bluuebunny/crossref_metadata_2025 huggingface dataset:
The Inheritance and Variation of Floral Scent Compounds in Parent–Progeny Relationships of Malus
Authors: Fan Junjun , Zai Yu , Peng Ye , Peng Qin , Sun Meng , Xiong Qingqing , Ma Jingze , Zhou Chenchen , Zhang Wangxiang
Published: December 2024

Improving floral scent quality is an important goal in Malus breeding. However, the inheritance regularity for volatile components of Malus remains unclear. In this study, the floral scent compounds and scent characteristics of five Malus taxa with clearly defined parent–progeny relationships were analyzed by sensory evaluation, an electronic nose, and gas chromatography–mass spectrometry. A total of 51 volatile compounds were identified in five taxa. M. ioensis showed the highest sensory intensity with the maximum total content of compounds (8247.59 ng·g−1 FW·h−1). Compared to its progenies and ‘Lemoinei’, terpenoid compounds in M. ioensis accounted for the largest proportion (40.46%). Most compounds in the progenies were inherited from their maternal parent (60.61–75.00%), and most of them were significantly downregulated by hybridization. However, the content of several compounds in the progenies appeared transgressive, even unique. Progenies and their maternal parents exhibited similar sensory characteristics: earthy/woody, sweet, and rose. The content of characteristic compounds (geranylacetone, 6-methyl-5-hepten-2-one, 2-phenylethanol, α-ionone, β-ionone, decanal, and so on), total content, and the response of sensor W3S positively correlated with scent intensity. The response of sensor W1W correlated significantly and positively with the compound number and the total content. Our findings provided a reference for tracking maternal parents for cultivars and enabled rapid selection of fragrant flower cultivars by electronic nose.
Geospatial Modelling Predicts Agricultural Microplastic Hotspots from Biosolid Application Risks
Authors: McNulty Kieran , Nahar Kamrun
Published: December 2024

Microplastics are emerging as widespread modern pollutants, posing a variety of health and environmental risks. Microplastics are found in agriculture; they are often introduced via biosolids from wastewater treatment plants and are sold as alternatives to inorganic fertilizers. In Australia, there has been limited research on the agricultural concentrations of microplastics, and there has been no predictive modelling to identify which geographies are most at risk for pollution. Based on global emerging trends, this study uses geospatial modelling to map potential high-risk areas for agricultural microplastics within an area of the Murray-Darling Basin in New South Wales, Australia. In doing so, this study demonstrates the use of a geospatial methodology that may be used in future risk assessments, both within Australia and globally. Risk index mapping was conducted for three different pollutant transport pathways: rainfall-runoff of microplastics, in-soil retention of microplastics, and groundwater infiltration of microplastics. Particular areas of risk were identified for each transport pathway, providing visualised mapping results that represent the value of the study and its methodology.
Screening of Efficient Antifreeze Agents to Prevent Low-Temperature Stress in Vines
Authors: Zhang Yali , Han Mengyao , Wu Langjie , Wang Haoyu , Liang Yanying , Wang Xianhang , Xi Zhumei
Published: December 2024

Grapevines are widely cultivated and highly nutritious, making them economically significant among cultivated plants globally. However, important spring temperature fluctuations and frequent frost events in some production areas severely affect grape growth and yield. Therefore, enhancing the cold resistance of grapevines is of considerable importance for production. This study used the less cold-resistant grape varieties Vitis vinifera cv. Cabernet Sauvignon and Vitis vinifera cv. Chardonnay as test materials. They were treated with the antifreeze agents ‘GuoGuang KangXiu,’ ‘Biogas Liquid’, ‘SenHuo’, and ‘Ice Shield’ for 24 and 48 h before exposure to a low-temperature treatment of −2 °C. Compared to the control (water), all four antifreeze agents significantly reduced the relative conductivity and malondialdehyde content in the tender shoots of both grape varieties, increased the Fv/Fm ratio, enhanced osmotic adjustment substance levels, and improved antioxidant enzyme activity. Moreover, relative conductivity and malondialdehyde content were significantly lower, and the Fv/Fm ratio was higher when the antifreeze agents were applied 24 h before frost compared to 48 h. This suggests that all four antifreeze agents effectively improve grapevine cold resistance, with better results observed when applied 24 h before frost. Additionally, the antifreeze agents significantly increased the soluble solid content in berries and enhanced the levels of anthocyanins, total phenols, total tannins, total flavonoids, and total flavonols in the berries’ skin. They had minimal impact on the fruit set rate, cluster weight, and berries shape index, indicating that these agents can improve berries quality to some extent.
Effect of Urease and Nitrification Inhibitors on Heavy Metal Mobility in an Intensively Cultivated Soil
Authors: Vicar Nicoleta , Lațo Alina , Lațo Iaroslav , Crista Florin , Berbecea Adina , Radulov Isidora
Published: December 2024

Urease and nitrification inhibitors represent ways to reduce nitrogen losses; their presence modifies microbial and enzymatic activity in the soil, affecting pH and organic matter (OM), which in turn affects the mobility of heavy metals. To evaluate the effect of urea with inhibitors, pH, OM content, and pseudo-total and mobile metal content (Cu, Cd, Ni, Pb, Cr, Zn, and Mn) were determined in soil samples fertilized with six different urea variants with inhibitors. The modification in the pseudo-total content of heavy metals following fertilization (%) was as follows: Cu (−39.26 ÷ −8.82), Cd (10.74 ÷ 15.40), Ni (5.76 ÷ 18.84), Pb (−13.30 ÷ 12.46), Cr (−15.55 ÷ 11.60), Zn (35.10 ÷ 162.76), and Mn (−1.32 ÷ 12.17). The pH was situated in the range of 7.05 to 7.17, while OM content showed an average increase of 16%. The determined pollution indicators revealed an accumulation of Zn in the soil. The results showed a trend of accumulation of bioavailable heavy metals, with the greatest increase for Mn (43%). Only in the case of Zn, there was a decrease in mobile content with the lowest value when applying two urease inhibitors and one nitrification inhibitor. Inhibitors modify the OM content and soil pH, influencing the mobility and bioavailability of heavy metals.
Abiotic Stress Tolerance and Invasive Potential of Ornamental Plants in the Mediterranean Area: Implications for Sustainable Landscaping
Authors: Mircea Diana-Maria , Boscaiu Monica , Sestras Radu E. , Sestras Adriana F. , Vicente Oscar
Published: December 2024

Several ornamental species have demonstrated adaptive features that could increase their resilience to the current climate emergency but could also be linked to invasiveness in non-native environments. The present review examines the responses of ornamental plants’ grown in the Mediterranean region to relevant abiotic stress conditions such as drought, salinity, extreme temperatures, heavy metal toxicity, and atmospheric N deposition, emphasising physiological and biochemical responses that allow them to adapt to unfavourable environments. These response mechanisms, although useful for landscaping under challenging climate change effects, would help non-native ornamental plants outcompete native flora, change ecosystem functioning, and ultimately contribute to a loss in biodiversity. Effective management practices for sustainable landscaping ensure the sensible use of native species and the development of non-invasive cultivars to minimise ecological risks. This review attempts to provide information on the ornamental and environmental functions of plants in landscape architecture through the relationships between aesthetic and ecological values under abiotic stress, including their negative environmental impact due to their invasive potential.
First Detection of Wolbachia in Namibian Bird Ectoparasites (Acariformes: Syringophilidae) with a Description of New Quill Mite Species
Authors: Glowska-Patyniak Eliza , Kaszewska-Gilas Katarzyna , Laniecka Izabella , Olechnowicz Julia , Ostrowska Kamila , Dmuchowska Wiktoria , Schmidt Brian K. , Hubert Jan , Trzebny Artur
Published: December 2024

Wolbachia is a common intracellular bacterial genus that infects numerous arthropods and filarial nematodes. In arthropods, it typically acts as a reproductive parasite, leading to various phenotypic effects such as cytoplasmic incompatibility, parthenogenesis, feminization, or male-killing. Quill mites (Acariformes: Syringophilidae) are a group of bird parasites that have recently attracted increasing interest due to the detection of unique phylogenetic lineages of endosymbiotic bacteria and potentially pathogenic taxa. Our study used an unbiased 16S rRNA gene amplicon sequencing approach to examine several populations of Namibian quill mites for the presence of bacteria that could affect their biology. We detected Wolbachia in two mite populations collected from two species of larks. However, we did not find any other endosymbiotic bacteria or any that could be of epidemiological importance. Since the mite taxa we tested were previously unknown to science, we conducted comprehensive morphological and molecular systematic analyses on them. Our research revealed two new quill mite species of the genus Syringophilopsis Kethley, 1970 which parasitize three sub-Saharan alaudids, i.e., Syringophilopsis erythrochlamys sp. n. from the dune lark Calendulauda erythrochlamys (Strickland, HE) and S. christinae sp. n. from the Karoo long-billed lark Certhilauda subcoronata (Smith) and spike-heeled lark Chersomanes albofasciata (de Lafresnaye, NFAA). In addition, we provided the African reed warbler Acrocephalus baeticatus (Vieillot, LJP) as a new host for S. acrocephali Skoracki, 1999. Our study expands the knowledge on parasite diversity and provides new insights into Wolbachia infection among quill mites in Africa.
Comparative Metagenomic Analysis of the Gut Microbiota of Captive Pangolins: A Case Study of Two Species
Authors: Dai Zhengyu , Xie Bowen , Xie Chungang , Xiang Jinsuo , Wang Xinmei , Li Jing , Zheng Rongquan , Wang Yanni
Published: December 2024

Pangolins, one of the most trafficked mammals, face significant health challenges in captivity, including digestive disorders and immune dysfunctions. These issues are closely linked to alterations in their gut microbiota, which play vital roles in the host metabolism, immunity, and overall health. This study investigated the differences in the gut microbiota composition and function between two pangolin species, Chinese pangolins (Manis pentadactyla) and Malayan pangolins (Manis javanica), under identical captive conditions to better understand their ecological adaptability and health implications. Using metagenomic sequencing, fecal samples from eight adult captive pangolins were analyzed, including four male Malayan pangolins and three male and one female Chinese pangolins. Comparative analyses of the alpha and beta diversities, microbial community structure, and functional profiles were performed. Both species harbored gut microbiota dominated by Firmicutes, Bacteroidetes, and Proteobacteria. However, the Chinese pangolins exhibited higher microbial diversity (Shannon index, p = 0.042; Simpson index, p = 0.037) and lower relative abundance of Proteobacteria compared with the Malayan pangolins. A functional analysis revealed significant differences in the metabolic pathways, where the Chinese pangolins demonstrated a higher potential for fiber degradation, whereas the Malayan pangolins exhibited elevated levels of antibiotic resistance genes and pathogenic taxa, such as Escherichia coli. These findings suggest that captivity duration and environmental stress likely contribute to the observed differences, with the Malayan pangolins experiencing greater dysbiosis due to longer captivity periods. This study provides valuable insights into the role of gut microbiota in pangolin health and offers a foundation for improving conservation strategies and captive care protocols.
Development and Validation of a Machine Learning Model for the Prediction of Bloodstream Infections in Patients with Hematological Malignancies and Febrile Neutropenia
Authors: Gallardo-Pizarro Antonio , Teijón-Lumbreras Christian , Monzo-Gallo Patricia , Aiello Tommaso Francesco , Chumbita Mariana , Peyrony Olivier , Gras Emmanuelle , Pitart Cristina , Mensa Josep , Esteve Jordi , Soriano Alex , Garcia-Vidal Carolina
Published: December 2024

Background/Objectives: The rise of multidrug-resistant (MDR) infections demands personalized antibiotic strategies for febrile neutropenia (FN) in hematological malignancies. This study investigates machine learning (ML) for identifying patient profiles with increased susceptibility to bloodstream infections (BSI) during FN onset, aiming to tailor treatment approaches. Methods: From January 2020 to June 2022, we used the unsupervised ML algorithm KAMILA to analyze data from hospitalized hematological malignancy patients. Eleven features categorized clinical phenotypes and determined BSI and multidrug-resistant Gram-negative bacilli (MDR-GNB) prevalences at FN onset. Model performance was evaluated with a validation cohort from July 2022 to March 2023. Results: Among 462 FN episodes analyzed in the development cohort, 116 (25.1%) had BSIs. KAMILA’s stratification identified three risk clusters: Cluster 1 (low risk), Cluster 2 (intermediate risk), and Cluster 3 (high risk). Cluster 2 (28.4% of episodes) and Cluster 3 (43.7%) exhibited higher BSI rates of 26.7% and 37.6% and GNB BSI rates of 13.4% and 19.3%, respectively. Cluster 3 had a higher incidence of MDR-GNB BSIs, accounting for 75% of all MDR-GNB BSIs. Cluster 1 (27.9% of episodes) showed a lower BSI risk (<1%) with no GNB infections. Validation cohort results were similar: Cluster 3 had a BSI rate of 38.1%, including 78% of all MDR-GNB BSIs, while Cluster 1 had no GNB-related BSIs. Conclusions: Unsupervised ML-based risk stratification enhances evidence-driven decision-making for empiric antibiotic therapies at FN onset, crucial in an era of rising multi-drug resistance.
Tracking the Prevalence of Antibiotic Resistance in Enterococcus Within the Spanish Poultry Industry: Insights from a One Health Approach
Authors: Garcia-Llorens Josep , Monroy Isaac , Torres-Boncompte Jan , Soriano Jose , Catalá-Gregori Pablo , Sevilla-Navarro Sandra
Published: December 2024

Background/Objectives: Antimicrobial resistance (AMR) in Enterococcus species from poultry production represents a significant public health threat due to the potential transmission of AMR through the food chain. This study aimed to examine the relative prevalence, resistance patterns, and mannitol fermentation capacity of Enterococcus isolates from various poultry production systems in Spain over a seven-year period (2017–2023). Methods: A total of 215 Enterococcus isolates were analyzed. Phenotypic assessments were conducted to determine resistance rates and metabolic capacities, while genotypic characterization focused on detecting vancomycin-resistance genes (vanA, vanB, vanC, and vanD). Results: Enterococcus faecalis (62.3%) and Enterococcus faecium (29.77%) were the predominant species, primarily isolated from broilers (74.88%), with the highest frequency observed in one-week-old chicks (31.16%). High resistance rates to tetracyclines and streptogramins were identified, while resistance to vancomycin (0.47%) and tigecycline (3.03%) was low. Interestingly, a significant reduction in tetracyclines resistance was shown in this period for Enterococcus faecalis (from 100% to 70% (2017–2023) and Enterococcus faecium (from 100% to 40% (2018–2023)). Multidrug resistance (MDR) was detected in 26.98% of isolates. Mannitol fermentation tests revealed high metabolic capacity in Enterococcus faecalis (99.25%) and Enterococcus faecium (96.88%), associated with adaptability and virulence potential. Genotypic analysis showed a very low prevalence of vanB and vanC genes. Conclusions: These findings highlight the critical need for targeted surveillance and intervention strategies in poultry production to mitigate the risks posed by MDR Enterococcus to public health.
Reuse of Spent Coffee Grounds: Alternative Applications, Challenges, and Prospects—A Review
Authors: Sidło Wiktoria , Latosińska Jolanta
Published: December 2024

Spent coffee grounds (SCGs) are a waste product generated after brewing coffee. For every ton of brewed coffee, approximately 650 kg of grounds are produced. Their disposal can have harmful environmental effects, making it necessary to find alternative uses for SCGs. The valorization of coffee’s bioactive compounds significantly contributes to environmental protection and management. According to the literature, SCGs can be utilized as fertilizers, sorbents, additives for biodiesel production, in bioelectricity, or as an enhancer for the calorific value of biomass. SCGs can also be used as substrates for producing various items, such as cups or plant pots, in the food, cosmetics, and construction industries. The applications of SCGs described in this review article were conducted under laboratory conditions. The aim of this article is to analyze the alternative uses of SCGs in various fields, highlighting the diversity of applications as well as key drawbacks and limitations associated with their use. Additionally, potential future research directions are outlined, which could contribute to a more efficient and sustainable utilization of SCGs.
Energy Management Strategies for Extended-Range Electric Vehicles with Real Driving Emission Constraints
Authors: Xu Hualong , Chen Yang , Zhang Li , Chen Guoliang , Han Jinlin , Zhang Qing , Li Chaokai
Published: December 2024

Fuel economy has long been the core control objective in the energy management strategies of extended-range electric vehicles (EREVs), but little research has considered real driving emissions. In this paper, the real driving emissions of an EREV are investigated, and the abnormal pollutant emissions caused by engine start–stop events are clarified. Accordingly, an interpolated-startup-corrected method is proposed to construct real driving emission models. Next, an optimization problem is constituted with real driving emissions as the constraints and fuel consumption as the objective. The optimization problem is solved using a dynamic programming (DP) algorithm embodying the interpolated-startup-corrected emission models, and the start–stop reduction strategies and condition migration strategies are derived. Compared to the strategy without the emission constraints, the CO and NOx emissions under the no-start–stop strategy are cut down by about 70%; the PN emissions are even orders of magnitude lower. Meanwhile, the condition migration strategy can compromise the fuel economy and pollutant emissions by adjusting the engine operating points, thus possibly limiting pollutant emissions beyond the start–stop reduction strategy.
Real-Time Automatic Configuration of Brain MRI: A Comparative Study of SIFT Descriptors and YOLO Neural Network
Authors: Almeida Rávison Amaral , Carvalho Júlio César Porto de , Vieira Antônio Wilson , de Oliveira Heveraldo Rodrigues , D’Angelo Marcos F. S. V.
Published: December 2024

This work presents two approaches to image processing in brain magnetic resonance imaging (MRI) to enhance slice planning during examinations. The first approach involves capturing images from the operator’s console during slice planning for two different brain examinations. From these images, Scale-Invariant Feature Transform (SIFT) descriptors are extracted from the regions of interest. These descriptors are then utilized to train and test a model for image matching. The second approach introduces a novel method based on the YOLO (You Only Look Once) neural network, which is designed to automatically align and orient cutting planes. Both methods aim to automate and assist operators in decision making during MRI slice planning, thereby reducing human dependency and improving examination accuracy. The SIFT-based method demonstrated satisfactory results, meeting the necessary requirements for accurate brain examinations. Meanwhile, the YOLO-based method provides a more advanced and automated solution to detect and align structures in brain MRI images. These two distinct approaches are intended to be compared, highlighting their respective strengths and weaknesses in the context of brain MRI slice planning.
A Novel Deep Learning Approach for Yarn Hairiness Characterization Using an Improved YOLOv5 Algorithm
Authors: Pereira Filipe , Lopes Helena , Pinto Leandro , Soares Filomena , Vasconcelos Rosa , Machado José , Carvalho Vítor
Published: December 2024

In textile manufacturing, ensuring high-quality yarn is crucial, as it directly influences the overall quality of the end product. However, imperfections like protruding and loop fibers, known as ‘hairiness’, can significantly impact yarn quality, leading to defects in the final fabrics. Controlling yarn quality in the spinning process is essential, but current commercial equipment is expensive and limited to analyzing only a few parameters. The advent of artificial intelligence (AI) offers a promising solution to this challenge. By utilizing deep learning algorithms, a model can detect various yarn irregularities, including thick places, thin places, and neps, while characterizing hairiness by distinguishing between loop and protruding fibers in digital yarn images. This paper proposes a novel approach using deep learning, specifically, an enhanced algorithm based on YOLOv5s6, to characterize different types of yarn hairiness. Key performance indicators include precision, recall, F1-score, mAP0.5:0.95, and mAP0.5. The experimental results show significant improvements, with the proposed algorithm increasing model mAP0.5 by 5% to 6% and mAP0.5:0.95 by 11% to 12% compared to the standard YOLOv5s6 model. A 10k-fold cross-validation method is applied, providing an accurate estimate of the performance on unseen data and facilitating unbiased comparisons with other approaches.
A Novel Traffic Analysis Zone Division Methodology Based on Individual Travel Data
Authors: Du Kai , Song Jingni , Chen Dan , Li Ming , Zhu Yadi
Published: December 2024

Urban rail transit passenger flow forecasting often relies on the traditional “four-step” method, where the division of traffic analysis zones (TAZs) is critical to ensuring prediction accuracy. As the fundamental units for describing trip origins and destinations, TAZs also encompass socioeconomic attributes such as land use, population, and employment. However, traditional TAZs, typically based on administrative boundaries, fail to reflect evolving urban travel behavior, particularly when transit stations are located near TAZ boundaries. Additionally, the emergence of urban big data allows for more refined spatial analyses based on individual travel patterns, addressing the limitations of administrative divisions. This study proposes an innovative TAZ aggregation model based on travel similarity, integrating public transit smart-card data and GIS data from bus networks. First, individual spatiotemporal travel patterns are mapped and discretized in both the spatial and temporal dimensions. Travel characteristic data are then extracted for spatial grid units. The TAZ division problem is defined as a multiobjective optimization problem, including factors such as travel similarity, the homogeneity of travel intensity, the statistical accuracy of the area, geographic information preservation, travel ratio constraints, and shape constraints. Multiple TAZ division schemes are produced and assessed using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), resulting in the selection of the optimal scheme. The proposed method is implemented on bus passenger travel data in Beijing, showing that the optimized scheme significantly reduces the number of zones with travel ratios exceeding 10%. Compared with existing schemes, the optimized division yields more uniform distributions of travel ratios, area, and travel density, while significantly minimizing the number of zones with a high travel concentration. These results demonstrate that the proposed method better reflects residents’ actual travel behaviors, offering a notable improvement over traditional approaches. This research provides a novel and practical framework for data-driven TAZ optimization.
Part A: Innovative Data Augmentation Approach to Enhance Machine Learning Efficiency—Case Study for Hydrodynamic Purposes
Authors: Majidiyan Hamed , Enshaei Hossein , Howe Damon , Gubesch Eric
Published: December 2024

These days, AI and machine learning (ML) have become pervasive in numerous fields. However, the maritime industry has faced challenges due to the dynamic and unstructured nature of environmental inputs. Hydrodynamic models, vital for predicting ship responses and estimating sea states, rely on diverse data sources of varying fidelities. The effectiveness of ML models in real-world applications hinges on the diversity, range, and quality of the data. Linear simulation techniques, chosen for their simplicity and cost-effectiveness, produce unrealistic and overly optimistic results. Conversely, high-fidelity experiments are prohibitively expensive. To address this, the study introduces an innovative feature engineering that incorporates uncertainty into features of linear models derived from higher fidelity modeling. This enhances productive data entropy, positively enhancing feature classification and improving the accuracy and feasibility of ML models in hydrodynamic responses of floating vessels. Tested with data from a known geometrical shape exposed to regular and irregular waves, the technique employs Ansys Aqwa for linear models. The results demonstrate the efficiency of the proposed technique, expanding the applicability of ML models in realistic scenarios. The application of the proposed approach extends beyond and can be further applied to any stochastic process, which expands the ML application for realistic use cases.
3D Segmentation and Visualization of Skin Vasculature Using Line-Field Confocal Optical Coherence Tomography
Authors: Mayer Oliver , Wirsching Hanna , Schlingmann Sophia , Welzel Julia , Schuh Sandra
Published: December 2024

This study explores the advanced imaging of skin vasculature using Line-Field Confocal Optical Coherence Tomography (LC-OCT), which offers high-resolution, three-dimensional (3D) visualization of vascular structures, especially within skin tumors. The research aims to improve the understanding of tumor angiogenesis and the complex vascular morphology associated with malignancies. The methodology involves converting original image stacks into negative images, manually tracing vessels using the Simple Neurite Tracer (SNT) plugin, and creating smoothed binary masks to reconstruct 3D models. The study’s results highlight the ability to visualize serpiginous, corkscrew-like, and irregular vessels across various skin cancers, including melanoma, squamous cell carcinoma, and basal cell carcinoma. These visualizations provide insights into vessel morphology, spatial arrangements, and blood flow patterns, which are crucial for assessing tumor growth and potential therapeutic responses. The findings indicate that 3D reconstructions from LC-OCT can uncover vascular details previously undetectable by two-dimensional imaging techniques, making it a valuable tool in dermatology for both clinical diagnostics and research. This method allows for better monitoring of skin cancer treatment and understanding of the role of vascular polymorphism in tumor development.
Deformation and Instability Mechanisms of a Shaft and Roadway Under the Influence of Rock Mass Subsidence
Authors: Rong Junfeng , Wang Bin
Published: December 2024

Investigating deformation and failure mechanisms in shafts and roadways due to rock subsidence is crucial for preventing structural failures in underground construction. This study employs FLAC3D software (vision 5.00) to develop a mechanical coupling model representing the geological and structural configuration of a stratum–shaft–roadway system. The model sets maximum subsidence displacements (MSDs) of the horsehead roadway’s roof at 0.5 m, 1.0 m, and 1.5 m to simulate secondary soil consolidation from hydrophobic water at the shaft’s base. By analyzing Mises stress and plastic zone distributions, this study characterizes stress failure patterns and elucidates instability mechanisms through stress and displacement responses. The results indicate the following: (1) Increasing MSD intensifies tensile stress on overlying strata results in vertical displacement about one-fifth of the MSD at 100 m above the roadway. (2) As subsidence increases, the disturbance range of the overlying rock, shaft failure extent, and number of tensile failure units rise. MSD transitions expand the shaft failure range and evolve tensile failure from sporadic to large-scale uniformity. (3) Shaft failure arises from the combined effects of instability and deformation in the horsehead and connecting roadways, compounded by geological conditions. Excitation-induced disturbances cause bending of thin bedrock, affecting the bedrock–loose layer interface and leading to shaft rupture. (4) Measures including establishing protective coal pillars and enhancing support strength are recommended to prevent shaft damage from mining subsidence and water drainage.
Classroom Expression Recognition Based on Deep Learning
Authors: Gao Yang , Zhou Linyan , He Jialiang
Published: December 2024

In the field of education, classroom expression recognition technology has important application value. It can help teachers understand students’ emotions and learning attitudes and adjust teaching strategies in time to improve learning outcomes. However, in a classroom environment with a large number of students, it is difficult for teachers to monitor individual learning progress, resulting in low classroom management efficiency, insufficient teaching personalization, and imperfect student evaluation mechanisms. To address the above problems, this paper constructs a dataset covering five typical classroom expressions (resistance, confusion, understanding, fatigue, and neutrality). To address the problem of insufficient data volume, this paper introduces a data enhancement method based on generative adversarial networks to improve the diversity and quality of the dataset. At the same time, a multi-attention fusion network (MAF-ER) is proposed, which combines spatial attention mechanism, channel attention mechanism, and self-attention mechanism. Experimental results show that the expression recognition accuracy of the MAF-ER algorithm reaches 88.34%, which is better than the baseline method. The ablation experiment further verifies the effectiveness of the multi-level attention (MLA) mechanism. The results show that the proposed method has a high accuracy in expression recognition tasks and provides strong support for smart classrooms.
Physico-Chemical Characteristics of Rosa canina L. Seeds and Determining Their Potential Use
Authors: Stryjecka Małgorzata , Kiełtyka-Dadasiewicz Anna , Michalak Monika
Published: December 2024

Rosa canina is well-known plant, and its fruits have been used for centuries. The seeds have been less utilized and remain untested. The objective of this study was to examine the physico-chemical properties of rosehip seeds and to identify potential applications based on their properties. The physico-chemical properties of the seeds were determined, including swelling index (2.51), color in the CIE L * a * b * space (b * = 26.2), as well as carbohydrate (79.2 g/100 g), soluble dietary fiber (71.6 g/100 g), protein (7.67 g/100 g), fat (6.23 g/100 g), and ash (1.96 g/100 g) content. Among the amino acids, glutamic acid dominated (1.58 mg/100 g), while among the fatty acids, linoleic acid (53.19%), linolenic acid (20.24%), and oleic acid (17.63%) were the most dominant. In addition, rosehip seeds contained large amounts of calcium (3851 mg/kg), potassium (2732 mg/kg), and phosphorus (991 mg/kg), as well as vitamin C (1783 μg/g). It was found that the content of other valuable phytochemicals, such as phenolic compounds (2633 μg/g) and carotenoids (3.13 μg/g) influenced the antioxidant properties of the tested raw material. This activity examined by the ferric-reducing antioxidant power (FRAP) and DPPH radical scavenging activity methods was assessed at a level of 191 and 229 μmol TE/g, respectively. The results indicate the potential use of rosehip seed, a by-product of the food processing industry, as a new high-value ingredient for health products, such as nutraceuticals, pharmaceuticals, and cosmeceuticals.
Curiosity-Driven Camouflaged Object Segmentation
Authors: Pang Mengyin , Sun Meijun , Wang Zheng
Published: December 2024

Camouflaged object segmentation refers to the task of accurately extracting objects that are seamlessly integrated within their surrounding environment. Existing deep-learning methods frequently encounter challenges in accurately segmenting camouflaged objects, particularly in capturing their complete and intricate details. To this end, we propose a novel method based on the Curiosity-Driven network, which is motivated by the innate human tendency for curiosity when encountering ambiguous regions and the subsequent drive to explore and observe objects’ details. Specifically, the proposed fusion bridge module aims to exploit the model’s inherent curiosity to fuse these features extracted by the dual-branch feature encoder to capture the complete details of the object. Then, drawing inspiration from curiosity, the curiosity-refinement module is proposed to progressively refine the initial predictions by exploring unknown regions within the object’s surrounding environment. Notably, we develop a novel curiosity-calculation operation to discover and remove curiosity, leading to accurate segmentation results. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms the existing competitors on three challenging benchmark datasets. Compared with the recently proposed state-of-the-art method, our model achieves performance gains of 1.80% on average for Sα. Moreover, our model can be extended to the polyp and industrial defects segmentation tasks, validating its robustness and effectiveness.
A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
Authors: Greif Lucas , Hauck Svenja , Kimmig Andreas , Ovtcharova Jivka
Published: December 2024

This study introduces a comprehensive knowledge graph (KG)-based framework designed to support sustainable decision-making by integrating, enriching, and analyzing heterogeneous data sources. The proposed methodology leverages domain expertise, real-world data, and synthetic data generated through language models to address challenges in life cycle assessment (LCA), particularly data scarcity and inconsistency. By modeling the entire product lifecycle, including engineering, production, usage, and disposal phases, the framework facilitates early-stage design decision-making and provides actionable insights for sustainability improvements. The methodology is validated through a case study on 3D printing (3DP), demonstrating its ability to manage complex data, highlight relationships between engineering decisions and environmental impacts, and mitigate data scarcity in the early phases of product development in the context of LCAs. In conclusion, the results demonstrate the framework’s potential to drive sustainable innovation in manufacturing.
Innovative Equation for Determining the Geochemical Background Values of Chromium Based on Major Components in Rock–Soil–Sediment Systems
Authors: Zhou Shuya , Gong Qingjie , Zhang Zhaochong , Lv Zhendong , Chen Shaoyu , An Yonglong
Published: December 2024

The geochemical background value of chromium (Cr) serves as a pivotal factor in environmental assessments and mineral exploration endeavors. Traditionally, geochemical background values have been determined using statistical parameters derived from dataset analysis, though this method may possess inherent limitations. In this study, we introduce a novel equation to calculate the geochemical background value of Cr, based on the premise that major elements can effectively delineate the geochemical background for trace elements. Using a dataset encompassing 791 abundance records from rocks, soils, and sediments, we derived and fitted the equation. Our findings revealed that when treating these 791 records as representative of background samples, approximately 95% of the records lie within a logarithmic error of ±0.2 from the calculated values. Based on this observation, we propose adopting a logarithmic error threshold of ±0.2 as a criterion for identifying background samples. To further validate the equation, we tested background samples from diverse regions with varying degrees of weathering. Among the 138 samples analyzed, only two exhibited background values with a logarithmic error exceeding ±0.2 from the measured values, although all samples fell within a ±0.3 error margin. We subsequently applied this equation to the orthopyroxenite and dunite zones within the Pados-Tundra complex, located in the western Kola Peninsula, Russia. According to the ±0.2 logarithmic error criterion, mildly altered orthopyroxenite samples could be deemed as background Cr samples, where certain altered orthopyroxenite samples exhibited a logarithmic error in the ±0.2–±0.3 range. In contrast, altered and mineralized samples in the dunite zone showed a significant deviation beyond the ±0.2 error range, indicating that they should be classified as anomalous Cr samples. This innovative methodology offers a refined approach for background determination and anomaly identification, thereby enhancing mineral exploration and Cr environmental quality assessments.
GLClick: Interactive Segmentation Combining Global and Local Features
Authors: Tang Jiaying , Wang Hongyuan , Ding Zongyuan , Xin Zihao
Published: December 2024

Convolutional neural networks (CNNs) are the backbone of most modern interactive segmentation algorithms. However, the limited receptive field of CNNs restricts their ability to capture long-range semantic relationships. Recently, transformers have gained significant attention for their capacity to capture long-range dependencies. Nevertheless, CNNs still outperform Transformer in extracting local information. An effective interactive segmentation algorithm should accurately capture fine-grained local details alongside global semantic relationships. Therefore, we propose GLClick, a global–local click-based interactive image segmentation model that integrates local and global information through a novel fusion mechanism. We design an efficient global–local feature fusion module (GLFM) that integrates fine-grained features from various layers of ResNet50 with those from the Transformer feature pyramid. This approach maintains ResNet50’s ability to extract local features while effectively leveraging the Transformer to capture global context. Additionally, we enhance the multi-layer perceptron (MLP) to improve performance. Extensive experiments on diverse benchmark datasets demonstrate significant improvements in interactive image segmentation, confirming the effectiveness of our approach. Moreover, we conduct experiments on medical image datasets, further illustrating the model’s versatility and effectiveness across different domains.
Non-Destructive Testing for Evaluation of Young’s Modulus by Using Free Vibration Response of Composite Materials
Authors: Apsan Mirela-Roxana , Mitu Ana-Maria , Neagoe Catalin-Andrei , Pop Nicolae , Sireteanu Tudor
Published: December 2024

This article presents a non-destructive method, based on the response to free vibrations, which can be used with efficiency and reliability to determine the Young’s modulus of polymer composite materials reinforced with natural or synthetic fibers. The non-destructive tests are carried out by measuring the frequencies of bending free vibrations of cantilever beams with additional masses. By using inverse methods, the experimental values of elasticity modulus E are assessed and validated by numerical simulation, using the finite element method (FEM). For FEM modeling, the materials are considered linear, homogeneous, isotropic, and viscoelastic.
Antibacterial and Cytotoxic Evaluation of Lespedeza cuneata Extract Against Periodontal Pathogens and Human Gingival Cells: A Novel Option for Periodontal Therapy
Authors: Yoon Hyo-Ju , Kim Gyoo-Cheon , Nam Seoul-Hee
Published: December 2024

Background: This study aims to evaluate the antibacterial effect of Lespedeza cuneata extract on Porphyromonas gingivalis (P. gingivalis), a principal bacterium in periodontal disease, and its impact on human gingival fibroblasts (HGFs). Methods: Dried Lespedeza cuneata was extracted using 70% ethanol, concentrated, and freeze-dried to obtain the Lespedeza cuneata extract in powder form. The antibacterial effect, indicated by the inhibition of P. gingivalis growth, was assessed by administering concentrations of 1, 3, 5, 10, 20, 30, and 40 mg/mL. After 24 h of anaerobic incubation, colony-forming units per milliliter (CFU/mL) were measured. Cytotoxicity on HGF cells was evaluated after treatment with WST-1 solution followed by incubation at 37 °C, 5% CO2 for 2 h. Cell morphology and proliferation were assessed using the Sulforhodamine B (SRB) assay. Results: The antibacterial effect of Lespedeza cuneata extract was concentration-dependent, with 99.98% inhibition observed at 5 mg/mL, 99.99% at 10 mg/mL, and no detectable CFUs were observed at 40 mg/mL under the tested conditions. Evaluating the change in growth rate of HGF cells showed a decrease in cell viability as the concentration increased, and the application of Lespedeza cuneata extract at 10 mg/mL was found to be a safe and effective concentration with a half-maximal inhibitory concentration (IC50). Conclusion: Based on the antibacterial effect, cytotoxicity, and safety profile of Lespedeza cuneata extract, it holds potential as a natural extract material for the prevention, improvement, or treatment of periodontal disease. Additionally, validation of the practical approach will be necessary via a clinical applicability evaluation.
Dynamic Response of a Single-Rotor Wind Turbine with Planetary Speed Increaser and Counter-Rotating Electric Generator in Starting Transient State
Authors: Saulescu Radu , Neagoe Mircea
Published: December 2024

The paper addresses the dynamic modeling and numerical simulation of a novel single-rotor wind system with a planetary speed increaser and counter-rotating direct current (DC) generator, patented by authors, during the transient stage from rest. The proposed analytical dynamic algorithm involves the decomposition of the wind system into its component rigid bodies, followed by the description of their dynamic equations using the Newton–Euler method. The linear mechanical characteristics of the DC generator and wind rotor are added to these dynamic equations. These equations allow for the establishment of the close-form equation of motion of the wind system and, implicitly, the time variation of the mechanical power parameters. Numerical simulations of the obtained analytical dynamic model were performed in MATLAB-Simulink in start-up mode from rest for the case study of a 100 kW wind turbine. These results allowed highlighting the time variation of angular velocities and accelerations, torques, and powers for all system shafts, both in the transient regime and steady-state. The implementation, in this case, of the counter-rotating generator indicates a 6.4% contribution of the mobile stator to the generator’s total power. The paper’s results are useful in the design, virtual prototyping, and optimization processes of modern wind energy conversion systems.
Effects of Arm Swing in Lower Limb Muscle Activation and Coordination During Treadmill Walking
Authors: Rodríguez-Millán Teresa , Sanz-Morère Clara B. , Cherubini Agnese , Urrutia Rocío , Oliveira Barroso Filipe , Pons José L. , Tornero Jesús
Published: December 2024

Arm swing is an inherent aspect of human locomotion that enhances lower limb (LL) muscle activation, which may explain its benefits for stroke rehabilitation over fixed-arm approaches. This study analyzed how restricting arm movement affects LL muscle coordination by comparing treadmill walking with arms (WWA) and walking with no arms (WNA) through muscle synergy analysis. Surface electromyography was recorded from eight LL muscles in ten healthy participants. Significant differences were found in muscle activity envelopes at 50% of the gait cycle (GC) for the Gluteus Medius, 0% and 100% GC for the Vastus Lateralis and Semitendinosus, and 25% GC for the Semitendinosus, Gastrocnemius Medialis, and Soleus. The Rectus Femoris and Vastus Lateralis showed higher variability and activation in WNA compared to WWA. Synergy analysis revealed four muscle synergies, with a median global variance of 95%. While spatial components were similar, temporal differences emerged at 0% GC for Synergy 1, 5% and 90% GC for Synergy 2, and 95% GC for Synergy 3 (p < 0.05). Our results suggest that arm swing influences LL muscle activity and coordination during walking. Future studies will aim at understanding the effects of arm swing in stroke rehabilitation, which could help design more effective gait rehabilitation protocols including arm swing.
Lifetime Prediction of Single Crystal Nickel-Based Superalloys
Authors: Kasar Çağatay , Kaftancioglu Utku , Bayraktar Emin , Aslan Ozgur
Published: December 2024

Single crystal nickel-based superalloys are extensively used in turbine blade applications due to their superior creep resistance compared to their polycrystalline counterparts. With the high creep resistance, high cycle fatigue (HCF) and low cycle fatigue (LCF) become primary failure mechanisms for such applications. This study investigates the fatigue life prediction of CMSX-4 using a combination of crystal plasticity and lifetime assessment models. The constitutive crystal plasticity model simulates the anisotropic, rate-dependent deformation behavior of CMSX-4, while the modified Chaboche damage model is used for lifetime assessment, focusing on cleavage stresses on active slip planes to include anisotropy. Both qualitative and quantitative data obtained from HCF experiments on single crystal superalloys with notched geometry were used for validation of the model. Furthermore, artificial neural networks (ANNs) were employed to enhance the accuracy of lifetime predictions across varying temperatures by analyzing the fatigue curves obtained from the damage model. The integration of crystal plasticity, damage mechanics, and ANNs resulted in an accurate prediction of fatigue life and crack initiation points under complex loading conditions of single crystals superalloys.