bluuebunny/crossref_metadata_2025_split huggingface dataset:
Vehicular Noise and Air Pollution in Bhubaneswar City, India and Socio-Health Analysis
Authors: Meher K K , Sahu A K , Pradhan M , Pradhan P K 
Published: December 2024

Abstract

Urban areas, with their dense populations and extensive transportation networks, often face significant challenges from vehicular noise and air pollution. Noise pollution occurs when sound levels exceed acceptable thresholds, leading to adverse impacts on human health and well-being. In urban environments, traffic noise constitutes the largest portion of overall noise pollution and it can lead to a range of health issues of the inhabitants. The pollutants are released to surrounding air when fuel burns inside an internal combustion engine. Air pollution has escalated into a significant environmental and public health issue over the past few decades, particularly in urban areas. The long-term exposure to air pollution has serious health consequences, leading to chronic conditions that significantly impair quality of life and increase mortality. In this work, the vehicular noise pollution in Bhubanswar city and its impact on exposed population have been studied. The important locations have been chosen for the assessment of noise level and noise pollution indices. The air pollution and its correlation with vehicular noise have been studied. The impact of noise pollution on wellness of the exposed population has been studied also. It's concerning to observe that the traffic noise levels in Bhubaneswar city have reached such an alarming stage.
Integration of Artificial Intelligence Algorithms for Defect Detection and Shape Recognition in Mechanical Quality Control
Authors: Melhaoui Ouafae El , Melhaoui Islam El , Said Sara 
Published: December 2024

Abstract

The integration of artificial intelligence algorithms for defect detection and shape recognition in the automotive industry is rooted in machine learning principles. Neural networks play a pivotal role in this domain, renowned for their effectiveness in tasks such as pattern recognition and data analysis. These systems are indispensable within the automotive industry and beyond, crucial for interpreting datasets and enabling machines to identify objects and defects in images or videos based on their geometrical and structural properties. Techniques for defect analysis, including convolutional neural network (CNN) and fuzzy min-max classifier (FMMC) methods, are employed.
In the context of quality control, where assessing shape is paramount, image processing techniques such as zoning, Loci features, profile projection, Freeman chain code, and histogram of oriented gradients (HOG) are utilized. These methods facilitate the analysis of physical shapes and surface characteristics, ensuring that automotive parts satisfy stringent quality standards. Experimental results underscore the efficacy of CNN combined with FMMC, demonstrating superior accuracy of 97% compared to traditional image analysis methods. This approach highlights the capability of CNN and FMMC to enhance the precision and reliability of shape recognition through advanced image analysis, thereby contributing to improved quality control measures in the automotive industry.
An Innovative Ensemble Approach of Deep Learning Models with Soft Computing Techniques for GIS-based drought-zonation mapping in Rarh Region, West Bengal
Authors: Chowdhury Gopal , Mandal Sayantan , Saha Ashis 
Published: December 2024

Abstract

Drought is a complex natural disaster impacting ecosystems and communities, making its identification crucial for mitigation efforts. This study aimed to assess drought scenarios in the Rarh Region of West Bengal at 3-, 6-, and 12-month intervals. The region is an amalgamation of the plateau and Gangetic delta, facing a decreasing rainfall trend, particularly in Birbhum and Purba Bardhhaman districts. Purba Bardhhaman, known for its good track of rice production, is now facing severe drought, which is a concerning matter. The study assessed their collinearity by evaluating 27 drought assessment variables grouped into meteorological, agricultural, hydrological, and socio-economic facets. A Multi-Layer Perceptron Neural Network (MLP NN) was applied as a benchmark, followed by a DenseNet neural network. Finally, a Hybrid Deep Learning Ensemble model was developed to compare precision and create a drought-prone map. Results indicated that, on average, 26.66% of the region is highly drought-prone at a 3-month interval, 20% at 6 months, and 25% at 12 months. The models were validated using ROC-AUC, Standard Error, and Asymptotic Significance. The Hybrid Deep Learning Ensemble model showed the highest accuracy, achieving 94.2%, 94.3%, and 95.3% at 3-, 6-, and 12-month intervals, respectively. This research provides valuable insights for policymakers in West Bengal to address the increasing drought risks in the region.
Modelling Soil Organic Carbon at Multiple Depths in Woody Encroached Grasslands Using Integrated Remotely Sensed Data
Authors: Mthiyane Sfundo , Mutanga Onisimo , Matongera Trylee Nyasha , Odindi John 
Published: December 2024

Abstract

Woody plants encroachment into grasslands has considerable hydrological and biogeochemical consequences to grassland soils that include altering the Soil Organic Carbon (SOC) pool. Consequently, continuous SOC stock assessment and evaluation at deeper soil depths of woody encroached grasslands is essential for informed management and monitoring of the phenomenon. Due to high litter biomass and deep root structures, woody encroached landscapes have been suggested to alter the accumulation of SOC at deeper soil layers, however, the level at which woody encroachment sequestrate SOC within localized protected grasslands is still poorly understood. Remote sensing methods and techniques have recently been popular in SOC analysis due to better spatial and spectral data properties as well as the availability of affordable and eco-friendly data. In this regard, the study sought to quantify the accumulation of SOC at various depths (30 cm, 60 cm, and 100 cm) in a woody-encroached grassland by integrating Sentinel-1 (S1), Sentinel-2 (S2), PlanetScope (PS) satellite imagery, and topographic variables. SOC was quantified from 360 field-collected soil samples using the loss-On-Ignition (LOI) method and spatial distribution of SOC across the Bisley Nature Reserve modelled through employing the Random Forest (RF) algorithm. The study’s results demonstrate that the integration of topographic variables, Synthetic Aperture Radar (SAR), and PlanetScope data effectively modelled SOC stocks at all investigated soil depths, with high R² values of 0.79 and RMSE of 0.254 t/ha. Interestingly, SOC was established to be high at 30 cm compared to 60 cm and 100 cm depths. The horizontal reception (VH), Slope, Topographic Weightiness Index (TWI), Band 11 and vertical reception (VV) were optimal predictors of SOC in woody encroached landscapes. These results highlight the significance of integrating RF model with spectral data and topographic variables for accurate SOC modelling in woody encroached ecosystems. The findings of this study are pivotal for developing a cost-effective and labour-efficient assessment and monitoring system for the appropriate management of SOC in woody encroached habitats
AT4CTIRE: Adversarial Training for Cyber Threat Intelligence Relation Extraction
Authors: Han Yue , Jiang Rong , Li Changjian , Huang Yanyi , Chen Kai , Yu Han , Li Aiping , Han Weihong 
Published: December 2024

Abstract

Cyber Threat Intelligence (CTI) plays a crucial role in cybersecurity. However, traditional information extraction has low accuracy due to the specialization of CTIs and the concealment of relations. To improve the performance of CTI relations extraction in the knowledge graph, we propose a relation extraction architecture called Adversarial Training for Cyber Threat Intelligence Relation Extraction (AT4CTIRE). Besides, we developed a large-scale cybersecurity dataset for CTI analysis and evaluation, called Cyber Threat Intelligence Analysis (CTIA). Inspired by the Generative Adversarial Network, we integrate contextual semantics into this framework to refine our study. Firstly, we use some wrong triples with incorrect relations to train the generator and produce high-quality generated triples as adversarial samples. Secondly, the discriminator used actual and generated samples as training data. Integrating the discriminator and the context embedding module facilitates a deeper understanding of contextual CTI within threat triples. Finally, training a discriminator identified the relation between the threat entities. Experimentally, we set two CTI datasets and only one baseline that we could find to test the effect of the proposed method in the cybersecurity domain. We also set other knowledge graph completion comparisons that conclude three entity completion datasets and nine baselines, one relation completion dataset, and eight baselines. Experimental results demonstrate that AT4CTIRE outperforms existing methods with substantially improved extraction accuracy and a remarkable expedited training convergence rate.
Evaluation of the effectiveness of Aquatain, Bacillus thuringiensis var. israelensis, and Temephos Anopheles arabiensis and Anopheles stephensi larvae in the laboratory and field settings.
Authors: Dugassa Sisay , Kebede Tilahun , Abdulatif Bedri , Assefa Gudissa , Solomon Hiwot , Getachew Dejene , Lelisa Kidane , Gebresilassie Araya 
Published: December 2024

Abstract

Background: The main tools for the control of malaria vectors in sub-Saharan Africa (SSA), including Ethiopia are long lasting insecticidal nets and indoor residual spraying. However, the sustainability of IRS and ITNs is threatened by the emergence of insecticide resistances, behavioral avoidance, as well as presence of outdoor biting mosquito populations. Thus, complementary interventions such as larval source management (LSM), which includes larviciding, are required to achieve better result in malaria vector control. This study aimed to evaluate the efficacy of three larvicides (Aquatain AMF®, temephos and Bacillus thuringiensis var. israelensis (Bti) against larvae of Anopheles arabiensisi and An. stephensi.
Method: The tests were conducted in the laboratory and in semi-natural habitats on the 3rd and 4th larval instars of both insectary-reared and wild-collected immature Anopheles mosquitoes, respectively. For the field study, larvae were collected from three sites (Awash Sebat Kilo, Metahara and Adama towns). Three productive larval habitats in three villages were treated with Aquatain, temephos and Bti, and compared with untreated habitats (control).
Results: In the laboratory, the percent mortality ranged 95% to 100% and 60% to 100% for An. arabiensis and An. stephensi colonies, respectively, while the percent larval mortality ranged 97% to 100% and 74% to 100% for field collected wild An. arabiensis and An. stephensi, respectively. In the natural field study, the percent immature stages reductions ranged 84% - 96%, 89% - 95% and 77% - 96% at Awash, Metahara and Adama respectively. The three larvicides (Aquatain, Temephos and Bti) clearly showed high larviciding efficacies both in the laboratory and field evaluations. Despite its efficacy, temephos changed the color of the water to whitish and has got strong smell. As a result, the community scared of the treated habitats.
Conclusion: It is highly recommendable that Aquatain and/or Bti larvicides be used in malaria control program as complementary malaria vector control tools.
Optimizing environmental remediation: Thermal tunability of bone char for selective remediation of water, soil, and mine waste
Authors: Loredo-Portales Rene , Ruiz-Ortega Héctor Adán , Rio-Salas Rafael Del , Moreno-Rodriguez Verónica , González-Méndez Blanca , Ramos-Pérez Daniel , Torre-Velázquez Víctor , Encinas-Yanez Martín Fernando 
Published: December 2024

Abstract

Bone char (BoC) is a bio-hydroxyapatite material derived from the pyrolysis of animal bones. It has been extensively studied for its applications in water, soil, and industrial waste remediation. Unlike other biological and mineral adsorbents, BoC exhibits pH, electrical conductivity, porosity, and crystallinity properties that vary proportionally with the pyrolysis temperature. This variability allows BoC to be a tunable, reproducible, and sustainable material suitable for targeted remediation efforts, including the removal of specific anions and cations from contaminated water, enhancing soil quality, and mitigating the adverse effects of mine tailings. This study evaluates the chemical and physical properties of BoC produced at pyrolysis temperatures ranging from 400 to 1000°C, proposing its use for targeted remediation applications. Our findings indicate that both physical and chemical properties of BoC vary proportionally positively or negatively with increasing pyrolysis temperature. The main key characteristics for remediation include ion exchange capacity, pH, and stability. We recommend using BoC pyrolyzed at low temperatures (< 500°C) for water pollutant remediation, while BoC produced at medium temperatures (500–800°C) is better suited for soil remediation and as a soil fertilizer. Additionally, high temperature (> 800°C) pyrolyzed BoC is effective for remediation in both water and soil under extremely acidic conditions.
Integrated chemical and genomic analysis of lipopeptides produced by Bacillus velezensis CMRP4489 with antifungal activity
Authors: Nicoletto Maria Luiza A. , Baptista Julia P. , Noriler Sandriele A. , Gouveia Paula O. , Bertoli Alicya M. , Silva Daniel V. , Camargo Priscila G. , Macedo Fernando C. , Oliveira João P. , Pereira Ulisses P. , Mello João C. P. , Novello Claudio R. , Rocha Ulisses , Oliveira Admilton Gonçalves 
Published: December 2024

Abstract

Background
Bacillus velezensis CMRP4489 shows promise as a biological control agent thanks to its production of secondary metabolites with antimicrobial properties. Nevertheless, the specific compounds contributing to its antifungal effects are not fully understood. This study aimed to identify and characterise bioactive compounds from B. velezensis CMRP4489 that exhibit antifungal activity against Sclerotinia sclerotiorum, a major phytopathogen affecting various crops.
Results
We utilised vacuum liquid chromatography (VLC) and thin-layer chromatography (TLC) to separate and identify antifungal compounds present in the cell-free supernatant of B. velezensis CMRP4489. Initial antifungal activity was detected in fraction F2, but further purification led to the identification of fraction F6F, which displayed significantly enhanced activity, evident from inhibition halos measuring 26.25 ± 1.8 mm in disk diffusion assays. High-performance liquid chromatography (HPLC) revealed four subfractions, with F6F.1 demonstrating the highest activity, forming inhibition halos of 27.5 ± 2.0 mm. Through spectroscopic and spectrometric analysis, we identified F6F.1 as Bacillopeptin A and B, structurally similar lipopeptides. Electron microscopy confirmed that Bacillopeptins disrupt the hyphal structure of S. sclerotiorum, while minimal inhibitory concentration (MIC) assays showed that as low as 25 µg/mL could be effective. Additionally, genomic analysis using antiSMASH showed the presence of biosynthetic gene clusters (BGCs) in B. velezensis CMRP4489 linked to lipopeptide production, particularly those related to bacillopeptins, iturins, and fengycins, reinforcing the strain's potential for the production of diverse bioactive compounds.
Conclusions
This research is the first to illustrate the effectiveness of Bacillopeptins A and B from B. velezensis CMRP4489 against S. sclerotiorum, highlighting their strong antifungal properties and potential as biocontrol agents. The identification of pertinent BGCs suggests that B. velezensis CMRP4489 can be optimised for increased production of secondary metabolites. This study highlights the potential of Bacillopeptins A and B as alternatives to chemical fungicides, warranting further investigation in greenhouse and field trials to evaluate their effectiveness in real-world applications. Our findings provide important insights into the antimicrobial potential of B. velezensis CMRP4489 and emphasise its promise in sustainable crop protection strategies.
Mycorrhization of Quercus dentata seedlings with Laccaria bicolor enhances salt tolerance of plants only under relatively moderate soil salinity level
Authors: Sun Wenlong , Qi Luyu , Chen Haonan , Song Yixin , Jiang Jiaqi , Zhang Puyi , Wang Bojian , Wang Qiang , Meng Gaode , Ji Tianyu , Sun Xinke , Guo Weihua , Du Ning , Zwiazek Janusz J. 
Published: December 2024

Abstract

Background
Soil salinization is a growing global concern in many ecosystems. Although ectomycorrhizal fungi have been shown to alleviate the effects of salinity in some tree species, uncertainties persist concerning their effectiveness when plants are exposed to different salinity levels that are commonly present in salt-affected soils.
Objective
This study explored the effects of Laccaria bicolor on Quercus dentata seedlings under different levels of salt stress.
Methods
Q. dentata seedlings were grown in pots and were either non-inoculated (mycorrhizal control) or inoculated with the ectomycorrhizal fungus L. bicolor. The seedlings were then treated with three NaCl concentrations (0, 0.4%, and 0.8%), that was added to the soil for 30 days, after which plant physiological, stoichiometric, and growth characteristics were examined.
Results
NaCl exposure significantly impaired growth and physiological parameters in all seedling groups. The influence of L. bicolor on Q. dentata seedlings varied with salt concentration. Under moderate salinity, the seedlings colonized by L. bicolor exhibited an enhancement in root biomass and leaf chlorophyll concentrations, concomitant with a reduction in leaf Na+ concentrations and the Na+/K+ ratios. Conversely, under relatively high salinity, colonization of L. bicolor was associated with a reduction in leaf water content and fluorescence parameters, as well as an elevation in leaf Na+ concentrations.
Conclusions
The effect of ectomycorrhizal fungus L. bicolor on Q. dentata seedlings was dependent on NaCl concentration, and our results indicate that the use of L. bicolor in afforestation efforts with Q. dentata would only be effective under relatively low soil salinity levels.
A IoT-enabled Obstacle Detection and Recognition Technique for Blind Persons
Authors: Ikram Sunnia , Bajwa Prof. Imran , Gyawali Sujan , Ikram Amna , Alsubaie Najah 
Published: December 2024

Abstract

Object detection is a critical task in computer vision, essential for real-time applications ranging from autonomous vehicles to surveillance systems. This proposed work presents a comparative evaluation of Single Shot Multibox Detector (SSD algorithms). YOLOv3, MobileNetv3, RetinaNet and Faster R-CNN, in the context of real-time obstacle detection from camera images. The study evaluates these algorithms on performance metrics Precision, Recall and F1 score across various Intersection Over Union (IoU) thresholds. Also, the computational efficiency in terms of the time taken per frame is assessed to determine the effectiveness of each algorithm. The workflow includes image processing, augmentation, and application of SSD models to detect objects like vehicles, pedestrians and traffic signals. Results indicate that YOLOv3 achieves the highest precision of 96% demonstrating robust performance in real-time scenarios, while MobileNetv3 follows closely with 92%, RetinaNet and Faster RCNN achieves accuracy 90% and 90% respectively. These findings contribute to understanding the trade-offs between accuracy and computational efficiency in selected suitable SSD models for practical deployment.
Proteomic analysis reveals the sperm protein difference among fresh and frozen semen of Niangya yak in Xizang
Authors: Su Kuiyuan , Fu Hanyu , Xu Yefen , Liu Bohua , Zan Ruilong , Oujie Ciren , Ciren Quzhen , Duoji Ciren , Zhou Zhihao , Liu Suozhu , Niu Jiaqiang 
Published: December 2024

Abstract

Background To enhance Niangya yak's reproductive efficiency and breeding process, this study employed Label-free quantitative proteomics technology to analyze fresh (F) and chilled (C) semen samples.Result The analysis revealed an extensive repertoire of 18,338 peptide segments and 2,579 proteins within Niangya yak semen. Among them, 2,300 proteins were successfully annotated in the database. Notably, a total of 300 DEPs were identified between the C and F groups; comprising 129 up-regulated DEPs and 171 down-regulated DEPs. GO enrichment analysis highlighted that these DEPs may be involved in lipid metabolism as well as protein binding with proteases and phosphorylation processes associated with sperm cryodamage. KEGG enrichment analysis showed that 35 of the mapped pathways may be related to sperm motility, plasma membrane integrity and apoptosis. Specifically, the ribosome pathway, glycerophospholipid metabolism pathway, ferroptosis pathway, and phospholipase D signaling pathway might be closely linked to decreased sperm vitality, damaged plasma membrane integrity, and sperm apoptosis induced by freezing in semen. Based on the PPI analysis of groups C and F, this study has preliminarily identified potential proteins HSPBP1, HSP90AB1, CCDC83, PMSD4, ISYNA1, KLHL10, and UAB1 in Niangya yak semen that are associated with decreased vitality and cell apoptosis caused by cryodamage.Conclusion This study represents the first analysis of DEPs and their involvement in biological pathways in frozen yak semen using Label-free quantitative proteomics technology. It provides a theoretical foundation for comprehending the mechanism of cryodamage in Niangya yak semen as well as enhancing reproductive efficiency and breeding processes of yaks.
A decision-space model explains context-specific decision-making
Authors: Friedman Alexander , Beck Dirk , Heaton Cory , Davlia Luis , Rakocevic Lara , Drammis Sabrina , Tyulmankov Danil , Vara Paulina , Giri Atanu , Beck Shreeya Umashankar , Zhang Qingyang , Pokojovy Michael , Negishi Kenichiro , Batson Serina , Salcido Alexis , Reyes Neftali , Macias Andrea , Ibanez-Alcala Raquel , Houssain Safa , Waller Graham , Moschak Travis , Goosens Ki 
Published: December 2024

Abstract

Optimal decision-making requires consideration of internal and external contexts. Biased decision-making is a transdiagnostic symptom of neuropsychiatric disorders. We created a computational model demonstrating how the striosome compartment of the striatum constructs a context-dependent mathematical space for decision-making computations, and how the matrix compartment uses this space to define action value. The model explains multiple experimental results and unifies other theories like reward prediction error, roles of the direct versus indirect pathways, and roles of the striosome versus matrix, under one framework. We also found, through new analyses, that striosome and matrix neurons increase their synchrony during difficult tasks, caused by a necessary increase in dimensionality of the space. The model makes testable predictions about individual differences in disorder susceptibility, decision-making symptoms shared among neuropsychiatric disorders, and differences in neuropsychiatric disorder symptom presentation. The model provides new evidence for the central role that striosomes play in neuroeconomic and disorder-affected decision-making.