bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling Non-Quasi-Static Magnetic Coupling of Parallel Flat Inductors for Wireless Power Transfer Applications in Close Proximity to the Ground
Authors: Parise Mauro
Published: February 2025

Presented here is a novel rigorous analytical solution for investigation of the magnetic coupling of two parallel flat inductors located near conducting earth. The solution is derived by replacing the integrand of the double-integral expression for the flux linkage with its power series expansion and then carrying out analytical integration of each term of the resulting series of integrals. The mutual inductance is given as a combination of special functions, depending on the electromagnetic and geometrical parameters of the problem. The validity of the derived solution is assessed through comparison with the outcomes of conventional analytical and numerical algorithms. The results from the conducted simulations confirm that the proposed formulation offers advantages over the previous techniques in terms of both accuracy and efficiency.
Short-Term Building Electrical Load Prediction by Peak Data Clustering and Transfer Learning Strategy
Authors: Li Kangji , Zhou Shiyi , Zhao Mengtao , Wei Borui
Published: February 2025

With the gradual penetration of new energy generation and storage to the building side, the short-term prediction of building power demand plays an increasingly important role in peak demand response and energy supply/demand balance. The low occurring frequency of peak electrical loads in buildings leads to insufficient data sampling for model training, which is currently an important factor affecting the performance of short-term electrical load prediction. To address this issue, by using peak data clustering and knowledge transfer from similar buildings, a short-term electrical load forecasting method is proposed. First, a building’s electrical peak loads are clustered through peak/valley data analysis and K-nearest neighbors categorization method, thereby addressing the challenge of data clustering in data-sparse scenarios. Second, for peak/valley data clusters, an instance-based transfer learning (IBTL) strategy is used to transfer similar data from multi-source domains to enhance the target prediction’s accuracy. During the process, a two-stage similar data selection strategy is applied based on Wasserstein distance and locality sensitive hashing. An IBTL strategy, iTrAdaboost-Elman, is designed to construct the predictive model. The performance of proposed method is validated on a public dataset. Results show that the data clustering and transfer learning method reduces the error by 49.22% (MAE) compared to the Elman model. Compared to the same transfer learning model without data clustering, the proposed approach also achieves higher prediction accuracy (1.96% vs. 2.63%, MAPE). The proposed method is also applied to forecast hourly/daily power demands of two real campus buildings in the USA and China, respectively. The effects of data clustering and knowledge transfer are both analyzed and compared in detail.
Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism
Authors: Chen Yang , Tang Zeyang , Cui Yibo , Rao Wei , Li Yiwen
Published: February 2025

The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper proposes a forecasting approach that integrates dynamic time warping (DTW) with a spatial–temporal attention graph convolutional neural network (ASTGCN). First, this method delves into the correlations between various regions within the target city, establishing intricate coupling relationships among them. Subsequently, the FastDTW algorithm is employed to construct an adjacency matrix, capturing the spatiotemporal correlation among different urban regions. Finally, the ASTGCN model is applied to predict the power load of each region, which can accurately capture the spatiotemporal characteristics of the power load. The experimental results indicate that the proposed model has a more powerful comprehensive ability to capture spatiotemporal relationships and improve accuracy and stability in different prediction steps.
On-Line Insulation Monitoring Method of Substation Power Cable Based on Distributed Current Principal Component Analysis
Authors: Yang Haobo , Wang Jingang , Zhao Pengcheng , Yu Chuanxiang , You Hongkang , Dou Jinyao
Published: February 2025

Monitoring the insulation condition of power cables is essential for ensuring the safe and stable operation of the substation power supply system. Leakage current is an important indicator of insulation performance of power cables. However, the application of leakage current monitoring methods in substations is limited due to issues such as neutral line shunting on the load side and the spatial isolation of the phase-to-neutral line in the power cabinet. This paper proposes an insulation monitoring method based on distributed current principal component analysis for power cables in substations. Firstly, the leakage current of substation power cable is measured by a distributed current extraction method, and the cable insulation condition is preliminarily judged. Then, considering the problem of measurement error interference in the process of distributed current synthesis, an evaluation method of power cable insulation state based on principal component analysis of distributed current is proposed. To verify the feasibility of the proposed method, both simulation and laboratory tests were conducted. The results indicate that the proposed method can effectively measure the leakage current of power cables in substations and realize the accurate distinction between measurement error and cable insulation degradation characteristics. The method offers a novel idea for insulation monitoring of substation power cables.
Application of State Models in a Binary–Temporal Representation for the Prediction and Modelling of Crude Oil Prices
Authors: Stasiak Michał Dominik , Staszak Żaneta , Siwek Joanna , Wojcieszak Dawid
Published: February 2025

Crude oil prices have a key meaning for the economies of most countries. Their levels shape the general production costs in many sectors. Oil prices are also a base for financial derivatives like CFD contracts, which are popular nowadays. Due to these reasons, the possibility of an effective prediction of the direction of future changes in the price of crude oil is especially significant. Most existing works focus on the analysis of daily closing prices. This kind of approach results, on the one hand, in losing important information about the dynamics of changes during the day. On the other hand, it does not allow for the modelling of short-term price changes that are especially important in cases of financial derivatives having crude oil as their base instrument. The goal of the following article is the analysis of possible applications of a binary–temporal representation in the modelling and construction of effective decision support systems on the crude oil market. The analysis encompasses all researched state models, e.g., those applying mean and trend analysis. Also, the selection of parameters was optimized for Brent crude oil rates. The presented research confirms the high effectiveness of our state modelling system in predicting oil prices on a level that allows for the construction of financially effective investment decision support systems. The obtained results were verified based on proper backtests from different quotation periods. The presented results can be used both in scientific analyses and in the construction of investment support tools for the crude oil market.
Transmission Line Icing Prediction Based on Physically Guided Fast-Slow Transformer
Authors: Wang Feng , Ma Ziming
Published: February 2025

To improve the accuracy of the icing prediction model for overhead transmission lines, a physics-guided Fast-Slow Transformer icing prediction model for overhead transmission lines is proposed, which is based on the icing prediction model with meteorological input characteristics. First, the ice cover data is segmented into different time resolutions through Fourier transform; a transformer model based on Fourier transform is constructed to capture the local and global correlations of the ice cover data; then, according to the calculation model of the comprehensive load on the conductor and the conductor state equation, the variation law of ice thickness, temperature, wind speed, and tension is analyzed, and the model loss function is constructed according to the variation law to guide the training process of the model. Finally, the sample mixing enhancement algorithm is used to reduce the overfitting problem and improve the generalization performance of the prediction model. The results show that the proposed prediction model can consider the mechanical constraints in the ice growth process and accurately capture the dependence between ice cover and meteorology. Compared with traditional prediction models such as LSTM (Long Short-Term Memory) networks, its mean square error, mean absolute error, and mean absolute percentage error are reduced by 0.464–0.674, 0.41–0.53, and 8.87–11.5%, respectively, while the coefficient of determination (R2) is increased by 0.2–0.29.
Holistic Hosting Capacity Enhancement Through Sensitivity-Driven Flexibility Deployment and Uncertainty-Aware Optimization in Modern Distribution Networks
Authors: Pan Wenjie , Han Jun , Cai Chao , Chen Haofei , Liu Hong , Xu Zhengyang
Published: February 2025

This study presents a novel sensitivity-driven distributionally robust optimization framework designed to enhance hosting capacity in renewable-powered distribution networks through targeted flexibility resource deployment. The proposed approach integrates temporal sensitivity mapping with robust optimization techniques to prioritize resource allocation across high-sensitivity nodes, addressing uncertainties in renewable energy generation and load demand. By leveraging a dynamic interaction between sensitivity scores and temporal system conditions, the framework achieves efficient and resilient operation under extreme variability scenarios. Key methodological innovations include the incorporation of a social force model-based sensitivity mapping technique, a layered optimization approach balancing system-wide and localized decisions, and a robust uncertainty set to safeguard performance against distributional shifts. The framework is validated using a synthesized test system, incorporating realistic renewable generation profiles, load patterns, and energy storage dynamics. Results demonstrate a significant improvement in hosting capacity, with system-wide enhancements of up to 35% and a 50% reduction in renewable curtailment. Moreover, sensitivity-driven resource deployment ensures efficient utilization of flexibility resources, achieving a peak allocation efficiency of 90% during critical periods. This research provides a comprehensive tool for addressing the challenges of renewable integration and grid stability in modern power systems, offering actionable insights for resource allocation strategies under uncertainty. The proposed methodology not only advances the state-of-the-art in sensitivity-based optimization but also paves the way for scalable, resilient energy management solutions in high-renewable penetration scenarios.
Likert-Type Scale
Authors: Koo Malcolm , Yang Shih-Wei
Published: February 2025

The Likert-type scale is a widely used psychometric instrument for measuring attitudes, opinions, or perceptions in research contexts. It presents respondents with a series of statements accompanied by symmetrical response options, typically structured on a five-point scale ranging from “Strongly Disagree” to “Strongly Agree”. Each point on the scale represents a gradation of agreement or sentiment, allowing researchers to transform subjective responses into quantifiable data for statistical analysis and interpretation.
Fungicides in English Rivers: Widening the Understanding of the Presence, Co-Occurrence and Implications for Risk Assessment
Authors: Porter Nick , Collins Rob
Published: February 2025

Fungicides are commonly found in freshwater; however, the understanding of their wider presence, co-occurrence, and potential risk remains limited. This study examined English national datasets to highlight knowledge gaps and identify improvements to monitoring and risk assessment. The analysis found that at least one fungicide was present in 91% of samples collected from English rivers over a 5-year period, with four fungicides detected at rates exceeding 50%. Co-occurrence occurs widely, with up to nine different fungicides detected within the same sample and four detected the most frequently, raising concerns for synergistic interactions. The semi-quantitative nature of much of the available data precludes a clear determination of the potential risk of detrimental effects on aquatic biota. Fully quantitative analysis is required, and ecotoxicity-based water quality standards need to be agreed upon. The monthly sampling regime reflected in the national datasets will infrequently capture high flow events and so is unlikely to fully represent fungicides transported to rivers via rainfall-driven processes. Several information gaps exist, including the risk posed by fungicides in sewage sludge applied to land and the extent to which fungicides in the aquatic and terrestrial environments contribute to antifungal resistance. Improvements in spatial and temporal information on fungicide use are needed.
Impact of Air Pollution and Smog on Human Health in Pakistan: A Systematic Review
Authors: Iram Shazia , Qaisar Iqra , Shabbir Rabia , Pomee Muhammad Saleem , Schmidt Matthias , Hertig Elke
Published: February 2025

Air pollution is a serious public health issue in Pakistan’s metropolitan cities, including Lahore, Karachi, Faisalabad, Islamabad, and Rawalpindi. Pakistan’s urban areas are vulnerable due to air pollution drivers such as industrial activities, vehicular emissions, burning processes, emissions from brick kilns, urbanization, and other human activities that have resulted in significant human health issues. The purpose of this study was to examine the impact of air pollutants and smog, as well as their causes and effects on human health. The PRISMA technique was used to assess the impact of environmental contaminants on human health. This study looked at air pollution sources and pollutants such as PM2.5, PM10, CO2, CO, SOX, and NOx from waste combustion and agriculture. The population included people of all ages and sexes from both urban and rural areas of Pakistan. Data were retrieved and analyzed using SRDR+ software and Microsoft Excel spreadsheets. The data suggested that Karachi and Lahore had the highest levels of air pollution and disease prevalence, which were attributed to heavy industrial activity and traffic emissions. Smog was a serious concern in Lahore during winter, contributing to the spread of several diseases. Other cities, including Islamabad, Rawalpindi, Jhang, Sialkot, Faisalabad, and Kallar Kahar, were impacted by agricultural operations, industrial pollutants, brick kilns, and urbanization. Due to these drivers of air pollution, some diseases such as respiratory and cardiovascular diseases had notably higher incidences in these cities. Other diseases were connected with air pollution exposure, asthma, eye and throat problems, allergies, lung cancer, morbidities, and mortalities. To reduce air pollution’s health effects, policies should focus on reducing emissions, supporting cleaner technologies, and increasing air quality monitoring.
Extracting Individual Tree Positions in Closed-Canopy Stands Using a Multi-Source Local Maxima Method
Authors: Lai Guozhen , Cao Meng , Zhou Chengchuan , Liu Liting , Zhong Xun , Guo Zhiwen , Ouyang Xunzhi
Published: February 2025

The accurate extraction of individual tree positions is key to forest structure quantification, and Unmanned Aerial Vehicle (UAV) visible light data have become the primary data source for extracting individual tree locations. Compared to deep learning methods, classical detection methods require lower computational resources and have stronger interpretability and applicability. However, in closed-canopy forests, challenges such as crown overlap and uneven light distribution hinder extraction accuracy. To address this, the study improves the existing Revised Local Maxima (RLM) method and proposes a Multi-Source Local Maxima (MSLM) method, based on UAV visible light data, which integrates Canopy Height Models (CHMs) and Digital Orthophoto Mosaics (DOMs). Both the MSLM and RLM methods were used to extract individual tree positions from three different types of closed-canopy stands, and the extraction results of the two methods were compared. The results show that the MSLM method outperforms the RLM in terms of Accuracy Rate (85.59%), Overall Accuracy (99.09%), and F1 score (85.21%), with stable performance across different forest stand types. This demonstrates that the MSLM method can effectively overcome the challenges posed by closed-canopy stands, significantly improving extraction precision. These findings provide a cost-effective and efficient approach for forest resource monitoring and offer valuable insights for forest structure optimization and management.
Fire Severity Outperforms Remote Sensing Indices in Exploring Post-Fire Vegetation Recovery Dynamics in Complex Plateau Mountainous Regions
Authors: Liu Pengfei , Zhuang Weiyu , Kou Weili , Wang Leiguang , Wang Qiuhua , Deng Zhongjian
Published: February 2025

Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach combining differenced Normalized Burn Ratio (dNBR) and visual interpretation on Google Earth Engine (GEE) to generate high-quality training samples from Landsat 5 TM/7 ETM+/8 OLI imagery. Four supervised machine learning algorithms were evaluated, with Random Forest (RF) demonstrating superior accuracy (OA = 0.90) for fire severity classification compared to Support Vector Machine (SVM) OA of 0.88, Classification and Regression Tree(CART) OA o f0.85, and Naive Bayes(NB) OA of 0.78. Using RF, we generated annual fire severity maps alongside the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR) from 2005 to 2020. Key findings include the following: (1) fire severity classification outperformed traditional remote sensing indices in characterizing vegetation recovery; (2) distinct recovery trajectories emerged across severity levels, with moderate areas recovering in 7 years, severe areas transitioning within 2 years, and low severity areas peaking at 2 years post-fire; (3) southern mountainous regions exhibited 1–2 years faster recovery than northern areas. These insights advance understanding of post-fire ecosystem dynamics in complex terrains and support more effective recovery strategies.
Whole-Genome Identification and Expression Profiles of WRKY Genes Related to the Leaf Expansion Period in the Camphor Tree
Authors: Long Shilin , Jiang Jingyao , Lin Hanyang
Published: February 2025

The camphor tree (Cinnamomum camphora) is of great economic and ecological value, and the WRKY transcription factor (TF) family plays a crucial role in regulating plant growth and development as well as the responses toward environmental changes. However, the research on WRKY TFs in C. camphora remains scarce, and their roles in the leaf expansion period are unknown. In this study, we identified WRKY TFs across the C. camphora genome, followed by a phylogenetic analysis. Then, we conducted RNA sequencing and qPCR experiments on leaves collected from three distinct stages during leaf expansion (S1, S2, and S3) to determine which WRKY genes showed significant up-regulation during these stages. Here, a total of 72 CcWRKY TFs were found in the C. camphora genome, and they were phylogenetically clustered with corresponding subfamilies of Arabidopsis thaliana. These CcWRKY proteins were divided into three major groups (I, II, and III), where group II consisted of five subgroups. We found that three genes (CcWRKY24, CcWRKY42, and CcWRKY70) were upregulated from both S1 to S2 and from S1 to S3. The expression level of CcWRKY24 increased gradually from S1 to S3, while CcWRKY42 and CcWRKY70 exhibited higher expression levels in S2 and S3 than in S1. These predicted gene expression profiles were further confirmed by qPCR experiments. In summary, this study analyzed WRKY TFs in C. camphora from a genome-wide perspective and paves the way for future research on the functions of CcWRKYs.
Study of the Relationship Between Nitrogen, Phosphorus Content, and Microbial Community Changes in Deer Manure Compost with Different Conditioners
Authors: Xu Jianling , Wang Xinyu , Feng Jiayin , Zhang Wenqiu , Sun Jitian
Published: February 2025

Composting is an environmentally friendly method for disposing of solid waste. To enhance the fermentation rate and quality of deer manure composting, we investigated the effects of various conditioners (biochar, zeolite, biochar + zeolite) on the aerobic composting process of deer manure. The results indicated that the combination of biochar and zeolite significantly promoted the degradation of organic matter, resulting in a 34.83% decrease in total organic carbon (TOC) content. The addition of biochar was particularly beneficial for nitrogen retention in the compost, with the total nitrogen content reaching its highest level at 39.55 g/kg. Furthermore, the inclusion of zeolite and biochar altered the phosphorus content of the compost, with zeolite demonstrating a more favorable effect. The addition of a conditioner increased the relative abundance of Ascomycota and Proteobacteria and decreased the relative abundance of Firmicutes; the changes in Corynebacterium, Acinetobacter, and Glutamicibacter were positively correlated with the changes in the carbon-to-nitrogen ratio (C/N ratio) and negatively correlated with total nitrogen (TN) and total phosphorus (TP) levels. The mixed conditioner of biochar + zeolite used in composting exhibited low toxicity and the highest degree of decomposition. In summary, the combination of biochar + zeolite as a mixed conditioner is the optimal choice for reducing the toxicity of compost and promoting its maturation. Further research will be conducted in the future to promote the resource utilization of agricultural wastes such as deer manure.
The Health Benefits and Functional Properties of Gochujang: A Comprehensive Review of Fermentation and Bioactive Compounds
Authors: Park Young Kyoung , Kim Jinwon , Ryu Myeong Seon , Yang Hee-Jong , Jeong Do-Youn , Shin Dong-Hwa
Published: February 2025

Gochujang, a traditional Korean fermented red pepper paste, is celebrated for its unique spicy and fermented flavor. This natural, whole food offers several health benefits due to the bioactive compounds formed during fermentation and its diverse ingredients. These bioactive compounds have been shown to have anti-cancer properties and anti-inflammatory effects by reducing inflammatory cytokines and suppressing pathways associated with diseases such as colitis and hepatitis. Gochujang has also been shown to help prevent obesity by promoting weight loss, inhibiting fat accumulation, and improving lipid profiles. It has also been shown to aid in the prevention of diabetes by suppressing hepatic glucose production and improving insulin sensitivity. The influence of gochujang on the gut microbiota is remarkable, with the ability to increase beneficial bacteria, improve microbial balance, and alleviate metabolic disorders. The primary agents responsible for these effects are capsaicin, fermentation by-products, and other bioactive compounds. The fermentation process, driven by microorganisms, enhances the nutritional and functional properties of gochujang, strengthening its health-promoting potential. This paper provides a comprehensive review of gochujang’s historical background, production methods, the role of microorganisms in fermentation, and its functional properties, emphasizing its value as a functional food for overall health improvement.
Identification of Corn Chaff as an Optimal Substrate for the Production of Rhamnolipids in Pseudomonas aeruginosa Fermentations
Authors: Bava Adriana , Carnelli Sara , Vaccari Mentore , Beffa Trello , Beltrametti Fabrizio
Published: February 2025

Waste biomass deriving from agricultural activities has different destinations depending on the possibility of applying it to specific processes. As the waste biomass is abundant, cheap, and generally safe, it can be used for several applications, biogas production being the most relevant from the quantitative point of view. In this study, we have used a set of agricultural by-products (agro-waste) deriving from the post-harvest treatment of cereals and legumes as the growth substrate for selected biosurfactant-producing microbial strains. The agricultural by-products were easily metabolized and highly effective for the growth of microorganisms and the production of rhamnolipids and surfactin by Pseudomonas aeruginosa and Bacillus subtilis, respectively. In particular, the use of corn chaff (“bee-wings”) was suitable for the production of rhamnolipids. Indeed, in corn-chaff-based media, rhamnolipids yields ranged from 2 to 18 g/L of fermentation broth. This study demonstrated that the use of waste raw materials could be applied to reduce the carbon footprint of the production of biosurfactants without compromising the possibility of having a suitable fermentation medium for industrial production.
Intelligent Virtual Reality and Augmented Reality Technologies: An Overview
Authors: Lampropoulos Georgios
Published: February 2025

The research into artificial intelligence (AI), the metaverse, and extended reality (XR) technologies, such as augmented reality (AR), virtual reality (VR), and mixed reality (MR), has been expanding over the recent years. This study aims to provide an overview regarding the combination of AI with XR technologies and the metaverse through the examination of 880 articles using different approaches. The field has experienced a 91.29% increase in its annual growth rate, and although it is still in its infancy, the outcomes of this study highlight the potential of these technologies to be effectively combined and applied in various domains transforming and enriching them. Through content analysis and topic modeling, the main topics and areas in which this combination is mostly being researched and applied are as follows: (1) “Education/Learning/Training”, (2) “Healthcare and Medicine”, (3) “Generative artificial intelligence/Large language models”, (4) “Virtual worlds/Virtual avatars/Virtual assistants”, (5) “Human-computer interaction”, (6) “Machine learning/Deep learning/Neural networks”, (7) “Communication networks”, (8) “Industry”, (9) “Manufacturing”, (10) “E-commerce”, (11) “Entertainment”, (12) “Smart cities”, and (13) “New technologies” (e.g., digital twins, blockchain, internet of things, etc.). The study explores the documents through various dimensions and concludes by presenting the existing limitations, identifying key challenges, and providing suggestions for future research.
Identification and Characterization of Myxobolus pronini (Cnidaria: Myxozoa) from Gibel Carp Carassius auratus gibelio and Goldfish C. auratus: New Fish Host, Infection Site, and Geographic Distribution in China
Authors: Zhou Fan , Zhang Xiaoyi , Ding Peng , Sun Ronghua , Wang Zhe , Liu Yang
Published: February 2025

Myxozoans represent a significant group of parasitic pathogens affecting both wild and farmed fish. For accurate and comprehensive early detection, the composition of myxozoan pathogens in fish has consistently been a primary focus for parasitologists. In China, we undertook an investigation into myxozoan infection in fish from Shandong Province, China, successfully isolating a myxozoan species from gibel carp Carassius auratus gibelio Bloch, 1782 and goldfish C. auratus Linnaeus, 1758. In the present study, this myxozoan species was identified by detailed morphological and molecular analysis. This parasite is characterized by the presence of visible plasmodia in various organs (intestine, liver, gallbladder, and abdominal cavity) of gibel carp as well as the abdominal cavities of goldfish. In comparison to all documented myxozoan species, this myxozoan species exhibits morphological identity with Myxobolus pronini Liu, Batueva, Zhao, Zhang, Zhang, Li, Li, 2016, in terms of spore morphology. Molecular sequence analysis, including sequence similarity, variable site, and the secondary structure of SSU rRNA, indicates that the present myxozoan species closely resembles M. pronini. Furthermore, phylogenetic analyses revealed that all isolates collected from different organs and hosts in this study clustered with M. pronini, forming a distinct branch within the Carassius-infecting clade. Consequently, the present myxozoan species can be confidently identified as M. pronini. Compared with the previous reports on M. pronini, this study documents, for the first time, goldfish as a host, intestines and gallbladders as the sites of infection, and Shandong Province as the geographic distribution of this myxozoan species.
A Machine Learning Approach to Volume Tracking in Multiphase Flow Simulations
Authors: Mak Aaron , Raessi Mehdi
Published: February 2025

This work presents a machine learning (ML) approach to volume-tracking for computational simulations of multiphase flow. It is an alternative to a commonly used procedure in the volume-of-fluid (VOF) method for volume tracking, in which the phase interfaces are reconstructed for flux calculation followed by volume advection. Bypassing the computationally expensive steps of interface reconstruction and flux calculation, the proposed ML approach performs volume advection in a single step, directly predicting the volume fractions at the next time step. The proposed ML function is two-dimensional and has eleven inputs. It was trained using MATLAB’s (R2021a) Deep Learning Toolbox with a grid search method to find an optimal neural network configuration. The performance of the ML function is assessed using canonical test cases, including translation, rotation, and vortex tests. The errors in the volume fraction fields obtained by the ML function are compared with those of the VOF method. In ideal conditions, the ML function speeds up the computations four times compared to the VOF method. However, in terms of overall robustness and accuracy, the VOF method remains superior. This study demonstrates the potential of applying ML methods to multiphase flow simulations while highlighting areas for further improvement.
The Potential Use of ChatGPT as a Sensory Evaluator of Chocolate Brownies: A Brief Case Study
Authors: Torrico Damir D.
Published: February 2025

ChatGPT, a recently developed natural large language processing tool, has been widely explored in various fields of science and research. This study aimed to evaluate the potential use of ChatGPT as a sensory evaluator of hypothetical formulations of chocolate brownies. ChatGPT was prompted to act as an experienced taster to provide a detailed description of sensory characteristics for fifteen chocolate brownie formulations grouped into three categories (standard, common ingredients replacements, and uncommon ingredients replacements). Sentiment analysis, emotions/descriptors classification, and correspondence analysis were conducted to analyze ChatGPT responses. Results showed that the terms “trust”, “anticipation”, and “joy” were the most frequently expressed sentiments in the ChatGPT responses. The valence of all ChatGPT responses was mostly positive. The overall quality scores of all chocolate brownie formulations given by ChatGPT were extremely high, in the range of 8.5–9.5 (out of 10). ChatGPT tended to have higher positive emotions to formulations (some including worm meals and fish oil) that might have the opposite reactions with real consumers. Further research should focus on validating ChatGPT sensory descriptors with the outcomes of a human sensory panel. Additionally, future studies can explore the potential use of ChatGPT in evaluating the sensory characteristics of other food products for optimizing the product development process.
Comparative Analysis of the Nutrient Composition of Caulerpa lentillifera from Various Cultivation Sites
Authors: Zhou Wenchuan , Wang Yun , Xu Rui , Tian Jialin , Li Ting , Chen Suwen
Published: February 2025

The nutrient, amino acid, and fatty acid compositions of Caulerpa lentillifera from various aquaculture regions were assessed to analyze their nutritional characteristics and potential for aquaculture development. The nutrient composition of C. lentillifera was determined according to the standard national nutrient determination methods of the Association of Official Analytical Chemists, and the following data were revealed. (1) The basic nutritional components of C. lentillifera were relatively more abundant in the three aquaculture areas in Guangdong Province. The crude protein content in C. lentillifera was measured at (8.70 ± 0.36)% and (18.57 ± 1.59)% for samples collected from the Dapeng and Daya areas, respectively. These values were significantly higher compared to those obtained from the Beihai, Dongshan, and Nha Trang regions (p < 0.05). Additionally, the crude lipid content of C. lentillifera from the Daya population was measured at (5.10 ± 1.59)%, which was significantly higher than that of samples from the Beihai, Dongshan, and Nha Trang areas (p < 0.05). Furthermore, the total sugar contents in C. lentillifera were quantified as (32.50 ± 4.22)% and (32.87 ± 2.59)% for samples from the Daya and Shanwei regions, respectively. These values were significantly greater (p < 0.05) compared to those observed in the other four populations. (2) The total amino acid (TAA) content of C. lentillifera ranged from 7.05% to 12.37%, with notable concentrations of the fresh amino acids aspartic acid (Asp) and glutamic acid (Glu). Significant variations in the TAA and essential amino acid (EAA) levels were observed among the cultivation regions (p < 0.05), with the Shanwei population exhibiting the highest TAA content of 12.37% and EAA content of 4.65%, surpassing all other populations except for Guangdong Province (p < 0.05). (3) The fatty acid composition analysis revealed that the total fatty acid (TFA) and unsaturated fatty acid (UFA) levels in the long-stemmed grape fern alga from Daya were 2.400% and 1.048%, respectively, and significantly greater than those in the other populations except for Dapeng (p < 0.05). These results imply that the nutritional quality of the Daya population of C. lentillifera is relatively high. C. lentillifera exhibits a palatable flavor profile, making it suitable for consumption and the development into high-quality seafood condiments, thereby contributing to the environmentally sustainable advancement of C. lentillifera aquaculture.
Evaluation and Validation of Colloidal Gold Immunochromatographic Qualitative Testing Products for the Detection of Emamectin Benzoate, Isocarbophos, and Fipronil in Cowpea Samples
Authors: Song Anning , Wang Miao , She Yongxin , Jin Maojun , Cao Zhen , Abd El-Aty A. M. , Wang Jing
Published: February 2025

Pesticide residues still pose a risk to human health. With the rapid development of rapid testing technology, the levels of different types of pesticide residues in agricultural products can be identified in a shorter period; thus, the safety of food can be guaranteed. However, the effectiveness of commercially available testing products has yet to be evaluated. In this study, colloidal gold immunochromatographic qualitative testing products manufactured by 34 companies were tested for their assay performance on Emamectin Benzoate, Isocarbophos, and fipronil with standardized cowpea samples. The results indicated that most of the evaluated products were identified as having ‘passed’. Most pesticide residue rapid test immunoassay products can be considered ideal means for testing certain pesticide residues. However, further evaluation of pesticide residue rapid test immunoassay products is needed, as detection technologies are still developing.
Destructive and Non-Destructive Evaluation of Anthocyanin Content and Quality Attributes in Red Kiwifruit Subjected to Plant Spray Treatment with Cis-3-Hexenyl Butyrate
Authors: Lembo Micaela , Eramo Vanessa , Riggi Riccardo , Forniti Roberto , Bellincontro Andrea , Botondi Rinaldo
Published: February 2025

This work evaluated red kiwifruit plants’ spray treatment with cis-3-hexenyl butyrate (HB) as an inductor of some metabolic mechanisms related to fruit ripening, including an increase in anthocyanin content and the red hue color parameter. Considering their key role as ripening parameters for postharvest fruit quality and sorting assessment, the soluble solid content (SSC) and the flesh firmness penetrometer (FFP) were also measured. Treated plants received an application of 50 mM HB, administered exactly 2 and 4 weeks before the commercial harvest. At harvest time and during postharvest fruit ripening, near-infrared (NIR) spectral acquisitions were performed in order to check the feasibility of a rapid and non-destructive prediction of fruit anthocyanin content and SSC, coupled to destructive measurements and chemometric modelling. Regarding technological and chemical results, HB treatment indicates an optimum overall qualitative storage at 30 days. The fruit from treated plants is characterized by good quality parameters, including higher SSC, enhanced red hue (a* value) and increased anthocyanin content, despite similar weight loss to the untreated fruit. The obtained chemometric results underscore the promise and feasibility of NIRs in terms of detecting and estimating anthocyanin content and SSC in red kiwifruit, in order to pursue an evident perspective of improvement.
Effect of Ultrasonic Intensity Treatment on the Physicochemical and Functional Properties of Coregonus peled Protamine
Authors: Wang Feifei , Shu Dong , Wei Yabo , Guo Xin , Liu Pingping , Deng Xiaorong , Zhao Yunfeng , Lei Yongdong , Zhang Jian
Published: February 2025

As the most valuable protein in the sperm of testes tissues of Coregonus peled, Coregonus peled protamine (CPP) had a natural antibacterial and antiseptic effect, but its physicochemical properties and functions are easily affected by the ultrasound-assisted extraction process. In this study, ultrasound-assisted extraction of CPP was used to investigate the effects of different ultrasonic intensities (0, 3.03, 6.07, 9.10, 12.13, and 15.16 W/cm2) on the structural and functional properties of CPP. The results showed that at moderate ultrasonic intensity (9.10 W/cm2), the protein was the most successful, as it was subjected to cavitation shear and microjet by ultrasound, which resulted in changes in protein structure, moderate unfolding of peptide chains, and changes in the secondary and tertiary structures of CPP. SEM images confirmed the changes in the microstructure of CPP. Ultrasound oxidized the proteins to varying degrees with the highest sulfhydryl and carbonyl and surface hydrophobicity content at an ultrasonic intensity of 9.10 W/cm2. At the same time, the solubility, antimicrobial activity, and heparin binding of CPP were affected. It is worth mentioning that the ultrasonicated CPP exhibited a stronger heparin-binding capacity compared to the non-ultrasonicated CPP. In conclusion, 9.10 W/cm2 was determined as the optimal ultrasonic intensity parameter for this study. In conclusion, the incorporation of appropriate ultrasonic intensity in the acidic extraction process could help to improve the functional properties of CPP, and ultrasound-assisted protein extraction has emerged as a reliable technique capable of modifying the structure and function of CPP.
Characterization of Plant-Based Raw Materials Used in Meat Analog Manufacture
Authors: Bulgaru Viorica , Mazur Mihail , Netreba Natalia , Paiu Sergiu , Dragancea Veronica , Gurev Angela , Sturza Rodica , Şensoy İlkay , Ghendov-Mosanu Aliona
Published: February 2025

The purpose of this research was to investigate the characteristics of different plant-based sources rich in protein, chickpea flour (CPF), hazelnut oil cake (HOC), soy protein isolate (SPI) and concentrate (SPC), and pea protein isolate (PPI) for their subsequent use in the manufacture of meat analogs. The protein sources were analyzed for dry matter, ash, protein, fat, starch, dietary fiber, water holding capacity, granulosity, color parameters (L*, a*, b*, C*, YI), antioxidant activity before and after gastrointestinal in vitro digestion, and amino acid and mineral compositions. The highest dry matter content was determined in hazelnut oil cake and pea protein isolate. For the protein content, maximum values were obtained for the protein isolate and concentrate samples, from 52.80% to 80.50%, followed by hazelnut oil cake and chickpea flour. The water-holding capacity of all plant sources was directly influenced by the values of protein content, dietary fiber, and granulosity. The results obtained after gastrointestinal digestion also showed quite significant antioxidant activity, which is due to the process of hydrolysis and denaturation of plant-based protein sources in the gastrointestinal tract. Major amino acids identified in the analyzed samples were glutamic acid, leucine, arginine, phenylalanine, serine, valine, alanine, and tyrosine from minerals P, Na, Mg, and Ca. Principal component analysis (PCA) was used to illustrate the relationship between physicochemical characteristics, amino acid composition, mineral composition, and antioxidant activity determined in the plant-based materials.
Modeling and Evaluating the Impact of Mobile Usage on Pedestrian Behavior at Signalized Intersections: A Machine Learning Perspective
Authors: Haque Faizanul , Kidwai Farhan Ahmad , Thapa Ishwor , Ghani Sufyan , Mtapure Lincoln M.
Published: February 2025

Pedestrian safety is a growing global concern, particularly in urban areas, where rapid urbanization and increased mobile device usage have led to an increase in distracted walking. This study investigates the impact of technological distractions, specifically mobile usage (MU), on pedestrian behavior and safety at signalized urban intersections. Data were collected from 11 signalized intersections in New Delhi, India, using video recordings. Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). The outputs of the models focus on predicting mobile usage behavior and its association with compliance behaviors such as crosswalk and signal adherence. The results show that 6.9% of the pedestrians used mobile phones while crossing the road. Advanced machine learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN), have been applied to analyze and predict MU behavior. Key findings reveal that younger pedestrians and females are more likely to exhibit distracted behavior, with pedestrians crossing alone being the most prone to mobile usage. MU was significantly associated with increased levels of crosswalk violation. Among the machine learning models, the CNN demonstrated the highest prediction accuracy (94.93%). The findings of this study have a practical application in urban planning, traffic management, and policy formulation. Recommendations include infrastructure improvements, public awareness campaigns, and technology-based interventions to mitigate pedestrian distractions and to enhance road safety. These findings contribute to the development of data-driven strategies to improve pedestrian safety in rapidly urbanizing regions.
Stomatitis Healing via Hydrogels Comprising Proline, Carboxyvinyl Polymer, and Water
Authors: Hanaki Raichi , Harada Koji , Sasaki Yoshihiro , Matsumoto Michiaki , Tahara Yoshiro
Published: February 2025

Chemotherapy using anticancer agents and radiotherapy of cancers frequently induce the development of stomatitis as a side effect. In the present study, hydrogels for effective stomatitis healing under anticancer drug administration were developed using three components, namely proline, carboxyvinyl polymer, and water (denoted proline gels). Characterization including tilting, Fourier transform infrared spectra, and viscoelasticity measurements indicated that proline gels with proline concentrations over 300 μmol/g could retain water on the tongue of mice. The degradation and release behavior of proline gels in serological environments were evaluated, revealing that proline gels were degraded by serological salt concentrations, and the cumulative amount of proline released from proline gels depended on the concentration of proline in the gel. Proline gels were applied to the stomatitis area on the tongue of mice under anticancer drug administration, with subsequent reduction in the stomatitis area and regeneration of the mucosal epithelium layer, demonstrating effective stomatitis healing by proline gels with proline concentrations over 500 μmol/g. Other control samples including the carboxyvinyl polymer or proline alone did not reduce the stomatitis area in model mice. These results suggested that the proline gel is promising for the mucosa regeneration of anticancer drug-induced stomatitis.
Transcriptional Regulatory Network of the Embryonic Diapause Termination Process in Artemia
Authors: Wang Bin , He Zhen , Zhang Mingzhi , Zhang Ruiqi , Song Zhentao , Li Anqi , Hao Tong
Published: February 2025

Artemia is a typical animal used for the study of the diapause mechanism. The research on the regulation mechanism of diapause mainly focuses on the occurrence and maintenance of diapause. There are few studies on the mechanism of embryonic pause termination (EDT), especially for its transcriptional regulation mechanism. This study integrated transcriptional regulatory data from ATAC-seq and gene expression data from RNA-seq to explore the transcriptional regulatory mechanisms involved in the EDT process. Through integrated analysis, four important transcription factors (TFs), SVP, MYC, RXR, and SMAD6, were found to play a role in the EDT process, in which SVP, MYC, and RXR were upregulated, while SMAD6 was downregulated in the EDT stage. Through co-expression analysis, a transcription regulatory network for these four TFs was constructed and the functions of the TFs were analyzed. The expression of the TFs was further verified by RT-qPCR. Through functional analysis, SVP was found to be predominantly involved in cell adhesion and signal transduction. MYC probably played a role in protein binding. RXR may function in the process of RNA binding and the transfer of phosphorus-containing groups. Smad6 regulated the signal transduction, cell adhesion, and oxidation–reduction processes. The expression of the key TFs was verified by RT-qPCR. The results of this work provide important clues for the mechanism of transcriptional regulation in the EDT process of Artemia.