comparison of WPGF methods in a framework to be used by distribution system operators (DSOs). The methodology applied comprised five stages: pre-processing, feature selection, forecasting models, post-processing, and validation, using the historical wind power generation data (measured at secondary substations) of 20 wind farms connected to the medium voltage (MV) distribution network in Portugal. After comparing the accuracy of eight different models in terms of their relative root mean square error (RRMSE), extreme gradient boosting (XGBOOST) appeared as the best-suited forecasting method for wind power generation. The best average RRMSE achieved by the proposed XGBOOST model for 1-year training (January–December of 2020) and 6 months forecast (January–June of 2021) corresponds to 13.48%, outperforming the predictions of the Portuguese DSO by 20%.
Hydrogen Refueling Process: Theory, Modeling, and In-Force Applications
Authors: Genovese Matteo , Cigolotti Viviana , Jannelli Elio , Fragiacomo Petronilla
Published: March 2023

Among the alternative fuels enabling the energy transition, hydrogen-based transportation is a sustainable and efficient choice. It finds application both in light-duty and heavy-duty mobility. However, hydrogen gas has unique qualities that must be taken into account when employed in such vehicles: high-pressure levels up to 900 bar, storage in composite tanks with a temperature limit of 85 °C, and a negative Joule–Thomson coefficient throughout a wide range of operational parameters. Moreover, to perform a refueling procedure that is closer to the driver’s expectations, a fast process that requires pre-cooling the gas to −40 °C is necessary. The purpose of this work is to examine the major phenomena that occur during the hydrogen refueling process by analyzing the relevant theory and existing modeling methodologies.
Microgrid Applications and Technical Challenges—The Brazilian Status of Connection Standards and Operational Procedures
Authors: Castro José F. C. , Roncolatto Ronaldo A. , Donadon Antonio R. , Andrade Vittoria E. M. S. , Rosas Pedro , Bento Rafael G. , Matos José G. , Assis Fernando A. , Coelho Francisco C. R. , Quadros Rodolfo , Ota João I. Y. , Silva Luiz C. P. , Carneiro Rafael K.
Published: March 2023

One of the challenges faced by Brazilian distribution utilities to enable the connection and operation of microgrids (MGs) is the absence of a solid set of technical standards in the country. An alternative has been to use and adapt existing standards applied to micro- and mini-distributed generation. In this context, this paper presents an analysis of the development status of norms, standards, and general requirements for the connection and operation of microgrids, as well as a proposal for the regulation and structuring of technical and operational requirements related to the implementation of microgrid projects. Some critical points highlighted in the paper include: the modes of operation, the minimum requirements for the different modes of operation, interoperability of systems, a conceptual model with attribution of responsible actors for the decentralized management of microgrids adapted to the institutional standards of the Brazilian sectorial model, a proposal for a standard connection structure considering the point of connection (PoC) implanted using multifunctional relay and recloser, procedures for technical feasibility assessment (operational studies) of MGs connection, and, finally, a discussion of operational issues of storage systems in a microgrid environment.
Identifying Nutrient Export Hotspots Using a Spatially Distributed Model in Boreal-Forested Catchments
Authors: Leinonen Antti , Salmivaara Aura , Palviainen Marjo , Finér Leena , Peltola Heli , Laurén Annamari
Published: March 2023

The implementation of the Water Framework Directive (WFD) aimed to reduce nutrient export from catchments to water courses. Forest operations cause diffuse loading, which challenges the efficient targeting of water protection measures. We formed 100 equally probable clear-cut scenarios, to investigate how the location of the clear-cuts influenced the total nitrogen (TN) and phosphorous (TP) export on different scales. The nutrient export was calculated by using a distributed nutrient export model (NutSpaFHy). The clear-cut-induced excess TN and TP exports varied by 4.2%–5.5% and 5.0%–6.5%, respectively, between the clear-cut scenarios. We analyzed how the sub-catchment characteristics regulated the background export. The results also suggested that there was no single sub-catchment feature, which explained the variation in the TN and TP exports. There were clear differences in the background export and in the clear-cut-induced export between the sub-catchments. We also found that only 5% of the forest area could contribute up to half of the total nutrient export. Based on our results, we presented a conceptual planning framework, which applied the model results to finding areas where the nutrient export was high. Application of this information could improve the overall effectiveness of the water protection measures used in forestry.
Landslides in Forests around the World: Causes and Mitigation
Authors: Wen Haijia , Li Weile , Xu Chong , Daimaru Hiromu
Published: March 2023

Landslides are a common natural disaster in forested mountainous regions [...]
Predicting Distribution and Range Dynamics of Three Threatened Cypripedium Species under Climate Change Scenario in Western Himalaya
Authors: Chandra Naveen , Singh Gajendra , Rai Ishwari Datt , Mishra Arun Pratap , Kazmi Mohd. Yahya , Pandey Arvind , Jalal Jeewan Singh , Costache Romulus , Almohamad Hussein , Al-Mutiry Motrih , Abdo Hazem Ghassan
Published: March 2023

Climate change and anthropogenic pressure have significantly contributed to the decline of biodiversity worldwide, particularly in mountain ecosystems such as the Himalaya. In addition to being relatively sensitive to disturbances, orchids may also respond more quickly to climate change impacts than other plant species. Because of their complex biology and anthropogenic pressures on their habitat in the Himalayan region, lady’s slipper orchids are considered to be a highly vulnerable group of orchids. In the present study, we examine the effect of climate change on the distribution of three threatened Cypripedium species (Cypripedium cordigerum, Cypripedium elegans, and Cypripedium himalaicum), utilizing ecological niche modeling for present and future climatic scenarios to identify key environmental determinants and population parameters. A community climate system model (CCSM ver. 4) was used to identify suitable distribution areas for future scenarios. Based on the least correlated characteristics of the species bioclimatic, topographical, and physiological characteristics, the species’ climatic niche was determined. According to the results, the true skill statistic (TSS), area under the receiver operating characteristic curve (AUC), and Cohen’s kappa provide more reliable predictions. Precipitation during the wettest month and precipitation during the coldest quarter are the primary climatic variables that influence the distribution of suitable areas. A total of 192 km2 of the area was estimated to be suitable for all three species under current climate conditions. Under future climate conditions, the model predicts a trivial increase in suitable habitat areas with a shift toward the northwest. However, highly suitable habitat areas will be severely diminished. There are currently highly suitable habitats in Tungnath and the Valley of Flowers, but due to climatic factors, the habitats will become unsuitable in the future. Additionally, under future climatic scenarios, viable habitats will be identified for priority conservation to cope with the effects of climate change and anthropogenic activities. In light of these findings, conservation methods for the target species may be designed that will be successful and have the potential to prevent local extinctions.
Exploring the Potential Applications of the Noninvasive Reporter Gene RUBY in Plant Genetic Transformation
Authors: Yu Jingjing , Deng Shiling , Huang Han , Mo Jinhui , Xu Zeng-Fu , Wang Yi
Published: March 2023

Betalains can be conveniently observed and quantified and, accordingly, have the potential as naked-eye visual screening reporters during plant transformation. RUBY is a new reporter system that uses “2A” peptides to fuse three key genes, CYP76AD1, DODA, and glucosyl transferase, for betalain biosynthesis, and has been successfully used for transformation of rice, Arabidopsis, and cotton, but its potential applications in the genetic transformation of various other plant species remain to be verified. In this study, RUBY was transferred into the hairy roots of Plukenetia volubilis and Nicotiana benthamiana, and was transferred into Arabidopsis by the floral-dip method. The expression levels of CYP76AD1, DODA, and glucosyl transferase were detected by RT−PCR and RT−qPCR, the relationship between the expression level of RUBY and red coloration was analyzed, and the genetic stability of RUBY in transgenic Arabidopsis was studied. The results showed that the expression of RUBY could reconstruct the betalain biosynthesis pathway in the hairy roots of P. volubilis, N. benthamiana, and Arabidopsis plants, indicating that it has the potential for versatile use across species. As a reporter, betalain did not affect callus induction, plant regeneration, development, or fertility. However, when used in plant transformation for observation and visual screening, it needed to accumulate to a certain extent to show red coloration, and it was positively correlated with gene expression. In general, RUBY is a convenient reporter for plant transformation, and has no obvious side effects during plant growth and development. However, the potential application of RUBY for visual screening is highly determined by the expression level, and further improvement is needed.