bluuebunny/crossref_metadata_2025_split huggingface dataset:
Acaricidal and repellent activities of essential oils against the two-spotted spider mite
Authors: Loyola-Zapata Patricia , Silva-Aguayo Gonzalo , Rodríguez-Maciel J. C. , Cabrera-Barjas Gustavo , Figueroa-Cares Inés , Oyarce-Meléndez Gabriela 
Published: November 2024

Abstract

Tetranychus urticae Koch, is a harmful pest and its control is usually performed with synthetic acaricides. However, T. urticae has developed resistance. Hence, control alternatives such as essential oils are needed. This research aimed to assess, under laboratory conditions, the acaricidal and repellent activities of essential oils of Eucalyptus globulus, Salvia officinalis, Mentha × piperita, Thymus vulgaris, Foeniculum vulgare and Dysphania ambrosioides against T. urticae. The main components were 1,8-cineole (92.57%) in E. globulus, thujone (25.44%) in S. officinalis, menthol (63.52%) in M. piperita, thymol (37.91%) in Thymus vulgaris, anethol (45.44%) in F. vulgare and ascaridole (33.23%) in D. ambrosioides. Thymus vulgaris achieved the highest mortality by contact toxicity with a CL50 = 1.71 µL mL− 1 water, but its contact toxicity did not significantly differ from that of F. vulgare (CL50 = 1.80 µL mL− 1), D. ambrosioides (CL50 = 1.99 µL mL− 1 water), M. piperita (CL50 = 2.73 µL mL− 1 water) or S. officinalis (CL50 = 2.82 µL mL− 1 water). In the fumigant bioassay, D. ambrosioides was the most toxic treatment, with an LC50 = 1.83 µL L− 1 air, although it did not significantly differ from that of M. piperita (LC50 = 2.10 µL L− 1 air) or T. vulgaris (LC50 = 2.58 µL L− 1 air). All the treatments resulted in at least 30% repellent activity at the lowest concentration assessed (5.0%), and as the concentration of essential oil increased, the repellency potency also increased. Thymus vulgaris, Dysphania ambrosioides and Mentha × piperita essential oils are promising for managing Tetranychus urticae.
Characterizing Ionospheric TEC Gradients over Nigerian Longitudes During Increasing Phase of Solar Cycle #24
Authors: Abe Oladipo E. , Olawuyi J. S. , Fakomiti M. O. , Ojo Olakunle L. , Olumodimu O. O. , Abimbola O. A. , Akinyemi G. A , Chukwu Jonathan O. 
Published: November 2024

Abstract

Over the last decades, there has been growing interest in the characterization of ionospheric total electron content (TEC) for the effective planning, implementation and optimum performance of the satellite-based technology applications. This paper essentially examines the ionospheric TEC gradients using Global Navigation Satellite Systems (GNSS) data during the increasing phase of solar cycle 24, years 2011–2013. The data used were mined from the African GNSS Network of stations (AFREF, www.afrefdata.org) and Nigeria GNSS ground-based Network Observation Stations (NIGNET) located within the belt of the Equatorial Ionization Anomaly (EIA) region. The ionospheric TEC gradients were analysed during distinct geomagnetic conditions. The monthly mean results have shown that ionospheric TEC gradients are dominant during the disturbed conditions when the ionospheric storm has positive phase leading to an increase in the ionization, while the ionospheric TEC gradients are lower during the quiet conditions when the ionospheric storm is in the negative phase and this is evidenced in the decreased ionization at the disturbed conditions. The study records that the magnitude of ionospheric gradients increases with solar activities and could be as high as 300 mm/km within the Nigeria longitudes at the high of solar activity and the conjugate distance may not solely be responsible for the magnitude ionospheric gradient observe spatially. The results of the bounding errors revealed that the ionospheric TEC gradients are not well bounded over the Nigerian longitudes during moderate to high solar activities, which could pose a serious challenge to the seamless GNSS critical applications in terms of navigation and positioning.
Horses and ADHD: The ASTride intervention for Cognitive and Emotional Growth
Authors: Helmer Anne , Delore Elaina , Bart Orit 
Published: November 2024

Abstract

Background
This study aimed to evaluate the efficacy of the Attention Skill Training (ASTride) protocol, an Equine-Assisted Occupational Therapy (EAOT) intervention, for children diagnosed with Attention Deficit/Hyperactivity Disorder (ADHD) in enhancing and promoting cognitive and emotional aspects including executive functions, self-efficacy, anxiety and hope perception.
Methods
A prospective cohort study with an Interrupted Time-Series design was conducted. Fifty participants (mean age = 9.51 years, SD = 1.52) were assessed at four time points: baseline, pre-test (following a 12-week waiting period), post-test (after 12 weeks of intervention), and after three-month follow-up. The intervention was administered by a licensed equine assisted occupational therapists, and assessments were conducted by a blinded occupational therapist. Measures included The Behavior Rating Inventory of Executive Function (BRIEF) for executive functions, and the Hope, Child Anxiety Related Emotional Disorders (SCARED), and The New General Self-Efficacy Scale (NGSE) questionnaires for emotional factors.
Results
Significant improvements were found in executive functions, hope perception, and self-efficacy. Anxiety levels significantly decreased post-intervention.
Conclusion
The ASTride intervention showed efficacy in improving executive functions, hope, and self-efficacy, while reducing anxiety in children with ADHD, supporting its potential as a comprehensive therapeutic approach.
Trial registration
The research is registered at Clinical Trials.gov, identifier number NCT05869253, first registration at 22/5/2023.
Face Recognition Based on Deep Learning Convolutional Neural Network in Cloud Internet of Things Environment
Authors: Ghahremani Yaser , Maihami Vafa 
Published: November 2024

Abstract

Through the domain of remote medical engineering, the implementation of online facial recognition and intelligent routing has facilitated significant transformations across numerous sectors. In order to harness these functionalities, it is imperative that algorithms are both optimized and rendered efficient. Facial recognition within the contexts of the Internet of Things can be leveraged in domains such as security, healthcare, and surveillance. Nonetheless, in uncontrolled environments, the accuracy of facial recognition typically does not reach its full potential due to factors such as lighting conditions, angles of view, and the extent to which the face is occluded. For applications in healthcare and real-time systems, it is critical to achieve facial recognition that operates instantaneously and with high precision. The present study proposes the adoption of a deep learning-based algorithm structured in a tree configuration to sustain the actual accuracy of facial recognition in uncontrolled scenarios. Within such environments, this algorithm enhances recognition accuracy by partitioning the input volume into numerous smaller segments. The sequence in which the volume is divided is determined by the outputs of the deep learning algorithm, leading to a reduction in error rates in subsequent feedback iterations. This methodology supports deep learning processes and enhances the speed of algorithmic iteration cycles. The findings suggest that the proposed algorithm, which holds utility for real-time applications in security, healthcare, and surveillance, yields superior facial recognition accuracy in uncontrolled environments when compared to alternative methodologies.
A morphological, morphometric and geochemical characterization of the El Jobo projectile points – diversity and significance in early human populations across the Americas
Authors: Vargas Diego , Verger Kévin Le , Wiesenberg Guido L.B. , von Büren Carlos , Carrillo-Briceño Jorge Domingo , Jaimes Arturo , R. Sánchez-Villagra Marcelo 
Published: November 2024

Abstract

The El Jobo projectile points represent a distinctive ballistic innovation of Late Pleistocene Neotropical groups. This technology, characterized by its fusiform/lanceolate shape, has been recorded mainly in northwestern Venezuela in a variety of geographical areas and in association with megafauna killing/butchering sites. To address its significance and possible continental relationships, broader characterization and analysis are needed. Four consecutive approaches were conducted on a representative sample of El Jobo projectile points: A morphological classification, a technological approximation, an outline-based geometric morphometric analysis, and an elemental composition analysis. Six morphological types were recognized, for which no major differences in manufacture techniques were observed. Mainly collateral and irregular flaking patterns were identified, also recording new basal variability, including fluting, fluting-like and reconfiguration techniques. Due to fragmentation of the material, only the four most representative morphological types could be subjected to morphometric analysis. Elliptic Fourier and Principal Component analyses identified at least three clusters, with width variation mainly distinguishing their shapes, and statistical tests determined significative differences between the main morphological types. The elemental analysis revealed the main use of quarzitic rocks, with variations in elemental composition indicative of diverse sources. The observed variability and cumulative evidence of El Jobo projectile points suggests their adaptation to diverse hunting strategies and leads us to consider long-distance connections with other projectile point technologies across the continent.
The phase-specific dynamics in gene expression of Salmonella Typhimurium during Acanthamoeba castellanii infection
Authors: Balkin Alexander , Cherkasov Sergey , Gogolev Yuri , Plotnikov Andrey 
Published: November 2024

Abstract

Being facultative intracellular pathogens, the bacterium often is found in the environment. In natural habitats Salmonella are able to survive and multiply inside free-living protists that support preservation and distribution of the pathogen, its virulence and resistance to antimicrobial agents. At the same time, the expression profile of Salmonella genes in the eukaryotic cells has been shown not to be stable, but changes dramatically according to sequential stages of infection. Previously, we had described the gene expression profile of S. enterica serovar Typhimurium 14028S cells at the early stage of their interaction with Acanthamoeba castellanii. In this study, we have assessed the phase-specific dynamics in expression of key clusters and functional groups of S. Typhimurium 14028S genes simultaneously with enumeration viable bacteria in co-culture. The early stage of invasion is characterized by a maximum response to oxidative stress, and is accompanied by activation of SPI-1 and chemotaxis genes that are necessary for successful internalization into the host cell. At the second stage increase in expression of virulence (SPI-2 and SPI-3) is observed, accompanied with a maximum expression of iron uptake genes and lysozyme inhibitors. At the third stage of persistence, downregulation of carbon metabolism and oxidative stress response genes, as well as a decrease in the expression of all other genes is observed that may be an evidence of adaptation of Salmonella to intracellular conditions. The obtained results might be useful further for search of factors reducing persistence of pathogens like Salmonella in the environment.
Molecular detection of Wolbachia sp. and cytoplasmic incompatibility factors (CifA/B) in wild caught mosquitoes in Côte d'Ivoire
Authors: YAO Raymond Karlhis , GOMGNIMBOU Michel Kiréopori , COULIBALY Ibrahima Zanakoungo , ESSOH Christiane You , Traoré Issouf , AMARA Miriam Félicité , AKO Berenger Aristide , DIABATE Abdoulaye , BILGO Etienne 
Published: November 2024

Abstract

Background: Wolbachia is an endosymbiont bacterium known to stimulate host immunity against arboviruses and protozoa. Côte d'Ivoire is in a malaria-endemic region, and has experienced several dengue epidemics in recent decades as well. In order to help reduce the transmission of pathogens by mosquito vectors, we studied the prevalence of Wolbachia and the distribution of Cif genes in different mosquito species caught in the wild in Cote d’Ivoire.
Methods and Results:Mosquitoes of the genera Anophele, Aedes, Culex and Mansonia were captured in five cities. Mosquitoes were collected at larval stage in breeding sites and adults were captured using BG sentinel traps. The mosquitoes were identified morphologically and Wolbachia and cytoplasmic incompatibility factors were screened using qPCR targeting the 16s rRNA gene and the CifA,B genes. A total of 518 mosquito samples belonging to 15 species and 4 genera were examined. Sixty percent of the species were infected with Wolbachia. The three medically important mosquito species Aedes aegypti, Anopheles gambiae s.l. and Culex quinquefasciatus had a prevalence of 12.84%, 13.46% and 72.64% respectively. The Wolbachia strains infecting the different mosquito species of the genus Culexencoded 98.46% for the CifA gene and 88.46 % for the CifB gene.
Conclusion: The presence of Wolbachia and CifA,B genes in mosquitoes of different species in Côte d'Ivoire offer a promising opportunity to reduce the competence of mosquito vectors. Characterization of Wolbachia strains and cytoplasmic incompatibility factors will provide a better understanding of these endosymbionts, enabling the development of vector control strategies.
Performance of Generative AI Used by Chatgpt and Attention Mechanism for Remote Sensing Image Captioning (RSIC): a Comparative and Comprehensive Analysis
Authors: A Dhinesh , P Sumathy , A Vadivel 
Published: November 2024

Abstract

Remote Sensing Image Captioning (RSIC) is a challenging research area that generates descriptive captions of Remote Sensing Images (RSI). Recent advances in Deep Learning have contributed in RSIC and improved the capability to generate captions. In this paper, we present a comparative analysis of two prominent approaches: Channel Attention Mechanism and Generative AI. The channel attention mechanism emphasises on learning channel-wise with encoding-decoding, feature extraction and channel attention mechanism. On the other hand, Generative AI, particularly models like GPT, leverages large-scale pretraining to diverse datasets for generates coherent and contextually relevant captions. It extracts vision-based feature and contains auto-tokenizer within the Vi Encoder-Decoder. The objective of this study is to analytically understand the performance of Generative AI and other well-known schemes. The performance is evaluated in terms of caption quality, relevance, diversity, etc., and BLEU, METEOR, ROUGE and CIDEr are used as metrics in addition to Precision, Recall, F1-Score, F2-Score and Accuracy. We have used RSICD datasets and conducted experiments using Channel Attention Mechanism and Generative AI models. The experiment and analysis shed light on the advantages and disadvantages of each approach. It is observed that Generative AI performs well in terms of Precision, Recall, F1-Score, F2-Score and Accuracy. However, It has failed in describing the quantity of objects present in RSI and their Coordinates.
EHHO-EL: Hybrid Method for Software defect Detection in Software Product Lines using Extended Harris Hawks Optimization and Ensemble Learning
Authors: khameneh Mehdi Habibzadeh , Nabiollahi-Najafabadi Akbar , Tavoli Reza , Rastegari Hamid 
Published: November 2024

Abstract

Software Product Line (SPL) aims to reduce development costs and time while improving quality, but the complexity and involvement of multiple design teams often lead to defects and delays. Detecting and resolving defects in large-scale industrial SPLs remains a significant research area. This study proposes a hybrid approach that combines the Harris Hawks Optimization (HHO) algorithm with stacking-based ensemble learning for defect detection in SPLs. Enhanced by the Chaos Optimization Algorithm (COA) to avoid local optima and improve accuracy, the approach is evaluated on two datasets, LVAT and NASA, This study incorporates four datasets from each of these repositories. The experiment results show that the proposed method achieves detection accuracy rates of 92.7%, 91.1%, 96.3%, 98.4% for the LTS1, LTM2, LTL3, LTV4 and 97.91%, 99.01%, 94.21%, 90.93% for the CM1, JM1, KC1, PC1. Statistical tests confirm that this method offers superior accuracy and faster convergence compared to existing methods.
WITHDRAWN: The Impact of Artificial Intelligence-Powered Writing Assistance Systems on Metacognitive Writing Strategies
Authors: Berk Ece Hatice , Aydin Selami 
Published: November 2024

Abstract

While writing plays an important role in the English as a foreign language (EFL) context as it is a tool to express ideas, apply what has been learned, stimulate higher-order skills, and be a measurement of writing proficiency, metacognitive writing strategies involve planning, monitoring, and evaluating stages, learners can improve writing performance and encourage self-regulation so that learners can control their learning process. As a facilitator for self-directed learning, AI-powered systems in writing may help learners manage their learning process and empower their metacognitive skills for writing. However, there has been a limited number of studies carried out on the impact of AI-powered writing assistance systems on metacognitive writing strategies in the EFL learning context. This study aims to examine the possible effects of integrating AI-powered writing tools on EFL learners’ metacognitive writing strategies. It also compares AI-powered tools and conventional activities regarding the use of metacognitive strategies. In a quasi-experimental design, the study was conducted with 40 EFL learners. The study used a background questionnaire and the Metacognitive Writing Strategies Scale to collect data. The findings revealed that there was no significant difference between the use of traditional activities and AI-powered systems in terms of using metacognitive writing strategies. Therefore, it is recommended that the advantages and disadvantages of both means should be taken into consideration and that AI systems should be used as a supplementary tool to provide interaction, and immediate suggestions, and to address individual learning needs to enhance metacognitive strategy use.
Perception of agroindustrial engineering students on global innovation indices
Authors: -Tarazona Vitelio Asencios , Medrano Segundo Edilberto Vergara- , Salazar Edilberto Valencia – , –Yance Iris Olivia Ruíz , Díaz-Gonzales Eudualdo 
Published: November 2024

Abstract

The objective was to evaluate the level of conceptual perception of higher education students on the Global Innovation Indices. The sample consisted of 77 agro-industrial engineering students from UNIA, divided into three groups: group I students of cycle III; group II, cycle VI; and group III, cycles VII and X, which were selected by intentional non-probabilistic sampling. The Kruskal Wallis non-parametric test was extracted, which showed the non-significance of the differences between groups and within each group. 57.4% of students presented a perception in the values that they do not understand and do not give importance to the Global Innovation Indices, compared to 42.7% who indicate the values do understand and give importance to it. No significant statistical difference was demonstrated between the groups and within each reference group at the levels of perception of the Global Innovation Indices.
Global warming facilitated environmental change effects on CO2 releasing microbes in Antarctic sediments
Authors: Aromokeye David A. , Willis-Poratti Graciana , Wunder Lea C. , Yin Xiuran , Richter-Heitmann Tim , Otersen Carolin , Maeke Mara D. , Henkel Susann , Neder Camila , Vázquez Susana , Elvert Marcus , Cormack Walter Mac , Friedrich Michael W. 
Published: November 2024

Abstract

Rapid melting of the Western Antarctic Peninsula (WAP) glaciers is a compelling piece of evidence of how climate change affects our planet. This study investigated the impact of global warming-facilitated environmental change on microbial community structure and function by subjecting sediments sampled near the Fourcade Glacier in Potter Cove, WAP, to a temperature gradient and supply of metabolic nutrients relevant for the fate of carbon in marine ecosystems. We found that (i) temperature as a key environmental change driver will significantly impact microbial community structure, but ecological functions supported by fresh supply of nutrients from glacial meltwater will prevail; (ii) keystone species responsible for specialized functions are metabolically flexible, persisting from 2°C to 25°C; and (iii) in addition to keystone species, global warming will activate certain hitherto inactive but endogenous microorganisms in response to either changes in temperature or nutrient flux to sustain ecosystem functions. Our study presents evidence of sediment microbiome resilience in response to strong temperature or nutrient flux shifts, thereby adding another layer of evidence of nature’s adaptability to global warming.
A Systematic Review About Moral Implications in Autonomous Vehicles Between 2005 and 2023
Authors: Davar Mohamad Mahdi , Khojasteh MM , Zaemi Mahdi , Mamourian Shahrzad , Jalali Hanieh 
Published: November 2024

Abstract

Background: With the expansion of Artificial Intelligence (AI) in the contemporary era and the emergence of autonomous vehicles as a result, different ethical challenges have also arisen. Further, these challenges can be answered and investigated with different ethical and moral approaches. Therefore, we will find that this is a significant issue and also reviewing the researches that have been done in this regard is also of great importance.
Methods: Using the four-steps method to conduct a systematic review, we first extracted related documents by searching for relevant keywords in the Web of Science (WoS) databases, and also conducted a systematic review using the VOSviewer (version 1.6.20).
Results: After extracting these documents and using the VOSviewer, active countries in this field have been examined in terms of the number of documents and citations, active journals, active publishers, documents in terms of the number of citations, and also active authors in this field, as well as keywords and terms.
Comparative Analysis of Linear Regression, Decision Tree, Xgboost, Catboost, and Artificial Neural Network Machine Learning Algorithms for
Authors: Agaji Augustine Alul , Igah Godspower Charles 
Published: November 2024

Abstract

The compressive strength of concrete was predicted using various machine learning algorithms: Linear Regression (LR), Decision Tree (DT), Xgboost, Catboost, and Artificial Neural Network (ANN), to determine the best performant for concrete compressive strength prediction. From the analysis report, the best-performing model was the Catboost model, followed by the Xgboost model, with mean absolute errors of 2.72 N/mm2 and 3 N/mm2 respectively. The least performing models for concrete prediction from the research are the LR and the ANN with very high mean absolute errors of 7.75 N/mm2 and 4.9 N/mm2 respectively. The dataset from which the models were built, and insights drawn was obtained from kaggle.com.