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Found 6374 publications
First report of Haemaphysalis bispinosa, molecular-geographic relationships of Ixodes granulatus and a new Dermacentor species from Vietnam
Publication Name: Parasites and Vectors
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Background: Vietnam and its region are regarded as an ixodid tick biodiversity hotspot for at least two genera: Haemaphysalis and Dermacentor. To contribute to our knowledge on the tick fauna of this country, ticks from these two genera as well as an Ixodes species were analyzed morphologically and their molecular-phylogenetic relationships were examined in taxonomic and geographical contexts. Methods: For this study, seven Haemaphysalis sp. ticks were removed from dogs and collected from the vegetation. These showed morphological differences from congeneric species known to occur in Vietnam. In addition, three Ixodes sp. ticks were collected from pygmy slow lorises (Xanthonycticebus pygmaeus), and a Dermacentor female had been previously collected from the vegetation. After DNA extraction, these were molecularly or phylogenetically analyzed based on the cytochrome c oxidase subunit I (cox1) and 16S rRNA genes. Results: The three species were morphologically identified as (i) Ixodes granulatus, which had nearly or exactly 100% sequence identities to conspecific ticks reported from large (approximately 2000 km) geographical distances but was more different (having lower, only 94.2% cox1 and 96.7% 16S rRNA sequence identity) from samples collected within 1000 km of Vietnam in Southern China and Malaysia, respectively; (ii) Haemaphysalis bispinosa, which showed 100% sequence identity to samples reported within both narrow and broad geographical ranges; and (iii) a new species, Dermacentor pseudotamokensis Hornok sp. nov., described here morphologically and shown to be phylogenetically a sister species to Dermacentor tamokensis. Conclusions: Haemaphysalis bispinosa shows genetic homogeneity in the whole of South and Southeast Asia, probably owing to its frequent association with domestic ruminants and dogs (i.e. frequently transported hosts). However, I. granulatus, the Asian rodent tick, has a mixed geographical pattern of haplotypes, probably because it may associate with either synanthropic or wild-living rodents as primary hosts. This tick species is recorded here, for the first time to our knowledge, as parasitizing lorises in Vietnam and its region. Based on phylogenetic analyses, D. pseudotamokensis Hornok sp. nov., recognized and described here for the first time, was almost certainly misidentified previously as Dermacentor steini, drawing attention to the need to barcode all Dermacentor spp. in Southern Asia.
Open Access: Yes
Relationship Between Age at First Calving and 305-Day Milk Yield in Hungarian Holstein-Friesian Cows: Trends and Genetic Parameters
Publication Name: Animals
Publication Date: 2025-12-01
Volume: 15
Issue: 24
Page Range: Unknown
Description:
Age at first calving (AFC) and 305-day milk yield in the first lactation (MY) data of 18,545 Holstein-Friesian cows born between 2008 and 2018 in six herds were evaluated. The effects of some genetic and environmental factors, population genetic parameters, breeding value (BV), and phenotypic and genetic trends of AFC and MY traits were estimated. The GLM method (ANOVA Type III) and BLUP animal model were used for the estimations. One-way linear regression analysis was used for trend calculations. The adjusted overall mean value (±SE) of the AFC and MY traits was 25.19 ± 0.02 months and 10,287.14 ± 24.79 kg, respectively. The percentage proportion contribution of the different factors in the phenotype in the case of AFC was as follows: herd 94.41%, birth year of cow 3.26%, birth season of cow 1.39%, and sire 0.71%. For MY, the contribution was as follows: herd 89.17%, birth season of cow 5.38%, birth year of cow 4.09%, and sire 1.05%. The heritability of AFC and MY traits by two different models proved to be moderate (0.26 ± 0.02, 0.19 ± 0.01 and 0.30 ± 0.02, 0.34 ± 0.01, respectively). There were relatively small differences between the sires in the estimated BV for the traits AFC and MY. The phenotypic and genetic correlations between AFC and MY traits were weak (between −0.05 and −0.16). Based on the phenotypic trend calculation, AFC showed a decreasing direction (−0.12 months per year) and MY an increasing direction (+42.30 kg per year). However, the genetic trend was very slightly decreasing for AFC (−0.00 and −0.05 months per year) and slightly increasing for MY (+5.52 and +16.49 kg per year) over the period studied.
Open Access: Yes
DOI: 10.3390/ani15243648
Food safety risk analysis utilising K-lexicographic-max product of neutrosophic graph
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-12-01
Volume: 16
Issue: 12
Page Range: Unknown
Description:
In this study, we introduce the concept of the K-Lexicographic Max Product (K−LMP) of neutrosophic graphs and explore its associated degree structure to enhance decision-making frameworks in food safety applications related to risk assessment, including freshness, contamination, and spoilage. Neutrosophic graphs, capable of handling indeterminacy, inconsistency, and incompleteness, provide a flexible mathematical foundation for modelling complex systems. By incorporating the K−LMP into neutrosophic graphs, we offer a novel approach to comparing and ranking food safety scenarios where multiple attributes and uncertain information coexist. We present example graphs and theorems related to K−LMP and further define the K-Lexicographic degree to quantify node significance within the context of neutrosophic graphs. To validate the practical utility of this approach, a food safety analysis is implemented, demonstrating how the model identifies critical control points and supports more robust, transparent decision-making under uncertainty. This work contributes to the advancement of neutrosophic graph theory and its interdisciplinary application in food quality and safety management.
Open Access: Yes
The research landscape of industry 5.0: a scientific mapping based on bibliometric and topic modeling techniques
Publication Name: Flexible Services and Manufacturing Journal
Publication Date: 2025-12-01
Volume: 37
Issue: 4
Page Range: 1203-1250
Description:
Industry 5.0 (I5.0) marks a transformative shift toward integrating advanced technologies with human-centric design to foster innovation, resilient manufacturing, and sustainability. This study aims to examine the evolution and collaborative dynamics of I5.0 research through a bibliometric analysis of 942 journal articles from the Scopus database. Our findings reveal a significant increase in I5.0 research, particularly post-2020, yet highlight fragmented collaboration networks and a noticeable gap between institutions in developed and developing countries. Key thematic areas identified include human-robot collaboration, data management and security, AI-driven innovation, and sustainable practices. These insights suggest that a more integrated approach is essential for advancing I5.0, calling for strengthened global collaborations and a balanced emphasis on both technological and human-centric elements to fully realize its potential in driving resilient and sustainable industrial practices. This study provides the first comprehensive bibliometric analysis of I5.0, offering valuable insights for both researchers and practitioners.
Open Access: Yes
Unveiling temporal and frequency spillovers: Climate-risk indices and energy futures markets
Publication Name: Journal of Environmental Management
Publication Date: 2025-12-01
Volume: 395
Issue: Unknown
Page Range: Unknown
Description:
This study investigates the time- and frequency-domain spillover dynamics between climate-risk indices, namely the Transition Risk Index (TRI), Physical Risk Index (PRI), Global Climate Policy Uncertainty (GCPU))and major energy futures markets, including ICE Europe Brent crude oil futures (continuation), the global crude benchmark (Brent), ICE Europe Low Sulphur Gasoil futures, a European middle-distillate benchmark (Gasoil), Intercontinental Exchange (ICE) Abu Dhabi Murban crude oil futures (continuation), a Middle Eastern light-sweet benchmark (Murban), Shanghai Crude, New York Harbor Ultra-Low Sulphur Diesel futures (NYMEX) continuation (ULSD), and West Texas Intermediate crude oil futures (NYMEX Light Sweet Crude Oil futures continuation) (WTI). Employing a flexible econometric framework based on TVP-VAR and quantile connectedness, the analysis uncovers non-linear, asymmetric, and time-varying spillovers, with markedly stronger linkages during extreme market conditions and in the short term. Energy commodities, particularly Gasoil and WTI, emerge as significant net transmitters of transition risks, amplifying volatility during periods of stress, while long-term spillovers remain relatively weak, reflecting gradual decarbonization trends. The unique contribution of this paper lies in extending the Arbitrage Pricing Theory (APT) by integrating climate risks as dynamic, state-dependent, and non-diversifiable factors, thereby demonstrating how energy asset sensitivities fluctuate across regimes and quantiles. This approach advances the asset pricing and climate finance literature beyond static models by embedding dynamic connectedness into risk transmission analysis. The findings highlight the systemic nature of climate risks and underscore the importance of adaptive financial regulation, forward-looking climate policy, and flexible risk management practices to mitigate volatility and support the global energy transition. These insights provide actionable guidance for policymakers, regulators, and investors navigating the evolving interplay between climate risks and energy markets.
Open Access: Yes
Scenario-driven decision models for rare element waste management by integrating koch snowflake fuzzy sets and euclidean expert weighting
Publication Name: Sustainable Futures
Publication Date: 2025-12-01
Volume: 10
Issue: Unknown
Page Range: Unknown
Description:
The most critical factors must be determined to effectively manage environmental wastes generated during the extraction of rare elements. Otherwise, businesses may not be able to effectively manage their limited financial and human resources. This situation negatively affects the financial performance of the projects. The limited number of existing studies in the literature causes environmental risks to be insufficiently managed and recycling processes to be unoptimized. This study aims to determine priority strategies to increase the effectiveness of rare element waste management processes. Comprehensive and original decision-making models are created under three different scenarios. Koch Snowflake fuzzy sets, Euclidean based expert weighting and cognitive information modelling and analysis system (CIMAS) approaches are integrated in this model. The main contribution of this study is that a new type of fuzzy numbers called Koch Snowflake fuzzy sets is developed by considering the concept of fractal numbers. Fractal geometry is a powerful tool for modelling complex and dynamic systems. Hence, these new sets provide more flexible and more detailed uncertainty modelling. Moreover, considering different scenarios dynamic strategies can be developed that can adapt to changing conditions, such as pandemics or trade wars. The findings denote that technological developments are determined as the most critical factor under normal conditions. In the scenario where trade wars occur, it is revealed that political and regulatory measures should be addressed as a priority. In the event of a new epidemic disease such as COVID-19, it is concluded that more importance should be given to long-term storage strategies.
Open Access: Yes
Biogeography-Based Optimization of Machine Learning Models for Accurate Penetration Rate Prediction Using Rock Texture Coefficient
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Predicting drill penetration rate (PR) in rock environments remains a significant challenge due to the complex interplay between rock texture, drilling fluid properties, and operational parameters. Traditional empirical models often lack generalizability and are based on inconsistent datasets, limiting their reliability. To address these limitations, this study develops a comprehensive experimental dataset using rock samples collected from various mines in Iran, tested under controlled laboratory conditions with different drilling fluids, bit loads, and rotational speeds. Texture coefficient (TC), electrical conductivity (EC), load on bit (LOB), and bit rotational velocity (BRV) were selected as input features. Four machine learning models—support vector regression (SVR), stochastic gradient descent (SGD), K-nearest neighbors (KNN), and decision tree (DT)—were trained to predict PR. A biogeography-based optimization (BBO) algorithm was employed to fine-tune hyperparameters and enhance model accuracy. Additionally, a novel hybrid error index (HEI) was introduced to comprehensively evaluate model performance. Among all models, the DT achieved the best accuracy with an HEI of 0.3753, followed by KNN, SVR, and SGD. These findings demonstrate the potential of the DT model, combined with optimized learning and a robust dataset, to reliably predict penetration rate in rock-based engineering projects.
Open Access: Yes
Impact of recycling on polymer binder integrity in metal injection molding
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Metal Injection Molding (MIM) is a manufacturing process that integrates polymer binders with metal powders to produce high-precision components, offering both material efficiency and design flexibility. This study explores the recyclability of polymer-based feedstocks used in Metal Injection Molding, specifically evaluating how repeated recycling affects the structural integrity and thermal stability of polymer binders. Given the high cost of raw materials in MIM, optimizing recyclability is essential for reducing production costs and minimizing material waste, contributing to more sustainable manufacturing practices. To assess the feasibility of repeated material reuse, the study systematically subjected molded specimens to grinding and reinjection molding over eight consecutive cycles. The effects of reprocessing were analyzed using melt flow index (MFI) measurements, differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA) to track changes in polymer viscosity, thermal behavior, and degradation. The results indicate that wax precipitation during processing alters polymer viscosity and thermal stability, leading to gradual material property changes over successive recycling cycles. However, polymer degradation-induced viscosity reduction counterbalances these effects up to the fourth cycle, ensuring processability within standard injection molding conditions. The findings underscore the significance of analytical techniques in evaluating polymer binder integrity during multi-cycle reuse. Melt flow index (MFI) initially increased, peaking at the fourth recycling cycle, and then declined, while linear shrinkage rose by approximately 3% within the first three cycles before stabilizing. SEM–EDS analyses indicated around a 20% wax loss after multiple recycling cycles, significantly influencing binder rheology. Polymer binders can thus be successfully recycled up to four times while maintaining acceptable thermal and rheological properties, supporting resource-efficient and sustainable manufacturing strategies in MIM production.
Open Access: Yes
Exploring public discourse on green hydrogen via YouTube comments: A comparative sentiment analysis using VADER and ChatGPT
Publication Name: Economic Analysis and Policy
Publication Date: 2025-12-01
Volume: 88
Issue: Unknown
Page Range: 2012-2030
Description:
This study investigates public attitudes toward green hydrogen (GH) by analyzing YouTube comments through sentiment analysis and topic modelling. Unlike previous research that situates hydrogen within broader climate or energy debates and focuses on platforms such as Twitter or Bilibili, this work examines GH as a standalone topic and leverages YouTube's longer, context-rich comments to capture richer public discourse. Comments were collected via the YouTube API (Application Programming Interface) from a curated set of videos and analyzed for sentiment using both the rule-based VADER (Valence Aware Dictionary and sEntiment Reasoner) and the generative model ChatGPT-3.5, enabling a qualitative comparison of their performance. Latent Dirichlet Allocation (LDA) was then applied to identify major discussion themes, which were subsequently linked to sentiment trends. The results indicate that ChatGPT-3.5 outperforms VADER in interpreting sarcasm, slang, emoticons, and mixed sentiments. Topic modelling revealed eight key themes, including skepticism about institutional barriers and costs, optimism regarding GH's role in hard-to-decarbonize sectors, comparisons with nuclear energy and electric vehicles, and concerns about environmental and technical challenges. Overall, the study enhances understanding of online public discourse on GH by demonstrating how advanced sentiment analysis tools, combined with topic modelling, can generate deeper insights to inform strategies that better integrate public perceptions with the economic and policy conditions of GH deployment.
Open Access: Yes
