bluuebunny/crossref_metadata_2025 huggingface dataset:
Source Code Vulnerability Detection Based on Joint Graph and Multimodal Feature Fusion
Authors: Jin Dun , He Chengwan , Zou Quan , Qin Yan , Wang Boshu
Published: February 2025

Code vulnerability detection is a crucial approach to ensuring software security, aiming to automatically identify potential security vulnerabilities in source code. However, existing static vulnerability detection methods often face challenges such as feature loss and an insufficient expressive power when extracting program features. To address these issues, in this paper, we propose a source code vulnerability detection method based on joint graph and multimodal feature fusion. Innovatively, we construct a joint graph that integrates multiple program dependencies and semantic edges to achieve more comprehensive feature extraction. Additionally, by combining Graph Attention Networks (GATs) with the Transformer architecture, both structural and sequential features of code snippets are captured, further enhancing the model’s expressive capabilities. Finally, we introduce pre-fusion and post-fusion strategies to fully integrate multimodal features, thereby improving the accuracy and performance of vulnerability detection. Experimental results on the SARD dataset demonstrate the method’s excellent performance in detecting various types of vulnerabilities, achieving an F1 score of 85.20% and an accuracy of 86.50%. On the Real-Vul real-world dataset, the method achieves an F1 score of 73.9% and an accuracy of 86.50%. The detection results exhibit remarkable stability, ensuring a reliable and consistent performance. Overall, the proposed method surpasses the performance of existing mainstream detection approaches.
Enhanced DetNet: A New Framework for Detecting Small and Occluded 3D Objects
Authors: Zhang Baowen , Su Chengzhi , Cao Guohua
Published: February 2025

To mitigate the impact on detection performance caused by insufficient input information in 3D object detection based on single LiDAR data, this study designs three innovative modules based on the PointRCNN framework. Firstly, addressing the issue of the Multi-Layer Perceptron (MLP) in PointNet++ failing to effectively capture local features during the feature extraction phase, we propose the Adaptive Multilayer Perceptron (AMLP). Secondly, to prevent the problem of gradient vanishing due to the increased parameter scale and computational complexity of AMLP, we introduce the Channel Aware Residual module (CA-Res) in the feature extraction layer. Finally, in the head layer of the subsequent processing stage, we propose the Dynamic Attention Head (DA-Head) to enhance the representation of key features in the process of target detection. A series of experiments conducted on the KITTI validation set demonstrate that in complex scenarios, for the small target “Pedestrian”, our model achieves performance improvements of 2.08% and 3.46%, respectively, at the “Medium” and “Difficult” detection difficulty levels. To further validate the generalization capability of the Enhanced DetNet network, we deploy the trained model on the KITTI server and conduct a comprehensive evaluation of detection performance for the “Car”, “Pedestrian”, and “Cyclist” categories.
Efficient Graph-Based Multi-Story Path Planning with Optimized Elevator Selection for Indoor Delivery Robots
Authors: Kim Seungmin , Jang Hyunseo , Ha Jiseung , Lee Daekug , Ha Yeongho , Song Young Eun
Published: February 2025

Multi-story path planning (MSPP) for indoor delivery robots presents unique challenges in modern logistics systems, particularly in efficiently handling multiple deliveries across different floors. This paper proposes a graph-based path planning algorithm that optimizes delivery routes in multi-story buildings while maintaining computational efficiency. The algorithm, with a complexity of O(|E|log|V|), integrates three key components: (1) a floor-based parcel grouping strategy, (2) an optimized Dijkstra-based path planning system, and (3) an efficient elevator selection mechanism. The modular design allows for adaptability to diverse building layouts and logistics scenarios. Evaluated in a 15-story simulation with varying parcel counts (n = 1 to 7), the algorithm demonstrated minimal runtime increase (1.28 s to 1.35 s) while ensuring optimal path selection. Delivery distances ranged from 48 m (single-parcel) to 126 m (seven parcels), demonstrating the system’s scalability and efficiency in complex multi-story environments. Unlike traditional methods such as A*, the proposed approach guarantees optimality while effectively addressing vertical navigation challenges. These results validate its practical applicability in smart building environments and modern logistics systems, offering a robust solution for real-world delivery challenges.
xIIRS: Industrial Internet Intrusion Response Based on Explainable Deep Learning
Authors: Xue Qinhai , Zhang Zhiyong , Fan Kefeng , Wang Mingyan
Published: February 2025

The extensive interconnection and intelligent collaboration of multi-source heterogeneous devices in the industrial Internet environment have significantly improved the efficiency of industrial production and resource utilization. However, at the same time, the deployment characteristics of open-network architecture and the promotion of the concept of deep integration of OT/IT have led to an exponential growth of attacks on the industrial Internet. At present, most of the detection methods for industrial internet attacks use deep learning. However, due to the black-box characteristics caused by the complex structure of deep learning models, the explainability of industrial internet detection results generated based on deep learning is low. Therefore, we proposed an industrial internet intrusion response method xIIRS based on explainable deep learning. Firstly, an explanation method was improved to enhance the explanation by approximating and sampling the historical input and calculating the dynamic weighting for the sparse group lasso based on the evaluation criteria for the importance of features between and within feature groups. Then, we determined the defense rule scope based on the obtained explanation results and generated more fine-grained defense rules to implement intrusion response in combination with security constraints. The proposed method was experimented on two public datasets, TON_IoT and Gas Pipeline. The experimental results show that the explanation effect of xIIRS is better than the baseline method while achieving an average malicious traffic blocking rate of about 95% and an average normal traffic passing rate of about 99%.
Efficiency of Renewable Energy Potential Utilization in European Union: Towards Responsible Net-Zero Policy
Authors: Chodakowska Ewa , Nazarko Joanicjusz , Nazarko Łukasz
Published: February 2025

This study evaluates the efficiency of EU countries in utilizing their geographical potential for wind and solar energy production. A two-stage radial network data envelopment analysis (NDEA) is used to estimate the efficiency of the utilization of natural resources. The research is of a computational-empirical nature on the basis of publicly available data. The basic variables included in the model are: mean wind speed, Global Horizontal Irradiance, population, land area, wind energy capacity, solar PV capacity, wind energy generation, and solar power generation. The relationship between the environmental potential and the installed power capacity is evaluated in the first stage. In the second stage, the actual production from the installed capacity is analyzed. The efficiency trends over time are also investigated. This approach offers a comprehensive assessment by considering both the technical performance and environmental constraints. Considering all studied countries together, a slight increase in the relative efficiency of renewable energy potential utilization is observed—from 23.2% in 2018 to 28.7% in 2022. Germany and the Netherlands achieved 100% relative efficiency in 2022. The results reveal that the development of alternative energy sources and the efficiency of the installed power capacity utilization are not always in line with the local environmental conditions. The average efficiency of the analyzed countries from this perspective was 26.8% in 2018, with an improvement to 37.4% in 2022. The relative efficiency of the installed capacity utilization was high in both periods (76.3% and 74.9%, respectively). The impact of exogenous variables on performance (GDP and R&D expenditures) is discussed. Broader implications of the results for a responsible renewable energy policy in the EU demonstrate the need to combine overarching targets with a flexible governance system. That flexibility should allow for individual energy transition pathways, cooperative mechanisms, market integration, and targeted funding in order to account for the diversity of renewable resource utilization potentials among countries.
Energy Ladder, Decarbonisation and Energy Poverty: The European Union Inside
Authors: Dobrowolski Zbysław , Adamišin Peter , Sługocki Waldemar , Kotylak Sławomir
Published: February 2025

In the forthcoming decades, the energy sector will confront significant transitions related to climate change, supply stability, and energy poverty. The initial two aspects have undergone thorough scientific analysis, whilst the third has received comparatively less analysis despite its significant impact on the lives of millions. Using a systematic literature review, laws and regulations and document analysis, and based on the assumption of an energy ladder, this study provides an overview of energy poverty and its implications. The research focuses on energy poverty in developed countries and answers whether the European Union’s implemented and planned actions to reduce greenhouse gas emissions cause energy poverty. Based on the Polish case, the study shows that the EU’s scheduled actions aimed to stabilise climate change, although right in intention, require modification because they do not fully consider the significant economic and energy development diversity of the European Union Member States and may increase energy poverty affecting the European inhabitants. The findings show that due to the implementation of the EU Emission Trading System 2 in 2027, transport costs may increase by almost one-third, which will raise the prices of goods and services and affect energy poverty. This study develops the stream of research on energy poverty, modifies the concept of the energy ladder and shows threats resulting from the ETS2 implementation. The paper proposes the avenues of future research.
The Role of Lightweight AI Models in Supporting a Sustainable Transition to Renewable Energy: A Systematic Review
Authors: Miller Tymoteusz , Durlik Irmina , Kostecka Ewelina , Kozlovska Polina , Staude Marek , Sokołowska Sylwia
Published: February 2025

The transition from fossil fuels to renewable energy (RE) sources is an essential step in mitigating climate change and ensuring environmental sustainability. However, large-scale deployment of renewables is accompanied by new challenges, including the growing demand for rare-earth elements, the need for recycling end-of-life equipment, and the rising energy footprint of digital tools—particularly artificial intelligence (AI) models. This systematic review, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, explores how lightweight, distilled AI models can alleviate computational burdens while supporting critical applications in renewable energy systems. We examined empirical and conceptual studies published between 2010 and 2024 that address the deployment of AI in renewable energy, the circular economy paradigm, and model distillation and low-energy AI techniques. Our findings indicate that adopting distilled AI models can significantly reduce energy consumption in data processing, enhance grid optimization, and support sustainable resource management across the lifecycle of renewable energy infrastructures. This review concludes by highlighting the opportunities and challenges for policymakers, researchers, and industry stakeholders aiming to integrate circular economy principles into RE strategies, emphasizing the urgent need for collaborative solutions and incentivized policies that encourage low-footprint AI innovation.
Probabilistic Small-Signal Modeling and Stability Analysis of the DC Distribution System
Authors: Liu Wenlong , Zhang Bo , Lu Zimeng , Liao Yuming , Nian Heng
Published: February 2025

With the advent of large-scale electronic transportation, the construction of electric vehicle charging stations (EVCSs) has increased. The stochastic characteristic of the charging power of EVCSs leads to a risk of destabilization of the DC distribution network when there is a high degree of power electronification. Current deterministic stability analysis methods are too complicated to allow for brief descriptions of the effect of probabilistic characteristics of EVCSs on stability. This paper develops a probabilistic small-signal stability analysis method. Firstly, the probabilistic information of the system is obtained by combining the s-domain nodal impedance matrix based on the point estimation method. Then, the probability function of stability is fitted using the Cornish–Fisher expansion method. Finally, a comparison experiment using Monte Carlo simulation demonstrates that this method performs well in balancing accuracy and computational efficiency. The effects of line parameters and system control parameters on stability are investigated in the framework of probabilistic stability. This will provide a probabilistic perspective on the design of more complex power systems in the future.
Development of Advanced Machine Learning Models for Predicting CO2 Solubility in Brine
Authors: Du Xuejia , Thakur Ganesh C.
Published: February 2025

This study explores the application of advanced machine learning (ML) models to predict CO2 solubility in NaCl brine, a critical parameter for effective carbon capture, utilization, and storage (CCUS). Using a comprehensive database of 1404 experimental data points spanning temperature (−10 to 450 °C), pressure (0.098 to 140 MPa), and salinity (0.017 to 6.5 mol/kg), the research evaluates the predictive capabilities of five ML algorithms: Decision Tree, Random Forest, XGBoost, Multilayer Perceptron, and Support Vector Regression with a radial basis function kernel. Among these, XGBoost demonstrated the highest overall accuracy, achieving an R2 value of 0.9926, with low root mean square error (RMSE) and mean absolute error (MAE) of 0.0655 and 0.0191, respectively. A feature importance analysis revealed that pressure has the most impactful effect and positively correlates with CO2 solubility, while temperature generally exhibits a negative effect. A higher accuracy was found when the developed model was compared with one well-established empirical model and one ML-based model from the literature. The results underscore the potential of ML models to significantly enhance prediction accuracy over a wide data range, reduce computational costs, and improve the efficiency of CCUS operations. This work demonstrates the robustness and adaptability of ML approaches for modeling complex subsurface conditions, paving the way for optimized carbon sequestration strategies.
Forestry Segmentation Using Depth Information: A Method for Cost Saving, Preservation, and Accuracy
Authors: Wołk Krzysztof , Niklewski Jacek , Tatara Marek S. , Kopczyński Michał , Żero Oleg
Published: February 2025

Forests are critical ecosystems, supporting biodiversity, economic resources, and climate regulation. The traditional techniques applied in forestry segmentation based on RGB photos struggle in challenging circumstances, such as fluctuating lighting, occlusions, and densely overlapping structures, which results in imprecise tree detection and categorization. Despite their effectiveness, semantic segmentation models have trouble recognizing trees apart from background objects in cluttered surroundings. In order to overcome these restrictions, this study advances forestry management by integrating depth information into the YOLOv8 segmentation model using the FinnForest dataset. Results show significant improvements in detection accuracy, particularly for spruce trees, where mAP50 increased from 0.778 to 0.848 and mAP50-95 from 0.472 to 0.523. These findings demonstrate the potential of depth-enhanced models to overcome the limitations of traditional RGB-based segmentation, particularly in complex forest environments with overlapping structures. Depth-enhanced semantic segmentation enables precise mapping of tree species, health, and spatial arrangements, critical for habitat analysis, wildfire risk assessment, and sustainable resource management. By addressing the challenges of size, distance, and lighting variations, this approach supports accurate forest monitoring, improved resource conservation, and automated decision-making in forestry. This research highlights the transformative potential of depth integration in segmentation models, laying a foundation for broader applications in forestry and environmental conservation. Future studies could expand dataset diversity, explore alternative depth technologies like LiDAR, and benchmark against other architectures to enhance performance and adaptability further.
How Do Climate and Latitude Shape Global Tree Canopy Structure?
Authors: Rahimi Ehsan , Dong Pinliang , Jung Chuleui
Published: February 2025

Understanding global patterns of tree canopy height and density is essential for effective forest management and conservation planning. This study examines how these attributes vary along latitudinal gradients and identifies key climatic drivers influencing them. We utilized high-resolution remote sensing datasets, including a 10 m resolution canopy height dataset aggregated to 1 km for computational efficiency, and a 1 km resolution tree density dataset derived from ground-based measurements. To quantify the relationships between forest structure and environmental factors, we applied nonlinear regression models and climate dependency analyses, incorporating bioclimatic variables from the WorldClim dataset. Our key finding is that latitude exerts a dominant but asymmetric control on tree height and density, with tropical regions exhibiting the strongest correlations. Tree height follows a quadratic latitudinal pattern, explaining 29.3% of global variation, but this relationship is most pronounced in the tropics (−10° to 10° latitude, R2 = 91.3%), where warm and humid conditions promote taller forests. Importantly, this effect differs by hemisphere, with the Southern Hemisphere (R2 = 67.1%) showing stronger latitudinal dependence than the Northern Hemisphere (R2 = 35.3%), indicating climatic asymmetry in forest growth dynamics. Tree density exhibits a similar quadratic trend but with weaker global predictive power (R2 = 7%); however, within the tropics, latitude explains 90.6% of tree density variation, underscoring strong environmental constraints in biodiverse ecosystems. Among climatic factors, isothermality (Bio 3) is identified as the strongest determinant of tree height (R2 = 50.8%), suggesting that regions with stable temperature fluctuations foster taller forests. Tree density is most strongly influenced by the mean diurnal temperature range (Bio 2, R2 = 36.3%), emphasizing the role of daily thermal variability in tree distribution. Precipitation-related factors (Bio 14 and Bio 19) moderately explain tree height (~33%) and tree density (~25%), reinforcing the role of moisture availability in structuring forests. This study advances forest ecology research by integrating high-resolution canopy structure data with robust climate-driven modeling, revealing previously undocumented hemispheric asymmetries and biome-specific climate dependencies. These findings improve global forest predictive models and offer new insights for conservation strategies, particularly in tropical regions vulnerable to climate change.
Effect of Added Composite Bacteria on Fermentation Quality and Microbial Diversity of Mixed Microstorage of Reed Straw and Inferior Jujube
Authors: Sun Yaoqiang , Zhang Ning , Shao Yongbin , Wang Tengyu , Shen Wen , Wang Xinfeng , Sun Xinwen , Hua Dengke
Published: February 2025

Reed straw is a potential roughage resource that limits its application in livestock production due to its higher fiber content. Inferior jujube is widely used as a non-conventional feed in livestock production due to its high nutritional value. In this study, a two-factor experimental design was used to investigate the effects of fermentation with Composite A (BA) and Composite B (BB) under the condition that the dry matter ratio of reed straw to residual red dates was 1:1, and mixed microstorage was fermented for different times (0 d, 30 d, and 60 d) with the effects of nutrient indexes, fermentation quality, and microbial community dynamics changes. A control group (CON) was set up, six replicates were set up for each treatment, and the amount of bacteria added was 1% of the dry matter basis. The results showed that compared with the control group, both composite bacterial liquids could effectively reduce the nutrient loss of micro-silage feeds, in which with the addition of composite bacterium B (BB) treatment group at the end of fermentation, the crude protein (CP) content increased by 1.91%, and the neutral detergent fiber (NDF) and the acid detergent fiber (ADF) were reduced by 11.32% and 10.61%, respectively. The volatile fatty acid content was significantly higher than that of the control group, which was 26.41 μg·mL−1. The BB group had the highest abundance of Lacticaseibacillus during the fermentation process, which could produce a large amount of lactic acid and lower the pH to improve the quality of feed fermentation. In addition, the differences in the indexes were smaller in the BB group compared with the BA group, but the fermentation effect of the BB group was better than that of the BA group.
Modelling Relative Fire Sensitivity for Geodiversity Elements
Authors: Hoyland Ruby O. , McHenry Melinda T.
Published: February 2025

The integration of geodiversity elements and contexts into fire management frameworks remains limited due to a lack of actionable tools for assessing geosite sensitivity. This study addresses this gap by developing and testing a mechanistic model to evaluate soil and lithological fire sensitivity, using a geodiversity database of Tasmanian geosites at various temperature thresholds. Initial results indicate the utility of the approach to distinguish between sensitive and robust geosites, providing a simple delineation between the relative sensitivities of in situ elements. A subsequent iterative approach applied modelled outputs to an existing geosite database, giving coarse indicators of sites with a propensity to be modified by fire. With static inventory, this approach allows decision-makers to develop new risk parameters for the management of burns and wildfires. Geographically complex environments have led to misalignments between geosite boundaries and broader processes, data inaccessibility for remote or offshore sites, and fire as both a destructive and formative agent; these must all be resolved. Future work should consider the necessity of incorporating values, recovery trajectories, and hydrological processes into fire sensitivity assessments. The study concludes with recommendations for refining the model to enhance its utility for fire managers, ultimately contributing to the integration of geodiversity into fire management strategies and geoconservation planning.
Influence of Different Shaping Techniques on the Aroma Quality and Volatile Metabolites of Green Tea Revealed by Gas Chromatography Electronic Nose and Gas Chromatography–Tandem Mass Spectrometry
Authors: Tang Jiahao , Hu Jiajing , Zhou Xianxiu , Wang Qiwei , Jiang Yongwen , Yuan Haibo , Wang Yujie , Yang Yanqin
Published: February 2025

The shaping process is recognized as a crucial step in the manufacturing of green tea. However, its influence on aroma quality remains unclear. In this study, the effects of four shaping techniques, including flat green tea (FGT), straight green tea (SGT), phoenix green tea (PGT), and curled green tea (CGT), on the aroma quality and volatile metabolites of green tea were investigated by gas chromatography electronic nose (GC-E-Nose) and gas chromatography–tandem mass spectrometry (GC-MS/MS). The findings indicated that distinct shaping processes significantly influenced the development of the aroma quality and aroma components of green tea. The PGT processing facilitated the attainment of superior aroma quality of green tea. In total, 60 volatile components were identified by GC-MS/MS, with 54 of these compounds being consistently detected across four different shaping techniques. In particular, the PGT processing method was effective in yielding elevated levels of alcohols, esters and ketones. Moreover, 20 key odorants were screened out, with (E,E)-2,4-decadienal, (E,E)-2,4-nonadienal, phenylethyl alcohol, and benzeneacetaldehyde proven to be substantial contributors to the overall aromas of green tea under diverse shaping procedures. These key odorants were primarily derived from lipid degradation and the Maillard reaction. GC-E-Nose served as a significant adjunct to sensory evaluation, enabling the swift differentiation of green tea samples that have undergone various shaping processes. These findings offer both theoretical and technical perspectives that may guide the creation of innovative green tea products distinguished by their unique shapes.
Ziziphi Spinosae Semen Flavonoid Ameliorates Hypothalamic Metabolism and Modulates Gut Microbiota in Chronic Restraint Stress-Induced Anxiety-like Behavior in Mice
Authors: Yan Yan , Zhao Ni , Liu Jiaying , Zhang Shengmei , Zhang Yinjie , Qin Xuemei , Zhai Kefeng , Du Chenhui
Published: February 2025

Ziziphi Spinosae Semen (ZSS), a homology of medicine and a type of seed, has been widely used to improve sleep quality. The present study aimed to assess the effects of ZSS flavonoid (ZSSF) extracted and isolated from ZSS on gut microbiota and hypothalamus metabolomic profiles in a chronic restraint stress (CRS)-induced anxiety mouse model. ZSSF was prepared using microporous resin chromatography, and seven compounds were determined by UPLC-MS. ZSSF treatment dramatically reduced anxiety-like behaviors, exerted sedative–hypnotic effects, increased hippocampal 5-HT and 5-HTP, and enhanced intestinal barrier function through inhibiting colon ZO-1, Claudin-1, and Occludin expression and reducing TNF-α, IL-6, and IL-1β levels. Compared with the CRS group, the diversity of gut microbiota in ZSSF-group mice was increased, with an increase in Bacteroidetes and a decrease in Firmicutes, and it was accompanied by an increase in fecal SCFAs. Hypothalamus metabolomics and lipidomics were performed to achieve 25 differential metabolites and 44 lipids, respectively. Serum metabolomics showed a total of 13 metabolites associated with anxiety were remarkably regulated by ZSSF. Weighted correlation network analysis (WGCNA) showed that glycerophospholipids (GPs) as well as phenylalanine, tyrosine, and L-tryptophan in peripheral and central parts were significant metabolites, which contributed to the pharmacological action of ZSSF. The mRNA expression of TPH2 and DDC key enzymes associated with tryptophan metabolism were upregulated, and PLA2G12A, LACT, and PLA2G6 key enzymes associated with GP metabolism were downregulated in ZSSF compared with CRS. Briefly, ZSSF improved tryptophan and GP metabolism and regulated the gut microbiome. This study may lay a theoretical basis for potentially developing ZSSF as a natural functional food ingredient for the improvement of anxiety and sleep disorders.
Microbiota of Cheese Ecosystems: A Perspective on Cheesemaking
Authors: Neviani Erasmo , Gatti Monica , Gardini Fausto , Levante Alessia
Published: February 2025

This review contributes to the knowledge on the complex and adaptive microbial ecosystems within cheese, emphasizing their critical role in determining cheese quality, flavor, and safety. This review synthesizes the current knowledge on the microbial interactions and the dynamics of lactic acid bacteria (LAB), encompassing both starter (SLAB) and non-starter (NSLAB) strains, which are pivotal to the curd fermentation and ripening processes. The adaptability of these microbial consortia to environmental and technological stressors is explored, highlighting their contributions to acidification, proteolysis, and the development of distinctive organoleptic characteristics. Historical and technological perspectives on cheesemaking are also discussed, detailing the impact of milk treatment, starter culture selection, and post-renneting procedures on microbial activity and biochemical transformations. This review underscores the importance of microbial diversity and cooperative interactions in fostering ecosystem resilience and metabolic functionality, and it addresses the challenges in mimicking the technological performance of natural starters using selected cultures. By understanding the ecological roles and interactions of cheese microbiota, this review aims to guide improvements in cheese production practices. Additionally, these insights could spark the development of innovative strategies for microbial community management.
Double Emulsion Microencapsulation System for Lactobacillus rhamnosus GG Using Pea Protein and Cellulose Nanocrystals
Authors: Vanare Sanket Prakash , Singh Rakesh K. , Chen Jinru , Kong Fanbin
Published: February 2025

Microencapsulation using a double emulsion system can improve the viability of probiotic cells during storage and digestion. In this study, a double emulsion system WC/O/WF was designed to microencapsulate Lactobacillus rhamnosus GG using pea protein (PP) and cellulose nanocrystals (CNCs) at various proportions, and the effect of their proportions on the stability and efficacy of the encapsulation system was studied. The double emulsions were prepared by a two-step emulsification process: the internal aqueous phase containing probiotic strain (WC) was homogenized into the oil phase (O), which was then homogenized into the external aqueous phase (WF) containing 15% wall materials with varying proportions of PP and CNCs [F1 (100:0), F2 (96:4), F3 (92:8), F4 (88:12), F5 (84:16), F6 (80:20)]. The incorporation of CNCs significantly lowered the average particle size and improved the stability of the emulsions. The encapsulation efficiency did not differ significantly across the tested formulations (63–68%). To check the effectiveness of the designed system, a simulated digestion study was conducted in two phases: gastric phase and intestinal phase. The double emulsion microencapsulation significantly improved the viability of encapsulated cells during digestion compared against free cells. Microscopic analysis along with assessment of protein hydrolysis of the double emulsions during the simulated digestion demonstrated a two-stage protection mechanism. This study presented promising results for employing a double emulsion system for the microencapsulation of probiotics and the potential of PP and CNCs in designing such systems.
The Overall Quality Changes of Chinese Sauced Ducks at Different Stages During Processing and Storage
Authors: Yao Kaiyong , Cai Jie , Pan Daodong , Chen Bindan , Fan Jinghui , Ren Daxi , Xiao Yingping
Published: February 2025

This study reveals the physicochemical, microbial, flavor, and sensory changes in sauced duck from the marinating phase to the end of storage, divided into six stages (stages A–F). The changes in color, total plate count, total volatile basic nitrogen, and thiobarbituric acid reactive substance at different stages were clarified. We utilized 16S rRNA gene sequencing, GC-IMS, and GC-MS to explore the changes in bacterial flora, fatty acid composition, and flavor characteristics. The dominant bacteria identified in stages A–C included Psychrobacter, Flavobacterium, and Pseudomonas, while Lactobacillus and Staphylococcus dominated during stages D–F. Aldehydes, esters, alcohols, and ketones emerged as the main flavor compounds. Several unsaturated fatty acids significantly (p < 0.05) decreased from stage A to stage F. The sensory quality of sauced duck improved. The potential reactions were determined, and correlation analysis of sauced duck samples across different stages was performed. 3-Methy-1-butanol could be a crucial indicator of sauced duck’s overall quality. This research could support the treatment optimization of sauced duck products.
Screening for Safe and Efficient Monascus Strains with Functions of Lowering Blood Lipids, Blood Glucose, and Blood Pressure
Authors: Liu Chuling , Cheng Li , Yang Mingtian , He Zhengli , Jia Yanan , Xu Li , Zhang Yuansong
Published: February 2025

Monascus is a fungus widely used in food fermentation. This study employed microbial technology, combined with microscopic morphological observations and ITS sequence analysis, to isolate, purify, and identify 10 strains of red yeast mold from various Monascus products. After the HPLC detection of metabolic products, the M8 strain containing the toxic substance citrinin was excluded. Using the EWM-TOPSIS model, the remaining nine safe Monascus strains were evaluated for their inhibitory activities against pancreatic lipase, α-glucosidase, α-amylase, and the angiotensin-converting enzyme. The M2 strain with the highest comprehensive scores for lowering blood sugar, blood lipids, and blood pressure was selected. Its fermentation product at a concentration of 3 mg/mL had inhibition rates of 96.938%, 81.903%, and 72.215%, respectively. The contents of the blood lipid-lowering active substance Monacolin K and the blood sugar and blood pressure-lowering active substance GABA were 18.078 mg/g and 5.137 mg/g, respectively. This strain can be utilized for the biosynthesis of important active substances such as Monacolin K and GABA, as well as for the fermentation production of safe and effective functional foods to address health issues like high blood lipids, high blood sugar, and high blood pressure in people. This study also provides insights into the use of natural fungi to produce healthy foods for combating chronic diseases in humans.
Nutritional Composition, Physicochemical Properties, Antioxidant Activity, and Sensory Quality of Matricaria chamomilla-Enriched Wheat Bread
Authors: Kerbab Khawla , Sanah Ibtissem , Djeghim Fairouze , Belattar Nadjah , Santoro Valentina , D’Elia Maria , Rastrelli Luca
Published: February 2025

This study evaluates the effects of fortifying bread with different concentrations (3%, 10%, and 30%) of Matricaria chamomilla L. (MC) infusion and powder, derived from the plant’s aerial parts (stem, leaf, and flower). UPLC/MS-MS analysis of MC infusion and powder ethanolic extract confirmed the presence of polyphenolic compounds, including flavonoids, contributing to enhanced antioxidant and enzyme inhibitory properties. The physicochemical, antioxidant, and sensory properties of the enriched breads were assessed. Hierarchical cluster analysis revealed that breads enriched with 30% MC powder (BP-MC 30%) and infusion (BI-MC 30%) exhibited superior overall quality compared with other formulations. The enriched breads showed increased protein, fiber, and antioxidant content. Specifically, BI-MC 30% demonstrated superior antioxidant activity, while BP-MC 30% provided the highest fiber content. Sensory evaluation indicated that the enriched breads retained sensory properties similar to those of the control bread, despite the nutritional improvements. These findings suggest that incorporating Matricaria chamomilla, particularly at 30%, enhances the nutritional profile and antioxidant properties of bread while maintaining sensory characteristics close to those of traditional bread. This research highlights the potential of using chamomile’s aerial parts in the valorization of plant-based by-products for functional bakery product development.
Encapsulation Processes: Valorization, Stabilization, and Commercialization of Active and Natural Compounds
Authors: Estevinho Berta Nogueiro
Published: February 2025

Encapsulation techniques have emerged as an important strategy for creating nutraceuticals and fortified foods and beverages that contain various bioactive compounds, due to their potential health benefits [...]
Improving the Bioactivities of Apricot Kernels Through Fermentation: Investigating the Relationship Between Bioactivities, Polyphenols, and Amino Acids Through the Random Forest Regression XAI Approach
Authors: Zhao Zhiyu , Kantono Kevin , Kam Rothman , Le Thao T. , Kitundu Eileen , Chen Tony , Hamid Nazimah
Published: February 2025

Apricot kernels are known for being a rich source of oil, protein, and bioactive compounds. This study focused on enhancing the bioactivities of apricot kernels through fermentation. Additionally, this study explored the correlations between polyphenols, amino acids, antioxidant activities, and total phenolic content (TPC). The findings indicated that apricot kernels fermented with Lactiplantibacillus plantarum exhibited increased antioxidant activity, as assessed by the FRAP and CUPRAC methods, and an increased TPC compared to naturally fermented samples. The CUPRAC activity increased significantly from 1.03 to 1.82 mg of ascorbic acid per gram of sample on day 7, and the FRAP activity increased from 4.9 to 12.2 mg of ascorbic acid per gram of sample on day 3 of fermentation. Moreover, the TPC significantly increased from 1.67 to 7.58 mg of gallic acid per gram of sample on day 9 of fermentation. The results further demonstrated that, during the fermentation process, the concentration of hydroxybenzoic acid increased from 0.52 µg/g on day 0 to 5.3 µg/g on day 9. The DL-3-phenyllactic acid content demonstrated a significant increase from 0.42 µg/g on day 0 to 99.62 µg/g on day 5, while the benzoic acid content exhibited a notable increase from 45.33 µg/g to 138.13 µg/g over the fermentation period, with peak levels being observed on day 5. Similarly, most amino acids demonstrated a rise in concentration as the fermentation progressed, peaking on the ninth day. This study further employed random forest regression as a form of explainable artificial intelligence (XAI) to explore the relationships between phenolic compounds, amino acids, and antioxidant activities. Amino acids like L-cystine and L-anserine were found to positively impact FRAP values, while L-histidine and 1-methyl-L-histidine contributed to the CUPRAC antioxidant activity. Notably, hydroxybenzoic acid emerged as a key contributor to both the FRAP value and TPC, highlighting its significance in improving the overall antioxidant capacity of apricot kernels. These findings indicate that, under optimised fermentation conditions, apricot kernels hold promise as functional food ingredients due to the beneficial antioxidant properties observed in this study.
Functional Ingredients from Food Waste and By-Products: Processing Technologies, Functional Characteristics and Value-Added Applications
Authors: Seguí Lucía , Barrera Cristina
Published: February 2025

In the current scenario, food waste stands out as a pressing issue, accounting for a significant portion of the waste generated worldwide [...]
Exchange Rates, Supply Chain Activity/Disruption Effects, and Exports
Authors: Msomi Simiso , Muzindutsi Paul-Francios
Published: February 2025

In the past, South African monetary policy aimed to protect the external value of the domestic currency (Rand); however, these efforts failed. Later, its monetary policy approach changed to allow the foreign exchange rate market to determine the exchange rates. In such a change, the South African Reserve Bank (SARB) aimed to stabilize the demand for the Rand in the foreign exchange market by providing information to stabilize market expectations and create favorable market conditions. However, South African policymakers have struggled with currency depreciation since the early 60s, increasing the uncertainty of South African exports. This study aims to examine the effect of currency depreciation on exports using the Threshold Autoregressive (TAR) model. Additionally, this study created and validated the supply chain activity/disruption index to capture the sea trade activity. The sample period for the analysis is 2009 to 2023. The study finds that currency depreciation does not improve trade between South Africa and its trading partners over time. Furthermore, the currency depreciation was found to be asymmetric to the effect of international trade across the different regimes. The supply chain activity index shows that the effect of supply chain activity/disruption on exports is regime-dependent. This implies that the effect on exports is dependent on the economic environment.
Analysis Modulation Instability and Parametric Effect on Soliton Solutions for M-Fractional Landau–Ginzburg–Higgs (LGH) Equation Through Two Analytic Methods
Authors: Abdalla Mohamed , Roshid Md. Mamunur , Uddin Mahtab , Ullah Mohammad Safi
Published: February 2025

This manuscript studies the M-fractional Landau–Ginzburg–Higgs (M-fLGH) equation in comprehending superconductivity and drift cyclotron waves in radially inhomogeneous plasmas, especially for coherent ion cyclotron wave propagation, aiming to explore the soliton solutions, the parameter’s effect, and modulation instability. Here, we propose a novel approach, namely a newly improved Kudryashov’s method that integrates the combination of the unified method with the generalized Kudryashov’s method. By employing the modified F-expansion and the newly improved Kudryashov’s method, we investigate the soliton wave solutions for the M-fLGH model. The solutions are in trigonometric, rational, exponential, and hyperbolic forms. We present the effect of system parameters and fractional parameters. For special values of free parameters, we derive some novel phenomena such as kink wave, anti-kink wave, periodic lump wave with soliton, interaction of kink and periodic lump wave, interaction of anti-kink and periodic wave, periodic wave, solitonic wave, multi-lump wave in periodic form, and so on. The modulation instability criterion assesses the conditions that dictate the stability or instability of soliton solutions, highlighting the interplay between fractional order and system parameters. This study advances the theoretical understanding of fractional LGH models and provides valuable insights into practical applications in plasma physics, optical communication, and fluid dynamics.
Protein–Protein Interactions and Structure of Heat-Set Gels Based on Pea Protein and Egg White Mixtures
Authors: Kuang Jian , Hamon Pascaline , Lee Jeehyun , Bouhallab Said , Cases Eliane , Saurel Remi , Lechevalier Valérie
Published: February 2025

The substitution of animal proteins with plant-based ones to fit environmental and economic demands is a challenge in gel applications. This study examined the thermal elation of mixtures of pea protein isolate (PPI) and egg white proteins (EWPs) at different PPI/EWP weight ratios (100/0, 75/25, 50/50, 25/75, 0/100) at pH 7.5 and 9.0. Viscoelastic and texture properties of the composite gels, along with the microstructure and molecular interactions involved in the gel network, were investigated. Except for PPI-EWP 100/0 at pH 9.0, all systems gelled with increasing gel hardness, springiness, and cohesiveness when EWP content increased. This was explained by the microstructure of the gels, wherein the presence of PPI enhanced the formation of aggregates embedded in the EWP network, thus loosening it. The rheological properties of the mixed gels were primarily influenced by the EWP network, involving disulfide bonds. However, upon the addition of PPI, hydrogen bonds and hydrophobic interactions predominated and the structure of the gel became more sensitive to pH as electrostatic repulsions interfered. Adjusting the ratio of PPI/EWP allows the production of gels with varying textures, and suggests the possibility of partially substituting egg white with pea proteins in food gel formulation.
Virtual Imaginative Geographies: Generative AI and the Representation of Landscape Imagery
Authors: Alkhateeb Ghieth , Storie Joanna , Bell Simon , Suškevičs Monika
Published: February 2025

Generative AI (GenAI), particularly text-to-image (TTI) models, is reshaping landscape representation by transforming textual descriptions into visual outputs. However, these models often reinforce biases embedded in their training datasets, shaping how landscapes are perceived and represented. This research examines the biases in GenAI-generated landscape imagery through the lens of Edward Said’s “imaginative geographies”, focusing on how geographic references, cultural archetypes, and methodological factors influence AI outputs. We employed a structured approach to create prompts based on the Global Ecosystem Typology (GET), using Midjourney V6.1 as the primary tool for image generation. We extracted landscape descriptors from GET classifications and structured them into both simple and detailed prompts. The analysis involved comparing AI-generated images to ecological classifications and reference images to assess biases. The findings reveal the following three key types of biases: (1) geographic biases, where certain locations act as semantic triggers, leading to culturally stereotypical portrayals; (2) representation biases, where landscapes frequently depicted in AI training datasets appear with greater accuracy, while underrepresented landscapes are simplified into essentialized visual tropes; and (3) methodological biases, where prompt complexity influences representational accuracy but does not eliminate pre-existing cultural hierarchies. To address the bias challenges, the research presents the following four key recommendations for future research and practice: (1) incorporating finer-scale ecosystem classifications; (2) diversifying training datasets; (3) engaging local communities in participatory data collection; and (4) refining prompt-writing methodologies. These insights contribute to broader discussions on AI bias, emphasizing the necessity of critically evaluating the role of generative models in shaping landscape imaginaries.
Cultivating Complexity in an Age of Digital Dominance and Binary Oppositional Thinking
Authors: Nolan Amy
Published: February 2025

The work represented in the following essay explores meaning-making in the context of creating in a digitally saturated culture. The digital imperative, with its binary oppositional structure (ones and zeroes), has increasingly asserted itself as the only option, from learning management systems (LMSs) to nearly all financial transactions, to the deepening gulfs between very rich and very poor, to increased extremes in left- and right-wing politics, and has deepened an already-entrenched binary oppositional thinking in creativity, nature, identity, and how we imagine the future itself, i.e., the extinction ending versus the techno-utopia. Binary oppositional thinking persists when people want simple, direct answers to complex questions. We are living in such a time when anxiety and grief over climate change have left many people with deep uncertainties about the future. How might an embrace of complexity and creativity help us transmute binary oppositional thinking in the face of these challenges? Through personal and scholarly narrative, this study addresses this question through an exploration of narrative experiences.