bluuebunny/crossref_metadata_2025 huggingface dataset:
Long Short-Term Memory Neural Network with Attention Mechanism for Rice Yield Early Estimation in Qian Gorlos County, Northeast China
Authors: Li Jian , Xie Yichen , Liu Lushi , Song Kaishan , Zhu Bingxue
Published: January 2025

Rice is one of the most extensively cultivated food crops in Northeast China. Estimating pre-harvest rice yield is important for accurately formulating field management strategies and swiftly assessing overall rice production. This can be achieved using a pixel-scale model, which estimates crop yield based on information from each pixel. Previous studies predominantly employed remote sensing indices, climatic data, and yield statistics of rice across either single or all growth periods for yield estimation. These approaches are limited by a delay in yield estimation and are insufficient in the exploration of time-series feature variables at the pixel scale. This study presents the development of a novel deep-learning framework designed for the early estimation of rice yield in Qian Gorlos County, Northeast China. The framework utilizes a long short-term memory neural network integrated with an attention mechanism (ALSTM). In this framework, the heading stage–milk ripening stage is the time window for early yield estimation, and the vegetation indices Normalized Difference Red Edge (NDRE), Green Chlorophyll Vegetation Index (GCVI), and Normalized Difference Water Index (NDWI) from the rice transplanting to the milk ripening stage are time-series feature variables. The yield estimation precision is R2 = 0.88, RMSE = 341.82 kg/ha, MAE = 280.29 kg/ha, outperforming LASSO (R2 = 0.71, RMSE = 567.10 kg/ha, MAE = 487.38 kg/ha), RF (R2 = 0.79, RMSE = 506.70 kg/ha, MAE = 418.90 kg/ha), and LSTM (R2 = 0.83, RMSE = 451.11 kg/ha, MAE = 326.31 kg/ha). The ALSTM introduced in this paper demonstrates its robustness after being generalized to the 2022 growing season. It can forecast the current-year rice yield two months prior to harvest, providing a valuable reference for developing field management strategies to enhance rice productivity.
The Geochemical Characteristics and Exploitation Threshold of Copper in the Cultivated Soils of Guanzhong Plain, Shaanxi Province
Authors: Yan Yuchen , Yang Zhongfang , Yang Shengfei , Xu Anmin , Xu Duoxun
Published: January 2025

Moderate copper (Cu) intake is beneficial for human health; however, China has not established recommended Cu levels in major food crops and their cultivation lands. This study focuses on the Guanzhong Plain in Shaanxi Province, where we collected geochemical survey data at a scale of 1:250,000 and gathered 77 sets of wheat seed and root soil samples. We identified the Cu content and distribution characteristics within both soil and wheat grains in the area. Key factors influencing the bioaccumulation factor (BAF) of Cu in wheat were selected to establish a predictive model using artificial neural networks (ANN). Additionally, we determined recommended thresholds for Cu content in both the wheat and soil. The findings indicated, as follows, that: (1) the soil Cu content ranged from 13.00 to 98.00 mg/kg, with an average concentration of 32.21 mg/kg. Higher levels were found near alluvial deposits along the Qinling Mountains, showing a pattern of higher concentrations in the south than in the north; (2) the concentration of Cu in wheat grains varied from 2.94 to 6.34 mg/kg, with an average of 4.56 mg/kg; importantly, none exceeded the NY861-2004 permissible contamination limits; and (3) we recommend optimal ranges for Cu content for wheat grains of 3.16–10.00 mg/kg and establishing thresholds for cu-rich agricultural lands suitable for growing wheat of 20.80–50.00 mg/kg.
Coupling Artificial Intelligence with Proper Mathematical Algorithms to Gain Deeper Insights into the Biology of Birds’ Eggs
Authors: Narushin Valeriy G. , Volkova Natalia A. , Dzhagaev Alan Yu. , Griffin Darren K. , Romanov Michael N. , Zinovieva Natalia A.
Published: January 2025

Avian eggs are products of consumer demand, with modern methodologies for their morphometric analysis used for improving quality, productivity and marketability. Such studies open up numerous prospects for the introduction of artificial intelligence (AI) and deep learning (DL). We first consider the state of the art of DL in the poultry industry, e.g., image recognition and applications for the detection of egg cracks, egg content and freshness. We comment on how algorithms need to be properly trained and ask what information can be gleaned from egg shape. Considering the geometry of egg profiles, we revisit the Preston–Biggins egg model, the Hügelschäffer’s model, universal egg models, principles of egg universalism and “The Main Axiom”, proposing a series of postulates to evaluate the legitimacy and practical application of various mathematical models. We stress that different models have pros and cons, and using them in combination may yield more useful results than individual use. We consider the classic egg shape index alongside other alternatives, drawing conclusions about the importance of indices in the context of applying DL going forward. Examining egg weight, volume, surface area and air cell calculations, we consider how DL might be applied, e.g., for egg storage. The value of DL in egg studies is in pre-incubation egg sorting, the optimization of storage periods and incubation regimes, and the index representation of dimensional characteristics. Each index can thus be combined to provide a synergy that is on the threshold of many scientific discoveries, technological achievements and industrial successes facilitated through AI and DL.
Current Evidence on Raw Meat Diets in Pets: A Natural Symbol, but a Nutritional Controversy
Authors: Lyu Yang , Wu Caimei , Li Lian , Pu Junning
Published: January 2025

Feeding raw meat to cats and dogs has become a common practice in Western countries. Many pet owners cite their pets’ wild carnivore ancestry and believe raw meat diets are healthier, citing higher nutritional value, a lack of processing, low-carbohydrate content, and/or the presence of whole ingredients. However, due to the risks associated with raw meat-based diets (e.g., microbial contamination, nutritional imbalances) and growing concerns about food sustainability, most nutritional specialists and public health stakeholders question the practice of feeding raw meat. In this context, the vast majority of owners’ claims regarding the beneficial health effects of raw meat diets lack scientific substantiation, while professional advice primarily focuses on risks, often overlooking potential positive health effects. To better understand the effects of raw meat diets on the health of cats and dogs, this review summarises the latest scientific evidence and discusses both the potential health benefits and the risks associated with these diets.
Dynamic Adaptation of Hematological Parameters, Albumin, and Non-Esterified Fatty Acids in Saddlebred and Standardbred Horses During Exercise
Authors: Piccione Giuseppe , Arfuso Francesca , Giudice Elisabetta , Aragona Francesca , Pugliatti Pietro , Panzera Maria Francesca , Zumbo Alessandro , Monteverde Vincenzo , Bartolo Vincenzo , Barbera Adalberto , Giannetto Claudia
Published: January 2025

The response to exercise following a rest period may vary among horse breeds based on the importance of the hematological and hematochemical profiles of athletic horses. Ten Standardbred and ten Italian Saddlebred mares were subjected to a 4-week training program after a seasonal rest, and hematological parameters (red blood cells—RBCs; hemoglobin—Hb; hematocrit—Hct; platelets—PLTs; platelet aggregation—AG; aggregation slope—Slope; fibrinogen—Fb), as well as Albumin (Alb) and non-esterified fatty acids (NEFAs), were analyzed. Blood samples were obtained each week during the training program following a simulated exercise performed at T0 and T4 (T0pre-T0post; T1, T2, T3, and T4pre-T4post). A two-way ANOVA revealed an increase in all assessed parameters post vs. pre at T0 and T4 (p < 0.01) and a decrease in PLTs (p < 0.01) at T0 and T4 in both breeds. A significant effect of breed was observed, with higher values for RBCs and Hb at each time point (p < 0.001); Hct at T0 post, T2, T3, and T4 post; and NEFAs (p < 0.001) at T0 post and T4 post in Standardbred compared to Saddlebred horses. Positive correlations were identified among RBCs, Hb, Hct, PLTs, Alb, and NEFAs in both breeds and between AG and Slope in Saddlebred horses. Negative correlations were identified among AG and RBCs, Hb, Hct, PLTs, Alb, and NEFAs in Standardbred horses and for AG and Slope with RBCs, Hb, Hct, and PLTs in Saddlebred horses. A comparable reaction to training was observed in both breeds following the seasonal rest.
Early Identification of Geological Hazards Along the Power Transmission Line in Weinan Based on SBAS-InSAR
Authors: Shan Bo , Qi Jianguo , Tian Wucheng , Zhu Kuanxing , Jin Tie , Yang Qingkun , An Xiguan , Yang Guang , Hu Qi , Cao Chen
Published: January 2025

Landslides and ground subsidence pose significant threats to the successful construction and operation of transmission line projects in the Loess Plateau region. This study aims to explore an accurate early identification method for geological hazards, providing support for the construction and smooth operation of the transmission project along the route from Baishui County, Weinan City, Shaanxi Province to Lantian County, Xi’an City, Shaanxi Province. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to acquire the surface deformation data of the study area from 4 February 2018 to 21 May 2023. The deformation data were spatially analyzed through kernel density analysis, which quickly and intuitively identified 52 potential geological hazard points in the region, including eight landslides and 44 ground subsidence. Detailed field investigations of the hazards confirmed the accuracy of the identification results. A thorough analysis of typical hazards, such as landslide No. 9 and ground subsidence No. 29, revealed severe deformation, posing a threat to the proposed transmission project. This study indicates that combining InSAR, kernel density analysis, and field investigations can accurately and quickly identify geological hazards around transmission lines, providing support for the site selection and implementation of transmission projects.
Antitumor Activity of Whole-Plant Extracts from In Vitro Cultured and Wild-Growing Clinopodium vulgare Plants on a Panel of Human Tumor Cell Lines
Authors: Georgieva Ani , Sulikovska Inna , Toshkova-Yotova Tanya , Djeliova Vera , Amiri Sabrina , Tsonevski Nenad , Petkova-Kirova Polina , Tasheva Krasimira
Published: January 2025

Clinopodium vulgare L. is a valuable medicinal plant with various beneficial effects on health. In this study, water extracts from the aerial part of the wild and in vitro cultured C. vulgare plants were obtained. The polyphenol, flavonoid content and antioxidant activity of the extracts as well as their antitumor efficiency against a panel of cell lines were analyzed. The ability of C. vulgare to inhibit cancer cell migration and induce apoptosis in the tumor cells was examined by wound healing assay and fluorescence microscopic methods. The effect of the extracts on the cell cycle progression of the tumor cells was analyzed by flow cytometry. The presented results show that the antitumor activity of the extracts from in vitro cultured plants was similar to and even exceeded that of the wild plants. The cell viability and migration assays demonstrate the selective anticancer effect of the extract and significant inhibition of cancer cell proliferation and motility. The fluorescence microscopy and cell cycle analyses indicate that the antitumor activity of the in vitro plant extract was related to both antiproliferative and proapoptotic effects. These results show that C. vulgare plants obtained by in vitro micropropagation and cultivated ex vitro are promising candidates for anticancer drug therapy.
Resource-Efficient Acoustic Full-Waveform Inversion via Dual-Branch Physics-Informed RNN with Scale Decomposition
Authors: Lu Cai , Liu Jijun , Qu Liyuan , Gao Jianbo , Cai Hanpeng , Liang Jiandong
Published: January 2025

Full-waveform velocity inversion has long been a primary focus in seismic exploration. Full-waveform inversion techniques employing physics-informed recurrent neural networks (PIRNNs) have recently gained significant scholarly attention. However, these approaches demand considerable storage to capture spatiotemporal seismic wave propagation fields and their gradient information, often exceeding the memory capabilities of current GPU resources during field data processing. This study proposes a full-waveform inversion method utilizing a dual-branch PIRNN architecture to effectively minimize GPU resource consumption. The primary PIRNN branch performs forward-wave equation modeling at the original scale and computes the discrepancy between synthetic and observed seismic records. Additionally, a downscaled spatiotemporal PIRNN branch is introduced, transforming the original-scale error into a loss function via scale decomposition, which drives the inversion process in the downscaled domain. This dual-branch design necessitates recording only the spatiotemporal field and gradient information of the downscaled branch, significantly reducing GPU memory requirements. The proposed dual-branch PIRNN framework was validated through full-waveform inversions on synthetic horizontal-layer models and the Marmousi model across various scales. The results demonstrate that this approach markedly reduces resource consumption while maintaining high inversion accuracy.
Adaptive Constant False Alarm Detector Based on Composite Fuzzy Fusion Rules
Authors: Yang Yuyao , Xiu Chunbo
Published: January 2025

In order to improve the detection performance of the radar constant false alarm detector in a multiple-target environment, a Kaigh–Lachenbruch Quantile constant false alarm rate detector based on composite fuzzy fusion rules (CFKLQ-CFAR) is designed by combining fuzzy fusion rules and the Kaigh–Lachenbruch Quantile constant false alarm rate detector. Two sensors are used to collect environmental information, and the membership function value is calculated based on the collected information. Furthermore, the presence or absence of the target is judged compositely by four fuzzy fusion rules. CFKLQ-CFAR is applied to the variability index CFAR (VI-CFAR) detector, and an adaptive constant false alarm rate detector based on the composite fuzzy fusion rules (CFVI-CFAR) is designed to improve the performance of the radar constant false alarm detector in different environments. The simulation experiment results show that the average detection probability of CFKLQ-CFAR is 2.67% and 1.00% higher than that of KLQ-CFAR and the fuzzy logic fusion detector (FUMCA-CFAR) in a multiple-target environment. The average detection probability of CFVI-CFAR is 3.66% higher than that of the variability index heterogeneous clutter estimate modified ordered statistics CFAR (VIHCEMOS-CFAR) in a multiple-target environment, while in a clutter edge environment, the average false alarm probability of CFVI-CFAR is only 1.65% of that of VIHCEMOS-CFAR. Therefore, the performance of the radar constant false alarm detector has been effectively improved.
Analyzing Key Features of Open Source Software Survivability with Random Forest
Authors: Park Sohee , Kwon Gihwon
Published: January 2025

Open source software (OSS) projects rely on voluntary contributions, but their long-term survivability depends on sustained community engagement and effective problem-solving. Survivability, critical for maintaining project quality and trustworthiness, is closely linked to issue activity, as unresolved issues reflect a decline in maintenance capacity and problem-solving ability. Thus, analyzing issue retention rates provides valuable insights into a project’s health. This study evaluates OSS survivability by identifying the features that influence issue activity and analyzing their relationships with survivability. Kaplan–Meier survival analysis is employed to quantify issue activity and visualize trends in unresolved issue rates, providing a measure of project maintenance dynamics. A random forest model is used to examine the relationships between project features—such as popularity metrics, community engagement, code complexity, and project age—and issue retention rates. The results show that stars significantly reduce issue retention rates, with rates dropping from 0.62 to 0.52 as stars increase to 4000, while larger codebases, higher cyclomatic complexity, and older project age are associated with unresolved issue rates, rising by up to 15%. Forks also have a nonlinear impact, initially stabilizing retention rates but increasing unresolved issues as contributions became unmanageable. By identifying these critical factors and quantifying their impacts, this research offers actionable insights for OSS project managers to enhance project survivability and address key maintenance challenges, ensuring sustainable long-term success.
ResInceptNet-SA: A Network Traffic Intrusion Detection Model Fusing Feature Selection and Balanced Datasets
Authors: Liu Guorui , Zhang Tianlin , Dai Hualin , Cheng Xinyang , Yang Daoxuan
Published: January 2025

Network intrusion detection models are vital techniques for ensuring cybersecurity. However, existing models face several challenges, such as insufficient feature extraction capabilities, dataset imbalance, and suboptimal detection accuracy. In this paper, a new type of model (ResIncepNet-SA) based on InceptionNet, Resnet, and convolutional neural networks with a self-attention mechanism was proposed to detect network intrusions. The model used the PCA-ADASYN algorithm to compress network traffic features, extract high-correlation feature datasets, and oversample and balance the feature datasets to classify abnormal network traffic. The experimental results show that the accuracy, precision, recall, and F1-score of the proposed ResIncepNet-SA model using the NSL-KDD dataset reach 0.99366, 0.99343, 0.99339, and 0.99338, respectively. This model enhances the accuracy of abnormal network traffic detection and outperforms existing models when applied to imbalanced datasets, offering a new solution for network traffic intrusion detection.
A Theoretical Study on the Influence of the Functional Group Electronic Effect on the Electron Mobility of Cross-Linked Polyethylene
Authors: Du Yang , Zhang Hui , Du Xia , Shang Yan , Wang Xuan , Chen Qingguo , Li Zesheng
Published: January 2025

The effect of electron-donating and electron-withdrawing groups grafted onto polyethylene on electron mobility was studied using density functional theory. In order to ensure the accuracy of the calculation results, 17 basis sets from six methods were screened. 3-methylpentane was selected as the cross-linked polyethylene model. Compared with the experimental values, the theoretical calculation results show that wB97XD/6-311G(d,p) is more suitable for studying the electron mobility system. The roles of electron-donating and electron-withdrawing functional groups were studied. The results show that the electron mobility of grafting nitrobenzene (Ebnb) to polyethylene is the smallest among the studied molecules. As the ability of electron-donating groups increases, the electron mobility gradually increases, while the addition of the electron-withdrawing group reduces the electron mobility and the electron mobility gradually increases with increasing temperature. This investigation is expected to provide reliable information for the development of insulation materials for cables.
Predictions of Muscle Forces During the Cross-Body Adduction and Hand-Behind-the-Back Tests to Assess Osteoarthritis of the Acromioclavicular Joint
Authors: Gautam Kamal , Hefzy Mohamed Samir , Behrens Kyle , Mustapha Abdul A.
Published: January 2025

Acromioclavicular joint osteoarthritis is prevalent in middle-aged and older people, causing shoulder pain and functional limitations. Despite its prevalence, there are inconsistencies in the physical diagnosis procedures practiced in clinical tests. A recent study introduced a novel hand-behind-the-back (HBB) test, a promising alternative to the traditional cross-body adduction (CBA) test. However, further study was suggested to validate the results obtained. So, this study predicted muscle forces for the cross-body adduction and hand-behind-the-back tests using OpenSim and the AnyBody Modeling System™. This work redefined the joint kinematics for the tests and performed an inverse dynamics analysis to solve the muscle redundancy problem using the generic upper extremity dynamic models available in OpenSim and AnyBody Modeling System™. The results revealed some agreements and significant discrepancies in most muscle force predictions between the OpenSim and AnyBody Modeling SystemTM. Thus, this study underscores the necessity of integrating multiple modeling approaches and comprehensive validation, including experimental data, to enhance the accuracy and reliability of muscle force predictions in shoulder biomechanics during CBA and HBB tests.
Advancing Sustainable Materials: Synthesis and Analysis of Polyurethane Biocomposites from Hydrogenated Safflower Oil
Authors: Aydoğmuş Ercan , Yanen Cenk , Kıstak Celal
Published: January 2025

The growing demand for sustainable and environmentally friendly materials has highlighted the need for innovative alternatives to traditional plastics and composites. This study explores the development of polyurethane-based biocomposites synthesized using hydrogenated safflower oil (HSO), hydrogen gas, and a nickel catalyst. Safflower oil was hydrogenated via a catalytic hydrogenation mechanism, transforming carbon–carbon double bonds into saturated fatty acids. The process, optimized using response surface methodology (RSM), was conducted at 225 °C, 8 atmospheres, and 6 h, achieving a yield of 67%. Hydrogenation improved the physical and chemical properties of the biocomposites. Biocomposites were synthesized by combining HSO with methylene diphenyl diisocyanate (MDI) as a crosslinker. Optimal performance was observed at 2.5% HSO content, enhancing material properties. However, higher HSO content negatively affected biocomposite density and hardness, and surface morphology analyses revealed that increased HSO content led to irregular pore structures. These findings underscore the balance required in material composition to achieve optimal performance. This study presents a novel approach to producing hydrogenation-modified HSO-based biocomposites, providing an eco-friendly and sustainable alternative for industries such as packaging, construction, and automotive applications. The advancements contribute to reducing environmental impact and enhancing the performance of renewable material technologies.
Bayesian Hyperparameter Optimization of Machine Learning Models for Predicting Biomass Gasification Gases
Authors: Cihan Pınar
Published: January 2025

Predicting biomass gasification gases is crucial for energy production and environmental monitoring but poses challenges due to complex relationships and variability. Machine learning has emerged as a powerful tool for optimizing and managing these processes. This study uses Bayesian optimization to tune parameters for various machine learning methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Elastic Net, Adaptive Boosting (AdaBoost), Gradient-Boosting Regressor (GBR), K-nearest Neighbors (KNN), and Decision Tree (DT), aiming to identify the best model for predicting the compositions of CO, CO2, H2, and CH4 under different conditions. Performance was evaluated using the correlation coefficient (R), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Relative Absolute Error (RAE), and execution time, with comparisons visualized using a Taylor diagram. Hyperparameter optimization’s significance was assessed via t-test effect size and Cohen’s d. XGBoost outperformed other models, achieving high R values under optimal conditions (0.951 for CO, 0.954 for CO2, 0.981 for H2, and 0.933 for CH4) and maintaining robust performance under suboptimal conditions (0.889 for CO, 0.858 for CO2, 0.941 for H2, and 0.856 for CH4). In contrast, K-nearest Neighbors (KNN) and Elastic Net showed the poorest performance and stability. This study underscores the importance of hyperparameter optimization in enhancing model performance and demonstrates XGBoost’s superior accuracy and robustness, providing a valuable framework for applying machine learning to energy management and environmental monitoring.
A Review of Traditional and Advanced MPPT Approaches for PV Systems Under Uniformly Insolation and Partially Shaded Conditions
Authors: Endiz Mustafa Sacid , Gökkuş Göksel , Coşgun Atıl Emre , Demir Hasan
Published: January 2025

Solar photovoltaic (PV) is a crucial renewable energy source that converts sunlight into electricity using silicon-based semiconductor materials. However, due to the non-linear characteristic behavior of the PV module, the module’s output power varies according to the solar radiation and the ambient temperature. To address this challenge, maximum power point tracking (MPPT) techniques are employed to extract the maximum amount of power from the PV modules. This paper offers a comprehensive review of widely used traditional and advanced MPPT approaches in PV systems, along with current developments and future directions in the field. Under uniform insolation, these methods are compared based on their strengths and weaknesses, including sensed parameters, circuitry, tracking speed, implementation complexity, true MPPT, accuracy, and cost. Additionally, MPPT algorithms are evaluated in terms of their performance in reaching maximum power point (MPP) under partial shading condition (PSC). Existing research clearly demonstrates that the advanced techniques exhibit superior efficiency in comparison to traditional methods, although at the cost of increased design complexity and higher expenses. By presenting a detailed review and providing comparison tables of widely used MPPT techniques, this study aims to provide valuable insights for researchers and practitioners in selecting appropriate MPPT approaches for PV applications.
Automation and Decision Support in Nephrology: An Expert System Based on AI and ML for the Assessment, Treatment, and Management of Focal Segmental Glomerulosclerosis
Authors: Pawuś Dawid , Porażko Tomasz , Paszkiel Szczepan
Published: January 2025

Focal segmental glomerulosclerosis (FSGS) presents significant challenges in diagnosis, treatment, and management due to its complex etiology and clinical variability. Traditional approaches often rely on clinician judgment and are prone to inconsistencies. This study introduces an advanced expert system integrating Artificial Intelligence (AI) with Machine Learning (ML) to support nephrologists in assessing, treating, and managing FSGS. The proposed system features a modular design comprising diagnostic workflows, risk stratification, treatment guidance, and outcome monitoring modules. By leveraging ML algorithms and clinical data, the system offers personalized, data-driven recommendations, enhancing decision-making and patient care. The evaluation demonstrates the system’s efficacy in reducing diagnostic errors and optimizing treatment pathways. These findings underscore the potential of AI-driven tools in transforming nephrology practice and improving clinical outcomes for FSGS patients.
An Efficient Method for Generating a Super-Sized and Heterogeneous Pore-Throat Network Model of Rock
Authors: Yu Chunlei , Chen Wenbin , Li Junjian , Wang Shuoliang
Published: January 2025

The super-sized pore-throat network model can reflect both microscopic pore characteristics and macroscopic heterogeneity and is excellent in describing cross-scale flow fields. At present, there is no algorithm that can generate a micro pore-throat network model at a macro reservoir scale. This study examines algorithms for super-sized pore-throat network reconstruction using actual core sample data. It conducts a random simulation of mineral growth and dissolution under the constraints of four microscopic pore structure parameters: porosity, coordination number, pore radius, and throat radius. This approach achieves high-precision, super-sized, and regional pore-throat network modeling. Comparative analysis shows that these four parameters effectively guide the random growth process of super-sized pore-throat networks. The overall similarity between the generated pore-throat network model and real core samples is 88.7% on average. In addition, the algorithm can partition and control the generation of pore-throat networks according to sedimentary facies. The 100-megapixel model with 85,000 pores was generated in 455.9 s. This method can generate cross-scale models and provides a basis for cross-scale modeling in physical simulation experiments and numerical simulations.
Urban Geography Compression Patterns: Non-Euclidean and Fractal Viewpoints
Authors: Griffith Daniel A. , Arlinghaus Sandra Lach
Published: January 2025

The intersection of fractals, non-Euclidean geometry, spatial autocorrelation, and urban structure offers valuable theoretical and practical application insights, which echoes the overarching goal of this paper. Its research question asks about connections between graph theory adjacency matrix eigenfunctions and certain non-Euclidean grid systems; its explorations reflect accompanying synergistic influences on modern urban design. A Minkowski metric with an exponent between one and two bridges Manhattan and Euclidean spaces, supplying an effective tool in these pursuits. This model coalesces with urban fractal dimensions, shedding light on network density and human activity compression. Unlike Euclidean geometry, which assumes unique shortest paths, Manhattan geometry better represents human movements that typically follow multiple equal-length network routes instead of unfettered straight-line paths. Applying these concepts to urban spatial models, like the Burgess concentric ring conceptualization, reinforces the need for fractal analyses in urban studies. Incorporating a fractal perspective into eigenvector methods, particularly those affiliated with spatial autocorrelation, provides a deeper understanding of urban structure and dynamics, enlightening scholars about city evolution and functions. This approach enhances geometric understanding of city layouts and human behavior, offering insights into urban planning, network density, and human activity flows. Blending theoretical and applied concepts renders a clearer picture of the complex patterns shaping urban spaces.
Molecular Epidemiology Revealed Distinct Patterns Among Multidrug Resistant Clinical Acinetobacter baumannii Strains in Different Periods in the Main Hospital in Molise Region, Central Italy
Authors: Tamburro Manuela , Lombardi Adele , Sammarco Michela Lucia , Ripabelli Giancarlo
Published: January 2025

Background: Acinetobacter baumannii is a major cause of nosocomial infections in critically ill patients, and strains are frequently multidrug resistant (MDR). This study aimed to characterize 45 clinical A. baumannii isolates collected in different periods in the main hospital in the Molise Region, central Italy. Methods: Antimicrobial susceptibility was evaluated using an automated system, and PCRs were performed to detect resistance-associated genes. Pulsed-field gel electrophoresis (PFGE) was carried out with AscI and ApaI, and Multi-locus sequence typing (MLST) was performed according to the Oxford scheme. Results: All isolates exhibited MDR profiles, showing total susceptibility towards colistin. All strains harbored the blaOXA-23, blaOXA-51, and blaAmpC genes, as well as adeB, adeJ, adeG, abeS, and soxR. Dendrogram with AscI and ApaI revealed eleven and three clusters, respectively, and twenty-three and eighteen pulsotypes (Simpson’s index 0.96 and 0.93), and isolates from different periods were clearly distinguished. MLST revealed five sequence types, which varied depending on the isolation period, and ST1720 and ST369 were prevalent, followed by ST281, ST218, and ST513. Conclusions: Molecular characterization enables the identification of distinct patterns of MDR A. baumannii over time, underscoring its usefulness for improving epidemiological surveillance and combating antimicrobial resistance. This study provides previously unavailable information regarding A. baumannii circulating in the examined setting.
Density Functional Theory Study of Hydrogen Adsorption on Al-U Alloy Surfaces
Authors: Wang Xiaoting , Guan Min , You Duo , Xie Dong , Hou Mingxi , Leng Yongxiang
Published: January 2025

To better understand the hydrogen corrosion mechanism on uranium surfaces and assess the hydrogen penetration resistance of Al-U alloys, the adsorption of hydrogen atoms on U(110), Al(111), and nAl/U(110) alloy surfaces was systematically studied through density functional theory (DFT) calculations. The results reveal that when U is alloyed with Al, the adsorption behavior of H atom on its surface thereon changes greatly. Specifically, the adsorption energy of H decreases with increasing Al content, indicating a weakening of the interaction between the H atom and the surface. A correlation between binding strength and alloy composition was established using d-band center theory. The incorporation of Al atoms alters the electronic structure of the U(110) surface, shifting the d-band center of uranium atoms downward. This shift results in a weakened interaction between the adsorbed H atom and the alloy surface.
High-Resolution EEG Amplifiers Are Feasible for Electrocochleography Without Time Restriction
Authors: Schertenleib Florian Josef , Hochmuth Sabine , Müller Jana Annina , Sandmann Pascale , Radeloff Andreas
Published: January 2025

Objectives: The gold standard for electrocochleography (ECochG) is using dedicated recording devices for auditory evoked potentials. However, these have a very limited time window for recording. The aim of this study is to evaluate EEG amplifiers for ECochG, in particular for recording cochlear microphonics (CMs) without time restriction. Methods: Three high-resolution EEG amplifiers and different types of electrodes were analyzed and compared with a clinical system for recording auditory evoked potentials. For this, CMs were recorded after stimulation with various stimuli in a dummy and in human subjects. In the latter, recordings were made from the tympanic membrane and, during otosurgical procedures, from the promontory. Our evaluation focused on comparing signal amplifiers and electrode types, considering the signal-to-noise ratio, recording characteristics, and measurement reliability. Results: Using a dummy model, we observed significant differences among devices, electrode types, and stimulus frequencies. These findings were subsequently confirmed in human participant measurements. Nevertheless, EEG amplifiers proved to be feasible for ECochG recordings and offered a recording fidelity comparable to proprietary clinical methods. Importantly, with EEG amplifiers, we were able to record cochlear potentials in response to speech stimuli, revealing a strong correlation (r = 0.78) between recorded signals and the input stimulus. Conclusions: Our findings indicate that high resolution EEG amplifiers are suitable for recording cochlear potentials, in particular, CMs. This allows for evaluating cochlear signals in response to extended stimuli, in particular, speech stimuli.
k-Nearest Neighbour Estimation of the Conditional Set-Indexed Empirical Process for Functional Data: Asymptotic Properties
Authors: Souddi Youssouf , Bouzebda Salim
Published: January 2025

The main aim of this paper is to improve the existing limit theorems for set-indexed conditional empirical processes involving functional strong mixing random variables. To achieve this, we propose using the k-nearest neighbor approach to estimate the regression function, as opposed to the traditional kernel method. For the first time, we establish the weak consistency, asymptotic normality, and density of the proposed estimator. Our results are derived under certain assumptions about the richness of the index class C, specifically in terms of metric entropy with bracketing. This work builds upon our previous papers, which focused on the technical performance of empirical process methodologies, and further refines the prior estimator. We highlight that the k-nearest neighbor method outperforms the classical approach due to several advantages.
Metagenomic Insights into Pollutants in Biorefinery and Dairy Wastewater: rDNA Dominance and Electricity Generation in Double Chamber Microbial Fuel Cells
Authors: Shabangu Khaya Pearlman , Chetty Manimagalay , Bakare Babatunde Femi
Published: January 2025

This study evaluates the potential of biorefinery and dairy wastewater as substrates for electricity generation in double chamber Microbial Fuel Cells (DCMFC), focusing on their microbial taxonomy and electrochemical viability. Taxonomic analysis using 16S/18S rDNA-targeted DGGE and high-throughput sequencing identified Proteobacteria as dominant in biorefinery biomass, followed by Firmicutes and Bacteriodota. In dairy biomass, Lactobacillus (77.36%) and Clostridium (15.70%) were most prevalent. Biorefinery wastewater exhibited the highest bioelectrochemical viability due to its superior electrical conductivity and salinity, achieving a voltage yield of 65 mV, compared to 75.2 mV from mixed substrates and 1.7 mV from dairy wastewater. Elevated phosphate levels in dairy wastewater inhibited bioelectrochemical processes. This study recommends Biorefinery wastewater as the most suitable purely organic substrate for efficient bioelectricity generation and scaling up of MFCs, emphasising the importance of substrate selection for optimal energy output for practical and commercial viability.
Experimental Biomechanics of Neonatal Brachial Plexus Avulsion Injuries Using a Piglet Model
Authors: Singh Anita , Ghuge Kalyani , Patni Yashvy , Balasubramanian Sriram
Published: January 2025

Background: A brachial plexus avulsion occurs when the nerve root separates from the spinal cord during birthing trauma, such as shoulder dystocia or a difficult vaginal delivery. A complete paralysis of the affected levels occurs post-brachial plexus avulsion. Despite being reported in 10–20% of brachial plexus birthing injuries, it remains poorly diagnosed during the acute stages of injury, leading to poor intervention approaches. The poor diagnosis of brachial plexus avulsion injury can be attributed to the currently unavailable biomechanics of brachial plexus avulsion. While the biomechanical properties of neonatal brachial plexus are available, the forces required to avulse a neonatal brachial plexus remain unknown. Methods: This study aims to provide detailed biomechanics of the required forces and corresponding strains for neonatal brachial plexus avulsion. Biomechanical tensile testing was performed on an isolated, clinically relevant piglet spinal cord and brachial plexus complex, and the required avulsion forces and strains were measured. Results: The reported failure forces and corresponding strains were 3.9 ± 1.6 N at a 27.9 ± 6.5% strain, respectively. Conclusion: The obtained data are required to understand the avulsion injury biomechanics and provide the necessary experimental data for computational model development that serves as an ideal surrogate for understanding complicated birthing injuries in newborns.
Emerging Strategies for Revascularization: Use of Cell-Derived Extracellular Vesicles and Artificial Nanovesicles in Critical Limb Ischemia
Authors: Ravi Mythili Vijay Murali , Rajendran Ramya Lakshmi , Arun Raksa , Thasma Loganathbabu Vasanth Kanth , Reyaz Danyal , Nagarajan ArulJothi Kandasamy , Ahn Byeong-Cheol , Gangadaran Prakash
Published: January 2025

Critical limb ischemia (CLI) poses a substantial and intricate challenge in vascular medicine, necessitating the development of innovative therapeutic strategies to address its multifaceted pathophysiology. Conventional revascularization approaches often fail to adequately address the complexity of CLI, necessitating the identification of alternative methodologies. This review explores uncharted territory beyond traditional therapies, focusing on the potential of two distinct yet interrelated entities: cell-derived extracellular vesicles (EVs) and artificial nanovesicles. Cell-derived EVs are small membranous structures naturally released by cells, and artificial nanovesicles are artificially engineered nanosized vesicles. Both these vesicles represent promising avenues for therapeutic intervention. They act as carriers of bioactive cargo, including proteins, nucleic acids, and lipids, that can modulate intricate cellular responses associated with ischemic tissue repair and angiogenesis. This review also assesses the evolving landscape of CLI revascularization through the unique perspective of cell-derived EVs and artificial nanovesicles. The review spans the spectrum from early preclinical investigations to the latest translational advancements, providing a comprehensive overview of the current state of research in this emerging field. These groundbreaking vesicle therapies hold immense potential for revolutionizing CLI treatment paradigms.
Inhibition of MEK1/2 Signaling Pathway Limits M2 Macrophage Polarization and Interferes in the Dental Socket Repair Process in Mice
Authors: Fonseca Angélica Cristina , Colavite Priscila Maria , Azevedo Michelle de Campos Soriani , Passadori Daniela Carignatto , Melchiades Jessica Lima , Ortiz Rafael Carneiro , Rodini Camila Oliveira , Trombone Ana Paula Favaro , Garlet Gustavo Pompermaier
Published: January 2025

Dental socket repair theoretically involves a constructive inflammatory immune response, which evolves from an initial M1 prevalence to a subsequent M2 dominance. In this scenario, the MEK1/2 signaling pathway is allegedly involved in M2 polarization. This study aimed to evaluate the impact of MEK1/2 pharmacological inhibition in the local host response and repair outcome. C57Bl/6-WT 8-week-old male mice were submitted to the extraction of the right upper incisor and treated (or not, control group) with MEK1/2 inhibitor PD0325901 (10 mg/kg/24 h/IP, MEK1/2i group) and analyzed at 0, 3, 7, and 14 days using microcomputed tomography, histomorphometry, birefringence, immunohistochemistry, and PCR array analysis. The results demonstrate that MEK1/2 inhibition limits the development of M2 response over time, being associated with lower expression of M2, MSCs, and bone markers, lower levels of growth and osteogenic factors, along with a higher expression of iNOS, IL-1b, IL-6, and TNF-α, as well inflammatory chemokines, indicating a predominantly M1 pro-inflammatory environment. This modulation of local inflammatory immune response is associated with impaired bone formation as demonstrated by microtomographic and histomorphometric data. The results show that MEK1/2 inhibition delays bone repair after tooth extraction, supporting the concept that M2 macrophages are essential elements for host response regulation and proper repair.
Cocaine-Induced Limbic Encephalopathy Manifesting as Acute Amnesia: A Case Report
Authors: Bourgan Leah Mitra , Wong Lindsay Michelle , Setty Prithvi , Junaid Adan , Karim Shahnawaz , Lui Forshing
Published: January 2025

Background: Cocaine has been shown to cause cytotoxic neuronal damage, which has been implicated in cases of leukoencephalopathy. We present a case of cocaine-induced toxic encephalopathy resulting in predominant lesions to the gray matter on magnetic resonance imaging (MRI). Case Presentation: A 70-year-old female presented acutely with confusion, agitation, and disorientation. She was markedly hypertensive with other vital signs within normal range. On presentation to the emergency department, she was uncooperative and had an unsteady gait but showed no focal neurological deficits. Her lab work was positive for elevated cardiac troponins, elevated D-dimer, and a urine drug screen positive only for cocaine. Head computed tomography (CT) showed no hemorrhage and head CT angiogram showed no abnormalities and no significant vascular stenosis. Chest X-ray and CT showed diffuse ground glass opacities compatible with atypical pneumonia. Antibiotics were initiated to treat the pneumonia and antihypertensives were administered to manage her blood pressure. She was also given IV thiamine. Brain MRI showed restricted diffusion involving bilateral hippocampi, thalami, putamen, caudate, and right occipital lobe, findings suspicious for cytotoxic edema. After acute stabilization, the patient demonstrated profound anterograde and retrograde amnesia, which improved gradually over days to weeks. She was eventually discharged to a skilled nursing facility. Conclusion: To our knowledge, this is the first reported case of profound amnesia secondary to cocaine-induced toxic encephalopathy with bilateral hippocampal involvement. These symptoms correlate with the implicated neuroanatomical structures. This case demonstrates that cocaine may be implicated in toxic encephalopathy affecting the brain’s gray matter and highlights a unique presentation of these findings.
Butyrate Prevents Obesity Accompanied by HDAC9-Mediated Browning of White Adipose Tissue
Authors: Yang Jing , Li Guoli , Wang Shan , He Mingqian , Dong Sijing , Wang Ting , Shi Binyin , Rensen Patrick C. N. , Wang Yanan
Published: January 2025

Background/Objectives: Mounting evidence indicates that the short-chain fatty acid butyrate protects against obesity and associated comorbidities, partially through the induction of adipose tissue thermogenesis. However, the effects of butyrate on white adipose tissue (WAT) browning and its molecular mechanism are still elusive. The objective of this study was to investigate butyrate-induced thermogenesis in white adipose tissue and its underlying mechanism. Methods: We studied the effects of butyrate on diet-induced obesity in the humanized APOE*3-Leiden.CETP transgenic mouse model and explored factors related to white adipose browning. Specifically, mice were challenged with a high-fat diet supplemented with butyrate. Adiposity was measured to assess obesity development. Energy metabolism was detected using an indirect calorimetry system. RNA-seq analysis was conducted to analyze the transcription landscape of WAT and responsible targets. Furthermore, the revealed molecular mechanism was verified in vitro. Results: Butyrate alleviated high-fat diet-induced obesity and promoted energy expenditure accompanied by brown adipose tissue activation and WAT browning. Mechanistically, RNA-seq analysis revealed that butyrate downregulated HDAC9 in WAT. Additionally, butyrate decreased HDAC9 while increasing thermogenesis in vitro. Inhibition of HDAC9 with TMP269 promoted thermogenic gene expression, mimicking the effects of butyrate. Conclusions: Butyrate protects against diet-induced obesity accompanied by decreasing the expression of HDAC9 in white adipose tissue and inducing browning. This study reveals a new mechanism whereby butyrate activates adaptive thermogenesis and provides new insights for the development of weight-loss drugs targeting adipose HDAC9.