bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nudging consumers about the issue of microplastics: an experimental auction study on valuation for sustainable food packaging
Authors: Nagy László Bendegúz , Nayga Rodolfo M. , Temesi Ágoston 
Published: August 2024

AbstractPlastic, integral to food packaging since the 1950s, has become a global environmental concern due to its contribution to microplastic pollution. Microplastics harm ecosystems, impacting wildlife and human health. Amid increasing focus on sustainability, global initiatives target sustainable production and consumption, but consumers struggle to verify product claims, leading to potential greenwashing, particularly in the food industry. We conducted an experiment focusing on pasta products with varied packaging and labeling attributes. Findings suggest that consumers are willing to pay more for products with both biodegradable packaging and Product Environmental Footprint (PEF) labels, indicating heightened trust and perceived sustainability. Information about microplastics’ adverse environmental effects influenced consumer valuation, particularly among females, higher-income individuals, and those with stronger environmental concerns.
Noninstrumental Dysphagia Assessment and Screening: A Proposed Checklist
Authors: Brates Danielle , Peña-Chávez Rodolfo ,   on behalf of the FRONTIERS Collaborative
Published: August 2024

Purpose:In the field of dysphagia research, there is a need to establish a framework for the critical appraisal of methodological reporting. A working group was formed to develop a tool to aid in such critical appraisal across various domains of dysphagia research (called the FRONTIERS [Framework for RigOr aNd Transparency In REseaRch on Swallowing] Framework). The goal of the current paper is to present and describe one domain of this tool: noninstrumental assessment and screening tools.Method:Methods describing the development of the FRONTIERS Framework and the associated tool are detailed in the prologue of this series.Results:A set of questions specific to the critical appraisal of transparency and rigor of research involving noninstrumental screening and assessment tools was developed. This included 13 “yes/no” questions, each of which is presented with a rationale for its inclusion and an example of its implementation.Conclusions:The use of this framework will serve researchers and those appraising the quality of research that uses noninstrumental dysphagia assessment and screening tools. More broadly, the FRONTIERS Framework will facilitate improved rigor and transparency across dysphagia research. Special considerations and future goals are discussed.
Compression of Battery X-Ray Tomography Data with Machine Learning
Authors: Yan 颜 Zipei 子沛 , Wang 王 Qiyu 其钰 , Yu 禹 Xiqian 习谦 , Li 李 Jizhou 济舟 , K.-P. Ng 吴 Michael 国宝 
Published: August 2024

Abstract
With the increasing demand for high-resolution x-ray tomography in battery characterization, the challenges of storing, transmitting, and analyzing substantial imaging data necessitate more efficient solutions. Traditional data compression methods struggle to balance reduction ratio and image quality, often failing to preserve critical details for accurate analysis. This study proposes a machine learning-assisted compression method tailored for battery x-ray imaging data. Leveraging physics-informed representation learning, our approach significantly reduces file sizes without sacrificing meaningful information. We validate the method on typical battery materials and different x-ray imaging techniques, demonstrating its effectiveness in preserving structural and chemical details. Experimental results show an up-to-95 compression ratio while maintaining high fidelity in the projection and reconstructed images. The proposed framework provides a promising solution for managing large-scale battery x-ray imaging datasets, facilitating significant advancements in battery research and development.
Dual-objective optimal scheduling of grid-connected distributed photovoltaic systems considering life loss cost of energy storage
Authors: Li Yiming , Jin Xiaobing , Ma Tengfei , Sun Jian , Ke Deping , Xu Jian 
Published: August 2024

Abstract
Large-scale distributed photovoltaic (PV) connected to the grid brings great risk to the safety operation. Measures of cutting PV to reduce active power input are widely taken to stabilize the voltage during periods of large PV output, resulting in energy waste and economic losses for PV operators. To reduce PV cutting, energy storage (ES) units are considered to be applied to the distribution system with the cost of the ES operating model and the cost of the life loss model. Then, based on the Distflow model of power transmission constraint, a two-objective optimization model, including the minimum of comprehensive operating cost and the active power reduction by cutting PV is established. Lastly, to select a compromise solution, the linear membership function is used to search for the maximum satisfaction scheme from the Pareto solution set. The simulation of the IEEE 33-nodes example verifies the effectiveness of the proposed two-objective optimization model. Pareto solution sets with different weight coefficients provide a reference for decision makers and the maximum satisfaction scheme based on the linear membership function is a compromise solution of double objective function. The balance of the two strategies is realized while the voltage is not exceeded.
Short-term wind speed prediction based on time series trends and periodic characteristics
Authors: Li Jiawei , Han Xiaoming 
Published: August 2024

Abstract
In order to improve the accuracy of short-term wind speed prediction and ensure the stable operation of wind farms, a hybrid model combining contrast learning, convolutional neural network, and Transformer network is proposed to decouple and predict the period and trend characteristics of wind speed series to improve the accuracy of wind speed prediction. Based on the mutual information theory of contrast learning, the model uses the contrast loss function to train the adaptive convolutional module to extract the general information between the data of different periods and obtain the periodic features in the data. The trend characteristics of data are extracted through the Transformer network, and the two types of characteristics are combined for prediction. The model is verified by using an open data set, and the results show that the prediction error is effectively reduced by using this model to predict short-term wind speed.
Experimental research on heating performance of solar energy coupled with water source heat pump system
Authors: Gao Xuefeng , Zhang Xiaohan , Li Zhiyong , Han Bo 
Published: August 2024

Abstract
This paper establishes an experimental platform for a solar energy coupled water source heat pump system, tests the performance curve of the heat pump, and analyzes the system’s operational performance. The experimental study shows that under the conditions of compressor power (0.7 kW-2.2 kW) and evaporator inlet water temperature (14°C-21°C), for every 1°C increase in the heat pump inlet temperature, the Coefficient of Performance (COP) improves by an average of 0.07, and the heating capacity increases by an average of 0.15 kW; for every 0.4 kW increase in compressor power, the COP decreases by about 0.7, and the heating capacity increases by an average of 0.47 kW. The inlet and outlet temperatures of each component of the water source heat pump are related to the evaporator inlet water temperature. As the evaporator inlet water temperature rises, the inlet and outlet temperatures of all components increase, and vice versa. A standalone water source heat pump heating system, with a compressor power of 1 kW, has a COP of only 3.82, gradually decreasing as the power increases. The minimum COP of the solar-coupled water source heat pump system is 3.86, and it gradually increases with the intensity of solar radiation. This composite system not only improves the overall energy efficiency of the system but also avoids the high-power consumption drawbacks of traditional heat pumps. It can serve as one of the optional heating solutions for rural small-area residences.
Decarbonization management of solvent-storage carbon capture power plant
Authors: Wang Lingxian , Dai Bo , Zhao Xinyu , Mo Fanghua , Zhao Yueshan , Li Xiaodong 
Published: August 2024

Abstract
The imperative to mitigate climate change and protect the environment has placed traditional coal-fired power plants (CFPPs), major sources of carbon emissions, under severe scrutiny. Amid this context, the rapid growth of renewable energy sources, especially wind power, presents both an opportunity and a challenge in achieving a sustainable power system transition without undermining grid stability. Thus, this critical issue is settled by proposing the use of carbon capture and storage (CCS) technology to upgrade conventional CFPPs. The research highlights the urgent need to reduce carbon emissions and the pivotal role of CCS in this endeavor. Meanwhile, the implementation of solvent-storage carbon capture power plants (CCPP) and the integration with wind power are being conducted. Through rigorous simulation experiments, the study validates the effectiveness of CCPP in significantly lowering carbon emissions while enhancing the capability of power plants to incorporate wind energy. This work provides essential insights and a practical framework for the future application and development of CCS technology in optimizing power systems.
Research on heat distribution and heat efficiency improvement in the middle and late stages of SAGD
Authors: He Wanjun , Zhang Junhao , Luo Chihui , Guo Hao , Wang Zhuangzhuang , Li Boliang , Li Binfei 
Published: August 2024

Abstract
Challenges faced in heavy oil development include low efficiency in heat utilization and high energy consumption. Numerical simulation methods were employed to study the thermal distribution and clarify the thermal evolution law during the middle and later stages of SAGD, aiming to enhance heat utilization efficiency. Furthermore, the impacts of reservoir conditions and nitrogen injection on thermal distribution in the intermediate-to-late stages of SAGD were investigated. The research results indicate that in the intermediate-to-late stages of SAGD development, the proportion of heat loss gradually increases, while the proportion of heat absorbed by the reservoir decreases. By the later stage of SAGD, the thermal distribution is basically stable, with the reservoir absorbing only 35%-40% of the heat, the cap rock heat loss accounting for about 30%, and the heat carried by the output fluid comprising approximately 30%. Besides, an increase in permeability and reservoir thickness in reservoir conditions leads to an increase in the proportion of heat absorbed by the reservoir. Additionally, injecting nitrogen during the middle stage of SAGD can reduce heat loss in the later stage and increase the proportion of heat absorbed by the reservoir.
Design and economic analysis of photothermal coupled compressed air energy storage system
Authors: Hu Zhenhai , Tian Yingnan , Zhang Xuelin , Zhou Zuxu , Wang Zijie , Chi Ran , Dong Zhen , Lai Yanhua 
Published: August 2024

Abstract
A 10 MW photothermal coupled compressed air energy storage system is established. The cycle efficiency of the system is increased greatly by adding a solar heat collector. The cycle efficiency is 77.57%, which is increased by 12.88%. In addition, the sensitivity of the system parameters to the change of environmental parameters is low through sensitivity analysis. Finally, the system’s economic analysis is carried out by using the whole life cycle evaluation method, and the income under the power spot market model is calculated. According to the calculation results, the capacity cost of the system is 5, 897.23 yuan /kW, the kWh cost is 0.53 yuan /kWh, the net present value is 2, 306.09 thousand yuan, the static investment payback period is 9.1 years, the dynamic investment payback period is 14.5 years, the rate of return on investment is 12.36%, and the internal rate of return is 11.95%.
Impedance characterization of battery aging paths in energy storage operation mode
Authors: Wang Kaifeng , Chen Jizhong , Yan Tao , Yang Shuili , Zhao Jun , Chang Xiao , Zhang Min 
Published: August 2024

Abstract
Batteries in different energy storage operation modes show different aging paths, reflecting the differences in the electrochemical impedance characterization of batteries. This paper uses the rated capacity of 60Aꞏh lithium-ion battery for energy storage as an experimental object, analyzes the aging paths of battery in different energy storage operation conditions, respectively, and analyzes the impedance characteristics of the battery before and after the cycle. The results show that the battery ohmic impedance increases to varying degrees in various conditions, indicating that some degree of aging of the battery after cycling can be slowed down by applying a combined path to the battery. The results show that the ohmic impedance of the battery in each condition increases to different degrees, indicating that the battery has a certain degree of aging after cycling, and the aging speed of the battery can be slowed down. Analyzing impedance characteristics of the different aging paths of the battery under different operating conditions can provide support for the optimal operation strategy from the perspective of the internal mechanism of the battery.
Battery state of health estimation based on voltage relaxation and an improved online sequential extreme learning machine
Authors: Yang Zhigang , Tian Yi , Dong Xinyu , Wu Lifeng 
Published: August 2024

Abstract
Accurate online estimation of battery state of health (SOH) is crucial for the safety and stability of electronic devices. In reality, the complete operating information is difficult to obtain, making it hard to extract suitable features. Moreover, the online sequential extreme learning machine (OS-ELM) cannot dynamically adjust the model based on the temporal relations of samples, resulting in poor learning ability for temporal features. To address these problems, this paper proposes an OS-ELM with a forgetting learning mechanism (FLOS-ELM). First, features are extracted from the relaxation curves independent of the charging/discharging process. Then, a forgetting learning mechanism is introduced in the OS-ELM to update the model adaptively and improve the ability to capture temporal features and learn online. Finally, a mapping model of degradation features and SOH is constructed via the proposed FLOS-ELM to achieve accurate SOH estimation. Experimental results on the publicly available datasets show that the RMSE is 0.9%, verifying the validity and accuracy of the model.
State of lithium-ion battery health evaluation based on reconstructed health features and DBO-CNN-GRU
Authors: Li Yuqi , Zhang Yanrong 
Published: August 2024

Abstract
In the feature extraction process of battery health state estimation, redundant features will lead to the increase of model complexity and exponential growth of training parameters, while a single feature will lose local features and lead to the decrease of battery health state estimation accuracy. Therefore, it is very significant to choose appropriate features to represent the health state. In this paper, a CNN-GRU model combining reconstructed health features and a DBO optimization algorithm is proposed for battery health state estimation. Firstly, the health features are extracted from the voltage and current data of the lithium-ion battery during charging and discharging. Secondly, the optimal variational mode decomposition is used to decompose the health features, and the DBO is introduced to optimize the hyperparametric of the CNN-GRU model. Finally, the NASA battery dataset is selected to design comparative experiments of different models. The results show that SOH can be predicted more accurately based on the reconstructed health features and DBO-CNN-GRU model, which verifies the feasibility of the proposed method.
Design of on-board energy storage system using SiC MOSFET
Authors: Pu Weijie 
Published: August 2024

Abstract
The use of energy storage devices instead of braking resistors has been proposed to better achieve train braking energy recovery and reduce DC bus voltage fluctuation during traction braking. In order to minimize volume and weight and achieve on-board installation, this paper adopts a design scheme that integrates a single-phase bidirectional Buck/Boost circuit and a supercapacitor, using SiC MOSFET to further improve performance and power density. The control method adopts a double closed-loop control of the outer loop of the network voltage and the inner loop of the inductance current to achieve efficient constant current charging and discharging of the supercapacitors. Finally, the functionality of the system was verified by simulation and experiment.
Adaptive droop control strategy considering hybrid energy storage power allocation
Authors: Zheng Hua , Xie Li , Zhai Hao , Zhao Liangliang , Li Changling , Yi Geng , Ye Xiaoyu , Peng Pei 
Published: August 2024

Abstract
To maximize the advantages of hybrid energy storage in reducing frequency deviations, this study proposes a power allocation strategy for hybrid energy storage based on dynamic adjustable droop control. The strategy is demonstrated by utilizing an energy storage station composed of supercapacitors and lithium-ion batteries. Based on the state of charge (SOC) of the energy storage, the droop coefficient is dynamically adjusted using a logistic curve. Subsequently, the power demand of the hybrid power station is calculated. Additionally, an appropriate frequency regulation deadband is set to prevent system frequency from exceeding limits. Within the deadband, SOC self-recovery is considered to prevent frequency excursions. Outside the frequency regulation deadband, power allocation is determined by the state of charge of the hybrid energy storage system. Finally, simulation analyses using MATLAB/Simulink are conducted for step and continuous disturbances. The findings indicate that the devised control approach successfully enhances the performance of frequency control and refines frequency characteristics.
An Optimized charging method for lithium-ion batteries based on strain
Authors: Ren Wenju , Yi Yuan , Xie Xinyu , Qi Chenyang , Yang Yuhang , Zheng Taixiong 
Published: August 2024

Abstract
Lithium-ion batteries (LIBs) are widely used in multiple fields due to their high efficiency and environmental protection characteristics. However, during rapid charging, the internal strain of the battery may lead to performance degradation and shortened lifespan. In the study, the LIBs are charged at different charge rates, and their strain value is measured at the same time. It is found that the strain is closely related to the charge rate. Based on this, a 2C-1C CCCV charging strategy is proposed, which can ensure the charging speed while effectively suppressing the strain increase, reducing the stress of the battery, and prolonging the battery life. This discovery provides a new theoretical basis for rapid charging and helps promote the development of new energy technologies.
An EGLOF method for tidal stream turbine blade impact fault detection
Authors: Wu Zhen , Wang Tianzhen 
Published: August 2024

Abstract
The fault detection of blade impact fault using the stator current signals can improve the stability of the Tidal Stream Turbine (TST) operation. However, the current signals are disturbed by waves and turbulence, which leads to a high false alarm rate for fault detection. This paper proposes an envelope geometric local outlier factor method (EGLOF) to solve this problem, which is divided into three steps: Firstly, the signal envelope samples are extracted by using the Teager-Kaiser energy operator (TKEO). Then, the geometric features of the envelope samples are calculated by the sliding window technique. Finally, the local outlier factor (LOF) model is introduced to calculate EGLOF values to detect the impact fault. The experimental results show that the proposed method has a lower false alarm rate compared with the traditional methods.
Design and application of multi-level grid agc collaborative control scheme adapting to distributed new energy control
Authors: Jiang Xinfan , Hu Dijun , Zhang Tailei , Wu Jinbo , Liu Li , Wang Mingli 
Published: August 2024

Abstract
The proportion of installed capacity and output of new energy generation in China has been increasing year by year. Due to the volatility, randomness, and unpredictability of new energy, the load fluctuation in the power grid is more frequent, and the demand for AGC regulation is intensifying. The proportion of conventional power AGC resources capacity is decreasing, and new energy will increasingly become a new type of AGC resource. However, the low voltage and distributed characteristics of new energy pose a challenge to the existing power grid AGC system targeting high voltage and large capacity AGC resources. This paper designs a multi-level grid AGC collaborative architecture suitable for new energy access control, proposes a collaborative strategy between conventional power AGC and new energy AGC, and proposes a provincial and prefectural AGC collaborative control scheme including model design, information exchange, and security verification. The scheme presented in the paper has been successfully applied in provincial and prefectural power grid dispatch automation systems.
Enhancing multi-resonant MCR WPT systems: A novel multi-frequency transmission approach with unipolar PWM and QEP-based analysis
Authors: Guo Zhirou , Chen Yiwei , Liu Jiaqing , Wang Yifan , Chen Kaijie , Wang Weisen , Ye Huaiwen 
Published: August 2024

Abstract
This paper introduces a multi-frequency resonant wireless energy transmission system employing a single-transmit multi-receive magnetic coupling structure, effectively overcoming the limitations of traditional single-frequency systems. Implementing a multi-frequency pulse width modulation strategy optimizes the energy transmission across multiple magnetically coupled resonant frequencies, significantly enhancing system adaptability and device compatibility. Additionally, the paper utilizes the Quadratic Eigenvalue Problem (QEP) as an analytical tool, strengthening the theoretical foundation for system design and optimization. Experimental validation confirms the technical feasibility of the proposed method, paving a new path for the development of wireless energy transmission technology.
Short-term photovoltaic power combination prediction based on DBO-VMD and CPO-LSTM
Authors: Li Dahua , Hao Hao , Li Dong , Gao Qiang , Tian He 
Published: August 2024

Abstract
Regarding how to promote the accuracy of photovoltaic power generation prediction, a photovoltaic power prediction model based on Dung Beetle Optimizer (DBO), Variational Mode Decomposition (VMD), Crested Porcupine Optimizer (CPO), and Long Short-Term Memory Network (LSTM) is established. First, the DBO is used to optimize the VMD parameters. Then, the power data is decomposed by using VMD. Secondly, an LSTM prediction model is established for each decomposed component, and the hyperparameters of the LSTM model are optimized by using CPO. Last, each component model is trained and tested, and the final power prediction result is obtained by superimposing the prediction results of each component. The experiment shows that the model can achieve good prediction performance in different seasonal types and compares it with other models to verify the effectiveness of this model.