bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of Normalization on Entropy-Based Weights in Hellwig’s Method: A Case Study on Evaluating Sustainable Development in the Education Area
Authors: Roszkowska Ewa , Wachowicz Tomasz
Published: April 2024

Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig’s method. To compare the influence of various normalization methods in both the EWM and Hellwig’s method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries’ realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max–min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig’s method. The results are compared with the outcome obtained by Hellwig’s method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig’s method results.
Quo Vadis Particula Physica?
Authors: Calmet Xavier
Published: April 2024

In this brief paper, I give a very personal account on the state of particle physics on the occasion of Paul Frampton’s 80th birthday.
Bridging Theory and Practice: Using Goal Systems to Spark Professional Dialogue and Develop Personal Theories
Authors: Westbroek Hanna , de Vries Bregje , Kaal Anna , McDonnell Michelle
Published: April 2024

School-based mentors play a key role in the learning processes of student teachers. Ideally, they facilitate student teachers to scrutinize their approaches and underlying assumptions, and link these to theoretical notions. In this study we investigated how three mentors used a goal-system representation (GSR) tool in their mentoring conversations. The GSR tool is essentially a visual reflection of the student teacher’s personal theory regarding classroom practice. It was developed at our teacher training institute to help our students see the personal relevance of research literature and theory and apply it to their lesson plans, to bridge the gap between educational theory, their vision of good teaching and their educational practice. In three explorative case studies, we show how mentors use the GSR tool and to what extent they support three levels of personal theory development: sharing, investigating and transforming. In all cases, student teachers could relate their practices to theoretical notions, giving access to their mentors for further questioning of their sense-making of the situation. Mentors successfully use these opportunities for personal theory development in various ways. We conclude that the GSR tool functions as a boundary object between theory and practice and between institute-based and school-based teacher education.
Enhancing Competency-Based Education in Instrumental Analysis: A Novel Approach Using High-Performance Liquid Chromatography for Real-World Problem Solving
Authors: Vázquez-Espinosa Mercedes , Sancho-Galán Pau , González-de-Peredo Ana V. , Calle José Luis P. , Ruiz-Rodríguez Ana , Fernández Barbero Gerardo , Ferreiro-González Marta
Published: April 2024

Curricula related to instrumental analysis aim for competency-based education to promote the development of teaching strategies that encourage students to successfully tackle the problem-solving and tasks inherent to their profession. However, this endeavor is constrained by the complexity of equipment and the lack of time in laboratory classes. The objective of this work is to present an alternative high-performance liquid chromatography (HPLC) practice that adapts the desired competencies to a shorter and more effective timeframe. It seeks more active participation from the student and contextualizes chromatographic analysis within a real-world problem that encompasses the entire analytical process, from sample to final result. In this scenario, the student receives a solid sample of spicy paprika from a supermarket and must report the level of spiciness in terms of the total amount of capsaicinoids. To achieve this, they must first apply different experimental conditions for extracting the analytes of interest (varying temperatures and solvents) and selecting the most optimal condition. This practice is designed for short sessions, specifically conducted in two 2.5 h laboratory sessions, and has been implemented in the subject “Advanced Techniques in Instrumental Analysis (ATIA)” in the fourth year of the Degree in Biotechnology at the University of Cadiz. The results obtained demonstrated a significant increase in student motivation and an improvement in the acquisition of skills; 100% of the students achieved a grade higher than seven in the final evaluation of their learning process.
Motivation of Spanish University Students: A Regression Model
Authors: Sánchez-Bolívar Lionel , Rodríguez-Gamal Juan Francisco , Escalante-González Sergio , Tovar-Gálvez María Isabel
Published: April 2024

Student motivation is an axial variable in the choice of degree, academic performance, and future incorporation into the labor market. In this study, with a descriptive and cross-sectional design, 292 students enrolled in different university degrees of the University of Granada, in the campuses of Ceuta and Melilla; 66.80% (n = 195) women and 33.20% (n = 97) men, with a mean age of 22.03 years (SD = 5.80), were selected by stratified random sampling, in order to determine which type of motivation (extrinsic or intrinsic) was predominant and which population was more motivated. For data collection, a questionnaire was developed to assess students’ motivation according to socio-demographic factors and social competence. The results revealed that students pursuing Educational degrees showed higher adherence than those studying Nursing or Business Administration and Management. The regression model proved to be valid, predicting 81.8% of cases and explaining between 0.201 and 0.309 of the dependent variable, showing that the most intrinsically motivated students were students of Educational Sciences with high social activity.
Quality Assessors’ Feedback and Recommendations on Music Education in Hong Kong Kindergartens
Authors: Ho Yan Lam , Bautista Alfredo
Published: April 2024

In Hong Kong, quality assessors from the Education Bureau (EDB) visit kindergartens to conduct Quality Review (QR) assessments. Their written reports, therefore, reflect the official local perspective of quality kindergarten education. We conducted a content analysis of 323 QR reports focusing on the positive and negative feedback and recommendations for improvement regarding music education. Two coding schemes were developed using an inductive approach. High inter-reliability was obtained. The most frequently mentioned positive feedback codes were related to sufficient exposure to music and enjoyable musical learning experiences. In contrast, there were negative feedback codes pointing out that children’s musical exposure was insufficient at Kindergarten 3 (children aged 5–6 years), and some musical activities were poorly prepared and/or designed. Children’s music learning outcomes were rarely mentioned in the reports. The analyses indicate that the local understanding of kindergarten music education strongly emphasizes exposing children to enjoyable musical activities, with a lower focus on the nature and quality of musical learning. We conclude that the QR reports reflect a teacher-centric view of music education. Findings may inform curriculum designers and teacher educators about EDB’s expectations in the area of music education. Future research should explore the perspectives of other kindergarten stakeholders such as teachers, principals, and teacher educators.
Active Visual Perception Enhancement Method Based on Deep Reinforcement Learning
Authors: Yang Zhonglin , Fang Hao , Liu Huanyu , Li Junbao , Jiang Yutong , Zhu Mengqi
Published: April 2024

Traditional object detection methods using static cameras are constrained by their limited perspectives, hampering the effective detection of low-confidence targets. To address this challenge, this study introduces a deep reinforcement learning-based visual perception enhancement technique. This approach leverages pan–tilt–zoom (PTZ) cameras to achieve active vision, enabling them to autonomously make decisions and actions tailored to the current scene and object detection outcomes. This optimization enhances both the object detection process and information acquisition, significantly boosting the intelligent perception capabilities of PTZ cameras. Experimental findings demonstrate the robust generalization capabilities of this method across various object detection algorithms, resulting in an average confidence level improvement of 23.80%.
Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting
Authors: Oh Jinyeong , So Dayeong , Jo Jaehyeok , Kang Namil , Hwang Eenjun , Moon Jihoon
Published: April 2024

Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability to effectively learn unstable environmental variables and their complex interactions. However, NNs are limited in their practical industrial application in the energy sector because the optimization of the model structure or hyperparameters is a complex and time-consuming task. This paper proposes a two-stage NN optimization method for robust solar PV power forecasting. First, the solar PV power dataset is divided into training and test sets. In the training set, several NN models with different numbers of hidden layers are constructed, and Optuna is applied to select the optimal hyperparameter values for each model. Next, the optimized NN models for each layer are used to generate estimation and prediction values with fivefold cross-validation on the training and test sets, respectively. Finally, a random forest is used to learn the estimation values, and the prediction values from the test set are used as input to predict the final solar PV power. As a result of experiments in the Incheon area, the proposed method is not only easy to model but also outperforms several forecasting models. As a case in point, with the New-Incheon Sonae dataset—one of three from various Incheon locations—the proposed method achieved an average mean absolute error (MAE) of 149.53 kW and root mean squared error (RMSE) of 202.00 kW. These figures significantly outperform the benchmarks of attention mechanism-based deep learning models, with average scores of 169.87 kW for MAE and 232.55 kW for RMSE, signaling an advance that is expected to make a significant contribution to South Korea’s energy industry.
Characterizing Lossy Dielectric Materials in Shock Physics by Millimeter-Wave Interferometry Using One-Dimensional Convolutional Neural Networks and Nonlinear Optimization
Authors: Pham Ngoc Tuan , Lefrançois Alexandre , Aubert Hervé
Published: April 2024

When a dielectric material undergoes mechanical impact, it generates a shock wave, causing changes in its refractive index. Recent demonstrations have proven that the modified refractive index can be determined remotely using a millimeter-wave interferometer. However, these demonstrations are based on the resolution of an inverse electromagnetic problem, which assumes that the losses in the material are negligible. This restrictive assumption is overcome in this article, in which a new approach is proposed to solve the inverse electromagnetic problem in lossy and shocked dielectric materials. Our methodology combines a one-dimensional convolutional neural network architecture, namely Inverse problem of Lossless or Lossy Shocked Wavefront Network (ILSW-Net), with a nonlinear optimization technique based on the Nelder–Mead algorithm to estimate losses within dielectric materials under a mechanical impact. Experimental results for both simulated and real signals show that our method can successfully predict the velocities and the refractive index while accurately estimating the shock wavefront.
Thermal Safety Assessment Method for Power Devices in Natural Air-Cooled Converters
Authors: He Weichao , Zhu Yiming , Liu Zijian , Lin Jiaxue , Bao Fengshuo , Zu Wei , Ying Zhanfeng
Published: April 2024

The junction temperature of a power device in a natural air-cooled power converter fluctuates randomly due to the variation in airflow rate in ambient environments. Most of the existing thermal analysis methods do not pay attention to the uncertain influence of airflow on the heat-dissipation capacity of such converters, making it difficult to accurately evaluate the thermal safety of these devices. To address this issue, a thermal safety assessment method for power devices in natural air-cooled converters is proposed in this paper. In the proposed method, convective heat resistance samples of converter housing are extracted with an equivalent thermal network model and the historical operation temperature of the converter. Wavelet packet transform is used to analyze the time–frequency domain characteristics of the convective heat resistance, and Monte Carlo simulation is employed to simulate the random influence of the airflow rate on the device junction temperature. The thermal safety of power devices is assessed in the form of over-temperature probability, which is expressed by a two-variable growth function. An experimental platform is designed to validate the effectiveness of the proposed method. The results show that the proposed method can accurately estimate the over-temperature risk of a power device in a natural air-cooled converter under different ambient temperature and current levels, thus effectively improving the thermal reliability of converters.
Design and Implementation of Reconfigurable Array Adaptive Optoelectronic Hybrid Interconnect Shunting Network
Authors: Yang Bowen , Li Yong , Xi Chao , Shan Rui , Feng Yu , Luo Jiaying
Published: April 2024

Addressing challenges regarding Hybrid Optoelectronic Network-on-Chip systems, such as congestion control, their limited adaptability, and their inability to facilitate optoelectronic co-simulation, this study introduces an adaptive hybrid optoelectronic interconnection shunt structure tailored for reconfigurable array processors. Within this framework, an adaptive shunt routing algorithm and a low-loss non-blocking five-port optical router are developed. Furthermore, an adaptive hybrid optoelectronic interconnection simulation model and a performance statistical model, established using SystemVerilog and Verilog, complement these designs. The experimental results showcase promising enhancements: the designed routing algorithm demonstrates an average 17.5% improvement in mitigating congestion at network edge nodes; substantial reductions in the required number of cross waveguides and micro-ring resonators for optical routers lead to an average path insertion loss of only 0.522 dB. Moreover, the hybrid optoelectronic interconnection performance statistical model supports the design of routing strategies and topology structures, enabling resource usage, power consumption, insertion loss, and other performance metrics to be accurately assessed.
Continual Monitoring of Respiratory Disorders to Enhance Therapy via Real-Time Lung Sound Imaging in Telemedicine
Authors: Muhammad Murdifi , Li Minghui , Lou Yaolong , Lee Chang-Sheng
Published: April 2024

This work presents a configurable Internet of Things architecture for acoustical sensing and analysis for frequent remote respiratory assessments. The proposed system creates a foundation for enabling real-time therapy and patient feedback adjustment in a telemedicine setting. By allowing continuous remote respiratory monitoring, the system has the potential to give clinicians access to assessments from which they could make decisions about modifying therapy in real-time and communicate changes directly to patients. The system comprises a wearable wireless microphone array interfaced with a programmable microcontroller with embedded signal conditioning. Experiments on the phantom model were conducted to demonstrate the feasibility of reconstructing acoustic lung images for detecting obstructions in the airway and provided controlled validation of noise resilience and imaging capabilities. An optimized denoising technique and design innovations provided 7 dB more SNR and 7% more imaging accuracy for the proposed system, benchmarked against digital stethoscopes. While further clinical studies are warranted, initial results suggest potential benefits over single-point digital stethoscopes for internet-enabled remote lung monitoring needing noise immunity and regional specificity. The flexible architecture aims to bridge critical technical gaps in frequent and connected respiratory function at home or in busy clinical settings challenged by ambient noise interference.
A Nonlinear Subspace Predictive Control Approach Based on Locally Weighted Projection Regression
Authors: Wu Xinwei , Yang Xuebo
Published: April 2024

Subspace predictive control (SPC) is a widely recognized data-driven methodology known for its reliability and convenience. However, effectively applying SPC to complex industrial process systems remains a challenging endeavor. To address this, this paper introduces a nonlinear subspace predictive control approach based on locally weighted projection regression (NSPC-LWPR). By projecting the input space into localized regions, constructing precise local models, and aggregating them through weighted summation, this approach handles the nonlinearity effectively. Additionally, it dynamically adjusts the control strategy based on online process data and model parameters, while eliminating the need for offline process data storage, greatly enhancing the adaptability and efficiency of the approach. The parameter determination criteria and theoretical analysis encompassing feasibility and stability assessments provide a robust foundation for the proposed approach. To illustrate its efficacy and feasibility, the proposed approach is applied to a continuous stirred tank heater (CSTH) benchmark system. Comparative results highlight its superiority over SPC and adaptive subspace predictive control (ASPC) methods, evident in enhanced tracking precision and predictive accuracy. Overall, the proposed NSPC-LWPR approach presents a promising solution for nonlinear control challenges in industrial process systems.
State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU
Authors: Li Hao , Chen Chao , Wei Jie , Chen Zhuo , Lei Guangzhou , Wu Lingling
Published: April 2024

As a core component of new energy vehicles, accurate estimation of the State of Health (SOH) of lithium-ion power batteries is essential. Correctly predicting battery SOH plays a crucial role in extending the lifespan of new energy vehicles, ensuring their safety, and promoting their sustainable development. Traditional physical or electrochemical models have low accuracy in measuring the SOH of lithium batteries and are not suitable for the complex driving conditions of real-world vehicles. This study utilized the black-box characteristics of deep learning models to explore the intrinsic correlations in the historical cycling data of lithium batteries, thereby eliminating the need to consider the internal chemical reactions of lithium batteries. Through Pearson correlation analysis, this study selects health indicators (HIs) from lithium battery cycling data that significantly impact SOH as input features. In the field of lithium batteries, this paper applies ABC-BiGRU for the first time to SOH prediction. Compared with other recursive neural network models, ABC-BiGRU demonstrates superior predictive performance, with maximum root mean square error and mean absolute error of only 0.016799317 and 0.012626847, respectively.
Neurogaming in Virtual Reality: A Review of Video Game Genres and Cognitive Impact
Authors: GomezRomero-Borquez Jesus , Del-Valle-Soto Carolina , Del-Puerto-Flores J. Alberto , Briseño Ramon A. , Varela-Aldás José
Published: April 2024

This work marks a significant advancement in the field of cognitive science and gaming technology. It offers an in-depth analysis of the effects of various video game genres on brainwave patterns and concentration levels in virtual reality (VR) settings. The study is groundbreaking in its approach, employing electroencephalograms (EEGs) to explore the neural correlates of gaming, thus bridging the gap between technology, psychology, and neuroscience. This review enriches the dialogue on the potential of video games as a therapeutic tool in mental health. The study’s findings illuminate the capacity of different game genres to elicit varied brainwave responses, paving the way for tailored video game therapies. This review contributes meaningfully to the state of the art by offering empirical insights into the interaction between gaming environments and brain activity, highlighting the potential applications in therapeutic settings, cognitive training, and educational tools. The findings are especially relevant for developing VR gaming content and therapeutic games, enhancing the understanding of cognitive processes, and aiding in mental healthcare strategies.
An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation
Authors: Ma Shengjie , Wang Pei , Lee Hyukjoon
Published: April 2024

Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art.
Constant-Voltage and Constant-Current Controls of the Inductive Power Transfer System for Electric Vehicles Based on Full-Bridge Synchronous Rectification
Authors: Cai Jin , Sun Pan , Ji Kai , Wu Xusheng , Ji Hang , Wang Yuxiao , Rong Enguo
Published: April 2024

When an inductive power transfer (IPT) system conducts wireless charging for electric cars, the coupling coefficient between the coils is easily affected by fluctuations in the external environment. With frequent changes in the battery load impedance, it is difficult for the IPT system to achieve constant-voltage and constant-current (CVCC) controls. A CVCC control method is proposed for the IPT system that has a double-sided LCC compensation structure based on full-bridge synchronous rectification. The proposed method achieved good dynamic stability and was able to effectively switch between the output current and voltage of the system by adjusting only the duty cycle of the switch on the secondary side of the rectification bridge. As a result, the system efficiency was improved. The output characteristics of the double-sided LCC compensation structure was derived and the conduction condition with zero voltage was analyzed by using four switches through two conduction time series of the rectifier circuit. Then, the output voltage of the synchronized rectifier was derived. The hardware implementation of the full-bridge controllable rectifier was described in detail. Finally, a MATLAB/Simulink 2018a simulation model was developed and applied to an 11 kW prototype to analyze and validate the design. The results showed that the designed system had good CVCC output characteristics and could maintain constant output under certain coupling offsets. Compared with semi synchronous rectification methods, the proposed method had a higher efficiency, which was 95.6% at the rated load.
Analyzing the Effects of Atmospheric Turbulent Fluctuations on the Wake Structure of Wind Turbines and Their Blade Vibrational Dynamics
Authors: Farrell Alayna , Ponta Fernando , Baruah Apurva
Published: April 2024

In recent trends, a rising demand for renewable energy has driven wind turbines to larger proportions, where lighter blade designs are often adopted to reduce the costs associated with logistics and production. This causes modern utility-scale wind turbine blades to be inherently more flexible, and their amplified aeroelastic sensitivity results in complex multi-physics reactions to variant atmospheric conditions, including dynamic patterns of aerodynamic loading at the rotor and vortex structure evolutions within the wake. In this paper, we analyze the influence of inflow variance for wind turbines with large, flexible rotors through simulations of the National Rotor Testbed (NRT) turbine, located at Sandia National Labs’ Scaled Wind Farm Technology (SWiFT) facility in Lubbock, Texas. The Common Ordinary Differential Equation Framework (CODEF) modeling suite is used to simulate wind turbine aeroelastic oscillatory behavior and wind farm vortex wake interactions for a range of flexible NRT blade variations, operating in differing conditions of variant atmospheric flow. CODEF solutions of turbine operation in Steady-In-The-Average (SITA) wind conditions are compared to SITA wind conditions featuring a controlled gust-like pulse overimposed, to isolate the effects of typical wind fluctuations. Finally, simulations of realistic time-varying wind conditions from SWiFT meteorological tower measurements are compared to the solutions of SITA wind conditions. These increasingly complex atmospheric inflow variations are tested to show the differing effects evoked by various patterns of spatiotemporal atmospheric flow fluctuations. An analysis is presented for solutions of wind turbine aeroelastic response and vortex wake evolution, to elucidate the consequences of variant inflow, which pertain to wind turbine dynamics at an individual and farm-collective scale. The comparisons of simulated farm flow for SITA and measured fluctuating wind conditions show that certain regions of the wake contain up to a 12% difference in normalized axial velocity, due to the introduction of wind fluctuations. The findings of this study prove valuable for practical applications in wind farm control and optimization strategies, with particular significance for modern utility-scale wind power plants operating in variant atmospheric conditions.
Experimental Study on the Effect of Mixed Thermodynamic Inhibitors with Different Concentrations on Natural Gas Hydrate Synthesis
Authors: Luan Hengjie , Liu Mingkang , Shan Qinglin , Jiang Yujing , Yan Peng , Du Xiaoyu
Published: April 2024

Natural gas hydrate (NGH) is a potential future energy resource. More than 90% of NGH resources exist in the pore medium of seafloor sediments. During the development of deep-sea oil and gas fields, wellbore pipelines are often clogged due to the synthesis of gas hydrates, and the addition of thermodynamic inhibitors is a common solution to prevent hydrate synthesis. In this paper, the effects of two single inhibitors, sodium chloride and ethylene glycol, as well as hybrid inhibitors combining these two inhibitors on the synthesis of methane hydrates were investigated using the self-developed one-dimensional gas hydrate exploitation simulation test apparatus. The effects of single and hybrid inhibitors were investigated in terms of the hydrate synthesis volume and gas–water two-phase conversion rate. The results show that the hybrid inhibitor has a better inhibitory effect on hydrate synthesis with the same initial synthesis driving force. When the concentration of inhibitors is low, salt inhibitors can have a better inhibitory effect than alcohol inhibitors. However, in the mixed inhibitor experiment, increasing the proportion of ethylene glycol in the mixed inhibitor can more effectively inhibit the synthesis of hydrates than increasing the proportion of sodium chloride in the mixed inhibitor.
Detection and Screening of Organic Contaminants in A Riverine System of Georgia Using Non-Targeted Analysis
Authors: Basapuram Gayatri , Duttagupta Srimanti , Dutta Avishek
Published: April 2024

Numerous organic chemicals exist within aquatic environments, yet effectively screening and prioritizing them is a huge challenge. This study provides a comprehensive investigation into the ecological dynamics of the North Oconee River within Athens-Clarke County, Georgia, with a specific focus on the distribution of 33 identified compounds, including a prominent pesticide. The research, conducted in the riverine ecosystems proximal to the Firefly trail, employs advanced analytical techniques to elucidate potential contamination sources arising from agricultural and urban runoff. Intriguingly, the study reveals North Oconee River near the Firefly Trail as a notable site for heightened pesticide contamination, warranting a meticulous exploration of its origins. Furthermore, the investigation unveils the intricate microbial degradation processes of malathion within the North Oconee River, elucidating the pivotal role played by microbial activity in river water. The detection of degradant byproducts prompts the considerations of bioavailability and toxicity, associating potential implications for the river’s overall ecological health. Ongoing research endeavors to precisely quantify environmental risks and unravel indigenous microbial degradation pathways, presenting pivotal contributions to the scientific community’s understanding of complex riverine ecosystems. This research serves as a foundational piece in informing sustainable environmental management practices and emphasizes the urgency of comprehensive stewardship in safeguarding aquatic ecosystems.
Response of Soil CO2 Emission to Addition of Biochar and Dissolved Organic Carbon along a Vegetation Restoration Gradient of Subtropical China
Authors: Zhu Yulin , Tang Xinghao , Huang Yunpeng , Jiang Jing , Fang Xiong
Published: April 2024

Biochar, as a soil amendment, has been widely confirmed to increase soil carbon sequestration. However, how biochar addition affects soil carbon changes during the vegetation restoration process is still unclear, which constrains our ability to explore biochar’s application in the technology of soil carbon sequestration in forests. We conducted an incubation experiment on biochar and dissolved organic matter (DOM) addition to soil at three stages of revegetation (degraded land (DS), plantation forest (PS), and secondary natural forest (NS) in Changting County in Fujian province, China) to investigate the effects of vegetation restoration, biochar, DOM, and their interaction on soil CO2 emission and its relative mechanisms. We found that the accumulative release of CO2-C in the NS and PS soils was 7.6 and 6.8 times higher, respectively, in comparison to that from the DS soil. In the DS, biochar addition significantly increased the accumulative release of CO2-C, soil pH, NH4+-N content, qCO2, phenol oxidase, and peroxidase activities. Peroxidase activities were positively correlated with the accumulative release of CO2-C, and oxidase was the most important direct factor influencing the accumulative release of CO2-C in the DS. However, the accumulative release of CO2-C, soil NH4+-N content, qCO2, β-glucosidase, and N-acetylglucosaminidase activities was significantly reduced after the application of biochar in the PS and NS. These two hydrolases were positively associated with the accumulative release of CO2-C, and hydrolase was the most vital direct factor influencing the accumulative release of CO2-C from the PS and NS soils. The positive effect of DOM addition on CO2 emission under biochar application declined with a vegetation restoration age increase. Our results indicated that biochar could alter microbial physiological processes, inhibit qCO2 and hydrolase activities, and further decrease CO2 emission in relatively fertile soil from the PS and NS; but in the relatively barren soil from the DS, biochar might promote CO2 emission by stimulating microorganisms to enhance qCO2 and oxidase activities.
A Deep Learning Method for Log Diameter Measurement Using Wood Images Based on Yolov3 and DeepLabv3+
Authors: Lu Zhenglan , Yao Huilu , Lyu Yubiao , He Sheng , Ning Heng , Yu Yuhui , Zhai Lixia , Zhou Lin
Published: April 2024

Wood volume is an important indicator in timber trading, and log diameter is one of the primary parameters used to calculate wood volume. Currently, the most common methods for measuring log diameters are manual measurement or visual estimation by log scalers, which are laborious, time consuming, costly, and error prone owing to the irregular placement of logs and large numbers of roots. Additionally, this approach can easily lead to misrepresentation of data for profit. This study proposes a model for automatic log diameter measurement that is based on deep learning and uses images to address the existing problems. The specific measures to improve the performance and accuracy of log-diameter detection are as follows: (1) A dual network model is constructed combining the Yolov3 algorithm and DeepLabv3+ architecture to adapt to different log-end color states that considers the complexity of log-end faces. (2) AprilTag vision library is added to estimate the camera position during image acquisition to achieve real-time adjustment of the shooting angle and reduce the effect of log-image deformation on the results. (3) The backbone network is replaced with a MobileNetv2 convolutional neural network to migrate the model to mobile devices, which reduces the number of network parameters while maintaining detection accuracy. The training results show that the mean average precision of log-diameter detection reaches 97.28% and the mean intersection over union (mIoU) of log segmentation reaches 92.22%. Comparisons with other measurement models demonstrate that the proposed model is accurate and stable in measuring log diameter under different environments and lighting conditions, with an average accuracy of 96.26%. In the forestry test, the measurement errors for the volume of an entire truckload of logs and a single log diameter are 1.20% and 0.73%, respectively, which are less than the corresponding error requirements specified in the industry standards. These results indicate that the proposed method can provide a viable and cost-effective solution for measuring log diameters and offering the potential to improve the efficiency of log measurement and promote fair trade practices in the lumber industry.
Dynamic Changes of Environment and Gut Microbial Community of Litopenaeus vannamei in Greenhouse Farming and Potential Mechanism of Gut Microbial Community Construction
Authors: Li Hui , Gu Shuwen , Wang Libao , Shi Wenjun , Jiang Qi , Wan Xihe
Published: April 2024

The aim of this study was to investigate the dynamic changes in the microbial communities of both the environment and gut of Litopenaeus vannamei, as well as to elucidate the mechanisms underlying microbial community assembly in greenhouse farming. 16S rDNA high-throughput sequencing and bioinformatics methods were used to carry out the research on the community structure of the microorganisms under greenhouse culture conditions in water, sediment, and gut microorganisms; correlations pertaining to environmental factors; the feasibility of using Source Tracker; and the mechanisms of community construction. The results show that the dominant microorganisms in water, sediment, and gut farming in a greenhouse environment varied and were subject to dynamic change. A variety of beneficial microbiota such as Bacillus were found in the gut, whereas a variety of microorganisms such as Marivita and Pseudomonas, which function as nitrogen and phosphorus removers, were present in water. Source Tracker and environmental correlation analyses showed that changes in the gut were associated with eutrophication indicators (total nitrogen, total phosphorus, ammonia nitrogen) and changes in environmental microorganisms (in water and sediment). The results of the community-building mechanism analysis show that stochastic processes determine the community-building directions of environmental and gut microorganisms. These findings will help us to understand the microbiota characteristics of shrimp ponds under greenhouse farming conditions, and the complex interactions between the shrimp gut and the environmental microbiota and environmental variables, as well as revealing the changing rules of the gut microbiota.
The Cryoprotective Effect of an Antifreeze Collagen Peptide Complex Obtained by Enzymatic Glycosylation on Tilapia
Authors: Liu Shouchun , Zhang Luyao , Li Zhuyi , Chen Jing , Zhang Yinyu , Yang Xuebo , Chen Qiuhan , Cai Hongying , Hong Pengzhi , Zhu Chunhua , Zhong Saiyi
Published: April 2024

Antifreeze peptides have become effective antifreeze agents for frozen products, but their low quantity of active ingredients and high cost limit large-scale application. This study used the glycosylation of fish collagen peptides with glucosamine hydrochloride catalyzed by transglutaminase to obtain a transglutaminase-catalyzed glycosylation product (TGP) and investigate its antifreeze effect on tilapia. Compared with the blank group, the freshness (pH value of 6.31, TVB-N value of 21.7 mg/100 g, whiteness of 46.28), textural properties (especially hardness and elasticity), and rheological properties of the TGP groups were significantly improved. In addition, the protein structures of the samples were investigated using UV absorption and fluorescence spectroscopy. The results showed that the tertiary structure of the TGP groups changed to form a dense polymer. Therefore, this approach can reduce the denaturation and decomposition of muscle fibers and proteins in fish meat more effectively and has a better protective effect on muscle structure and protein aggregation, improving the stability of fish meat. This study reveals an innovative method for generating antifreeze peptides by enzymatic glycosylation, and glycosylated fish collagen peptide products can be used as new and effective green antifreeze agents in frozen foods.
Ovotransferrin Fibril—Gum Arabic Complexes as Stabilizers for Oleogel-in-Water Pickering Emulsions: Formation Mechanism, Physicochemical Properties, and Curcumin Delivery
Authors: Wei Zihao , Dong Yue , Si Jingyu
Published: April 2024

This project aimed to explore the influence of the interaction between ovotransferrin fibrils (OTF) and gum arabic (GA) on the formation mechanism, physicochemical properties, and curcumin delivery of the oleogel-in-water Pickering emulsion. Cryo-scanning electron microscopy results showed that OTF—GA complexes effectively adsorbed on the oil–water interface, generating spatial hindrance to inhibit droplet coalescence. The texture analysis also proved that OTF—GA complexes endowed oleogel-in-water Pickering emulsion with preferable springiness (0.49 ± 0.03 mm), chewiness (0.43 ± 0.07 mJ), and adhesion (0.31 ± 0.01 mJ). By exploring the coalescence stability, droplet size, and rheological properties of OTF—GA complexes–stabilized oleogel-in-water Pickering emulsion (OGPE), the higher coagulation stability, larger average droplet size (46.22 ± 0.08 μm), and stronger gel strength were observed. The microrheological results also exhibited stronger attraction between the OGPE droplets, a more pronounced solid-like structure, and a slower speed of movement than OTF-stabilized oleogel-in-water Pickering emulsion (OPE). Meanwhile, OGPE significantly enhanced the extent of lipolysis, stability, and bioaccessibility of curcumin, suggesting that it possessed superior performance as a delivery system for bioactive substances. This project provided adequate theoretical references for protein–polysaccharide complexes–stabilized oleogel-in-water Pickering emulsion, and contributed to expanding the application of oleogel-in-water Pickering emulsion in the food industry.
Metabolome and Transcriptome Association Analysis Reveals Mechanism of Synthesis of Nutrient Composition in Quinoa (Chenopodium quinoa Willd.) Seeds
Authors: Yang Jindan , Wang Yiyun , Sun Jiayi , Li Yuzhe , Zhu Renbin , Yin Yongjie , Wang Chuangyun , Yin Xuebin , Qin Lixia
Published: April 2024

Quinoa (Chenopodium quinoa Willd.) seeds are rich in nutrition, superior to other grains, and have a high market value. However, the biosynthesis mechanisms of protein, starch, and lipid in quinoa grain are still unclear. The objective of this study was to ascertain the nutritional constituents of white, yellow, red, and black quinoa seeds and to employ a multi-omics approach to analyze the synthesis mechanisms of these nutrients. The findings are intended to furnish a theoretical foundation and technical support for the biological breeding of quinoa in China. In this study, the nutritional analysis of white, yellow, red, and black quinoa seeds from the same area showed that the nutritional contents of the quinoa seeds were significantly different, and the protein content increased with the deepening of color. The protein content of black quinoa was the highest (16.1 g/100 g) and the lipid content was the lowest (2.7 g/100 g), among which, linoleic acid was the main fatty acid. A combined transcriptome and metabolome analysis exhibited that differentially expressed genes were enriched in “linoleic acid metabolism”, “unsaturated fatty acid biosynthesis”, and “amino acid biosynthesis”. We mainly identified seven genes involved in starch synthesis (LOC110716805, LOC110722789, LOC110738785, LOC110720405, LOC110730081, LOC110692055, and LOC110732328); five genes involved in lipid synthesis (LOC110701563, LOC110699636, LOC110709273, LOC110715590, and LOC110728838); and nine genes involved in protein synthesis (LOC110710842, LOC110720003, LOC110687170, LOC110716004, LOC110702086, LOC110724454 LOC110724577, LOC110704171, and LOC110686607). The data presented in this study based on nutrient, transcriptome, and metabolome analyses contribute to an enhanced understanding of the genetic regulation of seed quality traits in quinoa, and provide candidate genes for further genetic improvements to improve the nutritional value of quinoa seeds.
The Effect of High-Pressure Processing of Caprine Milk on the Production and Properties of Yoghurt
Authors: Jankowska Agnieszka , Kiełczewska Katarzyna , Wachowska Maria , Dąbrowska Aneta , Siemianowski Krzysztof , Haponiuk Elżbieta , Stasiewicz Katarzyna
Published: April 2024

The aim of this study was to determine the suitability of HP-treated caprine milk for yoghurt production and to evaluate the effect of HP treatment on yoghurt properties. Reconstituted caprine milk was subjected to HP treatment (350 MPa/10 min/20 °C); a lactic acid starter culture (YC-X16, Chr. Hansen) was added. Milk was fermented at a temperature of 43 °C until pH 4.60. Bacterial counts, pH, color, rheological characteristics, texture, microstructure, and the sensory attributes of the yoghurt were determined after production and after seven days of storage at a temperature of 4 °C. HP treatment increased color saturation and whiteness index and induced a minor increase in milk pH. Minor differences in the acidification curve were noted. During storage, Streptococcus thermophilus counts were significantly higher in yoghurt from HP-treated than from untreated milk, whereas Lactobacillus delbruecki ssp. bulgaricus counts remained stable. A color analysis did not reveal differences between the experimental and control yoghurts. After storage, yoghurt made from HP-treated milk was characterized by thicker consistency and lower rheological stability than the control yoghurt. The micrographs of the yoghurts confirmed the differences in rheological parameters. Yoghurt produced from HP-treated caprine milk and stored for seven days received the highest scores in the sensory evaluation.
Rediscovering Portuguese White Crowberries (Corema album): Cultural Insights and Nutritional Significance
Authors: Cunha Ana Margarida , Pereira Andreia , Cardoso Ana Paula , Silva Aida Moreira da , Barroca Maria João , Guiné Raquel P. F.
Published: April 2024

White crowberries (Corema album) are a fruit from an endemic shrub found in Southern European Atlantic costal dunes. Although this shrub and its fruits never became a formal commercial crop for a number of reasons, it has a long-lasting relevance and tradition, much associated with summer, beach and holidays. The main goal of this study was to conduct a thematic analysis of the words and small expressions people associate with white crowberries. For that, a questionnaire was used, and the participants were asked to indicate in an open-ended question which top-of-mind words/small expressions they associate with white crowberries. A total of 501 people participated in this study, of which only 394 knew about white crowberries, and from those, only 229 answered the open-ended question of interest to this purpose. The results showed that the words/small expressions given by the participants were distributed between five categories (1—Memories of places, people and times, 2—Emotions and experiences, 3—Sensorial perception, 4—Properties and uses, and 5—Natural resources’ valuation). Additionally, 18 subcategories were also identified. The most representative of the categories was sensorial perception and the most relevant of the subcategories was habitats (a subcategory from category 1). The most frequent words mentioned by the participants were beach, berry and summer. In addition, the effect of sociodemographic groups was investigated and some variations were observed in the categories of the words mentioned by the participants according to sex, living environment or region. This work allowed for the identification of a high variability in the words or expressions that account for a rich patrimony of tacit knowledge, memories, emotions and perceptions of the population towards white crowberries, thus confirming their social as well as nutritional relevance.