bluuebunny/crossref_metadata_2025 huggingface dataset:
A Nonlinear Local Approximation Approach for Catchment Classification
Authors: Khan Shakera K. , Sivakumar Bellie
Published: March 2024

Catchment classification plays an important role in many applications associated with water resources and environment. In recent years, several studies have applied the concepts of nonlinear dynamics and chaos for catchment classification, mainly using dimensionality measures. The present study explores prediction as a measure for catchment classification, through application of a nonlinear local approximation prediction method. The method uses the concept of phase-space reconstruction of a time series to represent the underlying system dynamics and identifies nearest neighbors in the phase space for system evolution and prediction. The prediction accuracy measures, as well as the optimum values of the parameters involved in the method (e.g., phase space or embedding dimension, number of neighbors), are used for classification. For implementation, the method is applied to daily streamflow data from 218 catchments in Australia, and predictions are made for different embedding dimensions and number of neighbors. The prediction results suggest that phase-space reconstruction using streamflow alone can provide good predictions. The results also indicate that better predictions are achieved for lower embedding dimensions and smaller numbers of neighbors, suggesting possible low dimensionality of the streamflow dynamics. The classification results based on prediction accuracy are found to be useful for identification of regions/stations with higher predictability, which has important implications for interpolation or extrapolation of streamflow data.
Machine Learning Classification of Event-Related Brain Potentials during a Visual Go/NoGo Task
Authors: Bryniarska Anna , Ramos José A. , Fernández Mercedes
Published: March 2024

Machine learning (ML) methods are increasingly being applied to analyze biological signals. For example, ML methods have been successfully applied to the human electroencephalogram (EEG) to classify neural signals as pathological or non-pathological and to predict working memory performance in healthy and psychiatric patients. ML approaches can quickly process large volumes of data to reveal patterns that may be missed by humans. This study investigated the accuracy of ML methods at classifying the brain’s electrical activity to cognitive events, i.e., event-related brain potentials (ERPs). ERPs are extracted from the ongoing EEG and represent electrical potentials in response to specific events. ERPs were evoked during a visual Go/NoGo task. The Go/NoGo task requires a button press on Go trials and response withholding on NoGo trials. NoGo trials elicit neural activity associated with inhibitory control processes. We compared the accuracy of six ML algorithms at classifying the ERPs associated with each trial type. The raw electrical signals were fed to all ML algorithms to build predictive models. The same raw data were then truncated in length and fitted to multiple dynamic state space models of order nx using a continuous-time subspace-based system identification algorithm. The 4nx numerator and denominator parameters of the transfer function of the state space model were then used as substitutes for the data. Dimensionality reduction simplifies classification, reduces noise, and may ultimately improve the predictive power of ML models. Our findings revealed that all ML methods correctly classified the electrical signal associated with each trial type with a high degree of accuracy, and accuracy remained high after parameterization was applied. We discuss the models and the usefulness of the parameterization.
Territorial Disparities in the Governance of Policies Promoting the School-to-Work Transition: An Analysis of the Italian Case
Authors: Giannoni Paola , Palumbo Mauro , Pandolfini Valeria , Torrigiani Claudio
Published: March 2024

The school-to-work alternance responds to the critical issues linked to the mismatch between school learning and labour market needs, aiming to enhance adolescents’ employability. However, recent studies have shown that in Italy, school-to-work transition pathways are diversified at the territorial level, reflecting regional disparities in opportunities that risk increasing inequalities. In this regard, this paper presents the main evidence emerging from an analysis on multilevel governance of the Pathways for Transversal Skills and Orientation (PCTOs), which are mandatory for Italian students in their last three years of upper secondary schooling. This focus is part of the national research project “Evaluating the School-Work Alternance: a longitudinal study in Italian upper secondary schools”, that aims to evaluate this policy. This part of the study, conducted through semi-structured qualitative interviews at the national, regional, and local level with stakeholders of public institutions, has examined PCTO implementation strategies, pointing out the transition mechanisms between the school and labour market, as well as roles, activities, and way of coordination between public and private sectors. The different perspectives that emerged underline the complexity of establishing networks that relate central and local governance in education systems. The results of the analysis provide a dynamic portrait of the PCTO in Italy, identifying relevant aspects that could enhance future planning or implementation of this policy.
Chilean Primary Learners’ Motivation and Attitude towards English as a Foreign Language
Authors: Inostroza Maria-Jesus , Perez-Villalobos Cristhian , Tabalí Pia
Published: March 2024

This study aims to identify motivational and attitude variables among Chilean young English learners from Concepción. A child-appropriate Likert scale questionnaire was distributed to 137 students from the 3rd, 4th, and 5th grade of two state-run primary schools. The instrument considered five variables: attitude towards English, intrinsic motivation, extrinsic motivation, Ideal L2 Self, and parent-oriented motivation, because the eclectic nature of early language learning has been shown to be influenced by these factors. The variables for teacher profile considered years of teaching experience, training in primary English language teaching, and access to multisensory and audio-visual materials. A confirmatory factor analysis was conducted to determine the internal consistency of the instrument. The Mann–Whitney U test and the Kruskal–Wallis H-test using Dunn’s post hoc contrast were used to compare the motivations and attitudes by gender, teacher profile, and grade. Spearman’s rho correlation coefficient was then used to identify the relationship between the target variables and the teacher profile. Results show that teacher profile is a key factor in motivation and attitude to learning English, particularly their access to multisensory and audio-visual materials. This study’s main novelty is that it is the first time a questionnaire focused on children’s attitudes and motivations has been applied in Chile, with clear implications for teacher education and policy implementation assessment.
Design of Half-Bridge Switching Power Module Based on Parallel-Connected SiC MOSFETs for LLC Resonant Converter with Symmetrical Structure and Low Parasitic Inductance
Authors: Park Hae-Chan , Min Sung-Soo , Lee Jeong-Ho , Park Su-Seong , Lee Sang-Hyeok , Kim Rae-Young
Published: March 2024

SiC MOSFETs are used in many power conversion applications because of their superior characteristics, such as fast switching speed, low on-resistance, and high operating temperature. In certain high-power systems, SiC MOSFETs are connected in parallel to enhance their current capacity and power efficiency. However, compared with Si-based devices, the current imbalance caused by the parasitic inductance difference becomes more severe when driving SiC MOSFETs in parallel, owing to the fast switching speed. Furthermore, the power loop inductance imbalance that occurs when constructing a half-bridge with parallel SiC MOSFETs has rarely been addressed in previous studies. In this study, a half-bridge switching power module based on parallel-connected SiC MOSFETs is proposed to solve the current imbalance through a symmetric structure of the gate and power loops. The effects of the magnitude and imbalance of the gate and power loop inductances in the half-bridge structure based on parallel-connected devices are also explained. A detailed printed circuit board layout of the proposed switching power module is provided, and the inductance symmetry is verified through simulations. A double-pulse test is conducted to verify the current-balancing capability of the proposed switching power module. In addition, an LLC resonant converter is designed using the proposed switching power module, and the power loss between parallel SiC MOSFETs is compared. The experimental results indicate the total power loss error between the parallel-connected SiC MOSFETs of the proposed power module is only 1.94%.
Exploring Public Data Vulnerabilities in Semi-Supervised Learning Models through Gray-box Adversarial Attack
Authors: Jo Junhyung , Kim Joongsu , Suh Young-Joo
Published: March 2024

Semi-supervised learning (SSL) models, integrating labeled and unlabeled data, have gained prominence in vision-based tasks, yet their susceptibility to adversarial attacks remains underexplored. This paper unveils the vulnerability of SSL models to gray-box adversarial attacks—a scenario where the attacker has partial knowledge of the model. We introduce an efficient attack method, Gray-box Adversarial Attack on Semi-supervised learning (GAAS), which exploits the dependency of SSL models on publicly available labeled data. Our analysis demonstrates that even with limited knowledge, GAAS can significantly undermine the integrity of SSL models across various tasks, including image classification, object detection, and semantic segmentation, with minimal access to labeled data. Through extensive experiments, we exhibit the effectiveness of GAAS, comparing it to white-box attack scenarios and underscoring the critical need for robust defense mechanisms. Our findings highlight the potential risks of relying on public datasets for SSL model training and advocate for the integration of adversarial training and other defense strategies to safeguard against such vulnerabilities.
A Bleeding Edge Web Application for Early Detection of Cyanobacterial Blooms
Authors: Chacón Jesús , Andrade Giordy A. , Risco-Martín Jose L. , Esteban Segundo
Published: March 2024

Harmful Algal and Cyanobacterial Bloom (HACB) threaten aquatic ecosystems, human health, and the economy. Many factors influence these dynamic events, which are often difficult to detect until the late stages of growth. The inclusion of an Early Warning System (EWS) can be instrumental in identifying hazards and preventing or at least minimizing their impact. Traditional monitoring approaches often fail to provide the real-time, high-resolution data needed for effective early warnings. The integration of Internet of Things (IoT) technologies offers a promising avenue to address these limitations by creating a network of interconnected sensors capable of continuously collecting and transmitting data from various aquatic environments. In this paper, we present DEVS-BLOOM-WebUI, an advanced web application that extends the capabilities of the DEVS-BLOOM framework, providing a user-friendly interface that supports different user roles. The application includes an interface to manage users and permissions, dashboards to inspect data (from sensors, Unmanned Surface Vehicles (USVs), weather stations, etc.), train AI models, explore their predictions, and facilitate decision-making through notification of early warnings. A key feature of DEVS-BLOOM-WebUI is the Scenario Configuration Editor (SCE). This interactive tool allows for users to design and configure the deployment of monitoring infrastructure within a water body, enhancing the system’s adaptability to user-defined simulation scenarios. This paper also investigates the practical implementation of an IoT-based EWS, discussing design considerations, sensor technologies, and communication protocols essential for seamless data integration and effective operation of the EWS.
Cooperative Coverage Path Planning for Multi-Mobile Robots Based on Improved K-Means Clustering and Deep Reinforcement Learning
Authors: Ni Jianjun , Gu Yu , Tang Guangyi , Ke Chunyan , Gu Yang
Published: March 2024

With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and operational limitations, such as terrain obstacles and the scope of the task area, in order to complete the CPP task better, this paper proposes an improved K-Means clustering algorithm to divide the multi-robot task area. The improved K-Means clustering algorithm improves the selection of the first initial clustering point, which makes the clustering process more reasonable and helps to distribute tasks more evenly. Simultaneously, it introduces deep reinforcement learning with a dueling network structure to better deal with terrain obstacles and improves the reward function to guide the coverage process. The simulation experiments have confirmed the advantages of this method in terms of balanced task assignment, improvement in strategy quality, and enhancement of coverage efficiency. It can reduce path duplication and omission while ensuring coverage quality.
ConGraph: Advanced Persistent Threat Detection Method Based on Provenance Graph Combined with Process Context in Cyber-Physical System Environment
Authors: Li Linrui , Chen Wen
Published: March 2024

With the wide use of Cyber-Physical Systems (CPS) in many applications, targets of advanced persistent threats (APTs) have been extended to the IoT and industrial control systems. Provenance graph analysis based on system audit logs has become a promising way for APT detection and investigation. However, we cannot afford to ignore that existing provenance-based APT detection systems lack the process–context information at system runtime, which seriously limits detection performance. In this paper, we proposed ConGraph, an approach for detecting APT attacks using provenance graphs combined with process context; we presented a module for collecting process context to detect APT attacks. This module collects file access behavior, network access behavior, and interactive relationship features of processes to enrich semantic information of the provenance graph. It was the first time that the provenance graph was combined with multiple process–context information to improve the detection performance of APT attacks. ConGraph extracts process activity features from the provenance graphs and submits the features to a CNN-BiLSTM model to detect underlying APT activities. Compared to some state-of-the-art models, our model raised the average precision rate, recall rate, and F-1 score by 13.12%, 25.61%, and 24.28%, respectively.
TSRNet: A Trans-Scale and Refined Low-Light Image Enhancement Network
Authors: Mu Qi , Ma Yueyue , Wang Xinyue , Li Zhanli
Published: March 2024

Retinex-based deep learning methods show good low-light enhancement performance and are mainstream approaches in this field. However, the current methods for enhancing low-light images are insufficient in accurately separating illumination and comprehensively restoring degraded information, especially in images with uneven or extremely low illumination levels. This situation often leads to the over-enhancement of bright regions, a loss of detail, and color distortion in the final images. To address these issues, we improved three subnetworks in the classic KinD network, and proposed a trans-scale and refined low-light image enhancement network. Compared with KinD, our method shows more precise image decomposition performance, enhancing the expressiveness of the reflection and illumination components in order to better depict image details, colors, and lighting information. For reflectance restoration, we use a U-shaped network for cross-scale denoising, incorporating attention mechanisms and a color saturation loss to restore image textures and colors. For light adjustment, we apply fine-grained light adjustment approaches to simultaneously enhance brightness in dark areas and prevent excessive enhancement in bright areas. The experimental results demonstrate that with the LOL dataset, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of TSRNet are improved by 2–31% and 5–34%, respectively, when compared with the mainstream methods.
Improving Steady State Accuracy in Field-Weakened Six-Phase Induction Machines with Integrator and Modulated Predictive Control
Authors: Ayala Magno , Doval-Gandoy Jesus , Rodas Jorge , Gonzalez Osvaldo , Gregor Raúl , Delorme Larizza , Romero Carlos , Fleitas Ariel
Published: March 2024

Finite-control-set model predictive control techniques are considered an exciting option for high-performance control multiphase drives due to their fast dynamic response, ability to handle multiple targets and constraints, and adaptability to different power converters or machine models. However, these techniques have some drawbacks, such as poor current reduction (x−y) and steady-state error (d−q), especially in the field weakening zone. Although some proposals have addressed these issues by adding modulation stages or designing new cost functions, there is still room for improvement, especially in steady-state error reduction. Therefore, this article proposes to include an integrator attached to a modulated predictive current controller applied to a six-phase induction machine to improve its performance throughout the entire speed range regarding steady-state error mitigation. Experimental tests were carried out to validate the effectiveness of the proposed controller. Tests were carried out evaluating the reduction of the steady-state error (d−q), the current tracking, the (x−y) currents reduction and the total harmonic distortion.
Performance Comparison of VVC, AV1, HEVC, and AVC for High Resolutions
Authors: Uhrina Miroslav , Sevcik Lukas , Bienik Juraj , Smatanova Lenka
Published: March 2024

Over the years, there has been growing interest in multimedia services, especially in the video domain, where firms and subscribers require higher resolutions, framerates, and sampling precision. This results in a huge amount of data that needs to be processed, stored, and transmitted. As a result, researchers face the challenge of developing new compression standards that can reduce the amount of data while maintaining the same quality. In this paper, the compression performance of the latest and most commonly used video codecs, namely H.266/VVC, AV1, H265/HEVC, and H.264/AVC was examined. The test set included seven sequences of various content at 8K, Ultra HD (UHD), and Full HD (FHD) resolutions, encoded to bitrates ranging from 1 to 15 Mbps for FHD and UHD resolutions and from 5 to 50 Mbps for 8K resolution. Objective quality metrics, such as peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), and video multi-method assessment fusion (VMAF) were used to measure codec performance. The results showed that H.266/VVC outperformed all other codecs, namely H.264/AVC, H.265/HEVC, and AV1, in terms of the Bjøntegaard delta (BD) model. The average bitrate savings were approximately 78% for H.266/VVC, 63% for AV1, and 53% for H.265/HEVC relative to H.264/AVC, 59% for H.266/VVC and 22% for AV1 compared to H.264/AVC, and 46% for H.266/VVC relative to AV1 (all for 8K resolution). The results also showed that codec performance varied depending on resolution, with higher resolutions showing greater efficiency for newly developed codecs, such as H.266/VVC and AV1. This confirms the fact that the H.266/VVC and AV1 codecs were primarily developed for videos at high resolutions, such as 8K and/or UHD.
Generating Artistic Portraits from Face Photos with Feature Disentanglement and Reconstruction
Authors: Guo Haoran , Ma Zhe , Chen Xuhesheng , Wang Xukang , Xu Jun , Zheng Yangming
Published: March 2024

Generating artistic portraits from face photos presents a complex challenge that requires high-quality image synthesis and a deep understanding of artistic style and facial features. Traditional generative adversarial networks (GANs) have made significant strides in image synthesis; however, they encounter limitations in artistic portrait generation, particularly in the nuanced disentanglement and reconstruction of facial features and artistic styles. This paper introduces a novel approach that overcomes these limitations by employing feature disentanglement and reconstruction techniques, enabling the generation of artistic portraits that more faithfully retain the subject’s identity and expressiveness while incorporating diverse artistic styles. Our method integrates six key components: a U-Net-based image generator, an image discriminator, a feature-disentanglement module, a feature-reconstruction module, a U-Net-based information generator, and a cross-modal fusion module, working in concert to transform face photos into artistic portraits. Through extensive experiments on the APDrawing dataset, our approach demonstrated superior performance in visual quality, achieving a significant reduction in the Fréchet Inception Distance (FID) score to 61.23, highlighting its ability to generate more-realistic and -diverse artistic portraits compared to existing methods. Ablation studies further validated the effectiveness of each component in our method, underscoring the importance of feature disentanglement and reconstruction in enhancing the artistic quality of the generated portraits.
Research on Fast Frequency Response Control Strategy of Hydrogen Production Systems
Authors: Shi Tao , Xu Zeyan , Gu Libo , Zhou Hangyu
Published: March 2024

With the large-scale integration of intermittent renewable energy generation presented by wind and photovoltaic power, the security and stability of power system operations have been challenged. Therefore, this article proposes a control strategy of a hydrogen production system based on renewable energy power generation to enable the fast frequency response of a grid. Firstly, based on the idea of virtual synchronous control, a fast frequency response control transformation strategy for the grid-connected interface of hydrogen production systems for renewable energy power generation is proposed to provide active power support when the grid frequency is disturbed. Secondly, based on the influence of VSG’s inertia and damping coefficient on the dynamic characteristics of the system, a VSG adaptive control model based on particle swarm optimization is designed. Finally, based on the Matlab/Simulink platform, a grid-connected simulation model of hydrogen production systems for renewable energy power generation is established. The results show that the interface-transformed electrolytic hydrogen production device can actively respond to the frequency disturbances of the power system and participate in primary frequency control, providing active support for the frequency stability of the power system under high-percentage renewable energy generation integration. Moreover, the system with parameter optimization has better fast frequency response control characteristics.
Face-Inception-Net for Recognition
Authors: Zhang Qinghui , Wang Xiaofeng , Zhang Mengya , Lu Lei , Lv Pengtao
Published: March 2024

Face recognition in general scenarios has been saturated in recent years, but there is still room to enhance model performance in extreme scenarios and fairness situations. Inspired by the successful application of Transformer and ConvNet in computer vision, we propose a FIN-Block, which gives a more flexible composition paradigm for building a novel pure convolution model and provides a foundation for constructing a new framework for general face recognition in both extreme scenarios and fairness situations. FIN-Block-A uses a combination of stacked large-size convolution kernels and parallel convolution branches to ensure a large spatial receptive field while improving the module’s deep feature embedding and extraction capabilities. FIN-Block-B takes advantage of stacked orthogonal convolution kernels and parallel branches to balance model size and performance. By applying FIN-Block with an adapted convolution kernel size in different stages, we built a reasonable and novel framework Face-Inception-Net, and the performance of the model is highly competitive with ConvNeXt and InceptionNeXt. The models were trained on CASIA-WebFace and MS-wo-RFW databases and evaluated on 14 mainstream benchmarks, including LFW, extreme scene, and fairness test sets. The proposed Face-Inception-Net achieved the highest average TAR@FAR0.001 of 95.9% in all used benchmarks, fully demonstrating effectiveness and generality in various scenarios.
Research on Lateral Load Bearing Characteristics of Deepwater Drilling Conductor Suction Pile
Authors: Li Shuzhan , Yang Jin , Zhu Guojing , Wang Jiakang , Huang Yi , Jiang Kun
Published: March 2024

The vast reserves of natural gas hydrates in offshore areas present significant challenges to development. Surface well construction technology is crucial for the extraction of deepwater natural gas hydrates. To ensure the safety of the subsea wellhead during the drilling process for deepwater natural gas hydrates, a novel conductor suction pile device has been designed, comprising a combination of suction piles and surface conductors. And research has been conducted to investigate the lateral stability characteristics of the conductor suction pile. Drawing upon the pile foundation load-bearing theory and the equilibrium of the differential element, a theoretical analysis model and corresponding governing equations of the conductor suction pile system are established. A solution for a multi-point boundary value problem by simplifying the conductor suction pile system into a two-end free beam is proposed. The governing equations are then converted into a first-order differential equation system, and the four-stage Lobatto IIIa collocation method program for the multi-point boundary value problem is developed and resolved using MATLAB 2023a. Furthermore, a case study of a well in the South China Sea elucidates the effects of wellhead load and seabed soil properties on the lateral load-bearing capacity of the conductor suction pile system, verifying the collocation method’s validity against the results from the finite difference method. After conducting a comparative analysis of the lateral load-bearing performance between conductor suction piles and traditional surface conductors, it is observed that conductor suction piles exhibit lower horizontal displacement and bending moments compared to surface conductors. Therefore, conductor suction piles demonstrate a substantial safety margin. The research findings provide a theoretical basis for the lateral stability of conductor suction piles during deepwater natural gas hydrate drilling. This offers a safe and efficient method for surface well construction in the extraction of natural gas hydrates.
Reservoir Characteristics and Exploration Potential Evaluation of Lower Cambrian Niutitang Shale in Northern Guizhou: A Case Study of Well QX1
Authors: Yang Cong , Zou Niuniu , Zhang Daquan , Chen Yi , Du Wei , Zhu Biao
Published: March 2024

The Lower Cambrian Niutitang Formation in the Northern Guizhou harbors abundant organic-rich mud shale, constituting the most significant marine shale gas reservoir in Guizhou. In this article, the reservoir characteristics of Lower Cambrian Niutitang Formation in Northern Guizhou are analyzed in terms of lithology, mineralogy, organic geochemistry, pore structure, gas content and continuous thickness of shale, and the exploration potential of shale gas in this area is evaluated. The results indicate that the content of brittle minerals in the shale of well QX1 is 65.29% to 95.22% (average of 82.10%). The total organic carbon (TOC) content ranges from 2.06% to 12.10% (average of 5.64%). The organic matter maturity (Ro) within the range of 2.29–2.67%, and the kerogen type is identified as type I. The shale samples from the Niutitang Formation have high TOC content, suitable thermal maturity, and a favorable kerogen type, suggesting good gas generation potential. The results of scanning electron microscopy (SEM) show that intergranular pores, intragranular pores and microfractures are developed in the shale of well QX1, which can provide sufficient storage space for shale gas. The shale exhibits a continuous thickness of 105.66 m in the QX1 well, comprising a gas-bearing interval of 32.89 m at the top (with an effective continuous thickness of 18 m) and a hydrocarbon source rock layer of 75.78 m at the bottom. In comparison with other shale gas regions, Niutitang Formation shale in Northern Guizhou exhibits characteristics such as favorable gas generation conditions, greater storage conditions, excellent gas-bearing, strong frackability, and substantial continuous thickness, it has greater potential for shale gas exploration.
Feasibility Investigation of Geothermal Energy Heating System in Mining Area: Application of Mine Cooling and Aquifer Thermal Energy Exploitation Technique
Authors: Ding Luwei , Zhang Zetian , Li Baiyi , Qi Shengming , Liu Hengfeng , Liu Shuo
Published: March 2024

Mine heat hazards have resulted in large amounts of high-quality coal resources in deep that cannot be mined. The mining industry is paying more and more attention to the extraction and utilization of geothermal energy in mines, while at the same time reducing the underground temperature to realize co-extraction of coal and heat. In addition, coal mines tend to burn large amounts of coal to heat mine buildings and provide hot water for workers’ daily baths, creating operating costs and increasing greenhouse gas emissions. Therefore, it is of great significance to investigate the feasibility of extracting geothermal energy to provide the daily heat load for mines. Currently, there is little research on the feasibility of geothermal energy extraction and utilization in productive mines instead of abandoned mines. In this study, according to the actual situation of Xinhu mine in eastern China, a combined geothermal water system and heat-pump heating system is proposed, aiming to effectively realize mine cooling and geothermal exploitation and utilization. The geothermal storage capacity in the area is analyzed, and an economic analysis is developed. The economic analysis indicates that the main factors affecting the feasibility of the system are the number of mine users, the distance from the geothermal production well to the mine buildings, and the coal price. The research shows that the economic efficiency of the system is better when the heating scale is larger and the distance is smaller. As coal prices rise, the combined geothermal water and heat-pump heating system will be more economical than traditional coal heating. If a mine has 2000 workers, the application of this system can prevent 334.584 t of CO2 emissions per year.
Influences of Average Temperature Difference and Measurement Period on Estimation of In Situ Thermal Transmittance of Building Exterior Walls Using the Average Method of ISO 9869-1
Authors: Lee Ye-Ji , Moon Ji-Hoon , Choi Doo-Sung , Ko Myeong-Jin
Published: March 2024

Many studies have sought to overcome the two main limitations of the average method of ISO 9869-1—its long test duration and low accuracy. These studies reported that the reliability of the results is dependent on the temperature differences. This consensus was based on the results of studies that measured a few cases with specific temperature differences, and the convergence criteria utilized for the representative thermal transmittance (U-value) were rarely examined. This study analyzed the influence of the average temperature difference and test duration on the convergence characteristics and accuracy of U-value estimation using the average method. Data from a north-facing exterior wall with a theoretical U-value of 0.145 W/m2·K were measured between June 2022 and May 2023. The influences of different measurement conditions were analyzed for cases divided based on four measurement periods and 11 average air temperature differences. The findings show that an accurate U-value can be obtained from 7 days’ worth of measurement data with an average temperature difference of 10 °C or higher and that the improvement in accuracy is minimal under stricter conditions. To achieve a convergence probability of over 90% for temperature differences of 10 °C or greater, the second and third criteria required measurement periods of 7 and 15 days respectively.
Smart AC-DC Coupled Hybrid Railway Microgrids Integrated with Renewable Energy Sources: Current and Next Generation Architectures
Authors: Jafari Kaleybar Hamed , Hafezi Hossein , Brenna Morris , Faranda Roberto Sebastiano
Published: March 2024

In recent years, there has been increasing interest in integrating the smart grid concept into railway networks, which has been driven by the need to enhance energy efficiency and reduce air pollution in such energy-intensive systems. Consequently, experts have actively sought innovative solutions with which to tackle these challenges. One promising strategy involves integrating renewable energy sources (RESs), energy storage systems (ESSs), and electric vehicle charging stations (EVCSs) into current electric railway systems (ERSs). This study begins by examining the concept of implementing smart grids in railway systems through bibliometric analysis. It then delves into the realization of a hybrid railway microgrid (H-RMG) designed to enhance power flow capacities, improve energy efficiency, and address power quality issues in traditional AC railway networks. This paper introduces various future AC–DC-coupled hybrid railway microgrid (ADH-RMG) architectures centered around a shared DC bus acting as a DC hub for upgrading conventional AC railway systems utilizing interfacing static converters. Through an exploration of different possible ADH-RMG configurations, this research aims to offer valuable insights and a roadmap for the modernization and reconstruction of existing railway networks using smart grid technologies. The integration of RESs and EV charging infrastructures within the ADH-RMG concept presents a promising pathway toward establishing more sustainable and environmentally friendly railway systems.
A Refined Wind Power Forecasting Method with High Temporal Resolution Based on Light Convolutional Neural Network Architecture
Authors: Zhang Fei , Ren Xiaoying , Liu Yongqian
Published: March 2024

With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks.
Examining the Coopetition Relationships in Renewable Energy Trade among BRI Countries: Complexity, Stability, and Evolution
Authors: Xu Yufei , Ji Zhangyi , Jiang Chenming , Xu Wei , Gao Cuixia
Published: March 2024

The Belt and Road Initiative (BRI) has significantly transformed the traditional energy market and reshaped international cooperation and conflict dynamics through its expanding trade in renewable energy resources. This study focuses on examining the complex and evolving nature of coopetition relationships in the renewable energy trade among BRI countries from 2013 to 2020. Understanding the interplay between cooperation and competition in this sector is crucial for comprehending the dynamics and stability of these trade relationships. Using a signed network approach, the findings of this study reveal that the countries predominantly exhibit a cooperative relationship. However, as time progresses, a notable pattern emerges, characterized by the coexistence of “competitive cooperation” and “cooperative competition”. In addition, coopetition group clustering is strongly influenced by geographical location. China, as a key player in the BRI, demonstrates a coopetition group characterized by a high inflow and low outflow pattern. Furthermore, the implementation of the BRI has greatly improved the overall stability of trade along the route. However, the coexistence of competition and cooperation among nations has increased the uncertainty of trade relations, thereby exerting a certain level of influence on their stability. Based on these findings, this study proposes policy recommendations to strength renewable energy trade relationships along the BRI route.
Comparison of the Roles of the South Korean and Japanese Electric Power Sectors in Their National Economies
Authors: Lee Seo-Young , Bak Kyung-Min , Yoo Seung-Hoon
Published: March 2024

The growing demand for electricity, driven by factors such as the shift to carbon neutrality and economic growth, is a challenge shared by South Korea and Japan. South Korea, a peninsula nation, and Japan, an island country, which are both heavily dependent on foreign energy sources and have manufacturing-based industrial structures, are actively working to secure stable power supplies for their economic development. This article carries out a quantitative analysis of the roles of the South Korean and Japanese electric power sectors (EPSs) in their respective economies, seeking to identify differences and generate actionable insights for decision making and policy formulation. Utilizing the input–output (IO) technique with the latest available data, the analysis includes a demand-side model, a supply-side model, and a price-side model to examine various effects of the EPSs. The key findings reveal differences in the production-inducing, value-added creation, and job-creation effects between the two countries, highlighting operational disparities in their electricity sectors. Additionally, South Korea exhibits higher wage-inducing, supply shortage, and price-side effects than Japan, because of its public enterprise-oriented high-wage structure and a substantial manufacturing sector. These quantitative results provide valuable reference material for future government decisions and policy development in the EPS and emphasize the significant role and impact of the power sector in both countries.
Determination of the Dynamic Modulus of Elasticity of Pine Based on the PZT Transducer
Authors: Li Shaocheng , Xu Guangzhou , Jiang Chenkan , Hu Hailong
Published: March 2024

A new method for the determination of the dynamic modulus of elasticity (Ed) of pine wood, based on the transverse vibration excitation and electromechanical impedance (EMI) response of the lead zirconate titanate (PZT) transducer is proposed. The influence of the length to thickness ratio of the pine specimen on the measurement accuracy was studied through modal simulation analysis. Based on the results of the modal simulation, the size of the pine specimen was optimized, and the scanning frequency range of the EMI response was determined. On this basis, the EMI simulation and test of the pine specimen coupled with a PZT patch were carried out to verify the effectiveness of the novel method. The impedance simulation results of three kinds of pine specimens show that a unique and significant formant appears in the real part of each EMI response curve, and the maximum relative errors of the rectangular PZT patch and circular PZT patch are 1.34% and 1.81%, respectively. The impedance test results of three kinds of pine specimens indicate that the maximum relative errors of the rectangular PZT patch and circular PZT patch are 1.41% and 1.68%, respectively, compared with the corresponding results obtained by the traditional transverse vibration method. Simulation and experimental results verify the validity of the proposed method for the elastic modulus determination of pine wood.
Effect of Soil Moisture Content on the Shear Strength of Dicranopteris Linearis-Rooted Soil in Different Soil Layers of Collapsing Wall
Authors: Zhou Man , Zhu Qin , Wang He , Wang Xiaopeng , Zhan Yuanyuan , Lin Jinshi , Zhang Yue , Huang Yanhe , Jiang Fangshi
Published: March 2024

The occurrence and development of Benggang is closely related to the decreased shear strength of collapsing walls. Plant roots can improve the soil shear resistance, and their soil reinforcing effect is restricted by soil moisture content (SMC). However, the effect and mechanism of SMC on the shear properties of rooted soil with different soil properties remain unknown. Therefore, the dominant soil erosion-resistant plant Dicranopteris linearis was selected as the research object, and shear tests were conducted to determine the shear strength response of rooted soil to SMC in the lateritic layer (LL), sandy layer (SL) and detritus layer (DL) with SMCs from 15% to 30%. The results showed that, compared with 15% SMC, the average decrease in shear strength of 30% SMC in the LL, SL and DL rooted soil were 17.37%, 21.96% and 23.36%, respectively. The rooted soil cohesion changed with increasing SMC in a binomial function, and the optimal SMC in the LL was 22.78%, which was higher than that of the SL (19.67%) and DL (18.39%). The cohesion increment of rooted soil weakened with increasing SMC, and the decrease was greatest in the SL. When the SMC increased from 15% to 30%, the internal friction angle of the rooted soil decreased by 34%, 11% and 12% in the LL, SL and DL, respectively. The Wu and Waldron’s model (WWM) correction parameters k′¯ of the LL, SL and DL were 0.59, 0.14 and 0.05, respectively. With the modified WWM, a new prediction model for the shear strength of rooted soil based on SMC was established. In short, a high SMC weakened the mechanical effect of Dicranopteris linearis-rooted soil, especially in the SL and DL of the collapsing wall, and attention should be given to drainage facilities when treating Benggang erosion.
Assessing the Relationship between Land Surface Temperature and Composition Elements of Urban Green Spaces during Heat Waves Episodes in Mediterranean Cities
Authors: Delgado-Capel Manuel José , Egea-Cariñanos Paloma , Cariñanos Paloma
Published: March 2024

In the context of escalating global temperatures and intensified heat waves, the Mediterranean region emerges as a noteworthy hotspot, experiencing a surge in the frequency and intensity of these extreme heat events. Nature-based solutions, particularly management of urban green infrastructure (UGI) areas, have shown promising outcomes in adapting urban areas to the challenges posed by heat waves. The objective of the current study is twofold: firstly, to identify the compositional patterns of strategically distributed small public green spaces, demonstrating their enhanced capacity to mitigate the impact of heat waves in the Mediterranean region; secondly, to assess the association, direction, and explanatory strength of the relationship between the composition elements of the UGI areas and area typology, specifically focusing on the variation in land surface temperature (LST) values during heat wave episodes spanning from 2017 to 2023. The methodology involved obtaining land surface temperature (LST) values from satellite images and classifying green areas based on composition, orientation, and typology. Ordinal multiple regressions were conducted to analyze the relationship between the considered variables and LST ranges during heat wave episodes that occurred from 2017 to 2023. The findings indicate an increase in LST ranges across many areas, emphasizing heightened thermal stress in a Mediterranean medium-sized compact city, Granada (in the southeast of the Iberian Peninsula). Traditional squares, pocket parks and gardens, and pedestrian areas with trees and impervious surfaces performed better in reducing the probability of exceeding LST values above 41 °C compared to other vegetated patches mainly occupied by herbaceous vegetation and grass. The study concludes by advocating for the strategic incorporation of vegetation, especially trees, along with traditional squares featuring semipermeable pavement with trees and shrubbery, as a potential effective strategy for enhancing resilience against extreme heat events. Overall, this research enhances our understanding of LST dynamics during heat waves and offers guidance for bolstering the resilience of urban green spaces in the Mediterranean region.
Study on the Root Characteristics and Effects on Soil Reinforcement of Slope-Protection Vegetation in the Chinese Loess Plateau
Authors: Zhang Xueli , Fu Yimin , Pei Qinghao , Guo Jinjun , Jian Shengqi
Published: March 2024

The Loess Plateau region of China suffers from severe soil erosion, and the selection of effective slope-protection vegetation is essential to prevent soil and water loss. This study focused on individual plants of common species in the Loess Plateau, such as Caragana korshinski Kom., Hippophae rhamnoides Linn., Pinus tabuliformis Carr., Robinia Pseudoacacia Linn., Populus tomentosa Carr., Prunus armeniaca Lam. The root spatial distribution, geometric morphology, and fractal characteristics of these plants were measured using the whole-root-excavation method, and the vertical pull-out force of their root systems was quantified using the in situ whole-plant root-pulling method. The results showed that H. rhamnoides dominates in the vertical spatial distribution of its root system through a larger number of inclined roots. C. korshinskii, P. tomentosa, R. pseudoacacia, and P. armeniaca dominate in the horizontal spatial distribution of their root systems through a greater number of horizontal roots. P. tabuliformis, on the other hand, achieves a relatively balanced distribution in both horizontal and vertical spaces through its well-developed taproot and numerous lateral roots. In terms of the geometric morphology and fractal characteristics of their root systems, H. rhamnoides and C. korshinskii exhibit a larger number of fine roots and complex branching, resulting in a higher total-root length, total-root surface area, and root fractal dimension. The soil-stabilizing ability of H. rhamnoides, C. korshinskii, and R. pseudoacacia was stronger, mainly influenced by their total-root length, total-root surface area, and inclined root quantity, and these species can be prioritized as typical vegetation for soil and water conservation in the construction of Loess Plateau vegetation. From the perspective of slope stabilization and soil conservation alone, we strongly recommend planting shrub vegetation in the Chinese Loess Plateau.