bluuebunny/crossref_metadata_2025 huggingface dataset:
Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality
Authors: Kundu Avisek , Kundu Seeboli Ghosh , Sahu Santosh Kumar , Badgayan Nitesh Dhar
Published: January 2025

The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, tangibility, empathy, and responsiveness—provides a measurable framework for evaluating the overall customer satisfaction. This study endeavors to ascertain whether all SERVQUAL dimensions carry equal weight in their effect on the overall service quality and to estimate the service quality based on various input features. To achieve this, questions were framed to assess the impact of variables such as gender, age, marital status, highest level of education, and frequency of hotel stays. The importance of each feature relative to the five SERVQUAL dimensions was investigated using machine learning models, specifically, CatBoost and Microsoft Azure Automated Machine Learning (AutoML) studio. This study revealed that both CatBoost and Azure AutoML identified the frequency of hotel stays and age group as the dominant predictors of service quality. Additionally, Azure AutoML highlighted the marital status as a more significant factor, suggesting its potential influence on customer preferences. The comparative modeling results demonstrated a strong alignment between the feature importance derived from CatBoost and Azure AutoML, enabling decision-makers to identify which dimensions are influenced by specific predictors and focus on targeted improvements.
Use of IoT with Deep Learning for Classification of Environment Sounds and Detection of Gases
Authors: Mishra Priya , Mishra Naveen , Choudhary Dilip Kumar , Pareek Prakash , Reis Manuel J. C. S.
Published: January 2025

The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their flammability and toxicity. LPG, widely used in residential and industrial settings, is especially hazardous because it is colorless, odorless, and highly flammable, making undetected leaks an explosion risk. To mitigate these dangers, modern gas detection systems employ sensors, microcontrollers, and real-time monitoring to quickly identify dangerous gas levels. This study introduces an IoT-based system designed for comprehensive environmental monitoring, with a focus on detecting LPG and butane leaks. Using sensors like the MQ6 for gas detection, MQ135 for air quality, and DHT11 for temperature and humidity, the system, managed by an Arduino Mega, collects data and sends these to the ThingSpeak platform for analysis and visualization. In cases of elevated gas levels, it triggers an alarm and notifies the user through IFTTT. Additionally, the system includes a microphone and a CNN model for analyzing audio data, enabling a thorough environmental assessment by identifying specific sounds related to ongoing activities, reaching an accuracy of 96%.
Mosaic Structure of GaN Film Grown on Sapphire Substrate by AP-MOCVD: Impact of Thermal Annealing on the Tilt and Twist Angles
Authors: Laifi J. , Hasaneen M. F. , Bouazizi H. , Alsahli Fatimah Hafiz , Lafford T. A. , Bchetnia A.
Published: January 2025

A GaN layer with a thickness of 2 µm was grown on a sapphire substrate using atmospheric pressure metal–organic chemical vapor deposition (AP-MOCVD). Subsequently, the layer was annealed under a nitrogen atmosphere at temperatures ranging from 1000 °C to 1120 °C. High-resolution X-ray diffraction (HRXRD) analysis reveals the impact of thermal annealing on the mosaic structure of the GaN, specifically the tilt and twist variations in four planes: (00.2), (10.3), (10.2), and (10.1). Interestingly, the observed trends suggest a differential effect of annealing on screw and edge dislocation densities. The annealing process reduces the edge and screw dislocation density. Lower values (Dscrew = 1.2 × 108 cm−2; Dedge = 1.6 × 109 cm−2) were obtained for the sample annealed at 1050 °C. Notably, both tilt and twist angles exhibited a minimum at 1050 °C (tilt = 252 arcsecs, and twist = 558 arcsecs), indicating improved crystal quality at this specific temperature. Photoluminescence (PL) spectroscopy further complemented the structural analysis. The intensity and broadening of the yellow band (YL) in the PL spectra progressively increased with the increasing annealing temperature, suggesting the presence of additional defect states. The near band edge PL emission (3.35 and 3.41 eV) variation upon thermal annealing was correlated with the mosaic structure evolution.
Thermodynamic and Kinetic Insights into the Formation of Metallic Rhenium via Solution Combustion Synthesis
Authors: Paryshev Andrey Alekseevich , Shishkin Roman
Published: January 2025

This study investigates the formation of metallic rhenium through Solution Combustion Synthesis (SCS), focusing on the thermodynamics, kinetics, and phase composition of the process. The impact of the fuel-to-oxidizer ratio (φ) on the synthesis of rhenium was evaluated, demonstrating that the stoichiometric ratio (φ = 1) leads to the highest combustion temperature and the formation of pure metallic rhenium, as confirmed by XRD. Deviation from this stoichiometric condition, either by fuel excess (φ > 1) or oxidizer excess (φ < 1), resulted in incomplete reduction and the formation of rhenium oxides (ReO2 and ReO3). Thermodynamic calculations revealed that under reducing conditions, metallic rhenium is the primary product while oxidizing conditions favor the formation of rhenium dioxide. Kinetic analysis of the thermal decomposition of ammonium perrhenate suggested that the process involves a multi-stage reaction, with the reduction of rhenium occurring in a stepwise manner. The findings provide new insights into the role of rhenium as both a metal and an oxidizer in SCS and emphasize the critical influence of the fuel/oxidizer ratio in controlling the phase composition and crystallinity of the final product.
Midterm Outcomes of Endovascular Treatment for Intracranial Atherosclerosis: High-Volume Versus Low-Volume Centres
Authors: Abualhasan Ahmed , Pero Guglielmo , Quilici Luca , Piano Mariangela , Valvassori Luca , Sobh Khaled , Mansour Ossama , Elbassiony Ahmed , El-Serafy Omar , Boccardi Edoardo , Abd-Allah Foad
Published: January 2025

Background: Intracranial stenting is still feasible, but its effectiveness is still investigational. Our study investigated outcomes of endovascular treatment in high-volume and low-volume centres. Methods: We retrospectively recruited 36 patients with intracranial atherosclerosis who underwent endovascular treatment from January 2014 to June 2016 at three low-volume centres (n = 18), and a single high-volume centre (n = 18). Detailed periprocedural records, as well as clinical and radiological follow-up data, were revised through at least one-year post-procedure. The outcome parameters included successful revascularization, occurrence of any death, stroke, and/or Transient Ischaemic Attack (TIA) after intervention or during the follow-up period, and restenosis (≥50%). Results: The successful revascularization rate was 97.2%. The 30-day rate of any death, stroke, and/or TIA was 13.9%. At a median clinical follow-up of 18 months, the rate of any death, stroke, and/or TIA was 27.8%. Rates of any death, stroke, and/or TIA at 30 days and 18 months were higher among patients treated in low-volume centres versus those treated in high-volume centres but without reaching statistical significance (22.2% versus 5.6%, p = 0.188; and 38.9% versus 16.7%, p = 0.137, respectively). Conclusions: Although not statistically significant, our study showed a higher incidence of death, stroke and/or TIA among patients treated in the low-volume centre compared to those treated in the high-volume centre.
A Systematic Review: The Role of Artificial Intelligence in Lung Cancer Screening in Detecting Lung Nodules on Chest X-Rays
Authors: Megat Ramli Puteri Norliza , Aizuddin Azimatun Noor , Ahmad Norfazilah , Abdul Hamid Zuhanis , Ismail Khairil Idham
Published: January 2025

Background: Lung cancer remains one of the leading causes of cancer-related deaths worldwide. Artificial intelligence (AI) holds significant potential roles in enhancing the detection of lung nodules through chest X-ray (CXR), enabling earlier diagnosis and improved outcomes. Methods: Papers were identified through a comprehensive search of the Web of Science (WOS), Scopus, and Ovid Medline databases for publications dated between 2020 and 2024. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 34 studies that met the inclusion criteria were selected for quality assessment and data extraction. Results: AI demonstrated sensitivity rates of 56.4–95.7% and specificities of 71.9–97.5%, with the area under the receiver operating characteristic (AUROC) values between 0.89 and 0.99, compared to radiologists’ mean area under the curve (AUC) of 0.81. AI performed better with larger nodules (>2 cm) and solid nodules, showing higher AUC values for calcified (0.71) compared to non-calcified nodules (0.55). Performance was lower in hilar areas (30%) and lower lung fields (43.8%). A combined AI-radiologist approach improved overall detection rates, particularly benefiting less experienced readers; however, AI showed limitations in detecting ground-glass opacities (GGOs). Conclusions: AI shows promise as a supplementary tool for radiologists in lung nodule detection. However, the variability in AI results across studies highlights the need for standardized assessment methods and diverse datasets for model training. Future studies should focus on developing more precise and applicable algorithms while evaluating the effectiveness and cost-efficiency of AI in lung cancer screening interventions.
Circulating Tumor DNA and [18F]FDG-PET for Early Response Assessment in Patients with Advanced NSCLC
Authors: Ryssel Heidi , Ahlborn Lise Barlebo , Dejanovic Danijela , Keller Sune Hoegild , Pøhl Mette , Østrup Olga , Loft Annika , Fischer Barbara Malene , Langer Seppo Wang , Kjaer Andreas , Christensen Tine Nøhr
Published: January 2025

Background/Objectives: Identifying treatment failure at earlier time points to could spare cancer patients from ineffective treatment and side effects. In this study, circulating tumor DNA (ctDNA) and [18F]FDG-PET/CT were investigated during the first cycle of anticancer therapy in patients with advanced non-small cell lung cancer (NSCLC) to explore their potential for early response evaluation. Methods: Patients with advanced NSCLC receiving first-line therapy with immune checkpoint inhibitors and/or chemotherapy were included. CtDNA and [18F]FDG-PET/CT assessments were conducted before treatment and at weeks 1 and 3 during the first cycle of therapy. ctDNA quantification was performed using a targeted next-generation sequencing (NGS) panel, and the least favorable change in any mutated allele frequency at a given time was used for analysis. [18F]FDG-PET/CT was quantified using sumSULpeak and metabolic tumor volume (MTV4.0). Early changes in ctDNA levels and [18F]FDG-PET parameters were compared with final treatment response, measured by RECIST after 12 weeks, as well as progression-free survival and overall survival. Results: Of the sixteen included patients, eight were non-responders. ctDNA mutations were detected in baseline blood samples in eight patients. Changes in ctDNA level, MTV4.0, and sumSULpeak at week 3 indicated response in 7 out of 8 patients, 13 out of 15 patients, and 9 out of 15 patients, respectively. At week 3, no false increases were seen with ctDNA and MTV4.0. Conclusions: These results suggest that early changes in ctDNA and [18F]FDG-PET/CT at 3 weeks of treatment could be used to early assess treatment response. Increased levels of ctDNA and MTV4.0 at week 3 were only observed in patients with treatment failure.
What Are Priorities and Didactic Choices of Teachers at Schools for Swedish Students with Intellectual Disability?—A Study of Teacher-Initiated Professional Development in a Swedish Context
Authors: Andersson Anna-Lena , Klang Nina , Östlund Daniel
Published: January 2025

This article presents findings from an ongoing project focused on teaching in Schools for Students with Intellectual Disabilities (SSIDs) in Sweden. Methodologically, the project employs professional development circles as a collaborative working method, aiming to bridge the gap between research and practice by facilitating dialog between teachers and researchers. Through qualitative content analysis of data collected from these circles, the study delves into professionals’ instructional priorities and didactic choices, offering insights into how teachers adapt instruction to meet students’ diverse needs and prerequisites. By fostering knowledge exchange and shared knowledge production, the project aims to support school development and enhance teacher professionalism in SSIDs.
SAT-GATv2: A Dynamic Attention-Based Graph Neural Network for Solving Boolean Satisfiability Problem
Authors: Chang Wenjing , Liu Wenlong
Published: January 2025

We propose SAT-GATv2, a graph neural network (GNN)-based model designed to solve the Boolean satisfiability problem (SAT) through graph-based deep learning techniques. SAT-GATv2 transforms SAT formulas into graph structures, leveraging message-passing neural networks (MPNNs) to propagate local information and dynamic attention mechanisms (GATv2) to accurately capture inter-node dependencies and enhance node feature representations. Unlike traditional heuristic-driven SAT solvers, SAT-GATv2 adopts a data-driven approach, learning structural patterns directly from graph representations and providing a complementary framework to existing methods. Experimental results demonstrate that SAT-GATv2 achieves an accuracy improvement of 1.75–5.51% over NeuroSAT on challenging random 3-SAT(n) instances, highlighting its effectiveness in handling difficult problem distributions, and outperforms other GNN-based models on SR(n) datasets, showcasing its scalability and adaptability. Ablation studies validate the critical roles of MPNNs and GATv2 in improving prediction accuracy and scalability. While SAT-GATv2 does not yet surpass CDCL-based solvers in overall performance, it addresses their limitations in scalability and adaptability to complex instances, offering an efficient graph-based alternative for tackling larger and more complex SAT problems. This study establishes a foundation for integrating deep learning with combinatorial optimization, emphasizing its potential for applications in artificial intelligence and operations research.
Open-Set Automatic Modulation Recognition Based on Circular Prototype Learning and Denoising Diffusion Model
Authors: Niu Huiying , Xie Xun , Cheng Xiaojing , Bai Jing
Published: January 2025

Automatic modulation recognition (AMR) technology is a critical component of modern communication systems. However, conventional AMR methods based on the closed-set assumption struggle to detect unknown classes that may appear during testing. To address this limitation, this paper proposes an open-set automatic modulation recognition (OSAMR) framework, termed CPLDiff, that integrates circular prototype learning (CPL) with a denoising diffusion model (DDM) to detect unknown classes. The core idea of CPLDiff is to jointly leverage the class-level and instance-level information of the training samples. To achieve this, CPL is used to extract class-level information, while the diffusion model is employed to extract instance-level information. (1) Circular Prototype Learning: Prototype vectors are pre-optimized and fixed, and a bias radius is introduced to expand the feasible encoding space. (2) Denoising Diffusion Model: Noise is added to the sample, and the DDM is used to remove this noise. The probability of a sample belonging to a known class is proportional to the extent of noise removal. (3) Final Integration: The outputs of the CPL and the DDM are combined to perform OSAMR. We conducted comparative experiments and evaluated the proposed method using diverse metrics to ensure a comprehensive assessment of its effectiveness. The experimental results demonstrate that the CPLDiff method significantly improves the detection capability for unknown classes compared to state-of-the-art methods.
Deep Learning-Based Approach for Microscopic Algae Classification with Grad-CAM Interpretability
Authors: Ali Maisam , Yaseen Muhammad , Ali Sikandar , Kim Hee-Cheol
Published: January 2025

The natural occurrence of harmful algal blooms (HABs) adversely affects the quality of clean and fresh water. They pose increased risks to human health, aquatic ecosystems, and water bodies. Continuous monitoring and appropriate measures must be taken to combat HABs. Deep learning models that utilize computer vision play a vital role in identifying and classifying harmful algal blooms in aquatic environments and water storage facilities. Inspecting algal blooms using conventional methods, such as algae detection under microscopes, is difficult, expensive, and time-consuming. Deep learning algorithms have shown a notable and remarkable performance in the image classification domain and its applications, including microscopic algae species classification and detection. In this study, we propose a deep learning-based approach for classifying microscopic images of algae using computer vision. This approach employs a convolutional neural network (CNN) model integrated with two additional blocks—squeeze and dense blocks—to determine the presence of algae, followed by adding Grad-CAM to the proposed model to ensure interpretability and transparency. We performed several experiments on our custom dataset of microscopic algae images. Data augmentation techniques were employed to increase the number of images in the dataset, whereas pre-processing techniques were implemented to elevate the overall data quality. Our proposed model was trained on 3200 images consisting of four classes. We also compared our proposed model with the other transfer learning models, i.e., ResNet50 and Vgg16. Our proposed model outperformed the other two deep learning models. The proposed model demonstrated 96.7% accuracy, while Resnet50, EfficientNetB0, and VGG16 showed accuracy of 85.0%, 92.96%, and 93.5%, respectively. The results of this research demonstrate the potential of deep learning-based approaches for algae classification. This deep learning-based algorithm can be deployed in real-time applications to classify and identify algae to ensure the quality of water reservoirs. Computer-assisted solutions are advantageous for tracking freshwater algal blooms. Using deep learning-based models to identify and classify algae species from microscopic images is a novel application in the AI community.
Controlling Engine Load Distribution in LNG Ship Propulsion Systems to Optimize Gas Emissions and Fuel Consumption
Authors: Martinić-Cezar Siniša , Jurić Zdeslav , Assani Nur , Račić Nikola
Published: January 2025

The increasing emphasis on environmental sustainability and stricter gas emissions regulations has made the optimization of fuel and emissions a crucial factor for marine propulsion systems. This paper investigates the potential to improve fuel efficiency and reduce emissions of LNG ship propulsion systems by using different load sharing strategies in Dual-Fuel Diesel-Electric (DFDE) propulsion systems. Using data collected from on-board cyclic measurements and an optimization model, the effects of different load sharing strategies for various types of fuel, such as HFO, MDO, and LNG, under different engine load conditions were investigated. The results of these strategies are compared with those of on-board power management systems (PMS), which evenly allocate power among the engines, irrespective of fuel usage and emission levels. The results show that load adjustments according to the optimization model can considerably increase fuel economy and contribute to the reduction of CO2 and NOx compared to standard practice at the equal load in different ship operating modes. Our approach introduces an innovative optimization concept that has been proven to improve fuel efficiency and reduce emissions beyond standard practices. This paper demonstrates the robustness of the model in balancing environmental and operational objectives and presents an effective approach for more sustainable and efficient ship operations. The results are in line with global sustainability efforts and provide valuable insights for future innovations in energy optimization and ship emission control.
A Three-Granularity Pose Estimation Framework for Multi-Type High-Voltage Transmission Towers Using Part Affinity Fields (PAFs)
Authors: Huo Yaoran , Dai Xu , Tang Zhenyu , Xiao Yuhao , Zhang Yupeng , Fang Xia
Published: January 2025

At present, Unmanned Aerial Vehicles (UAVs) combined with deep learning have become an important means of transmission line inspection; however, the current approach has the problems of high demand for manual operation, low inspection efficiency, inspection results that do not reflect the distribution of defects on transmission towers, and the need for a large number of manually annotated captured images. In order to achieve the UAV understanding the structure of transmission towers and identifying the defects in the parts of transmission towers, a three-granularity pose estimation framework for multi-type high-voltage transmission towers using Part Affinity Fields (PAFs) is presented here. The framework classifies the structural critical points of high-voltage transmission towers and uses PAFs to provide a basis for the connection between the critical points to achieve the pose estimation for multi-type towers. On the other hand, a three-fine-grained prediction incorporating an intermediate supervisory mechanism is designed so as to overcome the problem of dense and overlapping keypoints of transmission towers. The dataset used in this study consists of real image data of high-voltage transmission towers and complementary images of virtual scenes created through the fourth-generation Unreal Engine (UE4). In various types of electrical tower detection, the average keypoint identification AF of the proposed model exceeds 96% and the average skeleton connection AF exceeds 93% at all granularities, which demonstrates good results on the test set and shows some degree of generalization to electricity towers not included in the dataset.
Modelling Backward Trajectories of Air Masses for Identifying Sources of Particulate Matter Originating from Coal Combustion in a Combined Heat and Power Plant
Authors: Ciepiela Maciej , Sobczyk Wiktoria , Sobczyk Eugeniusz Jacek
Published: January 2025

The paper analyzes the processes of emission and dispersion of particulate contaminants from a large point source emitter: a hard coal-fired power plant. Reference is made to the European Green Deal and its main objective of reducing anthropogenic particulate and greenhouse gas emissions. CHPP, Krakow Combined Heat and Power Plant, Poland, as described in the article, has a strong impact on the mechanisms that shape the microclimatic factors of the Krakow agglomeration. This combined heat and power plant provides heat and electricity for the city, while simultaneously emitting significant amounts of suspended particulate matter into the atmosphere. Due to the adverse impact of non-conventional energy sources on the natural environment and the increasing effects of climate warming, radical changes need to be implemented. The HYSPLIT (Hybrid Single-Particles Lagrangian Integrated Trajectories) model was used to track the movement of contaminated air masses. A 5-day episode of increased hourly concentrations of PM2.5 particulate matter contamination was selected to analyze the backward trajectories of air mass displacement. From 15 August 2022 to 19 August 2022, high 24-h particulate matter concentrations were recorded, measuring around 20 µg/m3. The HYSPLIT model, a unique tool in the precise identification of point sources of pollution and their impact on the air quality of the region, was used to analyze the influx of polluted air masses. A 5-day episode of increased hourly concentrations of PM2.5 pollutants was selected for the study, with values of approximately 20 µg/m3. It was found that low-pressure systems over the North Atlantic brought wet and variable weather conditions, while high-pressure systems in southern and eastern Europe, including Poland, provided stable and dry weather conditions. The simulation results were verified by analyzing synoptic maps of the study area. The image of the displacement of contaminated air masses obtained from the HYSPLIT model was found to be consistent with the synoptic maps, confirming the accuracy of the applied model. This means that the HYSPLIT model can be used to create maps of contaminant dispersion directions. Consequently, it was confirmed that modeling using the HYSPLIT model is an effective method for predicting the displacement directions of particulate contamination originating from coal combustion in a combined heat and power plant. Identifying circulation patterns and front zones during episodes of increased contaminant concentrations is strategic for effective weather monitoring, air quality management, and alerting the public to episodes of increased health risk in a large agglomeration.
Physics-Based Modelling of Plate-Fin Heat Exchangers
Authors: Grespan Mattia , Leonforte Adriano , Calò Luigi , Cavazzuti Marco , Angeli Diego
Published: January 2025

Aluminium plate-fin heat exchangers are widely used in automotive, aerospace, and other industrial applications. Extensive research has been conducted on these coolers, yet accurate predictive tools for their thermo-hydraulic performance are still lacking, due to the wide variety of geometric parameters and working fluids involved. This work proposes an original approach based purely on physical principles and established models, combining detailed numerical models for the extended surfaces and manifolds, with global models aimed at accurately evaluating overall head losses and heat transfer rates in plate-fin heat exchangers. Extended surfaces are studied by means of computational models of unitary fin modules under fully developed flow conditions. Entrance effects are analysed through dedicated numerical models. Numerical results on extended surfaces are extended to whole heat exchangers by global models for heat transfer and head losses, based on the ε−NTU method and the Darcy–Weisbach equation, respectively. The proposed approach is presented and validated through the analysis of a case study comprising several heat exchangers featuring different geometries and working fluids. Numerically derived heat transfer rates and head losses are compared with experimental data showing maximum deviations of ±20% for most of the tested configurations, highlighting the strength of the proposed modelling methodology.
Effect of Zirconium Silicate Reinforcement on Aluminum 7075; Mechanical Properties, Thermomechanical Analysis and Vibrational Behavior
Authors: Kamanna Balbheem , Kivade S. B. , Nagamadhu M.
Published: January 2025

Aluminum 7075 alloys are widely utilized in aerospace, transportation, and marine industries due to their high strength and low density. However, further research is needed to understand their mechanical, thermomechanical, and vibrational behaviors when reinforced. This study focuses on the development of Al 7075 composites reinforced with zirconium silicate (ZrSiO4), processed via sand stir casting. The mechanical properties, including tensile, compression, and impact strength, as well as thermomechanical and vibrational behaviors, were thoroughly investigated. A planetary ball mill was used to mix ZrSiO4 with a wettability agent, and the results indicated that the addition of ZrSiO4 with the wettability agent significantly enhanced the mechanical properties. Fourier Transform Infrared Spectroscopy (FTIR) was employed to identify the compounds formed after adding the reinforcement and wettability agent. Scanning Electron Microscope (SEM) images and Energy-dispersive X-ray (EDX) analysis revealed a uniform distribution of the particles within the matrix. The tensile, compression, and impact strengths increased by 20%, 21%, and 19%, respectively, with the addition of 8 wt% ZrSiO4; however, strain decreased. Additionally, heat treatment further enhanced the mechanical properties of the composites. The thermomechanical properties showed improvement even at elevated temperatures, and the damping factor was enhanced with the addition of ZrSiO4. The elemental composition of the reinforced composites was analyzed using EDX, confirming the presence of the reinforcement. This research highlights the potential of Al 7075-ZrSiO4 composites for improved performance in various applications.
Variability and Relationship Between Phenological and Morphological Traits in Early and Late Pedunculate Oak
Authors: Živković Andrijana Bauer , Nikolić Mirjana Šijačić , Stojanović Dejan B. , Orlović Saša , Kovačević Branislav
Published: January 2025

Variability and relationship between phenological traits and leaf, acorn, and tree size morphometric traits were examined in early and late bud-flushing groups of the pedunculate oak population in the vicinity of Sremska Mitrovica, Serbia. According to the obtained three-year results, there were no significant differences in tree size and leaf morphometric characteristics between the early and late group. The effect of trees within phenological groups was statistically significant and considerable, especially in leaf blade width (lbw) and leaf area (la). Acorn length (acl) and acorn index (acinx) were significantly influenced by phenological group, achieving moderate contribution of phenological group to the total variation. There was a clear effect of phenological group on variation of examined phenological traits. The effect of year of monitoring on the same traits was not significant, but there was a clear effect of interaction between phenological group and year, especially in case of traits describing the period from bud swelling until the emergence of wrinkled leaves (f12a), unfolded leaves (f12b), and fully developed leaves (f12g) and their ratio with the period from 8 March until the emergence of wrinkled leaves: f12b/f02a and f12g/f02a ratio. Earlier phenology in warmer years is clearer in the early bud-flushing group than in the late one. Periods between different bud swelling and f12a, f12b, and f12g phenological phases were significantly shorter in 2017 (with a warm April) than in 2015 (with moderate temperatures in March and April) in the early group, but significantly longer in the late group. Examined traits were classified in six groups based on their factorial loadings with the first six principal components rotated by Varimax method, revealing strict distinction between traits by their original nature. In that sense, all examined groups of traits could be considered as informative in variability studies of pedunculate oak. The tree size traits (tree height and diameter at breast height) formed the separate, fifth group, suggesting no close relationship of these traits with any other examined characteristic. Both cluster analysis and PCA suggest distinct classification by trees’ phenology, but also considerable differences by the second principal component which is closely related to leaf size characteristics. The research should be continued on variability between populations and progenies, especially with respect to phenological and acorn morphometric traits. Understanding the phenological variations between early and late oaks could be essential for designing robust forest adaptation management strategies.
Research on Coal and Gas Outburst Prediction and Sensitivity Analysis Based on an Interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine Model
Authors: Wang Yanping , Qin Zhixin , Yan Zhenguo , Deng Jun , Huang Yuxin , Zhang Longcheng , Cao Yuqi , Wang Yiyang
Published: January 2025

Coal and gas outbursts pose significant threats to underground personnel, making the development of accurate prediction models crucial for reducing casualties. By addressing the challenges of highly nonlinear relationships among predictive parameters, poor interpretability of models, and limited sample data in existing studies, this paper proposes an interpretable Ali Baba and the Forty Thieves–Transformer–Support Vector Machine (AFT-Transformer-SVM) model with high predictive accuracy. The Ali Baba and the Forty Thieves (AFT) algorithm is employed to optimise a Transformer-based feature extraction, thereby reducing the degree of nonlinearity among sample data. A Transformer-SVM model is constructed, wherein the Support Vector Machine (SVM) model provides negative feedback to refine the Transformer feature extraction, enhancing the prediction accuracy of coal and gas outbursts. Various classification assessment methods, such as TP, TN, FP, FN tables, and SHAP analysis, are utilised to improve the interpretability of the model. Additionally, the permutation feature importance (PFI) method is applied to conduct a sensitivity analysis, elucidating the relationship between the sample data and outburst risks. Through a comparative analysis with algorithms such as eXtreme gradient boosting (XGBoost), k-nearest neighbour (KNN), radial basis function networks (RBFNs), and Bayesian classifiers, the proposed method demonstrates superior accuracy and effectively predicts coal and gas outburst risks, achieving 100% accuracy in the sample dataset. The influence of parameters on the model is analysed, highlighting that the coal seam gas content is the primary factor driving the outburst risks. The proposed approach provides technical support for coal and gas outburst predictions across different mines, enhancing emergency response and prevention capabilities for underground mining operations.
Antioxidant Peptides from Tiger Nut (Cyperus esculentus L.): Chemical Analysis and Cytoprotective Functions on HepG2 and Caco-2 Cells
Authors: Quan Yu , Chen Lin , Fan Meiqi , Zhao Xia , Hao Jianxiong
Published: January 2025

Tiger nuts were enzymatically hydrolyzed by Alcalase and then separated and purified by ultrafiltration classification and Sephadex G-15 fractionation to obtain tiger nut peptides. Their chemical antioxidant activities and cytoprotective functions on HepG2 and Caco-2 cells were systematically evaluated in this study. The tiger nut peptides (TNP) were found to perform excellent antioxidant activity supported by their chemical and cell antioxidant behaviors, amino acid composition, and morphological observation. Higher 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity (DPPH• RSA, 64.05–124.07%) and ferric ion-reducing antioxidant power (FRAP, 0.17–1.78 μmol/mL) were observed in the TNP with more hydrophobic amino acids (41.77 ± 1.36 g/100 g) compared with traditional soybean and peanut peptides. Furthermore, the peptides from tiger nut (TNP, TNP-4, T1, T2, T3) could effectively protect H2O2-induced HepG2 and Caco-2 cells from oxidative damage by enhancing endogenous antioxidant enzyme activities and reducing oxidative stress levels, especially the T3 peptides purified from the fraction less than 1 kDa molecular weight. The catalase, superoxide dismutase, and glutathione peroxidase activities significantly increased, and the contents of intracellular reactive oxygen species and malondialdehyde decreased. This study highlights the potential of the peptides from tiger nuts as antioxidant ingredients for food applications.
Mycotoxins in Cheese: Assessing Risks, Fungal Contaminants, and Control Strategies for Food Safety
Authors: Aranda Camila , Rodriguez Rodrigo , Fernández-Baldo Martín A. , Durán Paola
Published: January 2025

According to the scientific information reviewed, cheese is highly susceptible to contamination by mycotoxin-producing fungi, primarily species from the genera Aspergillus (A. niger, A. flavus) and Penicillium (P. commune, P. solitum, P. palitans, and P. crustosum). Studies on various types of cheese made from cow’s milk report an average concentration of Aflatoxin M1 (AFM1) at 13,000 ng kg−1, which is alarming since the regulatory limits for AFM1 in cheese range from 250 to 500 ng kg−1. For instance, limits set by Codex Alimentarius, the European Commission (EC), Turkey, and Iran are 250 ng kg−1. In the Netherlands, the limit is 200 ng kg−1, and in Italy, it is 450 ng kg−1. However, the concentration of mycotoxins frequently exceeds these regulatory limits, including critical mycotoxins such as ochratoxin A, citrinin, and cyclopiazonic acid, which pose significant global health concerns. Therefore, this study aims to review the mycobiota responsible for producing key mycotoxins in cheese and to assess the influence of physicochemical factors on fungal growth and mycotoxin production. By incorporating control strategies such as hygiene practices, pasteurization, and the use of preservatives, this study seeks to improve methodologies in the cheese production chain and mitigate contamination by fungi and mycotoxins.
Nutraceutical Prospects of Pumpkin Seeds: A Study on the Lipid Fraction Composition and Oxidative Stability Across Eleven Varieties
Authors: Grajzer Magdalena , Kozłowska Weronika , Zalewski Iwan , Matkowski Adam , Wiland-Szymańska Justyna , Rękoś Monika , Prescha Anna
Published: January 2025

The oxidative stability of nutritive and bioactive lipids is essential for their functionality. This study evaluated the potential of lipid fractions from pumpkin seeds obtained from eleven high-performing cultivars of Cucurbita maxima Duchesne, C. pepo L., and C. moschata Duchesne cultivated in Poland, aiming to evaluate their stability for nutraceutical applications. This study investigated the intrinsic relationship between chemical composition and oxidative stability to identify cultivars with promising functional potential and commercial value. The fatty acid, sterol, and lipid antioxidant profiles were characterized using gas chromatography (GC), GC–mass spectrometry (GC-MS), and ultra-high-performance liquid chromatography (UPLC), respectively. Antiradical activity was assessed via the DPPH assay, and oxidative stability was evaluated using differential scanning calorimetry (DSC). The oils exhibited high levels of polyunsaturated fatty acids (PUFAs) (59.5–68.6%), with n-6/n-3 fatty acid ratios ranging from 66.5 to 211.6. The lipid extracts contained up to 97.1% Δ7-sterols, while key antioxidants included squalene (616.6–3092.0 mg/kg) and γ-tocopherol (54.1–423.6 mg/kg). Notably, the C. pepo cultivar ‘Moonshine’ was the least abundant in these bioactive compounds. The carotenoid content ranged from 5.7 to 19.4 mg/kg across the extracts. Among the studied cultivars, ‘Show Winner’ and ‘Pink Jumbo Banana’ (C. maxima) stood out as promising candidates for nutraceutical applications due to their elevated levels of tocopherols, carotenoids, and squalene. A moderate n-6/n-3 fatty acid ratio (100–170), coupled with balanced levels of γ-tocopherol and squalene, was found to significantly enhance the oxidative stability of pumpkin seed lipids. These lipid fractions also show potential as stabilizing additives for oils rich in α-linolenic acid but deficient in natural antioxidants.
Isolation and Identification of Aspergillus spp. from Rotted Walnuts and Inhibition Mechanism of Aspergillus flavus via Cinnamon Essential Oil
Authors: Zhang Doudou , Luo Kangjing , Wen Shaocong , Zhou Qing , Li Bochao , Liang Wenhui , Di Jianbing
Published: January 2025

Walnuts are prone to contamination by rotting fungi. However, the microflora present in walnuts across various regions of China has not been thoroughly investigated. Cinnamon essential oil (CEO) is commonly used in food preservation because of its natural safety and strong antimicrobial properties. Additionally, studies on the antifungal potential of CEO to prevent walnut spoilage are limited. Therefore, we investigated Aspergillus spp. contamination in moldy walnuts stored across different locations in Shanxi, China. A total of 100 moldy walnut samples underwent traditional mycological analysis to isolate Aspergillus spp. The antibacterial properties and the mechanisms by which CEO targets Aspergillus spp. were thoroughly investigated. Five representative morphospecies were subsequently classified to the species level using Internal Transcribed Spacer sequence analysis. The dominant species were Aspergillus flavus and Aspergillus fumigatus, with frequencies of 100% and 93%, respectively, followed by Aspergillus nigers, Aspergillus terreus, and Aspergillus tubingensis, with frequencies of 78%, 47%, and 40%, respectively. Overall, 358 fungal species belonging to the Aspergillus genus were recovered. The MIC of CEO against A. flavus in vitro was 0.78 g/L. Furthermore, CEO compromised the permeability and integrity of the cell membrane, causing the leakage of intracellular components and promoting the accumulation of malondialdehyde compounds and a decrease in superoxide dismutase activity. Overall, we isolated and identified Aspergillus spp. in moldy walnuts and confirmed the feasibility of using CEO as a green anti-Aspergillus spp. agent for the preservation of walnuts.
We Not Like Them: Complex Economic Intergenerational Trauma (CEIT) and Black Women’s Economic Resistance
Authors: Younger Tiffany N.
Published: January 2025

Black women come from a lineage of survivors who have faced threats to their economic livelihoods since the emancipation of slavery. These threats are deeply rooted in the systemic expectation of free labor, which dates to the illegal enslavement of Black individuals beginning in 1619. A new emerging concept of complex economic intergenerational trauma (CEIT), grounded in Black feminist theory, examines the historical and contemporary economic exploitation of Black women at the intersection of gendered anti-Black racism. CEIT highlights ancestral capital—an essential form of wealth and survival—that has enabled Black women to succeed in a society built through their marginalization. Findings from an exploratory phenomenological study of thirteen Black women’s experiences with wealth accumulation reveal that Black women have thrived beyond the confines of the U.S. economy, despite its design to exploit their labor without providing them benefits. Ancestral capital, rooted in knowledge, values, cultural resilience, and community support, has been key to their survival and success. Today, Black women continue to face significant barriers to true economic security due to ongoing gendered anti-Black racism. However, they survive and adapt. A thematic analysis from the study shows that Black women approach economic security and wealth differently than others, placing a strong emphasis on community, collective organizing, and mutual aid. These approaches stem from a lack of access to traditional avenues of wealth accumulation available to others. Despite these ongoing challenges, Black women persist, drawing on both their inherited trauma and their unique forms of capital to navigate the economic system. This paper underscores the uniqueness of Black women by uplifting their resilience and survival, offering a testament to their ability to thrive beyond traditional financial means and continue their legacy of strength.
Genome-Wide Identification and Expression Analysis Under Abiotic Stress of the Lipoxygenase Gene Family in Maize (Zea mays)
Authors: Li Sinan , Hou Shuai , Sun Yuanqing , Sun Minghao , Sun Yan , Li Xin , Li Yunlong , Wang Luyao , Cai Quan , Guo Baitao , Zhang Jianguo
Published: January 2025

Background/Objectives: Abiotic stresses impose significant constraints on crop growth, development, and yield. However, the comprehensive characterization of the maize (Zea mays) lipoxygenase (LOX) gene family under stress conditions remains limited. LOXs play vital roles in plant stress responses by mediating lipid oxidation and signaling pathways. Methods: In this study, 13 ZmLOX genes were identified in maize and characterized to explore their functions under abiotic stresses. Results: Phylogenetics revealed that ZmLOX genes share evolutionary origins with LOX genes in Arabidopsis and rice. Promoter analysis identified cis-acting elements associated with growth, light response, hormone signaling, and stress response, indicating their diverse biological roles. Gene Ontology (GO) and KEGG enrichment analyses showed that ZmLOX genes are involved in jasmonic acid metabolism, lipid signaling, and photosynthetic processes, while protein–protein interaction (PPI) analysis positioned ZmLOX proteins as central hubs in stress-related regulatory networks. Differential expression and qRT-PCR analyses revealed stress-specific (including heat, drought, salt, and cold) expression patterns, with ZmLOX2 and ZmLOX13 showing key roles in drought and cold tolerance, respectively. Conclusions: These findings provide new insights into the regulatory functions of ZmLOX genes, offering potential targets for enhancing maize resilience to abiotic stresses and improving agricultural productivity.
Editorial for the Bioinformatics of Human Diseases Special Issue
Authors: Xu Hongyan
Published: January 2025

Bioinformatics plays an ever-increasing role in revealing the complexity of genomic information and how it is related to the susceptibility and pathophysiology of human diseases [...]
Comprehensive Guidelines for Numerical Simulation of Jet Grouting Technology Using MPS-CAE
Authors: Shakya Sudip , Hontani Yoji , Chao Kuo Chieh , Inazumi Shinya
Published: January 2025

This paper presents a thorough guide to simulating jet grouting using the Moving Particle Semi-Implicit (MPS) method for numerical analysis and Computer-Aided Engineering (CAE) for model development. It addresses the shortcomings of previous jet grouting simulation studies, which often lacked clear and comprehensive guidelines, by providing a detailed step-by-step approach. The key aspects of the simulation that define and shape the output of real-world jet grouting technology, such as jet grouting spray settings and material parameter configurations, are validated against benchmark experimental data. The previously challenging task of accurately determining material parameters for soil when modeled as a Bingham fluid bi-viscosity model, is simplified into a universal guideline that can be easily applied to any soil type with known unconfined compressive strength. Finally, the reliability of the jet grouting simulation is confirmed by comparing the simulation results with benchmark experimental data under similar conditions, demonstrating the robustness and accuracy of the proposed method.