bluuebunny/crossref_metadata_2025 huggingface dataset:
Correlation between Academic Achievement Results and Students’ Perceptions in Instant Response System-Based Language Learning Classes at the University
Authors: Koh Tae-Jin , Kim Yong-Jeong
Published: May 2024

In the contemporary university environment, there is a growing trend towards the use of innovative pedagogical methods aimed at increasing student engagement and learner-centeredness. Despite this shift, traditional lecture formats continue to be used, particularly in large classes or language courses. This is largely due to the perceived efficiency and convenience of the traditional lecture format for both teachers and students. However, the limited interaction and communication inherent in traditional lectures can hinder student satisfaction and participation. To address this, the integration of an instant response system (IRS) into the classroom environment offers a promising solution. These systems, which leverage technology and anonymity, facilitate real-time feedback and active student participation without placing a significant burden on the instructor. This study examines the implementation of IRSs in Hindi language learning courses at Hankuk University of Foreign Studies, Seoul, focusing on their impact on student satisfaction, engagement, and self-efficacy at different levels of academic achievement. The findings reveal nuanced differences in perceptions and outcomes between high, average, and low achievers, highlighting the potential of IRSs to foster engagement and communication in diverse learner cohorts. Contrary to expectations, satisfaction levels did not consistently correlate with academic performance. In fact, middle achievers showed significant benefits. Qualitative findings further elucidate students’ experiences and highlight the importance of tailored approaches to maximize the effectiveness of IRSs. Overall, this research highlights the adaptability and effectiveness of IRSs in promoting active learning environments, and offers valuable implications for instructional design and pedagogical practice.
The Promise of AI Object-Recognition in Learning Mathematics: An Explorative Study of 6-Year-Old Children’s Interactions with Cuisenaire Rods and the Blockplay.ai App
Authors: Rumbelow Michael , Coles Alf
Published: May 2024

We developed and trained a prototype AI-based object-recognition app, blockplay.ai, to recognise Cuisenaire rods placed on a tabletop, and speak the rods’ lengths. We challenged 6-year-olds in a primary school in England to play a ‘game’: can you make the app say the two times table? Drawing methodologically on theories of embodiment, we analyse two videoclips, each of a child interacting with rods, the app and the task set by the researchers, as a dynamic, complex child-rods-app-task body-artefact system. Theoretically we draw on Davydovian concepts of learning as a concrete-to-abstract-to-new-concrete cycle, using abstract artefacts such as mathematical language to coordinate new perceptually-guided actions on concrete objects. In one videoclip the child’s pattern of actions are consistent with a change, within a few minutes, from perceiving and acting on rods as counters, to perceiving and acting on rods as lengths; in the other videoclip, this does not happen. We analyse the changes in patterns of interactions as shifts to new stable attractors in a dynamic child-rods-app-task body-artefact system, driven by tensions generated by unexpected concrete-to-abstract relationships. We end by looking forward to the range of possible uses of object-recognition technology in the learning of mathematics, for example, provoking algebraic awareness.
A Study on the Influence of the Affective Domain on the Attitudes of Middle School Students toward Mathematics from a Gender Perspective
Authors: Gutierrez-Aguilar Mariana , Tejeda Santa
Published: May 2024

Women’s representation in Science, Technology, Engineering and Mathematics (STEM) is a powerful resource to motivate girls to study STEM degrees and fulfill the growing demands for professionals in these fields. From their youth, positive attitudes toward mathematics are characteristic of girls and boys who study STEM degrees. This research aims to identify the association between gender stereotypes and attitudes toward mathematics. The 6° grade generation from a middle school in Monterrey, Mexico, first answered tests on attitudes toward mathematics and gender stereotypes in mathematics. Afterwards, a sample group underwent a 4-week intervention during which students saw videos of STEM professionals and answered a questionnaire on student’s self-perception in STEM careers. Finally, the tests were reapplied with a questionnaire on the use and ease of mathematics. Quasi-statistical and discourse analysis were used to obtain the results. These are presented through a model that highlights the mediating role that the mathematical self-concept and the interest/enjoyment for mathematics have in the association between gender stereotypes and attitudes toward mathematics. The role of gender on female’s lower mathematical self-concept is also exposed, suggesting subsequent lines of research on improving self-concept as an approach to equitably increase students’ interests in STEM degrees from their youth.
Few-Shot Image Classification Based on Swin Transformer + CSAM + EMD
Authors: Sun Huadong , Zhang Pengyi , Zhang Xu , Han Xiaowei
Published: May 2024

In few-shot image classification (FSIC), the feature extraction module of the traditional convolutional neural networks is often constrained by the local nature of the convolutional kernel. As a result, it becomes challenging to handle global information and long-distance dependencies effectively. In order to address this problem, an innovative FSIC method is proposed in this paper, which is the integration of Swin Transformer and CSAM and Earth Mover’s Distance (EMD) technology (STCE). We utilize the Swin Transformer network for image feature extraction, and perform CSAM attention mechanism feature weighting on the output feature map, while we adopt the EMD algorithm to generate the optimal matching flow between the structural units, minimizing the matching cost. This approach allows for a more precise representation of the classification distance between images. We have conducted numerous experiments to validate the effectiveness of our algorithm. On three commonly used few-shot datasets, namely mini-ImageNet, tiered-ImageNet, and FC100, the accuracy of one-shot and five-shot has reached the state of the art (SOTA) in the FSIC; the mini-ImageNet achieves an accuracy of 98.65 ± 0.1% for one-shot and 99.6 ± 0.2% for five-shot tasks, while tiered ImageNet has an accuracy of 91.6 ± 0.1% for one-shot tasks and 96.55 ± 0.27% for five-shot tasks. For FC100, the accuracy is 64.1 ± 0.3% for one-shot tasks and 79.8 ± 0.69% for five-shot tasks. On two commonly used few-shot datasets, namely CUB, CIFAR-FS, CUB achieves an accuracy of 83.1 ± 0.4% for one-shot and 92.88 ± 0.4% for five-shot tasks, while CIFAR-FS achieves an accuracy of 86.95 ± 0.2% for one-shot and 94 ± 0.4% for five-shot tasks.
Parameter Optimization of Josephson Parametric Amplifiers Using a Heuristic Search Algorithm for Axion Haloscope Search
Authors: Kim Younggeun , Jeong Junu , Youn Sungwoo , Bae Sungjae , van Loo Arjan F. , Nakamura Yasunobu , Uchaikin Sergey , Semertzidis Yannis K.
Published: May 2024

The cavity haloscope is among the most widely adopted experimental platforms designed to detect dark matter axions with its principle relying on the conversion of axions into microwave photons in the presence of a strong magnetic field. The Josephson parametric amplifier (JPA), known for its quantum-limited noise characteristics, has been incorporated into the detection system to capture the weakly interacting axion signals. However, the performance of the JPA can be influenced by its environment, leading to the potential unreliability of a predefined parameter set obtained in a specific laboratory setting. Furthermore, conducting a broadband search requires the consecutive characterization of the amplifier across different tuning frequencies. To ensure more reliable measurements, we utilize the Nelder–Mead technique as a numerical search method to dynamically determine the optimal operating conditions. This heuristic search algorithm explores the multidimensional parameter space of the JPA, optimizing critical characteristics such as gain and noise temperature to maximize signal-to-noise ratios for a given experimental setup. Our study presents a comprehensive analysis of the properties of a flux-driven JPA to demonstrate the effectiveness of the algorithm. This approach contributes to ongoing efforts in axion dark matter research by offering an efficient method to enhance axion detection sensitivity through the optimized utilization of JPAs.
Study of 1500 V AlGaN/GaN High-Electron-Mobility Transistors Grown on Engineered Substrates
Authors: Liu An-Chen , Chen Pei-Tien , Chuang Chia-Hao , Chen Yan-Chieh , Chen Yan-Lin , Chen Hsin-Chu , Chang Shu-Tong , Huang I-Yu , Kuo Hao-Chung
Published: May 2024

In this study, we demonstrate breakdown voltage at 1500 V of GaN on a QST power device. The high breakdown voltage and low current collapse performance can be attributed to the higher quality of GaN buffer layers grown on QST substrates. This is primarily due to the matched coefficient of thermal expansion (CTE) with GaN and the enhanced mechanical strength. Based on computer-aided design (TCAD) simulations, the strong electric-field-induced trap-assisted thermionic field emissions (TA-TFEs) in the GaN on QST could be eliminated in the GaN buffer. This demonstration showed the potential of GaN on QST, and promises well-controlled performance and reliability under high-power operation conditions.
Modeling Wind-Speed Statistics beyond the Weibull Distribution
Authors: Lencastre Pedro , Yazidi Anis , Lind Pedro G.
Published: May 2024

While it is well known that the Weibull distribution is a good model for wind-speed measurements and can be explained through simple statistical arguments, how such a model holds for shorter time periods is still an open question. In this paper, we present a systematic investigation of the accuracy of the Weibull distribution to wind-speed measurements, in comparison with other possible “cousin” distributions. In particular, we show that the Gaussian distribution enables one to predict wind-speed histograms with higher accuracy than the Weibull distribution. Two other good candidates are the Nakagami and the Rice distributions, which can be interpreted as particular cases of the Weibull distribution for particular choices of the shape and scale parameters. These findings hold not only when predicting next-point values of the wind speed but also when predicting the wind energy values. Finally, we discuss such findings in the context of wind power forecasting and monitoring for power-grid assessment.
The Effect of Environmental Smart Technology and Renewable Energy on Carbon Footprint: A Sustainability Perspective from the MENA Region
Authors: Alofaysan Hind
Published: May 2024

This paper looks at the changing impact of renewable energy and green innovation on the carbon footprint of eight MENA nations between 2000 and 2020. We investigate this by using the panel Q-ARDL model for the first time, and we find that, with various impacts across different quantiles, a rise in green innovation and renewable energy greatly boosts environmental sustainability in the short run. In the long run, this effect becomes increasingly more noticeable. According to our analysis, the chosen MENA countries quickly embraced energy storage, solar hydrogen, and other technology pathways to diversify their energy mix, which was a turning point in the fight against climate change. Although these factors have been separately examined in different studies, our research merges them into a single non-parametric model. This research is significant as it provides empirical evidence on the efficiency of green innovation and renewable energy policies, and it will guide policymakers and energy stakeholders in developing strategies to achieve sustainable development goals.
The Role of Amphiphilic Nanosilica Fluid in Reducing Viscosity in Heavy Oil
Authors: Wang Yuejie , Zheng Wei , Zhang Hongyou , Tang Chenyang , Zhang Jun , Yu Dengfei , Lu Xuanfeng , Li Gang
Published: May 2024

Heavy oil accounts for a considerable proportion of the world’s petroleum resources, and its exploitation helps to mitigate reliance on conventional oil resources and diversify energy supply. However, due to the high viscosity and high adhesion characteristics of heavy oil, conventional methods such as thermal recovery, emulsification, and dilution have significant limitations and cannot meet the growing demands for heavy oil production. In this study, 3-propyltrimethoxysilane (MPS) was used to modify and graft amphiphilic surfactants (AS) onto nanosilica to prepare a salt-resistant (total mineralization > 8000 mg/L, Ca2+ + Mg2+ > 1000 mg/L) and temperature-resistant (250 °C) nanosilicon viscosity reducer (NSD). This article compares amphiphilic surfactants (AS) as conventional viscosity-reducing agents with NSD. FTIR and TEM measurements indicated successful bonding of 3-propyltrimethoxysilane to the surface of silica. Experimental results show that at a concentration of 0.2 wt% and a mineralization of 8829 mg/L, the viscosity reduction rates of thick oil (LD-1) before and after aging were 85.29% and 81.36%, respectively, from an initial viscosity of 38,700 mPa·s. Contact angle experiments demonstrated that 0.2 wt% concentration of NSD could change the surface of reservoir rock from oil-wet to water-wet. Interfacial tension experiments showed that the interfacial tension between 0.2 wt% NSD and heavy oil was 0.076 mN/m. Additionally, when the liquid-to-solid ratio was 10:1, the dynamic and static adsorption amounts of 0.2 wt% NSD were 1.328 mg/g-sand and 0.745 mg/g-sand, respectively. Furthermore, one-dimensional displacement experiments verified the oil recovery performance of NSD at different concentrations (0.1 wt%, 0.15 wt%, 0.2 wt%, 0.25 wt%) at 250 °C and compared the oil recovery efficiency of 0.2 wt% NSD with different types of demulsifiers. Experimental results indicate that the recovery rate increased with the increase in NSD concentration, and 0.2 wt% NSD could improve the recovery rate of heavy oil by 22.8% at 250 °C. The study of nano-demulsification oil recovery systems can effectively improve the development efficiency of heavy oil.
Buildup of Fault Protection for High-Current Single-Phase Transformers in Ore Furnaces
Authors: Rakhimberdinova Dilara , Novozhilov Aleksandr , Kolesnikov Evgeniy , Goryunov Vladimir , Novozhilov Timofey
Published: May 2024

High-power ore furnaces mainly use single-phase transformers supplied by phase-to-phase voltage with the secondary winding made in the form of four to eight isolated splits and a high-voltage busbar assembly made of the same number of pairs of tube buses. Due to these design features, turn-to-turn faults are the main faults in the primary winding of the transformer, and flexible coupling breaks and arc faults in splitting circuits prevail in the high-voltage system. This is why current and gas protections are currently used to protect transformers against short circuits. However, current protections are insensitive to turn faults, and the gas protection response time can be long since it strongly depends on the oil temperature. High-current systems are protected from arc faults only by insulating tube buses. However, this insulation is gradually destroyed under the impact of abrasive dust in the air, and the presence of coal dust in the air creates conditions for arc faults. There is no protection against breaks in the circuits of pairs of tube buses. The extent of damage in a transformer and its high-current system due to the faults can be significantly reduced by using new protection devices based on the measurements of the longitudinal and transverse asymmetry of the stray magnetic fields of the protected transformer. To implement these protections, this article suggests a method for protecting a transformer and a high-current system using magnetic current transformers (MCTs). The number and points for MCT winding coils are determined through the analysis of their EMF values versus their positions relative to the windings of the transformer operating under different modes. The results serve as the basis for the protection circuit design; the protection response threshold is calculated, the protection operation is described, and the results of the experiment are given.
Effects of Hydrogen Addition on the Thermal Performance and Emissions of Biomass Syngas Combustion in a Horizontal Boiler
Authors: Suxing Shengnan , Yu Xiao , Li Jinze , Liu Xuelai , Sui Lichao , Zhang Jingkui , Fu Zaiguo , Shao Yan
Published: May 2024

Due to its low calorific value, abnormal phenomena such as incomplete combustion and flameout may occur during the combustion process of biomass syngas. The applicability of adding hydrogen can assist in the combustion of biomass syngas in boilers to overcome the above defects, and the effects need to be investigated. In this study, a multi-mechanism model is employed to numerically simulate the flow and combustion of a horizontal boiler burning biomass syngas. The reliability verification of the model is conducted by comparing it with the experimental results of combustion in a domestic boiler with biomass syngas. From the views of multi-fields and synergy, the effects of hydrogen addition on the thermal performance and emissions of biomass syngas are further expounded. Two scenarios are taken into consideration: hydrogen addition at a constant fuel volume flow rate and constant heat input. The result indicates that hydrogen addition significantly affects the multi-field synergy, which is advantageous for improving the heat transfer performance and combustion efficiency of biomass syngas. However, when the hydrogen addition ratio exceeds 20% at a constant fuel volume flow rate and 25% at constant heat input, its impact may be reduced. When the hydrogen content increases, the outlet temperature of the combustion chamber decreases, and pollutant emissions are effectively controlled. The turbulent kinetic energy at the reversal section decreases, and the uniformity of the flow field improves. These results provide certain guidance for the efficient utilization of biomass syngas and the operation of boilers burning biomass syngas.
Coordinated Operation Strategy for Equitable Aggregation in Virtual Power Plant Clusters with Electric Heat Demand Response Considered
Authors: Liu Zixuan , Zhu Ruijin , Kong Dewen , Guo Hao
Published: May 2024

To tackle the variability of distributed renewable energy (DRE) and the timing differences in load demand, this paper perfects the integrated layout of “source-load-storage” energy control in virtual power plants (VPPs). Introducing a comprehensive control approach for VPPs of varying ownerships, and encompassing load aggregators (LAs), a robust and cost-efficient operation strategy is proposed for VPP clusters. Initially, the influence of real-time electricity prices on cluster energy utilization is taken into account. Flexible shared electricity prices are formulated cluster-wide, based on the buying and selling data reported by each VPP, and are distributed equitably across the cluster. Following this, a flexible supply and demand response mechanism is established. With the goal of minimizing operational costs, this strategy responds to demand (DR) on the end-user side, instituting shifts and reductions in electricity and heat loads based on electricity and heat load forecasting data. On the supply side, optimization strategies are developed for gas turbines, residual heat boilers, and ground-source heat pumps to restrict power output, thus achieving economical and low-carbon cluster operations. Finally, the efficacy of the proposed optimization strategy is demonstrated through tackling numerous scenario comparisons. The results showcase that the proposed strategy diminishes operational costs and carbon emissions within the cluster by 11.7% and 5.29%, respectively, correlating to the unoptimized scenario.
Adaptive Quasi-Super-Twisting Sliding Mode Control for Flexible Multistate Switch
Authors: Ma Wenzhong , Wang Xiao , Wang Yusheng , Zhang Wenyan , Li Hengshuo , Zhu Yaheng
Published: May 2024

The mathematical model of a flexible multistate switch (FMSS) exhibits nonlinear and strong coupling characteristics, whereas traditional power decoupling control makes it difficult to completely decouple the output power. The traditional proportional–integral control parameters are difficult to adjust, and their robustness and dynamic performance are poor, which affects the stability of the voltage of the power distribution network and feeder power. To address these problems, this study first converted the original system into a linear system via coordinate transformation using feedback-accurate linearization to decouple active and reactive currents. Thereafter, a super-twisting sliding mode control (ST-SMC) algorithm was introduced, and an adaptive quasi-super-twisting sliding mode control (AQST-SMC) algorithm comprising the quasi-sliding mode function and adaptive proportional term was proposed. An FMSS double closed-loop controller was designed to achieve improved vibration suppression and convergence speed. A three-port FMSS simulation model was developed using MATLAB/Simulink, and the simulation results show that the proposed control strategy enhances the robustness and dynamic performance of the system.
Investigation of the Near Future Solar Energy Changes Using a Regional Climate Model over Istanbul, Türkiye
Authors: Duran Yusuf , Yavuz Elif , Özkaya Bestami , Yalçin Yüksel , Variş Çağatay , Kuzu S. Levent
Published: May 2024

This study aims to assess potential changes in radiation values at the solar power plant facility in Istanbul using the RegCM. This analysis seeks to estimate the extent of the solar radiation changes and evaluate the production capacity of solar power in Istanbul in the future. The research involved installing an off-grid rooftop solar energy system. Meteorological parameters (temperature, etc.) and the system’s outputs were monitored to evaluate the energy production and its relationship with these parameters. The performance of the Regional Climate Model version 5.0 (RegCMv5) in accurately representing surface solar radiation and temperature patterns was assessed by comparing it with measured monocrystalline solar panel output data. The impact of different cumulus convection schemes was examined on the sensitivity of the RegCM by analyzing surface solar radiation data over the initial three months. Long-term simulations were conducted with the representational concentration path (RCP) scenarios of 2.6, 4.5, and 8.5 spanning from 2023 to 2050 with convection schemes yielding the best results. All scenarios project a slight decrease in incoming surface radiation.
Review and Recent Advances in Metal Compounds as Potential High-Performance Anodes for Sodium Ion Batteries
Authors: Choi Inji , Ha Sion , Kim Kyeong-Ho
Published: May 2024

Along with great attention to eco-friendly power solutions, sodium ion batteries (SIBs) have stepped into the limelight for electrical vehicles (EVs) and grid-scale energy storage systems (ESSs). SIBs have been perceived as a bright substitute for lithium ion batteries (LIBs) due to abundance on Earth along with the cost-effectiveness of Na resources compared to Li counterparts. Nevertheless, there are still inherent challenges to commercialize SIBs due to the relatively larger ionic radius and sluggish kinetics of Na+ ions than those of Li+ ions. Particularly, exploring novel anode materials is necessary because the conventional graphite anode in LIBs is less active in Na cells and hard carbon anodes exhibit a poor rate capability. Various metal compounds have been examined for high-performance anode materials in SIBs and they exhibit different electrochemical performances depending on their compositions. In this review, we summarize and discuss the correlation between cation and anion compositions of metal compound anodes and their structural features, energy storage mechanisms, working potentials, and electrochemical performances. On top of that, we also present current research progress and numerous strategies for achieving high energy density, power, and excellent cycle stability in anode materials.
A Dynamic Reserve Evaluation Method for an Ultra-Deep Fractured Tight Sandstone Gas Reservoir
Authors: He Xinxing , Wang Chenhui , Chang Baohua , Cao Zhenglin , Tang Haifa
Published: May 2024

Dynamic reserves evaluation is crucial for the development and assessment of gas reservoirs. However, ultra-deep fractured tight sandstone gas reservoirs pose unique challenges due to their abnormally high pressure, tight matrix, and complex fracture distribution. This paper proposes a simplified forward calculation method that incorporates the fracture length for the rapid estimation of dynamic reserves in fractured tight sandstone gas reservoirs. This method was based on the pressure change rate equation and considered the unique characteristics of fractured gas reservoirs. Numerical simulations were conducted to analyze the sensitivity of the proposed method. The proposed method was applied to estimate the dynamic reserves of a fractured gas reservoir, and the results closely approximate the well group method, indicating its accuracy. The main advantage of this method lies in its simplicity, allowing field engineers to perform rapid dynamic reserve evaluations.
Investigation of SiC MOSFET Body Diode Reverse Recovery and Snappy Recovery Conditions
Authors: Pennisi Giuseppe , Pulvirenti Mario , Salvo Luciano , Sciacca Angelo Giuseppe , Cascino Salvatore , Laudani Antonio , Salerno Nunzio , Rizzo Santi Agatino
Published: May 2024

This paper investigates the behavior of SiC MOSFETs body diode reverse recovery as a function of different operating conditions. The knowledge of their effects is crucial to properly designing and driving power converters based on SiC devices, in order to optimize the MOSFETs commutations aiming at improving efficiency. Indeed, reverse recovery is a part of the switching transient, but it has a significant role due to its impact on recovery energy and charge. The set of different operating conditions has been properly chosen to prevent or force the snappy recovery of the device under testing. The experimental results and specific software simulations have revealed phenomena unknown in the literature. More specifically, the analysis of the reverse recovery charge, Qrr, revealed two unexpected phenomena at high temperatures: it decreased with increasing gate voltage; the higher the device threshold, the higher the Qrr. TCAD-Silvaco (ATLAS v. 5.29.0.C) simulations have shown that this is due to a displacement current flowing in the drift region due to the output capacitance voltage variation during commutation. From the analysis of the snappy recovery, it has emerged that there is a minimum forward current slope, below which the reverse recovery cannot be snappy, even for a high current level. Once this current slope is reached, Qrr varies with the forward current only.
A Combined Model of Convolutional Neural Network and Bidirectional Long Short-Term Memory with Attention Mechanism for Load Harmonics Forecasting
Authors: Kuyumani Excellence M. , Hasan Ali N. , Shongwe Thokozani C.
Published: May 2024

In the increasingly complex and dynamic electrical power system, forecasting harmonics is key to developing and ensuring a clean power supply. The traditional methods have achieved some degree of success. However, they often fail to forecast complex and dynamic harmonics, highlighting the serious need to improve the forecasting performance. Precise forecasting of electrical power system harmonics is challenging and demanding, owing to the increased frequency with harmonic noise. The occurrence of harmonics is stochastic in nature; it has taken a long time for the development of dependable and efficient models. Several machine learning and statistical methods have produced positive results with minimal errors. To improve the prognostic accuracy of the power supply system, this study proposes an organic hybrid combination of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) method (CNN-BiLSTM-AM) to forecast load harmonics. CNN models intricate non-linear systems with multi-dimensionality aspects. LSTM performs better when dealing with exploding gradients in time series data. Bi-LSTM has two LSTM layers: one layer processes data in the onward direction and the other in the regressive direction. Bi-LSTM uses both preceding and subsequent data, and as a result, it has better performance compared to RNN and LSTM. AM’s purpose is to make desired features outstanding. The CNN-BiLSTM-AM method performed better than the other five methods, with a prediction accuracy of 92.366% and a root mean square error (RMSE) of 0.000000222.
Vehicle-to-Grid Revenue from Retail Time-of-Day Rates, Compared with Wholesale Market Participation under FERC Order 2222
Authors: Metz John G. , Kempton Willett
Published: May 2024

This article compares potential revenue from electric storage in retail and wholesale electric markets. The retail value can be extracted when storage responds to time-of-day retail prices. The wholesale value is enabled by the recent US Federal Energy Regulatory Commission Order 2222, which requires regional transmission operators (RTOs) to allow distributed storage behind the meter to participate in wholesale electric markets. To quantify the value of these markets, we use realistic time-of-day rates and market prices in one RTO’s ancillary service market. Formulae are developed to estimate the value of behind-the-meter storage in wholesale and retail markets, using an example electric vehicle in a fleet setting. The formulae are also used to compare whether or not net metering is available and different charging rates. The aggregate national storage behind the retail meter is very large, given the projected growth of electric vehicles. Our findings indicate the revenue from wholesale markets can be significantly more than that of retail opportunities. However, the potential in either retail or wholesale markets is currently limited by both state policy and incomplete RTO implementation of FERC orders.
The New Policy for Innovative Transformation in Regional Industrial Chains, the Conversion of New and Old Kinetic Energy, and Energy Poverty Alleviation
Authors: Chen Dongli , Huang Qianxuan
Published: May 2024

As the world’s largest emerging market country, not only has China faced the contradiction between its huge population size and per capita energy scarcity for a long time, but the rigid constraints brought by energy poverty have also plagued the lives and production of Chinese residents. Based on panel data from 30 provinces (except Tibet) in mainland China from 2009 to 2021, this study employs double machine learning and spatial difference-in-difference for causal inference to explore the impact of a medium- to long-term regional innovation pilot policy in China—the new policy for innovative transformation in regional industrial chains—on energy poverty alleviation. This study also introduces China’s conversion of new and old kinetic energy into this quasi-natural experiment. This study presents the following findings: (1) The new policy for innovative transformation in regional industrial chains and the concept of the conversion of new and old kinetic energy can both significantly promote energy poverty alleviation. (2) The mechanism pathway of “the new policy for innovative transformation in regional industrial chains → the conversion of new and old kinetic energy → the energy poverty alleviation in heating/household electricity/transportation segments” has proved to be an effective practice in China. (3) Based on the spatial double difference model, the spatial direct effect of the new regional industrial chain innovation and change policy on energy poverty alleviation is significantly positive, while the spatial direct effect and spatial spillover effect of the new and old kinetic energy transformation on energy poverty alleviation are both significantly positive. (4) Based on the counterfactual framework analysis, in addition to the causal mediating mechanism of the demand-side conversion of new and old kinetic energy being impeded, both the supply-side and the structural-side conversion of new and old kinetic energy are able to play a significant positive causal mediating role in both the treatment and control groups.
Feature Selection for Explaining Yellowfin Tuna Catch per Unit Effort Using Least Absolute Shrinkage and Selection Operator Regression
Authors: Yang Ling , Zhou Weifeng
Published: May 2024

To accurately identify the key features influencing the fisheries distribution of Pacific yellowfin tuna, this study analyzed data from 43 longline fishing vessels operated from 2008 to 2019. These vessels operated in the Pacific Ocean region (0° to 30° S; 110° E to 170° W), with a specific focus on 25 features of yellowfin tuna derived from marine environment data. For this purpose, this study opted for the Lasso regression analysis method to select features to predict Pacific yellowfin tuna fishing grounds, exploring the relationship between the catch per unit effort (CPUE) of yellowfin tuna and multiple features. This study reveals that latitude and water temperature at various depths, particularly the sea surface temperature of the preceding and subsequent months and the temperature at depths between 300 and 450 m, are the most significant features influencing CPUE. Additionally, chlorophyll concentration and large-scale climate indices (ONI and NPGIO) also have a notable impact on the distribution of CPUE for yellowfin tuna. Lasso regression effectively identifies features that are significantly correlated with the CPUE of yellowfin tuna, thereby demonstrating superior fit and predictive accuracy in comparison with other models. It provides a suitable methodological approach for selecting fishing ground features of yellowfin tuna in the Pacific Ocean.
Understanding How Chemical Pollutants Arise and Evolve in the Brewing Supply Chain: A Scoping Review
Authors: Pérez-Lucas Gabriel , Navarro Ginés , Navarro Simón
Published: May 2024

In this study, a critical review was carried out using the Web of ScienceTM Core Collection database to analyse the scientific literature published to date to identify lines of research and future perspectives on the presence of chemical pollutants in beer brewing. Beer is one of the world’s most popular drinks and the most consumed alcoholic beverage. However, a widespread challenge with potential implications for human and animal health is the presence of physical, chemical, and/or microbiological contaminants in beer. Biogenic amines, heavy metals, mycotoxins, nitrosamines, pesticides, acrylamide, phthalates, bisphenols, microplastics, and, to a lesser extent, hydrocarbons (aliphatic chlorinated and polycyclic aromatic), carbonyls, furan-derivatives, polychlorinated biphenyls, and trihalomethanes are the main chemical pollutants found during the beer brewing process. Pollution sources include raw materials, technological process steps, the brewery environment, and packaging materials. Different chemical pollutants have been found during the beer brewing process, from barley to beer. Brewing steps such as steeping, kilning, mashing, boiling, fermentation, and clarification are critical in reducing the levels of many of these pollutants. As a result, their residual levels are usually below the maximum levels allowed by international regulations. Therefore, this work was aimed at assessing how chemical pollutants appear and evolve in the brewing process, according to research developed in the last few decades.
Preparation and Characterization of a Novel Longzhua mushroom Polysaccharide Hydrogel and Slow-Release Behavior of Encapsulated Rambutan Peel Polyphenols
Authors: Zhao Lingxin , Li Jiapeng , Ding Yangyue , Sun Liping
Published: May 2024

Natural polyphenols have drawbacks such as instability and low bioavailability, which can be overcome by encapsulated slow-release systems. Natural polymer hydrogels are ideal materials for slow-release systems because of their high biocompatibility. In this study, Longzhua mushroom polysaccharide hydrogel (LMPH) was used to encapsulate rambutan peel polyphenols (RPP) and delay their release time to improve their stability and bioavailability. The mechanical properties, rheology, stability, swelling properties, water-holding capacity, RPP loading, and slow-release behavior of LMPH were investigated. The results showed that LMPH has adequate mechanical and rheological properties, high thermal stability, excellent swelling and water-holding capacity, and good self-healing behavior. Increasing the polysaccharide content not only improved the hardness (0.17–1.13 N) and water-holding capacity of LMPH (90.84–99.32%) but also enhanced the encapsulation efficiency of RPP (93.13–99.94%). The dense network structure slowed down the release of RPP. In particular, LMPH5 released only 61.58% at 48 h. Thus, a stable encapsulated slow-release system was fabricated using a simple method based on the properties of LMPH. The developed material has great potential for the sustained release and delivery of biologically active substances.
Kidney Bean Protein Prevents High-Fat and High-Fructose Diet-Induced Obesity, Cognitive Impairment, and Disruption of Gut Microbiota Composition
Authors: Jiang Chunyang , Li Shiyu , Su Hang , Zhou Nong , Yao Yang
Published: May 2024

A long-term intake of a high-fat and high-fructose diet (HFFD), even a high-fat, high-fructose but low-protein diet (HFFD + LP), could cause obesity associated with cognitive impairments. In the present study, rats were subjected to a normal diet (ND), an HFFD diet, an HFFD + LP diet, and an HFFD with kidney bean protein (KP) diet for 8 weeks to evaluate the effect of KP on HFFD- or HFFD + LP-induced obesity and cognitive impairment. The results demonstrated that compared with the HFFD diet, KP administration significantly decreased the body weight by 7.7% and the serum Angiotensin-Converting Enzyme 2 (ACE-2) and Insulin-like Growth Factor 1 (IGF-1) levels by 14.4% and 46.8%, respectively (p < 0.05). In addition, KP suppressed HFFD-induced cognitive impairment, which was evidenced by 8.7% less time required to pass the water maze test. The 16s RNA analysis of the colonic contents showed that the relative abundance of Bifidobacterium, Butyricimonas, and Alloprevotella was increased by KP by 5.9, 44.2, and 79.2 times. Additionally, KP supplementation primarily affected the choline metabolic pathway in the liver, and the synthesis and functional pathway of neurotransmitters in the brain, thereby improving obesity and cognitive function in rats.
Effects of a Functional Food Made with Salvia hispanica L. (Chia Seed), Amaranthus hypochondriacus L. (Amaranth), and an Ethanolic Extract of Curcuma longa L. (Curcumin) in a Rat Model of Childhood Obesity
Authors: Rivero-Salgado Gloria Manuela , Zamudio Sergio Roberto , Fregoso-Aguilar Tomás Alejandro , Quevedo-Corona Lucía
Published: May 2024

Obesity is a global health problem and is increasing in prevalence in most countries. Although obesity affects all age groups, children are the most vulnerable sector. Functional foods are novel formulated foods containing substances (i.e., nutrients, phytochemicals, probiotics, etc.) that have potential health-enhancing or disease-preventing value. The research objective was to study the possible beneficial effects of providing a functional food made with amaranth flour, chia seed, and curcumin extract on the metabolism and behavior of a rat model of childhood obesity. Male Wistar rat pups from two litters of different sizes, a normal litter (NL) (10 pups) and a small litter (SL) (4 pups), were used. After weaning, the rats were fed a hypercaloric diet (HD) or an HD supplemented with the functional food mixture. Body weight and energy intake were measured for seven weeks, and locomotor activity, learning, and memory tests were also performed. At the end of the experiment, glucose and lipid metabolism parameters were determined. The results showed that in this model of obesity produced by early overfeeding and the consumption of a hypercaloric diet, anxiety-like behaviors and metabolic alterations occurred in the rat offspring; however, the provision of the functional food failed to reduce or prevent these alterations, and an exacerbation was even observed in some metabolic indicators. Interestingly, in the NL rats, the provision of the functional food produced some of the expected improvements in health, such as significant decreases in body weight gain and liver cholesterol and non-significant decreases in adipose tissue and leptin and insulin serum levels.
Exploring the Impact of Online Medical Team Engagement on Patient Satisfaction: A Semantic Features Perspective
Authors: Wang Siqi , Zhang Xiaofei
Published: May 2024

Online medical teams (OMTs), a new mode of online healthcare service, have emerged in online health communities (OHCs) in China. This study attempts to explore the underlying mechanism of how OMTs’ engagement influences patient satisfaction through the lens of semantic features. This study also scrutinizes the moderating effect of multiple specializations on the link between OMTs’ engagement and semantic features. We utilized a linear model that had fixed effects controlled at the team level for analysis. A bootstrapping approach using 5000 samples was employed to test the mediation effects. The findings reveal that OMTs’ engagement significantly improves language concreteness in online team consultations, which subsequently enhances patient satisfaction. OMT engagement has a negative impact on emotional intensity, ultimately decreasing patient satisfaction. Multiple specializations strengthen the impact of OMT engagement on both language concreteness and emotional intensity. This study contributes to the literature on OMTs and patient satisfaction, providing insights into patients’ perceptions of OMTs’ engagement during online team consultation. This study also generates several implications for the practice of OHCs and OMTs.
Core Legal Challenges for Medical 3D Printing in the EU
Authors: Pettersson Ante B. V. , Ballardini Rosa Maria , Mimler Marc , Li Phoebe , Salmi Mika , Minssen Timo , Gibson Ian , Mäkitie Antti
Published: May 2024

3D printing has been adopted into routine use for certain medical applications, but more widespread usage has been hindered by, among other things, unclear legislation. We performed an analysis, using legal doctrinal study and legal informatics, of relevant EU legislation and case law in four issues relevant to medical 3D printing (excluding bioprinting or pharmacoprinting): pre-market approval, post-market liability, intellectual property rights, and data protection. Several gaps and uncertainties in the current legislation and interpretations were identified. In particular, we regard the current EU regulatory framework to be quite limiting and inflexible, exemplifying a cautionary approach common in EU law. Though the need to establish high safety standards in order to protect patients as a disadvantaged population is understood, both legal uncertainties and overregulation are seen as harmful to innovation. Hence, more adaptive legislation is called for to ensure continuous innovation efforts and enhanced patient outcomes.
Mindfulness Is Associated with Lower Depression, Anxiety, and Post-Traumatic Stress Disorder Symptoms and Higher Quality of Life in Patients with an Implantable Cardioverter–Defibrillator—A Cross-Sectional Study
Authors: Dörner Marc , von Känel Roland , König Noelle , Pazhenkottil Aju P. , Altwegg Rahel , Nager Ladina , Attanasio Veronica , Guth Lisa , Zirngast Sina , Menzi Anna , Zuccarella-Hackl Claudia , Princip Mary
Published: May 2024

Several studies suggested the efficacy of dispositional mindfulness and mindfulness-based interventions in reducing anxiety and depression in cardiovascular diseases. However, data on the impact of mindfulness on the psychological well-being of patients with an implantable cardioverter–defibrillator (ICD) are scarce. In this study, 422 patients with an ICD were prospectively recruited. Logistic regression was applied to determine associations between dispositional mindfulness (Freiburg Mindfulness Inventory), depression (Patient Health Questionnaire-8), anxiety (Generalized Anxiety Disorder-7 scale), and post-traumatic stress disorder (PTSD) symptoms (Post-Traumatic Stress Diagnostic Scale), adjusting for age, sex, educational status, number of ICD shocks after ICD implantation, and physical activity. The PROCESS regression path analysis modelling tool was used to identify indirect mediating effects of dispositional mindfulness on depression, anxiety, and PTSD symptoms and quality of life (QoL; EuroQol group 5-dimension questionnaire). Participants presented high baseline QoL (mean 1.06 to 1.72) and medium–high mindfulness scores (mean 40.85 points). Higher mindfulness scores were associated with lower levels of anxiety (OR 0.90, 95% CI 0.86 to 0.95, 0.001), depression (OR 0.93, 95% CI 0.88 to 0.98, p = 0.006), and PTSD symptoms (OR 0.94, 95% CI 0.89 to 0.98, p = 0.011). Furthermore, greater mindfulness partially mediated the relationship between anxiety (indirect effect 0.10, 95% CI 0.02 to 0.21), depression (indirect effect 0.08, 95% CI 0.01 to 0.17), or PTSD (indirect effect 0.04, 95% CI 0.01 to 0.17) as independent variables and the QoL as the dependent variable. This study suggests that greater dispositional mindfulness is associated with less anxiety, depression, and PTSD symptoms. Mindfulness might also increase the QoL in ICD patients by mitigating the impact of those with psychological distress.
Intense Pulsed Light Therapy for Dry Eye Disease: Analyzing Temporal Changes in Tear Film Stability and Ocular Surface between IPL Sessions
Authors: Pac Cristina-Patricia , Sánchez-González José-María , Rocha-de-Lossada Carlos , Mercea Nadina , Ferrari Francis , Preda Maria Alexandra , Rosca Cosmin , Munteanu Mihnea
Published: May 2024

Background: Dry eye disease (DED), a prevalent condition with a multifactorial etiology, significantly impacts global health by causing discomfort and visual disturbance. This historical cohort study evaluates the efficacy of Intense Pulsed Light (IPL) therapy on meibomian gland dysfunction (MGD)-related evaporative DED. Methods: The study involved 110 patients (220 eyes) who underwent IPL therapy. Ethical approval was secured, and informed consent was obtained from all participants. A Tearcheck® (ESWvision, Houdan, France) device was used for ocular surface evaluation, measuring tear film stability (NIFBUT, NIABUT), tear film quantity (CTMH, TTMH), and inflammation (OSIE). The study assessed tear film and ocular surface health across multiple IPL sessions. Results: Significant improvements were observed in subjective symptoms (EFT score increased from 29.10 ± 8.87 to 35.91 ± 7.03, p < 0.01), tear film stability (NIFBUT increased from 9.37 ± 6.04 to 10.78 ± 5.83 s, p < 0.01; NIABUT increased from 11.07 ± 4.98 to 12.34 ± 4.66 s, p < 0.01), and tear film surface evaluation (TFSE score decreased from 337.78 ± 414.08 to 206.02 ± 240.44, p < 0.01). Tear film quantity remained unchanged (CTMH and TTMH, p > 0.05). Conclusions: IPL therapy is a promising treatment for DED, improving symptoms and ocular surface health. Further research is warranted to explore long-term efficacy and optimization.