bluuebunny/crossref_metadata_2025 huggingface dataset:
Advanced Strategies for Mitigating Catalyst Poisoning in Low and High Temperature Proton Exchange Membrane Fuel Cells: Recent Progress and Perspectives
Authors: Choi Suyeon , Jang Injoon , Lee Sehyun
Published: January 2025

Catalyst poisoning remains a persistent barrier to the efficiency and longevity of electrocatalytic energy conversion devices, namely fuel cells. To address this challenge, this review provides a systematic investigation of recent advancements in mitigation strategies, with particular emphasis on surface engineering, alloying, and combined approaches. Notable developments include the rational design of Pt-alloy catalysts with enhanced CO, H2S, and H3PO4 tolerance as well as the implementation of anti-poisoning molecular architectures and carbon-based protective layers. These methods collectively show considerable promise for improving catalytic activity by fine-tuning electronic structures and minimizing interactions with undesired adsorbates. In addition to presenting a comprehensive overview of the current progress, this review identifies promising future directions, guiding the design and realization of robust, poison-tolerant catalysts crucial for sustainable energy technologies.
Automated Machine Learning in Dentistry: A Narrative Review of Applications, Challenges, and Future Directions
Authors: Shujaat Sohaib
Published: January 2025

The adoption of automated machine learning (AutoML) in dentistry is transforming clinical practices by enabling clinicians to harness machine learning (ML) models without requiring extensive technical expertise. This narrative review aims to explore the impact of autoML in dental applications. A comprehensive search of PubMed, Scopus, and Google Scholar was conducted without time and language restrictions. Inclusion criteria focused on studies evaluating autoML applications and performance for dental tasks. Exclusion criteria included non-dental studies, single-case reports, and conference abstracts. This review highlights multiple promising applications of autoML in dentistry. Diagnostic tasks showed high accuracy, such as 95.4% precision in dental implant classification and 92% accuracy in paranasal sinus disease detection. Predictive tasks also demonstrated promise, including 84% accuracy for ICU admissions due to dental infections and 93.9% accuracy in orthodontic extraction predictions. AutoML frameworks like Google Vertex AI and H2O AutoML emerged as key tools for these applications. AutoML shows great promise in transforming dentistry by facilitating data-driven decision-making and improving patient care quality through accessible, automated solutions. Future advancements should focus on enhancing model interpretability, developing large and annotated datasets, and creating pipelines tailored to dental tasks. Educating clinicians on autoML and integrating domain-specific knowledge into automated platforms could further bridge the gap between complex ML technology and practical dental applications.
Recognition of Idiopathic Inflammatory Myopathies Underlying Interstitial Lung Diseases
Authors: Morina Giulia , Sambataro Domenico , Libra Alessandro , Palmucci Stefano , Colaci Michele , La Rocca Gaetano , Ferro Francesco , Carli Linda , Baldini Chiara , Liuzzo Santa Valentina , Vancheri Carlo , Sambataro Gianluca
Published: January 2025

Interstitial Lung Disease (ILD) is one of the most common causes of mortality in idiopathic Inflammatory Myopathies (IIM). Despite these conditions being commonly associated with proximal weakness, skin rashes and arthritis, ILD can be the first or the sole clinical feature in up to 60% of patients, potentially leading to incorrect diagnosis. The early recognition of an underlying IIM in ILD patients can allow for prompt treatment, which could potentially stabilize or even improve the lung disease, also avoiding the development of other clinical features associated with the condition. The objective of this review is to describe the clinical, serological and radiological features associated with IIM-ILD, mainly focusing on dermatomyositis and antisynthetase syndrome.
The Effects of Maternal Subclinical Hypothyroidism on Fetal Thymus Size: A Prospective Study
Authors: Albayrak Mehmet , Yükcü Bekir
Published: January 2025

Objective: This study investigated the impact of maternal subclinical hypothyroidism on fetal thymus size and development and explored how inadequate thyroid hormone production in pregnant women affects the fetal thymus. Methods: Conducted at the Giresun Obstetrics, Gynecology, and Pediatrics Training and Research Hospital, this case–control study involved 86 pregnant women, 43 with hypothyroidism and 43 without. Maternal thyroid function was assessed using TSH and free T4 levels, and fetal thymus size and thymus–thorax ratio were measured using ultrasound. Exclusion criteria were chronic hypertension, gestational hypertension or eclampsia, multiple pregnancies, infectious diseases, renovascular diseases, diagnosed with hypothyroidism prior to pregnancy and other endocrine disorders, fetal cardiac diseases, and morbid obesity. Data collected included maternal age, gestational week, number of pregnancies, parity, number of living children, thyroid-stimulating hormone (TSH) and Free thyroxine 4 (T4) levels, and fetal thymus measurements (transverse diameter and thymus/thorax ratio). Statistical analyses were performed using the Mann–Whitney U test and logistic regression analysis. The relationships between TSH, thymus diameters, thorax diameters, and the thymus–thorax ratio were evaluated using Spearman’s correlation coefficient. Results: The thymus–thorax ratio was significantly reduced in the hypothyroid group (p = 0.003). Logistic regression analysis identified TSH as an independent risk factor for a low thymus–thorax ratio, with each unit increase in TSH associated with a 1.345-fold higher likelihood of having a low thymus–thorax ratio. A significant negative correlation was found between TSH levels and the TTR ratio (Spearman’s correlation coefficient r = −0.338, p = 0.001). Conclusions: An association was identified between maternal TSH levels and the thymus–thorax ratio, with increasing TSH levels correlating with a decrease in the thymus–thorax ratio. Regular monitoring of thyroid hormone levels during pregnancy and appropriate replacement treatment in cases of deficiency are crucial for optimal fetal thymus development. Further multicenter studies are needed to confirm these findings and investigate the long-term implications of altered fetal thymus development.
Optical Coherence Tomography Image Enhancement and Layer Detection Using Cycle-GAN
Authors: Kim Ye Eun , Lee Eun Ji , Yoon Jung Suk , Kwak Jiyoon , Kim Hyunjoong
Published: January 2025

Background/Objectives: Variations in image clarity across different OCT devices, along with the inconsistent delineation of RNFL boundaries, pose a challenge to achieving consistent diagnoses for glaucoma. Recently, deep learning methods such as GANs for image transformation have been gaining attention. This paper introduces deep learning methods to transform low-clarity images from one OCT device into high-clarity images from another, concurrently estimating the retinal nerve fiber layer (RNFL) segmentation lines in the enhanced images. Methods: We applied two deep learning methods, pix2pix and cycle-GAN, and provided a comparison of their performance by evaluating the similarity between the generated and actual images, as well as comparing the generated RNFL boundary delineation with the actual boundaries. Results: The image conversion performance was compared based on two criteria: Fréchet Inception Distance (FID) and curve dissimilarity. In the comparison of FID values, the cycle-GAN method showed significantly lower values than the pix2pix method (p-value < 0.001). In terms of curve similarity, the cycle-GAN method also demonstrated higher similarity to the actual curves compared to both manually annotated curves and the pix2pix method (p-value < 0.001). Conclusions: We demonstrated that the cycle-GAN method produces more consistent and precise outcomes in the converted images compared to the pix2pix method. The resulting segmented lines showed a high degree of similarity to those manually annotated by clinical experts in high-clarity images, surpassing the boundary accuracy observed in the original low-clarity scans.
Comparative Analysis of Blood MMP-9 Concentration in Alcohol- and Opioid-Addicted Patients
Authors: Kartvelishvili Tamar , Sapojnikova Nelly , Asatiani Nino , Asanishvili Lali , Sokhadze Victor , Sichinava Nestan , Chikovani Zaza
Published: January 2025

Background/Objectives: In brain physiology and disease, MMP-9 is a significant and apparently peculiar factor. Numerous studies have implicated neuroinflammatory processes involving MMP-9 in the pathophysiology of addiction. This study aims to evaluate plasma MMP-9 level as a biomarker for the stages of alcohol and opioid addiction. Methods: The case subjects were patients with opioid and alcohol addiction. The quantitative assessment of MMP-9 plasma concentration was performed using monoclonal antibodies against human MMP-9. Results: MMP-9 levels in the plasma of patients with alcohol and opioid dependence differ from MMP-9 concentrations in apparently healthy donors. During the intoxication stage, MMP-9 concentrations in individuals with alcohol and opioid dependence are similar and higher than in the control group. While the MMP-9 level is close to the control level after alcohol withdrawal, it stays increased during opioid withdrawal. When MMP-9 levels in plasma were measured in three distinct intoxicated states (light, moderate, and heavy) in cases of alcohol addiction, the results were all similar. Two distinct opioid intoxicated states (methadone and buprenorphine) and three withdrawals—following methadone, buprenorphine, and heroin abuse—were associated with high MMP-9 levels.
Summed Tissue Resistance of Periodontal Ligaments and Alveolar Bone in Orthodontic Distal Retraction of Maxillary Canines: Mathematical Simulation of Clinical Data and Interpretation of Results
Authors: Bunta Olimpia , Muresan Vlad , Festila Dana , Baciut Mihaela
Published: January 2025

Background: The mechanical properties of either alveolar bone or periodontal ligaments under orthodontic loading, as well as orthodontic tooth movement, have been studied in recent years using computational approaches. In previous studies, we developed a theoretical mathematical approach that uses a weighting coefficient of the summed resistance of periodontal structures, namely the bone and periodontal ligaments, in relation to apex movement, the center of rotation, orthodontic force loading, and time in order to quantify the biological response to orthodontic biomechanics. Methods: We analyzed the distal retraction of three maxillary canines and integrated the clinical data obtained in the previously developed mathematical programs. Results: The values of the (σ) weighting coefficient of the tissue resistance were interpreted in the context of the clinical data obtained: the smaller the value of (σ), the higher the actual tissue resistance, with a greater difference between the crown and root movement; also, the higher the value of (σ), the lower the actual tissue resistance, with a small difference between the crown and apex movement. Conclusions: The clinical interpretation of the results allows us to set a premise for the refinement of the mathematical programs so that we can use them in assessing the orthodontic biomechanics of larger patient groups over longer periods of time and create premises of treatment protocol simplification and adjustment.
A Novel Drone Sampling Method for Lower Atmospheric Fungal Spores
Authors: Bangay Rohit , Matsuki Atsushi , Tuno Nobuko
Published: January 2025

Novel and practical methods are always sought across all disciplines; within bioaerosol research, portable, lightweight, and low-cost sampling pumps are few and far between. Fungal spores, key components of bioaerosols, have attracted attention due to their negative effects on human populations, agricultural systems, and ubiquitous nature. In terms of spatial scales, fungal spores across vertical gradients are frequently overlooked and in cases where atmospheric samples are collected, they are often a large distance away from the ground, occurring hundreds or thousands of meters into the atmosphere, which also requires substantial expenses for specialist apparatus. Here, we have utilized a drone and low-cost equipment to produce a new sampling method that can efficiently collect fungal spores and bridge the gap between ground sampling and atmospheric sampling, and sample in areas such as forest canopies or at building rooftop heights, in which planes, helicopters, or other UAVs may not be able to safely or practically maneuver. Additionally, we have created a novel approach to utilizing a drone for bioaerosol sampling during rain events, which, to our knowledge, is the first of its kind, opening up the possibilities for much needed comparisons of fungal spores in varying weather conditions.
Causal Discovery and Reasoning for Continuous Variables with an Improved Bayesian Network Constructed by Locality Sensitive Hashing and Kernel Density Estimation
Authors: Wei Chenghao , Li Chen , Liu Yingying , Chen Song , Zuo Zhiqiang , Wang Pukai , Ye Zhiwei
Published: January 2025

The structure learning of a Bayesian network (BN) is a crucial process that aims to unravel the complex dependencies relationships among variables using a given dataset. This paper proposes a new BN structure learning method for data with continuous attribute values. As a non-parametric distribution-free method, kernel density estimation (KDE) is applied in the conditional independence (CI) test. The skeleton of the BN is constructed utilizing the test based on mutual information and conditional mutual information, delineating potential relational connections between parents and children without imposing any distributional assumptions. In the searching stage of BN structure learning, the causal relationships between variables are achieved by using the conditional entropy scoring function and hill-climbing strategy. To further enhance the computational efficiency of our method, we incorporate a locality sensitive hashing (LSH) function into the KDE process. The method speeds up the calculations of KDE while maintaining the precision of the estimates, leading to a notable decrease in the time required for computing mutual information, conditional mutual information, and conditional entropy. A BN classifier (BNC) is established by using the computationally efficient BN learning method. Our experiments demonstrated that KDE using LSH has greatly improved the speed compared to traditional KDE without losing fitting accuracy. This achievement underscores the effectiveness of our method in balancing speed and accuracy. By giving the benchmark networks, the network structure learning accuracy with the proposed method is superior to other traditional structure learning methods. The BNC also demonstrates better accuracy with stronger interpretability compared to conventional classifiers on public datasets.
Future Evolutions of Precipitation and Temperature Using the Statistical Downscaling Model (SDSM), Case of the Guir and the Ziz Watershed, Morocco
Authors: Dafouf Safae , Lahrach Abderrahim , Tabyaoui Hassan , Benaabidate Lahcen
Published: January 2025

The current study is essential for obtaining an accurate representation of weather conditions in the Ziz and Guir watersheds, characterized by an arid climate. This study combined climate data from the ERA5 model with data from observation stations in order to evaluate the ERA5 model in Morocco’s arid environment and increase the temporal and geographical coverage of climate data. From the data collected, precipitation, minimum and maximum temperatures were predicted under the RCP4.5 and RCP8.5 scenarios by applying the SDSM model in the two watersheds for the 2025 and 2100 periods. These forecasts contribute to the development of adaptation strategies in the face of climate change by giving precise indications of future trends and providing local communities with tools for enhancing their resilience capacity. At all climatic stations, the temperature changes predicted under these scenarios showed a marked positive trend for both minimum and maximum temperatures. By the end of the century, minimum temperatures may increase by 1.84 °C and 2.39 °C under the RCP4.5 and RCP8.5 scenarios, respectively. Similarly, maximum temperatures may increase by 1.78 °C and 2.9 °C under the RCP4.5 and RCP8.5 scenarios, respectively. In addition, the precipitation forecast under the RCP 4.5 scenario showed a significant negative trend at the Ait Haddou station, while under the RCP 8.5 scenario, significant negative trends were predicted for the Sidi Hamza, Ait Haddou, Tit N’Aissa, and Bouanane stations.
The Role of AI-Assisted Learning in Academic Writing: A Mixed-Methods Study on Chinese as a Second Language Students
Authors: Chen Chen , Gong Yang (Frank)
Published: January 2025

This mixed-methods study examines the role of artificial intelligence (AI)-assisted learning in academic writing for Chinese as a Second Language (CSL) students in a Chinese university context. Fifty international CSL students were randomly assigned to experimental—AI-assisted learning using ChatGPT—and control—traditional learning—groups. Writing samples from the participants were evaluated using established scoring rubrics for Chinese academic writing. Based on pre- and post-test quantitative data and supplementary qualitative interviews with six participants from the experimental group, this study reveals that AI-assisted learning can enhance student outcome by supporting knowledge acquisition, helping to create a supportive learning environment, and increasing student motivation. However, this study also highlights concerns regarding over-reliance on AI, particularly in relation to ethical concerns, technical and networking issues, and the unreliability of AI-generated content. These findings contribute to a nuanced understanding of the impact of AI on CSL learners’ academic writing performance. Finally, we also discuss practical implications for educational stakeholders regarding the integration of AI into language education.
Research on Trajectory Planning Method Based on Bézier Curves for Dynamic Scenarios
Authors: Li Hongluo , Pang Hai , Xia Hongyang , Huang Yongxian , Zeng Xiangkun
Published: January 2025

With the increase in car ownership, traffic congestion, and frequent accidents, autonomous driving technology, especially for dynamic driving scenarios in the whole domain, has become a technological challenge for today’s researchers. Trajectory planning, as a crucial component of the autonomous driving technology framework, is gradually becoming a hot topic in intelligent research. In response to the challenges of planning lane-changing trajectories in complex dynamic driving scenarios under emergency evasive maneuvers, where it is difficult to consider surrounding vehicles and achieve dynamic adaptability, this paper proposes a dynamic adaptive trajectory planning method based on Bézier curves. Firstly, a mathematical model of Bézier curves is established and its curve characteristics are analyzed, which facilitates the correlation between the trajectory control points and the vehicle and the surrounding obstacles. Secondly, a mathematical function representing the Bézier curve is formulated, where the control points serve as the input and the lane-changing control curve as the output. Finally, the proposed method is validated through simulations on a jointly established simulation platform. The results indicate that the proposed method can plan lane-changing trajectories that are both safe and efficient under emergency evasive maneuvers, considering both static and complex dynamic conditions. This provides a novel solution for lane-changing trajectory planning in emergency evasive maneuvers for autonomous vehicles and holds significant theoretical research value.
A Coordinated Frequency Regulation Strategy Integrating Power Generation, Energy Storage, and DC Transmission for Offshore Wind Power MMC-HVDC Transmission Systems
Authors: Dan Yangqing , Wang Chenxuan , Lou Keheng , Huang Jinhua , Wang Guoteng , Xu Zheng , Huang Ying
Published: January 2025

With the increasing proportion of renewable energy in power grids, the inertia level and frequency regulation capability of modern power systems have declined. In response, this paper proposes a coordinated frequency regulation strategy integrating power generation, energy storage, and DC transmission for offshore wind power MMC-HVDC transmission systems, aimed at improving the frequency stability of onshore power grids. First, considering the inability of the receiving-end MMC-HVDC converter station under constant DC voltage control to directly respond to AC system frequency variations, a frequency regulation method is developed based on constant DC voltage control. The approach employs DC voltage as a transmission signal to coordinate the responses of wind turbines and energy storage systems. Subsequently, based on the energy storage configuration of the onshore renewable energy aggregation station, a secondary frequency regulation strategy is proposed. This strategy integrates offshore wind power, MMC-HVDC transmission system, and energy storage systems, balancing AC frequency regulation and the recovery of the state of charge (SOC) of the energy storage system. Finally, the proposed method is tested on a modified IEEE 39-bus system, the results demonstrate that the minimum frequency value can be in-creased by 37.5%, the system frequency can be restored to the initial state after secondary FM, and the results demonstrate its effectiveness.
Microalga-Based Electricity Production: A Comprehensive Review
Authors: Alrashidi Wid , Alhazmi Safiah , Sayegh Fotoon , Edris Sherif
Published: January 2025

This review evaluates the feasibility of using microalgal culture for sustainable energy production, emphasizing microbial fuel cells (MFCs) and biophotovoltaics (BPVs). This study’s uniqueness is rooted in its thorough examination of recent developments (2014–present) in microalgal strain selection, bioreactor design, and electrode materials. Furthermore, this review combines microalga cultivation with wastewater treatment, highlighting its importance. Notably, it examines advanced methodologies, such as the use of genetic engineering to enhance microalgal traits, nanotechnology to optimize electrode efficacy, and artificial intelligence (AI) to optimize bioelectrochemical systems. In addition, this study identifies possible future research avenues by examining microalga–bacterium consortia and cascaded biobattery systems. Consequently, the incorporation of case studies illustrating microalga biobatteries’ practical applications in low-power devices and wastewater treatment underscores the technology’s promise. Similarly, this study examines significant problems with enhancing farming methods, reconciling cost and yield, and integrating renewable energy sources with the grid, offering vital insights for academics and policymakers. Ultimately, this review emphasizes the need for economical cultivation methods, waste stream utilization, and scalable bioreactor designs, thereby considerably advancing sustainable energy options.
Closing the Loop of Biowaste Composting by Anaerobically Co-Digesting Leachate, a By-Product from Composting, with Glycerine
Authors: Le Thi Cam Tu , Bernat Katarzyna , Pokój Tomasz , Kulikowska Dorota
Published: January 2025

To achieve the required recycling rates, organic recycling via composting should be widely introduced in Poland for selectively collected biowaste. However, this process not only produces compost but also leachate (LCB), a nitrogen- and organics-rich liquid by-product. So far there has been limited information on the application of anaerobic digestion (AD) for treating LCB, which has fermentative potential. However, for effective methane production (MP) via AD, the ratio of chemical oxygen demand to total Kjeldahl nitrogen (COD/TKN) and pH of LCB are too low; thus, it should be co-digested with other organics-rich waste, e.g., glycerine (G). The present study tested the effect of G content in feedstock (in the range of 3–5% (v/v)) on the effectiveness of co-digestion with LCB, based on MP and the removal of COD. MP was accessed by using an automatic methane potential test system (AMPTS). Regardless of the feedstock composition (LCB, or LCB with G), the efficiency of COD removal was over 91%. Co-digestion not only increased MP by 6–15%, but also the methane content in the biogas by 4–14% compared to LCB only (353 NL/kg CODadded, 55%). MP and COD removal proceeded in two phases. During co-digestion in the 1st phase, volatile fatty acids (VFA) accumulated up to 2800 mg/L and the pH decreased below 6.8. The presence of G altered the shares of individual VFA and promoted the accumulation of propionic acid in contrast to LCB only, where caproic acid predominated. An initial accumulation of propionic acid and acidification in the mixtures decreased the kinetic constants of MP (from 0.79 to 0.54 d−1) and the rate of COD removal (from 2193 to 1603 mg/(L·d)). In the 2nd phase, the pH recovered, VFA concentrations decreased, and MP was no longer limited by these factors. However, it should be noted that excessive amounts of G, especially in reactors with constant feeding, may cause VFA accumulation to a greater extent and create a toxic environment for methanogens, inhibiting biogas production. In contrast, digestion of LCB only may lead to ammonium buildup if the COD/TKN ratio of the feedstock is too low. Despite these limitations, the use of AD in the treatment of LCB as a sustainable “closed-loop nutrient” technology closes the loop in composting of biowaste.
Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network
Authors: Du Muyuan , Zhang Zhimeng , Ji Chunning
Published: January 2025

Accurate and comprehensive wind speed forecasting is crucial for improving efficiency in offshore wind power operation systems in coastal regions. However, raw wind speed data often suffer from noise and missing values, which can undermine the prediction performance. This study proposes a systematic framework, termed VMD-RUN-Seq2Seq-Attention, for noise reduction, outlier detection, and wind speed prediction by integrating Variational Mode Decomposition (VMD), the Runge–Kutta optimization algorithm (RUN), and a Sequence-to-Sequence model with an Attention mechanism (Seq2Seq-Attention). Using wind speed data from the Shidao, Xiaomaidao, and Lianyungang stations as case studies, a fitness function based on the Pearson correlation coefficient was developed to optimize the VMD mode count and penalty factor. A comparative analysis of different Intrinsic Mode Function (IMF) selection ratios revealed that selecting a 50% IMF ratio effectively retains the intrinsic information of the raw data while minimizing noise. For outlier detection, statistical methods were employed, followed by a comparative evaluation of three models—LSTM, LSTM-KAN, and Seq2Seq-Attention—for multi-step wind speed forecasting over horizons ranging from 1 to 12 h. The results consistently showed that the Seq2Seq-Attention model achieved superior predictive accuracy across all forecast horizons, with the correlation coefficient of its prediction results greater than 0.9 in all cases. The proposed VMD-RUN-Seq2Seq-Attention framework outperformed other methods in the denoising, data cleansing, and reconstruction of the original wind speed dataset, with a maximum improvement of 21% in accuracy, producing highly accurate and reliable results. This approach offers a robust methodology for improving data quality and enhancing wind speed forecasting accuracy in coastal environments.
Voltage Unbalance Control Strategy for Local Shading Photovoltaic Grid-Connected System
Authors: Wan Pingye , Huang Miao , Mou Jinshan , Tao Lili , Zhang Shuping , Hu Zhihua
Published: January 2025

In view of the sudden grid voltage distortions, such as voltage sags and unbalances, that may occur in photovoltaic (PV) grid-connected systems under local shading conditions, this paper proposes a control strategy integrating a linear active disturbance rejection controller (LADRC)-based virtual synchronous generator (VSG) and an active disturbance rejection controller (ADRC)-based dynamic voltage restorer (DVR). To enhance the stability and response speed of the PV inverter system, a novel LADRC-based voltage–current dual closed-loop control strategy with pre-synchronization is designed, ensuring stable operation of the inverter and load. To address the overshooting issues found in traditional PI control under local shading, the ADRC-based DVR compensates for PV system voltage fluctuations, achieving rapid voltage distortion compensation and ensuring grid-connected system safety. Simulink experiments verify the feasibility and effectiveness of the proposed control strategy in improving transient voltage quality in PV systems affected by local shading. The total harmonic distortion rates of voltage and current are both less than 0.5%, which significantly improves the performance compared to existing research.
Research and Application of Oxygen-Reduced-Air-Assisted Gravity Drainage for Enhanced Oil Recovery
Authors: Wei Jiangfei , Yu Hongwei , Gao Ming , Yan Peifeng , Tan Kesheng , Yan Yutong , Wei Keqiang , Sun Mingyan , Yu Xianglong , Chen Zhihua , Chen Qiang
Published: January 2025

This paper summarizes the research progress and applications of oxygen-reduced-air-assisted gravity drainage (OAGD) in enhanced oil recovery (EOR). The fundamental principles and key technologies of OAGD are introduced, along with a review of domestic and international field trials. Factors influencing displacement performance, including low-temperature oxidation reactions, injection rates, and reservoir dip angles, are discussed in detail. The findings reveal that low-temperature oxidation significantly improves the recovery efficiency through the dynamic balance of light hydrocarbon volatilization and fuel deposition, coupled with the synergistic optimization of the reservoir temperature, pressure, and oxygen concentration. Proper control of the injection rate stabilizes the oil–gas interface, expands the swept volume, and delays gas channeling. High-dip reservoirs, benefiting from enhanced gravity segregation, demonstrate superior displacement efficiency. Finally, the paper highlights future directions, including the optimization of injection parameters, deepening studies on reservoir chemical reaction mechanisms, and integrating intelligent gas injection technologies to enhance the effectiveness and economic viability of OAGD in complex reservoirs.
Catalytic Methane Decomposition for the Simultaneous Production of Hydrogen and Low-Reactivity Biocarbon for the Metallurgic Industry
Authors: Khalil Roger A. , Jayakumari Sethulakshmy , Dalaker Halvor , Wang Liang , Tetlie Pål , Skreiberg Øyvind
Published: January 2025

To reach agreed-on climate goals, it is necessary to develop new energy carriers and industrial materials that are carbon-neutral. To combat global warming and keep Earth’s temperature from increasing by 1.5 °C, some of these solutions need to be carbon-negative. This study fulfills this criterion by producing clean hydrogen and biocarbon suitable for the metallurgic industry through the thermal decomposition of methane using biocarbon as a catalyst. Five different biomass samples were used to prepare biocarbons at a pyrolysis temperature of 1000 °C with a holding time of 90 min. When methane was cracked at 1100 °C with a holding time of 90 min, the highest hydrogen production was 105 mol/kg biocarbon, achieved using birch bark. The lowest hydrogen yield, of 68 mol/kg biocarbon, was achieved with steam-explosion pellets. All the biocarbons showed substantial carbon deposition from cracked methane on their surfaces, with the highest deposition on birch bark and spruce wood biocarbons of 42% relative to the biocarbon start weight. The carbon deposition increased with the decomposition temperature, the methane share in the purge gas and the holding time. The steam-explosion pellets, after deactivation, had a CO2 reactivity that was comparable to coke, a reducing agent that is commonly used in manganese-producing industries. About 90% of the potassium and sodium were removed from the biocarbon during catalytic decomposition of methane performed at 1100 °C. The alkali removal was calculated relative to the biocarbon produced under the same conditions, but with 100% N2 purge instead of CH4. After catalytic decomposition, the surface area of the biocarbon was reduced by 11–34%, depending on the biocarbon type.
Evidence About the Possible Role of Phthalates and Bisphenol A in Recurrent Pregnancy Loss and Endocrine Dysfunctions: A Case–Control Study
Authors: Caporossi Lidia , Viganò Paola , Paci Enrico , Capanna Silvia , Alteri Alessandra , De Rosa Mariangela , Pigini Daniela , Partenzi Elisa , Papaleo Bruno
Published: January 2025

Objectives. A case–control study was conducted to investigate the exposure levels to some specific chemicals, in women with infertility issues, compared with fertile women. Methods. A total of 186 cases and 196 controls were recruited. Each participant provided a urine sample for the determination of six phthalate metabolites (mono-ethyl phthalate, MEP; mono-n-butyl phthalate, MnBP; mono-n-ottyl phthalate, MnOP; monobenzyl phthalate, MBzP; and two metabolites of the diethyl-hexyl phthalate (DEHP): mono(2-ethyl-5-hydroxyhexyl) phthalate, MEHHP and mono(2-ethylhexyl) phthalate, MEHP) in addition to bisphenol A, BPA. Each woman also completed a questionnaire. The urine samples were analyzed using HPLC/MS/MS methods. Results. The analysis revealed significantly higher metabolite concentrations in cases than in controls for all metabolites, except MnOP. Stratification based on infertility factors, showed a significant association of MnBP, MBzP, BPA and DEHP with ovulatory and endocrine dysfunctions. Furthermore, higher mean concentrations of MEP and DEHP were observed in women with recurrent pregnancy loss (RPL) and idiopathic infertility, respectively. Conclusion. These findings suggest that some of the analyzed chemicals may play a role in female infertility. Exposure to DEP (diethyl phthalate) and DEHP appears to be associated with RPL and idiopathic infertility. Further investigation is required to explore potential sources of these risks.
Soil Aggregation, Aggregate Stability, and Associated Soil Organic Carbon in Huron Mountains Forests, Michigan, USA
Authors: Chen Xiaoyong , Gsell Timothy , Yunger John , Randa Lynda , Peng Yuanying , Carrington Mary
Published: January 2025

Soil organic carbon (SOC) plays a critical role in regulating the global carbon (C) cycle, with forest soils serving as significant C sinks. Soil aggregate stability and the distribution of SOC in different aggregate fractions would be affected by different forest types. In this study, we investigate the distribution and dynamics of SOC within different soil aggregate fractions across three main forest types in the Huron Mountains, Michigan, USA: white birch–eastern hemlock mixed forest, eastern-hemlock-dominated forest, and sugar maple forest. We hypothesize that variations in species composition and soil depth influence SOC storage and aggregate stability through mechanisms such as root interactions, microbial activity, and soil structure development. Soil samples were collected from three depth intervals (0–20 cm, 20–40 cm, and 40–60 cm) and analyzed for aggregate size distribution and SOC content. The results showed that aggregate size distribution and SOC stocks differ significantly across forest types, with the white birch–eastern hemlock mixed forest exhibiting the highest proportion of large aggregates (>1.0 mm), which contribute to more stable soil structures. This forest type also had the highest total aggregate mass and mean weight diameter, indicating enhanced soil stability. In contrast, sugar maple forest displayed a greater proportion of smaller aggregates and a lower macroaggregate-to-microaggregate ratio, suggesting fewer stable soils. SOC stocks were closely linked to aggregate size, with macroaggregates containing the highest proportion of SOC. These differences in SOC distribution and soil aggregate stability can be attributed to several underlying mechanisms, including variations in plant root interactions, microbial activity, and the physical properties of the soil. Forests with diverse species compositions, such as the white birch–eastern hemlock mixed forest, tend to support more complex root systems and microbial communities, leading to improved soil aggregation and greater SOC storage. Additionally, forest management practices such as selective thinning and mixed-species planting contribute to these processes by enhancing soil structure, increasing root biomass, and promoting soil microbial health. These interactions play a crucial role in enhancing C sequestration and improving soil health. Our findings emphasized the importance of forest composition in influencing SOC dynamics and soil stability, offering insights into the role of forest management in C sequestration and soil health. This study provided a reference to a deeper understanding of SOC storage potential in forest ecosystems and supports the development of sustainable forest management strategies to mitigate climate change.
Analysis of Wood Density to Compare the Amount of Accumulated Carbon Dioxide in the Stems of Selected Non-Native Tree Species in Poland
Authors: Wąsik Radosław
Published: January 2025

One of the priorities in European policy is the greater use of wood. In this context, it is important to know the total amount of CO2 absorbed by the tree and accumulated in the wood. In the timber industry, butt logs are mainly processed. The aim of this study is to analyze diameter at breast height (DBH), wood density (WD), and the amount of CO2 in grand fir (GF), Douglas fir (DF), northern red oak (NRO), and black locust (BL) wood. The DBH and bark thickness were measured, and cores were taken to study WD and calculate the amount of CO2. Analyses were conducted in three age classes of trees. It was found that in the youngest age class, DF had a significantly larger DBH compared to NRO and BL, and GF had a significantly larger DBH compared to NRO. The wood density of coniferous species was significantly lower compared to broadleaved species. DF absorbed the most CO2. In Class III, DF had significantly larger DBH and significantly lower wood density compared to NRO and BL. DF absorbed significantly more CO2 compared to NRO. In Classes IV and V, DF had larger DBH compared to NRO and lower wood density. The amount of CO2 absorbed by both species was similar. Taking into account the amount of absorbed CO2, the durability of the wood, and aspects related to sustainable forest management of the four studied non-native tree species, Douglas fir seems to be the best choice for cultivation in Polish forests.
Microbiota Composition in Raw Drinking Milk from Vending Machines: A Case Study in Croatia
Authors: Mikulec Nataša , Špoljarić Jasminka , Plavljanić Dijana , Darrer Monica , Oštarić Fabijan , Gajdoš Kljusurić Jasenka , Sarim Khan Mohd. , Zdolec Nevijo , Kazazić Snježana
Published: January 2025

According to the Regulation on the Quality of Fresh Raw Milk, up to 100,000 microorganisms/mL are allowed in milk obtained by the hygienic milking of healthy cows, which represents the natural microbiota of milk and has no negative impact on the overall quality of milk. However, with unprofessional handling during and after milking, milk is easily contaminated and becomes a potential medium for the growth and reproduction of microorganisms, some of which can be harmful to human health. Since the number of aerobic mesophilic bacteria in milk is one of the indicators of the hygienic quality of milk, their number and identification are fundamental in the control of raw milk from milk vending machines. From five different milk vending machines, 35 samples were collected, from which the total number of aerobic mesophilic bacteria was determined using the flow cytometry method and the classic method of counting colonies on a nutrient medium. Randomly selected colonies based on morphological differences (n = 700) were identified by comparing MALDI-TOF mass spectra with reference spectra stored in the microorganism library and processing using the MALDI Biotyper computer program. Thirty-eight genera and eighty-one bacterial species and five genera and seven fungal species were successfully identified. The species that predominate are Lactococcus lactis, Hafnia alvei, Escherichia coli, Leuconostoc mesenteroides, and Kluyveromyces lactis. By integrating advanced methods like flow cytometry and MALDI-TOF MS for precise microbial identification, this study highlights the need for enhanced monitoring and adherence to hygienic standards in raw milk vending machines. This approach not only safeguards public health but also supports consumer confidence in milk quality from vending machines.
Enhancing Probiotic Viability in Yogurt: The Role of Apple Fibers in Supporting Lacticaseibacillus casei ATCC 393 During Storage and Gastrointestinal Transit
Authors: Dimitrellou Dimitra , Sakadani Eleni , Kandylis Panagiotis
Published: January 2025

Probiotics are widely recognized for their health benefits, but their viability during food processing and digestion poses significant challenges. The present study evaluated the impact of incorporating apple fibers into yogurt on the viability of the probiotic strain Lacticaseibacillus casei ATCC 393 during production, storage, and simulated gastrointestinal digestion. Apple fibers, a by-product of apple processing, were used as a prebiotic ingredient due to their functional and technological benefits. The incorporation of apple fibers increased probiotic viability during 28 days of refrigerated storage, improving it from 90.4% in the control yogurt to 93.9%. Under simulated gastrointestinal conditions, yogurt alone acted as a protective matrix, preserving probiotic viability, during gastric (71.0% at pH 2 after 3 h) and intestinal digestion (73.3% at 0.3% bile salts after 6 h). The inclusion of apple fibers further enhanced this protection, reducing probiotic viability loss in both gastric (81.9% at pH 2 after 3 h) and intestinal (79.0% at 0.3% bile salts after 6 h) environments. Similar results were obtained using the INFOGEST 2.0 static protocol. After the completion of the protocol (oral, gastric and intestinal phase) a viability of 71.1% (6.61 logCFU/g) was observed in the yogurt with apple fibers compared to 64.5% (6.10 logCFU/g) in the control yogurt. This enhanced protection could be attributed to the potential prebiotic properties of apple fibers, including their pectin and cellulose content, which may shield probiotics from acidic and enzymatic degradation. These findings highlight the potential of apple fiber-enriched yogurt as a functional food that supports probiotic viability during storage and throughout gastrointestinal transit. These insights may open the way for developing new food products with enhanced health benefits, aligning with growing consumer demand for functional foods.
The Influence of Vegetable Oil Addition Levels on the Fatty Acid Profile and Oxidative Transformation Dynamics in Liver Sausage-Type Processed Meats
Authors: Bilska Agnieszka , Krzywdzińska-Bartkowiak Mirosława
Published: January 2025

In the production of meat products, animal fats, which are rich mainly in saturated fatty acids, are used as a recipe ingredient. To improve the quality and fatty acid profile of meat products, it is possible to partially replace animal fat with vegetable oils. This approach aims to achieve a more favorable PUFA/SFA ratio and n-6:n-3 PUFA ratio, bringing them closer to the values recommended by nutritional organizations. Therefore, the aim of this study was to determine the impact of replacing 20% and 40% of animal fat with selected plant fats on the change in the fat fraction composition of liver pâté-type processed meat and its oxidative stability. Fatty acid content was analyzed in the oils purchased from retailers and in experimental samples. During refrigerated storage of the experimental sausages, changes in the content of primary (peroxide value (PV)) and secondary oxidation products (TBARS), as well as changes in sensory quality, were evaluated. The analysis included cross-sectional color, aroma, texture, saltiness, and taste. The study showed that replacing 20% of animal fat with vegetable oils resulted in products with high sensory attractiveness and oxidative stability, outperforming those with 40% replacement. Among the tested vegetable oils, samples with rapeseed oil demonstrated the highest oxidative stability and the most favorable, nutrition-recommendation-approaching n-6 to n-3 fatty acid ratio, compared with samples with flaxseed, corn, sunflower, and soybean oils.
A Coarse-to-Fine Feature Aggregation Neural Network with a Boundary-Aware Module for Accurate Food Recognition
Authors: Liang Shuang , Gu Yu
Published: January 2025

Food recognition from images is crucial for dietary management, enabling applications like automated meal tracking and personalized nutrition planning. However, challenges such as background noise disrupting intra-class consistency, inter-class distinction, and domain shifts due to variations in capture angles, lighting, and image resolution persist. This study proposes a multi-stage convolutional neural network-based framework incorporating a boundary-aware module (BAM) for boundary region perception, deformable ROI pooling (DRP) for spatial feature refinement, a transformer encoder for capturing global contextual relationships, and a NetRVLAD module for robust feature aggregation. The framework achieved state-of-the-art performance on three benchmark datasets, with Top-1 accuracies of 99.80% on the Food-5k dataset, 99.17% on the Food-101 dataset, and 85.87% on the Food-2k dataset, significantly outperforming existing methods. This framework holds promise as a foundational tool for intelligent dietary management, offering robust and accurate solutions for real-world applications.
Impact of Encapsulation Position in Pickering Emulsions on Color Stability and Intensity Turmeric Oleoresin
Authors: Han Ahreum , Baek Youjin , Lee Hyeon Gyu
Published: January 2025

The emulsification of natural pigment is a widely utilized strategy to enhance its stability in the food industry. However, high turbidity in emulsions often causes color fading, limiting their application. Here, we developed a comprehensive Pickering emulsion (PE) system to improve the color intensity and stability of turmeric oleoresin (Tur) under various food processing conditions. Specifically, the effects of two encapsulation positions within the PE were compared: the inner oil phase (Tur-IPE) and the outer solid particle layer (Tur-OPE). Lysozyme and carboxymethyl cellulose nanoparticles (NPs) were used as natural solid particle surfactants, with their successful formation confirmed through physical property analysis and FTIR spectroscopy. The optimal oil fraction (φ) for suitable physical properties of PE was determined to be 0.2. Interestingly, Tur-OPE significantly exceeded Tur-conventional emulsions (Tur-CE) and Tur-IPE in terms of color vividness, exhibiting higher redness and lower lightness (p < 0.05). During thermal processing at 70 and 90 °C, all emulsions demonstrated significantly enhanced heat resistance, retaining 1.3 to 1.6 times more Tur, respectively, compared to unencapsulated Tur (free Tur) (p < 0.05). Furthermore, Tur’s pH instability was significantly overcome by encapsulation in all emulsion systems (p < 0.05). During 4 weeks of storage period, Tur-OPE demonstrated the highest retention rates, with the half-life of Tur increasing in the following order: free Tur < Tur-CE < Tur-IPE < Tur-OPE. Thus, we highlighted the important role of encapsulation position in PEs in improving and maintaining the color stability and vividness of natural pigments under various food processing conditions.