bluuebunny/crossref_metadata_2025 huggingface dataset:
Government Funding Allocations to Universities and the Business Cycle: An Analysis of Canada’s Provincial Governments
Authors: Rockerbie Duane , Easton Stephen
Published: April 2024

Canada’s universities each receive an annual operating grant from their provincial government to partially finance operating expenses. This paper estimates the sensitivity of provincial operating grants to the business cycle by disentangling the effects of procyclical income on government revenue and the countercyclical effect on student demand by utilizing an economic regression model composed of three equations. Our panel data include the total real operating grant paid to all universities within a province, total student enrolment, real per capita government revenue, and real per capita gross domestic product for Canada’s ten provinces over the 1992–2019 sample period. The results confirm that real per capita government revenues are procyclical and that full-time equivalent student enrolments are counter-cyclical. The total real operating grant is only weakly associated with cyclical changes in provincial government revenue. Instead, the total real operating grant is mainly determined by countercyclical changes in student demand. This partially offsets the potential reduction in funding to universities during an economic downturn. Provincial governments in Canada can smooth the total allocation over the business cycle by adjusting other expenditures and using debt financing. Our results suggest they do this to some extent, but not enough to avoid a net reduction in real operating grants during an economic downturn.
Implementation of Hybrid Education in Peruvian Public Universities: The Challenges
Authors: Colina-Ysea Félix , Pantigoso-Leython Nathalí , Abad-Lezama Irene , Calla-Vásquez Kriss , Chávez-Campó Soledad , Sanabria-Boudri Fanny Miriam , Soto-Rivera Colbert
Published: April 2024

Digital competencies and hybrid education have become fundamental tools to promote new learning styles in the context of higher education. The objective of the research was to evaluate the challenges that hybrid education creates with respect to the digital competencies of Peruvian university teachers in times of uncertainty. The approach used was mixed in order to collect both numerical and qualitative data. The population and sample were composed of 189 teachers from three national universities. The techniques used were a survey for quantitative data and an interview for qualitative data. The instruments used were a questionnaire and an interview protocol. The results show that the challenges that universities in Peru must face are the recognition of their own potential, technical–technological capacity, interpretation of the felt needs, the formative development of human talent, and reflecting themselves as a dynamic node that responds to the changes in society. It was concluded that Peruvian universities must proactively address the challenges presented by hybrid education and the development of digital competencies to ensure a high quality education that prepares students for the world of today and the world of tomorrow.
Nice for What? The Contradictions and Tensions of an Urban District’s Racial Equity Transformation
Authors: Virella Patricia , Liera Román
Published: April 2024

Diversity, equity, and inclusion training has exploded over the last decade. While many districts invest considerable resources in developing their leaders’ knowledge and skills on equity issues, “niceness” can perpetuate whiteness and present formidable obstacles to meaningful progress. Investigating a large urban-emergent district as a case study, we examine the efforts to eliminate the racial barriers perpetuated by its leaders and explore the contradictions that arise after a year of professional learning geared towards antiracist district transformation. We employ a theory of racialized organizations, seeking to understand how whiteness as niceness impeded school leaders’ efforts to engage in antiracist change work. The study provides valuable implications for policy, practice, and future research in education and equity.
Examining Front-Line Administrative Services in a Selected Public Higher Education Institution
Authors: Luthuli Mthokozisi , Nkomo Ntando , Moyane Smangele
Published: April 2024

The South African government’s commitment to people-friendly public service since 1994 has influenced the quality of service provided by front-line administrative staff in public higher learning institutions. This study explores the experiences of front-line administrative staff at the Durban University of Technology (DUT), focusing on the challenges faced and their impact on teaching, learning, and overall academic activities. Against the backdrop of public higher education institutions (HEIs) in South Africa, the study addresses the persistent challenges in service delivery and the crucial role of front-line administrative staff. Employing a post-positivist paradigm, the research adopts a hybrid methodological approach, combining qualitative and quantitative methods. A survey design is utilized to gather data from first-time entry students (FTENs) enrolled in the Business and Information Management program at DUT, employing convenience sampling and a self-administered questionnaire. The study’s findings illuminate the inefficiencies in front-line administrative services, elucidating their impact on diverse stakeholders and emphasizing the pressing need for enhancement. The study found that the majority of students perceived the services positively, with only a small number expressing dissatisfaction and nearly all participants noted the institution’s adherence to the Batho Pele Principles positively, though a few had contrasting experiences. The findings further revealed areas of improvement for the service. By focusing on the experiences of FTENs, the study contributes to the broader discourse on enhancing service delivery in public higher learning institutions. Addressing these challenges is crucial for the fulfilment of the core mission of HEIs providing quality education and fostering a positive learning environment.
Learning Multiplication by Translating across Microworlds
Authors: Tan Sheena , Chorney Sean , Sinclair Nathalie
Published: April 2024

In this article, we explore students’ experiences of using two different digital microworlds of multiplication, which can be found in the multitouch application TouchTimes. We draw on Diagne’s notion of translation to frame our study, focusing on the learning that occurs in the movement between the two microworlds. We study translation in terms of actions, strategies, perceptions, and preferences and highlight both the translatables and the untranslatables that emerged in the pair-based interviews that were conducted with grades 3–4 students.
Emotional Exhaustion Scale (ECE): Psychometric Properties in a Sample of Portuguese University Students
Authors: Ala Sílvia , Campos Francisco Ramos , Relva Inês Carvalho
Published: April 2024

Academic emotional exhaustion is the first stage of academic burnout syndrome, and it is necessary to assess it and intervene early, as the consequences can lead to harmful effects on psychological well-being. The main objectives of this study were to explore the psychometric properties of the Emotional Exhaustion Scale (ECE); to assess the association with suffering and positive well-being, using the MHI-5 Inventory; and to provide information on its validity and to compare it with these constructs. A total sample of 526 university students (81% female and 19% male) aged between 17 and 62 (M = 21.42 years and SD = 5.78) took part in this study. Exploratory and confirmatory factor analyses were carried out. In the internal consistency assessment, the ECE was 0.89 and the MHI-5 was 0.81 (Cronbach’s alpha). Exploratory factor analysis was carried out with Varimax rotation and confirmatory analysis, obtaining the factor that explains 50.5% of the variance. The results indicated that the ECE has adequate psychometric properties for use with higher education students in Portugal. Its use by mental health professionals in higher education institutions could be very relevant for screening for emotional exhaustion and thus preventing possible serious pathologies.
S3DK: An Open Source Toolkit for Prototyping Synchrophasor Applications
Authors: Baudette Maxime , Vanfretti Luigi , Tyagi Shashank
Published: April 2024

Synchrophasor data contain a trove of information on the power system and its dynamics. These measurements have a high potential to unlock our ability to cope with changing system conditions and challenges posed by distributed and intermittent energy sources. While Phasor Measurement Units (PMUs) have seen a large deployment in the grid, their applications are limited by the software platforms that are deployed in control centers to monitor the grid. In this paper, we present an open source toolkit that enables fast prototyping of PMU applications. The toolkit is akin to a software development kit (SDK) for synchrophasor applications, providing a number of functionalities that enable high-level PMU application development within the LabVIEW environment. This Smart-grid Synchrophasor SDK (S3DK) proposes a paradigm based on the concept of distributed applications, which allows development and deployment to be independent of the existing software stack deployed in control centers and to leverage PMU data at any level of a synchrophasor system hierarchy. This paper serves to introduce the S3DK, which is released as open source software to facilitate broader and fast prototyping of synchrophasor applications.
A Multi-Layered Defence Strategy against DDoS Attacks in SDN/NFV-Based 5G Mobile Networks
Authors: Sheibani Morteza , Konur Savas , Awan Irfan , Qureshi Amna
Published: April 2024

Software-defined networking (SDN) and network functions virtualisation (NFV) are crucial technologies for integration in the fifth generation of cellular networks (5G). However, they also pose new security challenges, and a timely research subject is working on intrusion detection systems (IDSs) for 5G networks. Current IDSs suffer from several limitations, resulting in a waste of resources and some security threats. This work proposes a new three-layered solution that includes forwarding and data transport, management and control, and virtualisation layers, emphasising distributed controllers in the management and control layer. The proposed solution uses entropy detection to classify arriving packets as normal or suspicious and then forwards the suspicious packets to a centralised controller for further processing using a self-organising map (SOM). A dynamic OpenFlow switch relocation method is introduced based on deep reinforcement learning to address the unbalanced burden among controllers and the static allocation of OpenFlow switches. The proposed system is analysed using the Markov decision process, and a Double Deep Q-Network (DDQN) is used to train the system. The experimental results demonstrate the effectiveness of the proposed approach in mitigating DDoS attacks, efficiently balancing controller workloads, and reducing the duration of the balancing process in 5G networks.
Synthetic Dataset Generation Using Photo-Realistic Simulation with Varied Time and Weather Axes
Authors: Lee Thomas , Mckeever Susan , Courtney Jane
Published: April 2024

To facilitate the integration of autonomous unmanned air vehicles (UAVs) in day-to-day life, it is imperative that safe navigation can be demonstrated in all relevant scenarios. For UAVs using a navigational protocol driven by artificial neural networks, training and testing data from multiple environmental contexts are needed to ensure that bias is minimised. The reduction in predictive capacity when faced with unfamiliar data is a common weak point in trained networks, which worsens the further the input data deviates from the training data. However, training for multiple environmental variables dramatically increases the man-hours required for data collection and validation. In this work, a potential solution to this data availability issue is presented through the generation and evaluation of photo-realistic image datasets from a simulation of 3D-scanned physical spaces which are theoretically linked in a digital twin (DT) configuration. This simulation is then used to generate environmentally varied iterations of the target object in that physical space by two contextual variables (weather and daylight). This results in an expanded dataset of bicycles that contains weather and time-varied components of the same images which are then evaluated using a generic build of the YoloV3 object detection network; the response is then compared to two real image (night and day) datasets as a baseline. The results reveal that the network response remained consistent across the temporal axis, maintaining a measured domain shift of approximately 23% between the two baselines.
An Automatic Near-Duplicate Video Data Cleaning Method Based on a Consistent Feature Hash Ring
Authors: Qin Yi , Ye Ou , Fu Yan
Published: April 2024

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issues caused by near-duplicate videos are becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can help uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which makes it difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and initial clustering centers under a condition that prior distribution is unknown, which seriously affects the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-head attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to the results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing studies, simulation results demonstrate the performance of the proposed method.
Evaluation of Entropy Analysis as a Fault-Related Feature for Detecting Faults in Induction Motors and Their Kinematic Chain
Authors: Jaen-Cuellar Arturo Y. , Saucedo-Dorantes Juan J. , Elvira-Ortiz David A. , Romero-Troncoso Rene de J.
Published: April 2024

The induction motors found in industrial and commercial applications are responsible for most of the energy consumption in the world. These machines are widely used because of their advantages like high efficiency, robustness, and practicality; nevertheless, the occurrence of unexpected faults may affect their proper operation leading to unnecessary breakdowns with economic repercussions. For that reason, the development of methodologies that ensure their proper operation is very important, and in this sense, this paper presents an evaluation of signal entropy as an alternative fault-related feature for detecting faults in induction motors and their kinematic chain. The novelty and contribution lie in calculating a set of entropy-related features from vibration and stator current signals measured from an induction motor operating under different fault conditions. The aim of this work is to identify changes and trends in entropy-related features produced by faulty conditions such as broken rotor bars, damage in bearings, misalignment, unbalance, as well as different severities of uniform wear in gearboxes. The estimated entropy-related features are compared to other classical features in order to determine the sensitivity and potentiality of entropy in providing valuable information that could be useful in future work for developing a complete methodology for identifying and classifying faults. The performed analysis is applied to real experimental data acquired from a laboratory test bench and the obtained results depict that entropy-related features can provide significant information related to particular faults in induction motors and their kinematic chain.
Key Information Extraction for Crime Investigation by Hybrid Classification Model
Authors: Park Yerin , Park Ro Seop , Kim Hansoo
Published: April 2024

The 2021 amendment to South Korea’s Criminal Procedure Law has significantly enhanced the role of the police as investigative authorities. Consequently, there is a heightened demand for advanced investigative expertise among the police, driven by an increase in the number of cases each investigator handles and the extended time required for report preparation. This situation underscores the necessity for an artificial-intelligence-supported system to augment the efficiency of investigators. In response, this study designs a hybrid model that fine-tunes two Transformer-based pre-trained language models to automatically extract 18 key pieces of information from legal documents. To facilitate this, “The Major Information Frame of Homicide Criminal Facts” was developed, and a large-scale training dataset specialized in the criminal investigation field was constructed. The hybrid classification model proposed in this research achieved an F1 score of 87.75%, indicating superior performance compared to using a single machine reading model. Additionally, the model’s top three predicted answers included the correct answer at a rate exceeding 98%, demonstrating a high accuracy level. These results suggest that the hybrid classification model designed in this study can play a crucial role in efficiently extracting essential information from complex legal and investigative documents. Based on these findings, it is confirmed that the hybrid classification model can be applied not only in drafting investigative reports but also in tasks such as searching for similar case precedents and constructing case timelines in various legal and investigative applications. The advancement is expected to provide a standardized approach that allows all investigators to perform objective investigations and hypothesis testing, thereby enhancing the fairness and efficiency of the investigative process.
Dip-NeRF: Depth-Based Anti-Aliased Neural Radiance Fields
Authors: Qin Shihao , Xiao Jiangjian , Ge Jianfei
Published: April 2024

Neural radiation field (NeRF)-based novel view synthesis methods are gaining popularity for their ability to generate detailed and realistic images. However, most NeRF-based methods only use images to learn scene representations, ignoring the importance of depth information. The Zip-NeRF method has achieved impressive results in unbounded scenes by combining anti-aliasing techniques and mesh representations. However, the method requires a large number of input images and may perform poorly in complex scenes. Our method incorporates the advantages of Zip-NeRF and incorporates depth information to reduce the number of required images and solve the scale-free problem in borderless scenes. Experimental results show that our method effectively reduces the training time.And we can generate high-quality images and fine point cloud models using few images, even in complex scenes with numerous occlusions.
Empirical Insights into 5G Deployments in Highway Operational Environments and Comparative Performance with 4G
Authors: Tsoulos George , Athanasiadou Georgia , Nikitopoulos George , Tsoulos Vassilis , Zarbouti Dimitra
Published: April 2024

Despite the promising benefits, the integration of 5G, particularly through Non-Standalone (NSA) architectures that rely on existing 4G infrastructures, introduces challenges in maintaining optimal radio network performance and service quality. This study evaluates 4G and 5G radio network performance through empirical field trials across highway operational scenarios, a domain less scrutinized compared with urban environments. By conducting simultaneous measurements and comparing all available networks, this research focuses on dissecting the performance of critical quality indicators to gauge the intricacies of radio network behavior in high-speed travel conditions. Our findings illuminate distinct behavioral patterns, highlighting the unique challenges and optimization opportunities in these scenarios. Variability in signal strength across the highway routes underscores the influence of geography and infrastructure on coverage, while the relative stability in signal quality suggests the networks’ capability to maintain signal quality amid fluctuating strength. Interference results indicate effective management of signal interference, crucial for high-quality links, whereas latency and throughput metrics highlight a lag behind anticipated goals for reduced latency but promising data rates. This study not only showcases the variances in network quality and performance, thus pinpointing areas for operator-specific enhancements, but also emphasizes the comparison between the robustness of 4G infrastructures and the challenges in optimizing 5G networks.
Weakening of Ledinegg Instability and Maldistribution of Boiling Flow in Parallel Microchannels by Entry Effects
Authors: Jiang Jieyan , Chen Changxu , Huang Haoxiang , Pan Zhenhai
Published: April 2024

In the pursuit of enhancing thermal management for miniaturized electronic devices, our study delves into the impact of entry effects on Ledinegg instability and flow maldistribution within parallel microchannels. Utilizing a coupled model that incorporates phase change and pressure drop dynamics in boiling flow, we examine microchannels characterized by a 50 length-to-diameter ratio and a 200 μm hydraulic diameter. Our findings unveil a significant influence of entry effects, which narrow the total flow excursion interval, thereby bolstering system stability. Specifically, as the heat flux escalates from 5 W/cm2 to 120 W/cm2, the entry effects increasingly mitigate flow instability and maldistribution in parallel channels, diminishing the total flow rate range susceptible to flow instability by 4.73% and 47.52%, while narrowing the total flow rate range corresponding to uneven flow distribution by 4.70% and 46.75%, respectively. Furthermore, entry effects expand the inlet subcooling range necessary for stabilizing the parallel channel system by 38.89% and 1000%. This research not only underscores the importance of considering entry effects in microchannel design but also opens avenues for further exploration into enhancing thermal management solutions.
Moisture Content Assessment of Commercially Available Diesel Fuel Using Impedance Spectroscopy
Authors: Macioszek Łukasz , Sobczyński Dariusz
Published: April 2024

Diesel is consistently a very popular fuel for internal combustion engines. Its consumption in the European Union has been rising over recent years, as has the number of engines increasingly sensitive to fuel contamination. One of the most important parameters affecting diesel quality is water content. Its exceedance of the standard’s permissible amount carries the risk of serious damage to expensive, modern fuel injection systems. Current methods of measuring water content require both specialised personnel and equipment, as well as transporting the sample to a suitable laboratory. Analysing the properties of diesel is challenging, mainly because many types of the fuel are sold, the chemical composition of which is always a well-guarded manufacturer’s secret. The research presented in this paper is aimed at verifying whether it is possible to test the moisture content of diesel using impedance spectroscopy. To date, it is the first such attempt with commercially available diesel using this method, which, once refined, could be used in situ. The authors propose and apply a novel technique that is able to be used when the electrical properties of tested oils are unknown. The experimental results obtained show that it is possible to use impedance spectroscopy to estimate the water content of different types of diesel fuel. The measurement accuracy achieved makes it possible to detect approaching or exceeding the permissible water content.
The Applications of WFEM in the Exploration of Medium-Depth Geothermal Resources
Authors: Fu Guoqiang , Li Zhuqiang , Zhang Qiangjiang , Guo Tao , Jiang Qiyun , Yang Yonghong , Chen Xueguo , Lei Chuang , Li Jie
Published: April 2024

Medium-depth geothermal energy mainly consists of two types, hydrothermal and dry-heat rock. With huge resource reserves, its exploration and development are of great significance to the transformation of China’s energy structure and the realization of the goal of “Double-Carbon”. From the differences in the electrical characteristics between hydrothermal and hot dry rock geothermal resources, this project uses the numerical modeling of the wide field electromagnetic (WFEM) method as a tool to design a model of typical geothermal resources. Then, we summarize and analyze the detection capability of the WFEM by calculating the WFEM response under the conditions of different depths, resistivity values, thickness-to-depth ratios and other parameters. Moreover, aiming at the key problems faced by the WFEM in real applications, we discuss the factors affecting the detection accuracy and give solutions to improve the reliability of the detection results. Finally, the medium-depth geothermal energy detection in the Jiyang Depression of Shandong is presented as an example, showing how the WFEM detection technique was used with the inversion result profile obtained. By comparing the results of geomagnetic and seismic detection on the same survey line, it is demonstrated that the WFEM method has great potential in the exploration of medium-depth geothermal energy and can be the preferred method.
A Comparative Study of Time-Evolution Characteristics of Single and Double Cavity Partial Discharges
Authors: Kyere Isaac Kwabena , Nyamupangedengu Cuthbert , Swanson Andrew Graham
Published: April 2024

Partial discharge (PD) in cavities can lead to a breakdown in solid insulation and, therefore, indicate the onset of aging in electrical equipment. It is necessary to investigate the activity of single- and double-cavity PD under aging conditions, which is the focus of this study. The results obtained can be useful in monitoring the condition of insulation systems. The factors (pressure, effective work function, and charge decay time constant) that influence PD behaviour under different test conditions were permutated in a PD model. The simulation results agree with measurements obtained for the same applied voltage. The model can generate PD pulse distribution shapes similar to the measured PD of single and double cavities. Both turtle-like and rabbit-ear-like phase-resolved PD (PRPD) patterns were observed during the aging process of the samples in both double and single cavities. This study concludes that the identification of the PD pattern achieved in this work for closely coupled cavities is a step towards characterizing multiple defects phenomena through PD evolution patterns.
Analysis of Energy Efficiency Opportunities for a Public Transportation Maintenance Facility—A Case Study
Authors: Higgins Jordan , Ramnarayan Aditya , Family Roxana , Ohadi Michael
Published: April 2024

A comprehensive energy audit of a light rail maintenance facility was performed to assess its energy performance and identify potential scope for improvements. The facility’s energy use intensity (EUI) for 2022 was 404 kWh/m2—more than double the benchmark EUI for maintenance facilities (151 kWh/m2) recommended by EnergyStar. Furthermore, the load factor was 0.22—significantly lower than the recommended minimum of 0.75 for an efficient building. The energy audit encompassed an in-depth evaluation of the facility’s structural and operational characteristics, comprising HVAC systems, lighting, the building envelope, and energy-intensive machinery. An energy model of the facility was developed to emulate the facility’s energy performance in 2022. Following the energy model’s validation, an analysis was conducted to identify opportunities for improving energy efficiency. Post-implementation of energy efficiency measures for the facility, the projected annual reductions are 1086 MWh of electricity, 5034 GJ of natural gas, utility savings of USD 162,402, and net GHG emissions reductions of 584 metric tons of CO2e. A subsequent 30% reduction in EUI to 283.6 kWh/m2 could be achieved with an 86% improvement in load factor, that is, increasing it from 0.22 to 0.41. This study emphasizes the need for energy audits and modeling for maintenance facilities to reduce Scope 1 and 2 emissions.
PV-Optimized Heat Pump Control in Multi-Family Buildings Using a Reinforcement Learning Approach
Authors: Bachseitz Michael , Sheryar Muhammad , Schmitt David , Summ Thorsten , Trinkl Christoph , Zörner Wilfried
Published: April 2024

For the energy transition in the residential sector, heat pumps are a core technology for decarbonizing thermal energy production for space heating and domestic hot water. Electricity generation from on-site photovoltaic (PV) systems can also contribute to a carbon-neutral building stock. However, both will increase the stress on the electricity grid. This can be reduced by using appropriate control strategies to match electricity consumption and production. In recent years, artificial intelligence-based approaches such as reinforcement learning (RL) have become increasingly popular for energy-system management. However, the literature shows a lack of investigation of RL-based controllers for multi-family building energy systems, including an air source heat pump, thermal storage, and a PV system, although this is a common system configuration. Therefore, in this study, a model of such an energy system and RL-based controllers were developed and simulated with physical models and compared with conventional rule-based approaches. Four RL algorithms were investigated for two objectives, and finally, the soft actor–critic algorithm was selected for the annual simulations. The first objective, to maintain only the required temperatures in the thermal storage, could be achieved by the developed RL agent. However, the second objective, to additionally improve the PV self-consumption, was better achieved by the rule-based controller. Therefore, further research on the reward function, hyperparameters, and advanced methods, including long short-term memory layers, as well as a training for longer time periods than six days are suggested.
Reconceptualizing Reliability Indices as Metrics to Quantify Power Distribution System Resilience
Authors: Abantao Gerald A. , Ibañez Jessa A. , Bundoc Paul Eugene Delfin C. , Blas Lean Lorenzo F. , Penisa Xaviery N. , Esparcia Eugene A. , Castro Michael T. , Buendia Roger Victor E. , Pilario Karl Ezra S. , Tio Adonis Emmanuel D. , Cruz Ivan Benedict Nilo C. , Ocon Joey D. , Odulio Carl Michael F.
Published: April 2024

In regions heavily affected by recurrent typhoons, the need for more resilient electricity infrastructure is pressing. This emphasizes the importance of integrating resilience assessment, including incorporating resilience metrics, into the planning process of power distribution systems against any disruptive events. Although standardized metrics exist for assessing distribution system reliability, the absence of formalized resilience metrics hampers informed investments in critical infrastructure such as microgrid development. In this work, a set of resilience metrics is proposed by reconceptualizing reliability metrics. The metrics were formulated to account for both the type of extreme event and its specific impact on loads with varying levels of criticality. The effectiveness of the proposed metrics is demonstrated through a Philippine microgrid case study. A Monte Carlo framework incorporating an extreme event model, component fragility model, and system response model was used to quantify the resilience improvement before and after stand-alone microgrid operation of the power distribution system. Results show that the proposed metrics can effectively evaluate resilience enhancement and highlight the value of a holistic approach of considering critical loads and types of extreme events to strengthen societal and community resilience, making a compelling case for strategic investments in infrastructure upgrades such as microgrids.
Influence of Electric Motor Manufacturing Tolerances on End-of-Line Testing: A Review
Authors: Popsi Nusrat Rezwana Shahreen , Anik Animesh , Verma Rajeev , Viana Caniggia , Iyer K. Lakshmi Varaha , Kar Narayan C.
Published: April 2024

Electric vehicles (EVs) are propelled by electric traction drive systems (ETDSs), which consist of various components including an electric motor, power electronic converter, and gear box. During manufacturing, end-of-line testing is the ultimate step for ensuring the quality and performance of electric motors in electric vehicle (EV) traction drive systems (ETDSs). The outcome of end-of-line testing of electric motors is significantly influenced by the tolerances of their structural parameters, such as stator inner and outer diameters, magnet dimensions, air gaps, and other geometric parameters. The existing literature provides insights into parametric sensitivity, offering guidance for enhancing the reliability of end-of-line testing. In this manuscript, the importance of end-of-line testing and the role of manufacturing tolerances of e-motor structural parameters in the manufacturing process of ETDSs are discussed. The impact of tolerances of e-motor structural parameters on the test results, such as torque, efficiency, back EMF, and e-NV (noise and vibration), is investigated. Finally, key challenges and research gaps in this area are identified, and recommendations for future research to mitigate the drawbacks of end-of-line testing are provided.
Analysis of Energy Flow in Hybrid and Electric-Drive Vehicles
Authors: Parczewski Krzysztof , Wnęk Henryk
Published: April 2024

Nowadays, the automotive market offers cars equipped with various types of drive systems, including a classic system in which the internal combustion engine burns fuel to power the vehicle, a hybrid system in which the internal combustion engine is supported by an electric machine (MHEV), and an electrical system in which energy to power the vehicle is drawn from the battery (BEV). Hybrid and electric vehicles have the ability to recover energy during deceleration and braking. Choosing a car with the right drive system is not easy for the average vehicle user. The article presents a comparison of vehicles with various drive systems. For this purpose, a diagnostic scanner was created that allows reading data from the car’s CAN network. The comparison of the obtained data allows for the presentation of more information about vehicles equipped with different drive systems. In particular, parameters such as energy consumption, the possibility of its recovery during braking and the instantaneous power of electric machines were subjected to comparative analysis. Vehicle tests were carried out on a chassis dynamometer in accordance with the WLTP test.
Application of Machine Learning for Productivity Prediction in Tight Gas Reservoirs
Authors: Fang Maojun , Shi Hengyu , Li Hao , Liu Tongjing
Published: April 2024

Accurate well productivity prediction plays a significant role in formulating reservoir development plans. However, traditional well productivity prediction methods lack accuracy in tight gas reservoirs; therefore, this paper quantitatively evaluates the correlations between absolute open flow and the critical parameters for Linxing tight gas reservoirs through statistical analysis. Dominant control factors are obtained by considering reservoir engineering theories, and a novel machine learning-based well productivity prediction method is proposed for tight gas reservoirs. The adaptability of the productivity prediction model is assessed through machine learning and field data analysis. Combined with the typical decline curve analysis, the estimated ultimate recovery (EUR) of a single well in the tight gas reservoir is forecasted in an appropriate range. The results of the study include 10 parameters (such as gas saturation) identified as the dominant controlling factors for well productivity and geological factors that impact the productivity in this area compared to fracturing parameters. According to the prediction results of the three models, the R2 of Support Vector Regression (SVR), Back Propagation (BP), and Random Forest (RF) models are 0.72, 0.87, and 0.91, respectively. The results indicate that RF has a more accurate prediction. In addition, the RF model is more suitable for medium and high-production wells based on the actual field data. Based on this model, it is verified that the productivity of low-producing wells is affected by water production. This study confirms the model’s reliability and application value by predicting recoverable reserves for a single well.
Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production
Authors: Eingartner Anna , Naumann Steffi , Schmitz Philipp , Worthmann Karl
Published: April 2024

In this paper, the application of the method of affinely adjustable robust optimization to a planning model of an energy system under uncertain parameters is presented, and the total scheduling costs in comparison with the deterministic model are evaluated. First, the basics of optimization under uncertain data are recapped, and it is described how these methods can be used in different applications for energy systems. This is followed by the methodology of adjustable robust optimization by defining the affinely adjustable robust counterpart. Finally, a numerical case study is conducted to compare the adjustable robust method with a rolling deterministic scheduling method. Both are implemented on a model of an energy system and compared with each other by simulation using real-world data. By calculating the total operating costs for both methods, it can be concluded that the adjustable robust optimization provides a significantly more cost-effective solution to the scheduling problem.
Research on Market Evaluation Model of Reserve Auxiliary Service Based on Two-Stage Optimization of New Power System
Authors: Qu Boyang , Fu Lisi
Published: April 2024

Large-scale fluctuating and intermittent new energy power generation in a new power system is gradually connected to the grid. In view of the impact of the uncertainty of wind power on the spinning reserve capacity of thermal power units in the new power system’s day-ahead dispatching and reserve auxiliary service market, the original dispatching mode and intensity can no longer meet the system demand. To address this problem, the establishment of a wind power grid-connected new power system’s standby auxiliary service market reward and punishment assessment mechanism is undertaken to fundamentally reduce the demand for auxiliary services of the new power system pressure. In the first part of this paper, a two-stage optimal scheduling strategy is proposed for the first day of the year that takes into account the operational risk and standby economics. First, a data-driven method is used to generate the forecast value of the wind power interval before the day, and a unit start–stop optimization model (the first-stage optimization model) is established by taking into account the CvaR (conditional value at risk) theory to optimize the risk loss of wind abandonment and loss of load and the fuel cost of each unit, and an optimization algorithm is used to carry out the three scenarios and the corresponding four scenarios to optimize the configuration of the start–stop state and power output of each unit. The optimization algorithm is used to optimize the starting and stopping status and output of each unit for three circumstances and four corresponding scenarios. Then, in the second stage, a standby auxiliary service market incentive and penalty assessment model is established to effectively coordinate the sharing of rotating standby capacity and cost among thermal power units through the incentive and penalty mechanism so as to make a reasonable and efficient allocation of wind power output, curtailable load, and synchronized standby capacity. The new power system with improved IEEE30 nodes is simulated and verified, and it is found that the two-stage optimization model obtains a scheduling strategy that takes into account the system operating cost, standby economy, and reliability, and at the same time, through the standby auxiliary service market incentive and penalty assessment mechanism, the extra cost caused by standby cost mismatch can be avoided. This evaluation model provides a reference for the safe, efficient, flexible, and nimble operation of the new power system, improves the economic efficiency and improves the auxiliary service market mechanism.
Blockchain-Enabled Provenance Tracking for Sustainable Material Reuse in Construction Supply Chains
Authors: Wilson Stanly , Adu-Duodu Kwabena , Li Yinhao , Sham Ringo , Almubarak Mohammed , Wang Yingli , Solaiman Ellis , Perera Charith , Ranjan Rajiv , Rana Omer
Published: April 2024

The growing complexity of construction supply chains and the significant impact of the construction industry on the environment demand an understanding of how to reuse and repurpose materials. In response to this critical challenge, research gaps that are significant in promoting material circularity are described. Despite its potential, the use of blockchain technology in construction faces challenges in verifiability, scalability, privacy, and interoperability. We propose a novel multilayer blockchain framework to enhance provenance tracking and data retrieval to enable a reliable audit trail. The framework utilises a privacy-centric solution that combines decentralised and centralised storage, security, and privacy. Furthermore, the framework implements access control to strengthen security and privacy, fostering transparency and information sharing among the stakeholders. These contributions collectively lead to trusted material circularity in a built environment. The implementation framework aims to create a prototype for blockchain applications in construction supply chains.
Prediction of DC Breakdown Voltage of Rod–Plate Gaps under Full-Flame Bridging Conditions
Authors: Pu Ziheng , Li Yuan , Li Peng , Ye Kuan , Zhou Kai , Zhang Ruizhe
Published: April 2024

In order to evaluate the risk of transmission line tripping due to wildfires, it is necessary to predict the breakdown voltage of the insulation gap under the flame. Firstly, this paper studies the breakdown prediction of rod–plane gaps under the full-flame bridging of wooden cribs; it then obtains the breakdown voltage and the leakage current values of full-flame bridging considering different sizes of wooden cribs and different gap distances. Then, a multi-physical field simulation is carried out to obtain the flame gap characteristic parameters, such as spatial temperature. The feature quantity is normalized and reduced in dimension, and a prediction model for gap breakdown voltage under flame conditions based on a support vector machine (SVM) is established. Finally, the DC withstand voltage values and corresponding characteristic quantities under different flame gap conditions are used as sample sets to test the prediction model. The results show that the prediction error for small gap breakdown voltage is less than 2.6%. The samples were tested under different flame intensities for training and prediction, and the error was less than 3.3%. The small gap data for 30~60 cm is used to predict the breakdown voltage of the long gap for 100~140 cm, and the error is less than 3.2%. Compared with the fitting correction formula method proposed in existing research, the error is reduced by 11.5% and 4.4%, respectively, which verifies the effectiveness of the SVM prediction model.
Anticipating Future Risks of Climate-Driven Wildfires in Boreal Forests
Authors: Corning Shelby , Krasovskiy Andrey , Kiparisov Pavel , San Pedro Johanna , Viana Camila Maciel , Kraxner Florian
Published: April 2024

Extreme forest fires have historically been a significant concern in Canada, the Russian Federation, the USA, and now pose an increasing threat in boreal Europe. This paper deals with application of the wildFire cLimate impacts and Adaptation Model (FLAM) in boreal forests. FLAM operates on a daily time step and utilizes mechanistic algorithms to quantify the impact of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. In our paper, we calibrate the model using historical remote sensing data and explore future projections of burned areas under different climate change scenarios. The study consists of the following steps: (i) analysis of the historical burned areas over 2001–2020; (ii) analysis of temperature and precipitation changes in the future projections as compared to the historical period; (iii) analysis of the future burned areas projected by FLAM and driven by climate change scenarios until the year 2100; (iv) simulation of adaptation options under the worst-case scenario. The modeling results show an increase in burned areas under all Representative Concentration Pathway (RCP) scenarios. Maintaining current temperatures (RCP 2.6) will still result in an increase in burned area (total and forest), but in the worst-case scenario (RCP 8.5), projected burned forest area will more than triple by 2100. Based on FLAM calibration, we identify hotspots for wildland fires in the boreal forest and suggest adaptation options such as increasing suppression efficiency at the hotspots. We model two scenarios of improved reaction times—stopping a fire within 4 days and within 24 h—which could reduce average burned forest areas by 48.6% and 79.2%, respectively, compared to projected burned areas without adaptation from 2021–2099.