bluuebunny/crossref_metadata_2025 huggingface dataset:
Biographically Driven Development of Translanguaging Stance for a Modern Language Teacher Candidate in a Graduate TESOL Class
Authors: Pontier Ryan W. , Abbasi Ehsan
Published: November 2024

This case study investigated a modern language teacher candidate’s developing translanguaging stance in a Masters-level TESOL course as she made sense of emerging tensions while drawing on her personal and teaching experiences. Data included the participant’s oral contributions to the course, the course syllabus, and student-produced artifacts. We used thematic analysis focusing on the participant’s perceptions of translanguaging as both theory and practice to analyze the data. A semi-structured interview was carried out five years later to review the impact of translanguaging on the participant’s teaching and ideology as a Spanish teacher in a dual language program. Findings uncovered five primary themes all related to the participant’s biographically related development of translanguaging stance: traditional second language acquisition theories as a concern, updating the notion of social justice, the ability to support positive identity development, contemplating translanguaging as a pedagogy, and the longevity of the biographical approach to lifelong learning. The findings highlight the importance of biographical approaches to lifelong learning in language education and suggest that teachers can foster bilingual development, establish inclusive teaching strategies, and create equitable language learning spaces by recognizing the potential of translanguaging, which may be made possible by fostering similar contexts in teacher education courses.
Braiding Sámi Diversity and Sámi Pedagogy into Early Childhood Teacher Education: An Example from Norway
Authors: Heim Greta , Bergan Veronica
Published: November 2024

The framework plan for kindergartens in Norway emphasizes knowledge of Indigenous Sámi culture among the core values of pedagogical practice. Preservice students in early childhood teacher education (ECTE) are thus obliged to learn about Sámi culture. We explored and developed collaborative teaching interventions for Sámi topics. We aimed to “braid” Sámi diversity into our teaching and make the lessons explorative, practical, and student-active, in line with the basics of Sámi pedagogy. The teaching emphasized how Sámi people were historically connected to the land through sustainable livelihoods and respect for natural resources. We developed the teaching interventions through action-based research in three cycles (2022–2024). Our primary material consisted of students’ responses to online surveys and group interviews. The findings show that students gained a broader understanding of diversity within Sámi culture after the interventions. They reported greater interest and better learning outcomes, especially from the active and practical lessons. The Sámi teaching content, structure, and methods explored in this study may be relevant to other ECTE or other teacher education programs, especially those related to teaching Indigenous topics to majority populations.
Light and Color: Art and Science Using an Interdisciplinary Approach in Primary Education Teacher Training
Authors: Serón Torrecilla Francisco Javier , de Echave Sanz Ana , Rodríguez Casals Carlos , Terrado Sieso Eva M. , Pozuelo Muñoz Jorge , Cascarosa Salillas Esther
Published: November 2024

Light and color present complex interactions whose understanding is not always intuitive. Over the past forty years, numerous studies have aimed to identify preconceived notions and the various models employed in schools. Numerous teaching and learning strategies, along with didactic analyses, often fail to consider the multidimensional and interdisciplinary nature of these phenomena. This article presents a teaching experience aimed at training future primary education teachers. The theoretical context of this proposal, designed within the domains of physical and chemical didactics and plastic and visual expression, will be discussed. Instruments consistent with qualitative action research were used. Some results from the analysis of students’ narratives, both in laboratory notebooks and in sessions recorded audiovisually, will be presented.
Massive Open Online Courses in Higher Education Institutions: The Pedagogical Model of the Instituto Superior Técnico
Authors: Azevedo Bárbara , Pedro Ana , Dorotea Nuno
Published: November 2024

In the context of 21st-century educational transformation, Massive Open Online Courses have emerged as a global and flexible learning opportunity. This study explores the impact of MOOC implemented at Instituto Super Técnico on student engagement and attendance, focusing on their effectiveness in higher education. A descriptive approach was applied, selecting three MOOC as the empirical field, combining quantitative data from participation logs and qualitative feedback from student surveys. The key characteristics, the challenges, and the effectiveness of educational resources were evaluated, particularly concerning student interaction and perceptions of course quality. The pedagogical analysis highlighted both successful strategies and areas for improvement. The findings offer valuable insights into the influence of MOOC on students and the institution, contributing to the broader understanding of Portugal’s educational landscape. This study also proposes a framework for enhancing MOOC strategies in higher education globally, considering the potential of technological innovations in the 21st century.
Overcoming the Challenges of Including Learners with Visual Impairments Through Teacher Collaborations
Authors: Maesala Manis , Ronél Ferreira
Published: November 2024

In this article we report on a study undertaken with 255 teachers working with learners with visual impairments. The focus of our discussion is teachers’ implementation of inclusive education policies with learners with visual impairments in full-service schools in South Africa. We foreground the ways in which the teacher participants relied on teacher collaborations to overcome some of the challenges they faced as a result of limited resource provisions in schools in this country. We implemented an instrumental case study design and followed the approach of participatory reflection and action (PRA). The sample included teachers (n = 255) from seven full-service and ten special schools from five provinces in South Africa. In addition, 50 expert stakeholders who work in the field of visual impairment were involved. For data generation and documentation, we utilised PRA-based workshops, the observation-as-context-of-interaction method, audio-visual techniques, field notes, and reflective journals. The findings of our research confirm that full-service schools face distinct challenges regarding limited resources as well as teachers that are inexperienced to accommodate learners with visual impairments. Even though the teachers in our study were initially reluctant to implement inclusive education practices, their collaboration with fellow teachers and other informed stakeholders enabled them to address some of the challenges they experienced and implement inclusive practices. They subsequently formed a team and learnt from one another to facilitate positive changes through the implementation of inclusive practices, thereby following a socio-ecological approach to inclusive practices in full-service schools in South Africa.
Object Tracking Algorithm Based on Integrated Multi-Scale Templates Guided by Judgment Mechanism
Authors: Wang Jing , Wang Yanru , Que Yuxiang , Huang Weichao , Wei Yuan
Published: November 2024

The object tracking algorithm TransT, based on Transformer, achieves significant improvements in accuracy and success rate by fusing the extracted features of convolutional neural networks with the structure of Transformer. However, when dealing with the deformation of the object’s appearance, the algorithm exhibits issues such as insufficient tracking accuracy and drift, which directly affect the stability of the algorithm. In order to overcome this problem, this paper demonstrates how to expand scene information at the scale level during the fusion process and, on this basis, achieve accurate recognition and positioning. The predicted results are promptly fed back to the subsequent tracking process, from which temporal templates are embedded. Starting from both location and time can effectively improve the adaptive ability of the tracking model. In the final experimental comparison results, the algorithm proposed in this paper can adapt well to the situation of object deformation, and the overall performance of the tracking model has also been improved.
Diagnosing and Characterizing Chronic Kidney Disease with Machine Learning: The Value of Clinical Patient Characteristics as Evidenced from an Open Dataset
Authors: Figueroa Juan , Etim Patrick , Shibu Adithyan Karanathu , Berger Derek , Levman Jacob
Published: November 2024

Applying artificial intelligence (AI) and machine learning for chronic kidney disease (CKD) diagnostics and characterization has the potential to improve the standard of patient care through accurate and early detection, as well as providing a more detailed understanding of the condition. This study employed reproducible validation of AI technology with public domain software applied to CKD diagnostics on a publicly available CKD dataset acquired from 400 patients. The approach presented includes patient-specific symptomatic variables and demonstrates performance improvements associated with this approach. Our best-performing AI models, which include patient symptom variables, achieve predictive accuracies ranging from 99.4 to 100% across both hold-out and 5-fold validation with the light gradient boosting machine. We demonstrate that the exclusion of patient symptom variables reduces model performance in line with the literature on the same dataset. We also provide an unsupervised learning cluster analysis to help interpret variability among, and characterize the population of, patients with CKD.
Computing Unit and Data Migration Strategy under Limited Resources: Taking Train Operation Control System as an Example
Authors: Yuan Jianjun , Sun Laiping , Chu Pengzi , Yu Yi
Published: November 2024

There are conflicts between the increasingly complex operational requirements and the slow rate of system platform upgrading, especially in the industry of railway transit-signaling systems. We attempted to address this problem by establishing a model for migrating computing units and data under resource-constrained conditions in this paper. By decomposing and reallocating application functions, optimizing the use of CPU, memory, and network bandwidth, a hierarchical structure of computing units is proposed. The architecture divides the system into layers and components to facilitate resource management. Then, a migration strategy is proposed, which mainly focuses on moving components and data from less critical paths to critical paths and ultimately optimizing the utilization of computing resources. Specifically, the test results suggest that the method can reduce the overall CPU utilization by 27%, memory usage by 6.8%, and network bandwidth occupation by 35%. The practical value of this study lies in providing a theoretical model and implementation method for optimizing resource allocation in scenarios where there is a gap between resource and computing requirements in fixed-resource service architectures. The strategy is compatible for distributed computing architectures and cloud/cloud–edge-computing architectures.
Text Command Intelligent Understanding for Cybersecurity Testing
Authors: Yi Junkai , Liu Yuan , Jiang Zhongbai , Liu Zhen
Published: November 2024

Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for network security evaluation tools by combining knowledge graphs and information entropy for automatic entity annotation. Additionally, a novel NER approach based on the KG-BERT-BiLSTM-CRF model is proposed. Compared to the traditional BERT-BiLSTM model, the KG-BERT-BiLSTM-CRF model demonstrates superior performance when applied to the specialized corpus of network security evaluation tools. The graph attention network (GAT) component effectively extracts relevant sequential content from datasets in the network security evaluation domain. The fusion layer then concatenates the feature sequences from the GAT and BiLSTM layers, enhancing the training process. Upon successful NER execution, in this study, the identified entities are mapped to pre-established command-line data for network security evaluation tools, achieving automatic conversion from textual content to evaluation commands. This process not only improves the efficiency and accuracy of command generation but also provides practical value for the development and optimization of network security evaluation tools. This approach enables the more precise automatic generation of evaluation commands tailored to specific security threats, thereby enhancing the timeliness and effectiveness of cybersecurity defenses.
Assessment of Insulation Coordination and Overvoltage for Utility Girds Integrated with Solar Farms
Authors: Soomro Mansoor , Abbasi Riaz , Baloch Mazhar , Chauhdary Sohaib Tahir , Siddiqui Mokhi Maan
Published: November 2024

Due to the economic and environmental concerns associated with fossil fuels, many government and private organizations are progressively shifting towards the integration of solar farms with Utility Grids. However, these systems are facing insulation failure issues due to internal and external transient overvoltage’s, in which their shape, magnitude, and duration are unpredictable, and consequently, the insulation stress also becomes unpredictable. To ensure the safety and integrity of the system against any transient overvoltage event, it is important to carry out an insulation coordination analysis. The primary goal of this research work is to achieve this optimization in an economically viable manner, ensuring both operational stability and cost-effectiveness in the design of electrical equipment like surge Arresters. The research work presented in the literature does not fully evaluate all International Electrotechnical Commission (IEC) overvoltage classes as specified in the insulation coordination standards for Utility Grids integrated with solar farms. Therefore, this research paper investigates the impact of various transient and switching overvoltage conditions, as defined in the IEC 60071.4 Insulation Coordination Standard at the Solar and Utility Grid Electrical power system using PSCAD 4.6/EMTP Software. Five distinct simulation scenarios were developed to assess the systems’ resilience against insulation stress events. The proposed system was also examined with and without the application of a lightning surge arrester.
Low-Carbon Optimization Scheduling of Integrated Energy Systems Based on Bilateral Demand Response and Two-Level Stackelberg Game
Authors: Pan Hua , Wu Qunli , Guo Huiling , Bai Jiayi
Published: November 2024

In the context of low-carbon energy transformation, fully utilizing the integrated demand response (IDR) resources on the load side can improve the operational flexibility and economy of the integrated energy system (IES). However, establishing a reasonable trading mechanism to enhance users’ participation in IDR has become a key issue that IES urgently needs to solve. To this end, this paper first establishes an IES model that includes electricity, heat, and gas. To reduce carbon emissions, a ladder-type carbon trading mechanism is introduced while adding low-carbon technologies such as carbon capture devices and power-to-gas conversion. Secondly, a bilateral IDR mechanism centered on the load aggregator (LA) is proposed, and a multi-agent operation model including IES, LA, and users is established. The IDR subsidy price is dynamically determined through a two-level Stackelberg game model involving IES, LA, and users. Then, through KKT conditions and the Big M method, the two-level game model is turned into an IES-LA game model, which is solved using a combination of the White Shark Optimization method and the Gurobi solver. The final simulation results show that the scheduling model can fully reflect the time value of IDR resources, and the IES cost is decreased by USD 152.22, while LA and user benefits are increased by USD 54.61 and USD 31.85. Meanwhile, the ladder-type carbon trading mechanism and low-carbon technology have effectively achieved low-carbon operation of the system.
Theoretical Advances in MBenes for Hydrogen Evolution Electrocatalysis
Authors: Wang Yanwei , Jia Qi , Gao Ge , Zhang Ying , Zhang Lei , Lu Shun , Fang Ling
Published: November 2024

Two-dimensional transition metal borides (MBenes) have emerged as promising electrocatalysts for hydrogen evolution reactions (HERs), attracting significant research interest due to theoretical computations that enhance the understanding and optimization of their performance. This review begins with a comprehensive summary of HER mechanisms, followed by an in-depth examination of the geometric and electronic properties of MBenes. Subsequently, this review explores MBene-based electrocatalysts for HERs, employing free-energy diagrams and an electronic structure analysis to assess both the intrinsic catalytic activity of MBenes and the theoretical performance of single-atom modified MBenes. Finally, the prospects and challenges associated with MBenes are discussed, providing valuable insights to guide future research in this area. Overall, this topic holds significant relevance for researchers in the HER field, and this review aims to deliver theoretical insights for the optimal design of advanced MBene electrocatalysts.
Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach
Authors: Michos Dimitrios , Catthoor Francky , Foussekis Dimitris , Kazantzidis Andreas
Published: November 2024

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.
Comparison of Bi-Directional Topologies for On-Board Charger: A 10.9 kW High-Efficiency High Power Density of DC-DC Stage
Authors: Oh Hyeong-Seok , Hong Seong-Yong , Lee Ju , Lee Jae-Bum
Published: November 2024

In recent years, the trend in power electronics has been toward high-efficiency and high-power-density converters. Additionally, this trend has allowed electric vehicles to accommodate larger batteries, which necessitate bi-directional capabilities not only for driving but also for vehicle to grid (V2G), etc. This article proposes a comparative analysis of GaN-based bi-directional topologies, namely the dual active bridge (DAB) converter and the CLLC converter. To ensure a fair analysis of the proposed topologies, prototypes with the same target of efficiency above 97.5% and a power density of 5.5 kW/L have been constructed. This research can support the adoption of 10.9 kW bi-directional topologies in GaN-based on-board chargers (OBCs) for EVs.
Resource Efficiency and the Role of Renewable Energy in Miskolc: The City’s Journey Towards Becoming a Smart City
Authors: Greutter-Gregus Éva , Koncz Gábor , Némedi-Kollár Kitti
Published: November 2024

Miskolc, which is the focus of our investigation, is the fourth most populous city in Hungary and the center of one of the most underdeveloped NUTS2 (basic territorial category for the regional policy of the European Union) regions in the European Union. The socialist heavy industry played a decisive role in the development of the city, the decline of which also left deep traces in the city. In its current position, the city tries to manage its available resources as efficiently as possible, and the city management is open to the use of modern urban development tools. This is supported by the fact that Miskolc was the first Hungarian city to join the Green Cities for Sustainable Europe movement in 2011, and then in 2015, it joined the Triangulum project of the EU Smart Cities and Communities program as a follower city. In the process of becoming a smart city, the dimensions of environmental sustainability and energy efficiency were given a prominent role, which should not be surprising considering the traditions of the city. Within this, we must first mention the construction of the geothermal central heating system, with which the city really took significant steps in this field. The main goal of the study is to develop a new smart local concept closely linked to regional development and the key energy sector, through which the local adaptation of the defining elements of the internationally defined smart city in several forms for the city of Miskolc will be presented. In our study, we review how the results achieved by Miskolc so far and the development plans for the future fit in with the smart energy developments of smart cities. Before exploring the processes in Miskolc, we will deal in more detail with the possibilities inherent in district heating and geothermal energy utilization and Hungary’s capabilities.
Exploring Biomaterial-Based CoolRoofs: Empirical Insights into Energy Efficiency and CO2 Emissions Reduction
Authors: Oukmi Hasna , Chegari Badr , Soun Roland , Mouhat Ouadia , Rougui Mohamed , Ganaoui Mohammed El
Published: November 2024

The Cool Roof concept, known for its efficiency in summer due to high temperatures during this period, employs a light coating that covers the roof to prevent the absorption of heat and maintain lower indoor temperatures. This study integrates a chemical component with biomaterials to enhance performance and reduce CO2 emissions. The composition investigated in this research is recognized for its durability and ability to lower outside temperatures, thereby mitigating the urban heat island effect. This experimental study evaluates the sustainability of CoolRoofs in a cold room located in Signes, France. Temperature measurements are conducted from 25 September 2023 to 27 July 2024, both with and without the coating, to assess energy performance and CO2 emissions. The selection of the building type ensures optimal performance in both summer and winter. Results show that the maximum outside and inside surface temperatures for a Cool Roof are 48.7 °C and 25.6 °C, respectively, compared to 72.9 °C and 32.2 °C for an uncoated roof. Additionally, implementing a CoolRoof reduces thermal load through the cold room by 56%, while CO2 emissions can be reduced by up to 27.31 kg CO2/m2 over a 20-year period. This study presents a solution for enhancing energy and environmental performance year-round using a resilient composite.
A Novel High-Gain Multi-Stage Switched-Capacitor-Based DC-DC Boost Converter with Closed-Loop Control
Authors: Kumar Priyanshu , Ajmeri Moina , Singh Ashutosh Kumar , Mandal Rajib Kumar , Saha Akshay Kumar
Published: November 2024

In this manuscript, a direct current (DC) boost converter based on a switched-capacitor circuit with closed-loop control, tailored for applications with high-voltage gain, is introduced. The converter utilizes a network with switching capacitors to enhance voltage gain without relying on inductors, making it ideal for high-voltage scenarios. An active disturbance rejection control (ADRC)-based control scheme is used to maintain output voltage stability in the presence of disturbances. The proposed converter’s functionality and performance are assessed through simulations and experimental tests under various load conditions. A loss analysis, considering the losses from switches and diodes, is provided to determine the net efficiency. The results from both simulations and experiments show that the proposed converter achieves a high-voltage gain, excellent load regulation, and rapid transient response, highlighting its potential for applications that require a high-voltage boosting operation.
Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO
Authors: Qiu Bo , Zhang Jian , Yang Yun , Qin Guangyuan , Zhou Zhongyi , Ying Cunrui
Published: November 2024

Accurately predicting oil well production volume is of great significance in oilfield production. To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov–Arnold network (KAN). The GRU-KAN model utilizes GRU to extract temporal features and KAN to capture complex nonlinear relationships. First, the MissForest algorithm is employed to handle anomalous data, improving data quality. The Pearson correlation coefficient is used to select the most significant features. These selected features are used as input to the GRU-KAN model to establish the oil well production prediction model. Then, the Particle Swarm Optimization (PSO) algorithm is used to enhance the predictive performance. Finally, the model is evaluated on the test set. The validity of the model was verified on two oil wells and the results on well F14 show that the proposed GRU-KAN model achieves a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) values of 11.90, 9.18, 6.0% and 0.95, respectively. Compared to popular single and hybrid models, the GRU-KAN model achieves higher production-prediction accuracy and higher computational efficiency. The model can be applied to the formulation of oilfield-development plans, which is of great theoretical and practical significance to the advancement of oilfield technology levels.
Impact of Impedances and Solar Inverter Grid Controls in Electric Distribution Line with Grid Voltage and Frequency Instability
Authors: Kaewnukultorn Thunchanok , Hegedus Steven
Published: November 2024

The penetration of solar energy into centralized electric grids has increased significantly during the last decade. Although the electricity from photovoltaics (PVs) can deliver clean and cost-effective energy, the intermittent nature of the sunlight can lead to challenges with electric grid stability. Smart inverter-based resources (IBRs) can be used to mitigate the impact of such high penetration of renewable energy, as well as to support grid reliability by improving the voltage and frequency stability with embedded control functions such as Volt-VAR, Volt–Watt, and Frequency–Watt. In this work, the results of an extensive experimental study of possible interactions between the unstable grid and two residential-scale inverters from different brands under different active and reactive power controls are presented. Two impedance circuits were installed between Power Hardware-in-the-loop (P-HIL) equipment to represent the impedance in an electric distribution line. Grid voltage and frequency were varied between extreme values outside of the normal range to test the response of the two inverters operating under different controls. The key findings highlighted that different inverters that have met the same requirements of IEEE 1547-2018 responded to grid instabilities differently. Therefore, commissioning tests to ensure inverter performance are crucial. In addition to the grid control, the residential PV installed capacity and physical distances between PV homes and the substation, which impacted the distribution wiring impedance which we characterized by the ratio of the reactive to real impedance (X/R), should be considered when assigning the grid-supporting control setpoints to smart inverters. A higher X/R of 3.5 allowed for more effective control to alleviate both voltage and frequency stability. The elimination of deadband in an aggressive Volt-VAR control also enhanced the ability to control voltage during extreme fluctuation. The analysis of sudden spikes in the grid responses to a large frequency drop showed that a shallow slope of 1.5 kW/Hz in the droop control resulted in a >65% lower sudden reactive power overshoot amplitude than a steeper slope of 2.8 kW/Hz.
The Impact of the Configuration of a Hydrogen Refueling Station on Risk Level
Authors: Rusin Andrzej , Stolecka-Antczak Katarzyna , Kosman Wojciech , Rusin Krzysztof
Published: November 2024

The paper discusses potential hazards at hydrogen refueling stations for transportation vehicles: cars and trucks. The main hazard analyzed here is an uncontrolled gas release due to a failure in one of the structures in the station: storage tanks of different pressure levels or a dispenser. This may lead to a hydrogen cloud occurring near the source of the release or at a given distance. The range of the cloud was analyzed in connection to the amount of the released gas and the wind velocity. The results of the calculations were compared for chosen structures in the station. Then potential fires and explosions were investigated. The hazard zones were calculated with respect to heat fluxes generated in the fires and the overpressure generated in explosions. The maximum ranges of these zones vary from about 14 to 30 m and from about 9 to 14 m for a fires and an explosions of hydrogen, respectively. Finally, human death probabilities are presented as functions of the distance from the sources of the uncontrolled hydrogen releases. These are shown for different amounts and pressures of the released gas. In addition, the risk of human death is determined along with the area, where it reaches the highest value in the whole station. The risk of human death in this area is 1.63 × 10−5 [1/year]. The area is approximately 8 square meters.
Two-Stage Locating and Capacity Optimization Model for the Ultra-High-Voltage DC Receiving End Considering Carbon Emission Trading and Renewable Energy Time-Series Output Reconstruction
Authors: Zhao Lang , Wang Zhidong , Sheng Hao , Li Yizheng , Zhang Tianqi , Wang Yao , Yu Haifeng
Published: November 2024

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model.
Single- and Three-Phase Dual-Active-Bridge DC–DC Converter Comparison for Battery Electric Vehicle Powertrain Application
Authors: Guennouni Nasr , Machkour Nadia , Chebak Ahmed
Published: November 2024

Dual-active-bridge (DAB) DC–DC converters are of great interest for DC–DC conversion in battery electric vehicle (BEV) powertrain applications. There are two versions of DAB DC–DC converters: single-phase (1p) and three-phase (3p) architectures. Many studies have compared these architectures, selecting the 3p topology as the most efficient. However, there is a gap in the literature when comparing both architectures when single-phase-shift (SPS) modulation is not used to drive the converter. The aim of this study was to compare 1p and 3p DAB DC–DC converters driven by optimal modulation techniques appropriate for BEV powertrain applications. Mathematical loss models were derived for both architectures, and their performances were compared. A case study of a 100 kW converter was considered as an example to visualize the overall efficiency of the converter for each layout. The 1p DAB DC–DC converter architecture outperformed the 3p layout in both its Y–Y and D–D transformer configurations. The higher performance efficiency, lower number of components, and reduced design complexity make the 1p DAB DC–DC converter topology a favorable choice for BEV powertrain applications.
Steady-State Temperature Prediction for Cluster-Laid Tunnel Cables Based on Self-Modeling in Natural Convection
Authors: Zhao Yingying , Si Wenrong , Fu Chenzhao , Yang Chenhan , Yang Jian
Published: November 2024

Accurate temperature prediction of the operating tunnel cable is crucial for its safe and efficient function. To achieve a rapid and accurate prediction of the steady-state temperature of the tunnel cable, the self-modeling pattern in natural convection on the cable surface in the rectangular tunnel is investigated, and the self-modeling method for the convective heat transfer coefficient calculation is proposed. A thermal circuit model for single cables is further established to predict the cable core temperature, and the model is extended to predict the cluster-laid cable core temperature based on the combined method. The results show that when the tunnel size is neglected, the maximum relative deviation of the convective heat transfer coefficient between the self-modeling method and the finite element simulation is only 1.78% in the studied cases, indicating that the natural convection on the cable surface approximately satisfies the self-modeling method. Additionally, applying the self-modeling method to the thermal circuit can accurately predict the temperature of the single cable core. Furthermore, for the three-phase four-circuit cable, the maximum deviation between the temperature prediction results and the finite element results is within 2 K in the studied cases, which verifies the predictive accuracy of the combined method for the cluster-laid tunnel cable.
Comparative Analysis of Material Efficiency and the Impact of Perforations on Heat Sinks for Monocrystalline Photovoltaic Panel Cooling
Authors: Cabrera-Escobar Raúl , Cabrera-Escobar José , Vera David , Jurado Francisco , Orozco-Cantos Lenin , Córdova-Suárez Manolo , García-Mora Félix
Published: November 2024

In this research, the design and simulation of a heat sink for photovoltaic panels were carried out using aluminum and copper, the most commonly used materials in heat dissipation systems. This heat sink consisted of fins that were tested both perforated and non-perforated to improve heat dissipation efficiency. This research stems from the need to reduce the temperature of photovoltaic panels during operation, as scientific evidence shows that photovoltaic panels experience a decrease in efficiency as the temperature increases, taking as a reference the temperature under standard test conditions. The simulations of photovoltaic panels with aluminum and copper fins, both perforated and non-perforated, followed a rigorous methodology. For validation, the simulation results were compared with field data, yielding a mean absolute percentage error of 1.71%. The findings indicate that copper fins reduced the temperature of the photovoltaic panel by 2.62 K, resulting in a 1.31% increase in efficiency. Similarly, aluminum fins reduced the temperature by 2.10 K, with a 1.05% increase in efficiency. Perforated copper fins achieved a temperature reduction of 3.07 K, increasing efficiency by 1.54%, while perforated aluminum fins reduced the temperature by 2.49 K, contributing to a 1.25% increase in efficiency.
Optimizing Models and Data Denoising Algorithms for Power Load Forecasting
Authors: Li Yanxia , Ugli Ilyosbek Numonov Rakhimjon , Ugli Yuldashev Izzatillo Hakimjon , Lee Taeo , Kim Tae-Kook
Published: November 2024

To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed. This method divides data into administrative regions, industries, and load characteristics using a four-step method, extracts periodic features using Fourier transform, and uses Kmeans++ for clustering processing. On this basis, a Transformer model based on an adversarial adaptive mechanism is designed, which aligns the data distribution of the source domain and target domain through a domain discriminator and feature extractor, thereby reducing the impact of domain offset on prediction accuracy. The mean square error of the Fourier transform clustering method used in this study was 0.154, which was lower than other methods and had a better data denoising effect. In load forecasting, the mean square errors of the model in predicting long-term load, short-term load, and real-time load were 0.026, 0.107, and 0.107, respectively, all lower than the values of other comparative models. Therefore, the load forecasting model designed for research has accuracy and stability, and it can provide a foundation for the precise control of urban power systems. The contributions of this study include improving the accuracy and stability of the load forecasting model, which provides the basis for the precise control of urban power systems. The model tracks periodicity, short-term load stochasticity, and high-frequency fluctuations in long-term loads well, and possesses high accuracy in short-term, long-term, and real-time load forecasting.
Gas Pipeline Leak Detection by Integrating Dynamic Modeling and Machine Learning Under the Transient State
Authors: Kim Juhyun , Han Sunlee , Kim Daehee , Lee Youngsoo
Published: November 2024

This study focused on developing machine learning models to detect leak size and location in transient state conditions. The model was designed for an onshore methane–hydrogen blending gas pipeline in Canada. Base case simulations revealed significant effects on mass flow and pressure due to leaks, with the system taking approximately 6 h to reach a steady state from transient conditions. This made it essential to analyze the flow characteristics during the transient state. Trend data from the pipeline’s inlet and outlet were examined, considering the leak size and location. To better represent the data over time, a method was used to create two-dimensional images, which were then fed into a CNN (convolutional neural network) for training. The model’s accuracy was assessed using classification accuracy and a confusion matrix. By refining the data acquisition process and implementing targeted screening procedures, the model’s classification accuracy increased to over 80%. In conclusion, this study demonstrates that machine learning can enable rapid and accurate leak detection in transient state conditions. The findings are expected to complement existing leak detection methods and support operators in making faster, more informed decisions.
Epigenetic Landscape of DNA Methylation in Pancreatic Ductal Adenocarcinoma
Authors: Liu Peiyi , Jacques Juliette , Hwang Chang-Il
Published: November 2024

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, characterized by its aggressive progression and dismal prognosis. Advances in epigenetic profiling, specifically DNA methylation analysis, have significantly deepened our understanding of PDAC pathogenesis. This review synthesizes findings from recent genome-wide DNA methylation studies, which have delineated a complex DNA methylation landscape differentiating between normal and cancerous pancreatic tissues, as well as across various stages and molecular subtypes of PDAC. These studies identified specific differentially methylated regions (DMRs) that not only enhance our grasp of the epigenetic drivers of PDAC but also offer potential biomarkers for early diagnosis and prognosis, enabling the customization of therapeutic approaches. The review further explores how DNA methylation profiling could facilitate the development of subtype-tailored therapies, potentially improving treatment outcomes based on precise molecular characterizations. Overall, leveraging DNA methylation alterations as functional biomarkers holds promise for advancing our understanding of disease progression and refining PDAC management strategies, which could lead to improved patient outcomes and a deeper comprehension of the disease’s underlying biological mechanisms.
Carbon Stock Dynamics in Rubber Plantations Along an Elevational Gradient in Tropical China
Authors: Razaq Mohsin , Huang Qicheng , Wang Feijun , Liu Changan , Gnanamoorthy Palingamoorthy , Liu Chenggang , Tang Jianwei
Published: November 2024

Carbon (C) losses due to the conversion of natural forests adversely affect the biotic and abiotic components of terrestrial ecosystems. In tropical China, rubber cultivation often extends from its traditional range to elevations of up to 1400 m. However, C stock in rubber plantations along elevation gradients is poorly understood. In this study, we investigated biomass and C stock along elevation gradients in two age groups (8- and 12-year-old) of rubber monoculture plantations in Xishuangbanna, Southwest China. The C distribution across various tree sections, ranging from aboveground biomass (AGB) to belowground biomass (BGB), including litter, big dead branches, and different soil depths were measured. A significant negative correlation was observed between AGB, BGB, litter, and total ecosystem C stocks and elevation gradients in both age groups. However, no correlation was observed between the total soil C stock and elevation gradients in 8-year-old rubber plantations, while significant decline was detected in 12-year-old rubber plantations. The highest ecosystem C stock of 197.90 Mg C ha−1 was recorded at 900 m in 8-year-old plantations; whereas, in 12-year-old rubber plantations, the highest value of 183.12 Mg C ha−1 was found at 700 m. The total ecosystem C stock decreased to their lowest level at 1000 m in both the 8-year-old and 12-year-old plantations, ranging between 113.05 Mg C ha−1 and 125.75 Mg C ha−1, respectively. Moreover, total ecosystem C stock significantly decreased from 51.55% to 8.05% and from 42.96% to 11.46% between 700 m and 1100 m, in both 8-year-old and 12-year-old plantations, respectively. Regardless of elevation gradients, the total ecosystem C stock of 12-year-old rubber plantations was 1.98% greater than that of 8-year-old rubber plantations. Biomass was the second largest contributor, while soil accounted for 82% to 90%, and the other components contributed less than 2% of the total ecosystem C stock in both age groups. These fluctuations in C stock along elevation gradients in both 8- and 12-year-old plantations suggested that rubber growth, biomass, and C stock capacity decreased above 900 m, and that age and elevation are key factors for biomass and C stock in rubber monoculture plantations.
Cost-Effective Strategy and Feasibility for Amylase Production from Okara by Bacillus subtilis J12
Authors: Mahfudz Muhamad Khairi , Jaikhan Somchai , Phirom-on Konlarat , Apiraksakorn Jirawan
Published: November 2024

Low-cost enzyme production is considered a feasibility factor in enzyme commercialization. Okara, a high-nutritional agro-industrial residue from soybean processing, was performed as a medium for bacterial amylase production to save costs and increase productivity. This study aimed to produce, characterize, activate amylase, and evaluate the material cost for media from okara. Under solid-state fermentation (SSF) of okara without pretreatment, Bacillus subtilis J12 could produce 983 U/g of amylase within 24 h. Bacillus subtilis J12 amylase had optimal activity at pH 6.0 and 50 °C and was stable at a moderate temperature for up to 120 min. Identified as a metalloenzyme, the activity was improved by ferric ions. The purification of amylase resulted in two fractions which contained at least two types of amylases. Compared with other producers, the production was evaluated using low-cost media without additional supplementations. Based on the productivity, characteristics, and evaluation, Bacillus subtilis J12 amylase was potentially commercialized, had economic value, possessed energy-saving features, and could be applied for industrial use.
Transcriptomic Analysis of Cell Stress Response in Wickerhamomyces anomalus H4 Under Octanoic Acid Stress
Authors: Yu Zhi-Hai , Li Li , Chen Qiu-Yu , Huang Bing-Xuan , Shi Ming-Zhi , Dong Wan-Jin , Zu Yuan , Huang Ming-Zheng , Liu Xiao-Zhu
Published: November 2024

The purified yeast strain H4, identified as W. anomalus through morphological, genetic, and phylogenetic analyses, was characterized and compared to a commercial Saccharomyces cerevisiae strain X16. W. anomalus H4 exhibited distinct morphological features. It demonstrated notable tolerance to 11% ethanol, 220 g/L glucose, and 200 mg/L octanoic acid, similar to X16, except for having a lower tolerance to SO2. Survival analysis under various stress conditions revealed that ethanol and octanoic acid had the most detrimental effects, with 56% cell mortality at 13% ethanol and 400 mg/L octanoic acid. Transcriptomic analysis under octanoic acid stress showed that at 200 mg/L, 3369 differentially expressed genes (DEGs) were induced, with 1609 being upregulated and 1760 downregulated, indicating broad transcriptional reprogramming. At 400 mg/L, only 130 DEGs were detected, suggesting a more limited response. KEGG pathway analysis indicated that most DEGs at 200 mg/L were associated with the “ribosome” and “proteasome” pathways, reflecting disruptions in protein synthesis and turnover. At 400 mg/L, the DEGs were primarily related to “DNA replication” and “pyruvate metabolism”. These findings highlight the adaptive mechanisms of W. anomalus H4 to environmental stresses, particularly octanoic acid, and its potential for use in brewing and fermentation processes.