bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diversity of sand flies and Leishmania parasites in an active cutaneous leishmaniasis transmission site in rural Guatemala, 2022
Authors: Lopez Yaimie , Casas-Sanchez Aitor , Arana Byron , Rizzo Nidia , Duran Erick , Padilla Norma , Vega Andrea De La , Bustamante Esteban , Acosta-Serrano Álvaro , Mendizabal-Cabrera Renata 
Published: March 2024

Abstract
Background
Cutaneous leishmaniasis (CL) is a parasitic vector borne disease endemic in 90 countries, including Guatemala. Despite the endemicity of the disease in the country, the most important factors that contribute to its transmission remain unknown. To address these knowledge gaps, we characterized the sand fly populations and Leishmania parasites circulating in patients and sand flies in an endemic community in Guatemala.
Methods
From March to August 2022, we visited the households of 23 patients recently diagnosed with CL to collect sand flies in three environments: indoors, outdoors in animal sheds and in the surrounding forest. The sand fly species were identified using end-point PCR targeting the cytochrome C gene followed by sequencing, and screened for Leishmania DNA by heat-shock protein 70 gene PCR and sequencing. Using the same methodology on lesion tissue smear material from patients, we identified the species of parasites causing infections in the community.
Results
We collected 93 sand flies (79% females) of at least five species, three of which have been previously associated with Leishmania transmission: Nyssomyia ylephiletor, Bichromomyia olmeca and Lutzomyia cruciata. The highly anthropophilic Ny. ylpehiletor, of which only females were captured, was the most common species (28%) and was primarily collected indoors (73.1%). Four female sand flies were positive for Leishmania DNA, and one of them (Ny. ylephiletor) was captured indoors. The parasite complex L. guyanensis (L. guyanensis and L. panamensis) was found in both sand flies and patients’ samples. In patients we also detected L. braziliensis and a cutaneous form of L. infantum. Three engorged Ny. ylephiletor were screened for blood meal identification, finding humans and domestic pig’s blood.
Conclusions
This is the first report in Guatemala of the presence of L. guyanensis in patients, and the first characterization of Leishmania infections in humans and sand flies that concur in time and geographical area. The capture of anthropophilic sand flies indoors, some with Leishmania DNA, could suggest indoor transmission of CL, but further studies are needed to confirm our results. These findings are key to local health authorities for decision-making on the appropriate prevention measures, such as the use of insecticide treated bed nets.
Bioremediation Potential of Soil Lead (Pb) Tolerant Fungi isolated from Owino Uhuru, a Suburb of Mombasa County, Kenya
Authors: Panyako Benjamin Wesonga , Wacira Teresia Nyambura , Omolo Ted David , Gicharu Gibson Kamau , Njeru Ezekiel Mugendi 
Published: March 2024

Abstract

A lead (Pb) smelter plant located in Owino Uhuru, a suburb of Mombasa County, caused lead poisoning and lifelong health effects. The Government of Kenya closed down the plant after reports of contamination. This study collected 24 soil samples from Owino Uhuru and analyzed them for lead levels using Microplasma Atomic Emission Spectrometry. The study also isolated and identified seven strains of fungi from the lead-contaminated soil and screened them for their ability to absorb lead. The genomic DNA of the fungal isolates was identified using ITS sequences of the rDNA. Blastn analysis indicated that the ITS gene sequences of the isolates had similarities of > 100% with known sequences in the nucleotide sequence database. These fungi were identified as Aspergillus welwitschiae, Aspergillus oryzae, Aspergillus piperis, Aspergillus austwickii, Aspergillus aflatoxiformans and Aspergillus tubingensis. The fungal isolates were tested for their ability to absorb lead at concentrations of 1.0 ppm, 0.8 ppm, 0.6 ppm, 0.4 ppm, and 0.2 ppm. The soil sample closest to the battery recycling plant contained more lead than those collected further away. Soil sample AU06, taken from the area bordering the wall of the lead smelter plant, had the highest lead concentration compared to all the samples. Aspergillus welwitschiae (GRY1) showed a higher potential for lead biosorption of 0.35 ± 0.1 ppm at 0.6 ppm lead concentration compared to other Aspergillus species. The study suggests using these fungal isolates for the removal and bio-treatment of lead-contaminated and polluted environments.
A-RepVGG: Research on Classification Algorithms based on Deep Learning and Wood CT Images
Authors: Zheng Zhishuai , Ge Zhedong , Yang Xiaoxia , Liu Xiaotong , Qin Lipeng , Wang Xu , Zhou Yucheng 
Published: March 2024

Abstract

To address the issue of limited expressive ability and performance degradation of the model caused by the limited depth and width of the CNN because of low computational overhead of lightweight convolutional neural networks, this paper introduces a classification method called A-RepVGG for wood CT images. A-RepVGG aims to enhance the model's classification accuracy in terms of wood microstructure by increasing the model complexity without increasing the depth and width of the network. The method utilizes adaptive convolution to dynamically aggregate convolution kernels based on the input image. This allows the convolution kernels to have optimal receptive fields on different layers of features, enabling a more comprehensive extraction of information, such as wood texture, tubular pore distribution, and cell arrangement. Additionally, a multi-scale null attention mechanism is incorporated into the network, multi-layer convolution is employed to extract feature maps of different scales and weighted fusion is adopted to emphasize important feature regions. This effectively captures both local and global information of the image. Finally, the ELU activation function is introduced to ensure that the feature information can be properly output in the negative half-axis, thereby facilitating a more thorough extraction of wood feature information and improving the model's classification accuracy. The study aimed to classify 20 species of wood cross-section microscopic images. The findings revealed that the A-RepVGG model outperformed other existing wood image classification models, such as ResNet, ResNeSt, and ViT. The A-RepVGG model achieved an impressive accuracy of 99.50% on the test set and 99.20% on the validation set. This model incorporates adaptive convolution and attention mechanisms, which effectively enhance the classification accuracy of wood microscopic images. These results highlight the potential of deep learning in automatic classification of wood species and provide valuable insights for micro-level wood classification.
Chemo-mechanical ageing of paper: effect of acidity, moisture and micro-structural features
Authors: Sadr Amir Parsa , Maraghechi Siavash , Suiker Akke S.J. , Bosco Emanuela 
Published: March 2024

Abstract
A multi-scale modeling framework is proposed for the prediction of the chemo-mechanical degradation of paper, with the particular aim of uncovering the key factors affecting the degradation process. Paper is represented as a two-dimensional, periodic repetition of a fibrous network unit cell, where the fibers are characterized by a moisture-dependent chemo-hygro-mechanical constitutive behavior. The degradation of paper occurs primarily as a result of the hydrolysis of cellulose, which causes a reduction of the degree of polymerization and a consequent decrease of the effective mechanical properties, ultimately leading to fiber embrittlement and a loss of material integrity. The interplay between the acidity of the paper, the ambient environmental conditions, and its chemo-mechanical degradation behaviour is a complex process. In the model, these interactions are accounted for by determining the coupled temporal evolution of the degree of polymerization, the acidity of the paper, and the moisture content, from which the time-dependent tensile strength of the paper is calculated. The internal stresses developing in the fibrous network under a change in moisture content lead to brittle fiber fracture once they reach the fiber tensile strength. The successive breakage of individual fibers results in damage development in the fibrous network, altering its effective constitutive properties. The temporal evolution of the effective hygro-mechanical properties of the fibrous network is calculated by employing asymptotic homogenization. For obtaining accurate model input, the strength and stiffness properties of individual fibers and the degree of polymerization of paper samples are measured at different ageing times by carrying out dedicated experiments. Subsequently, a series of numerical simulations is performed to analyze the chemo-mechanical degradation process of paper, highlighting the influence of the time-evolving acidity and moisture content. The numerical study further considers the effects of micro-structural features (i.e., the anisotropy of the fibrous network orientation and the fiber longitudinal elastic modulus) on the macroscopic degradation response of paper. The results of this work may help conservators of cultural heritage institutions determining optimal environmental conditions to limit or delay the time-dependent degradation of valuable historical paper artefacts.
Novel Approach for Addressing Data Imbalance
Authors: Patel Vaibhavi , Bhavsar Hetal 
Published: March 2024

Abstract
Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions towards the majority class, resulting in suboptimal performance for minority classes. In our research, we propose a novel approach to address this challenge specifically tailored for Support Vector Machines (SVM), a well-established family of learning algorithms. Our method leverages a kernel trick to enhance the SVM’s classification capabilities on imbalanced datasets named as KTI. It aims to streamline the classification process by incorporating adaptive data transformations within the algorithm itself, offering a more efficient and integrated solution for handling imbalanced data. Experimental evaluations conducted on diverse real-world datasets demonstrate the superior performance of our proposed strategy compared to existing methods , showcasing its potential for practical applications in classification tasks with skewed class distributions.
Seismic Resilience of G+6 Irregular RC Building: A Fragility Analysis
Authors: Mokashi Vishwajeet M. , Jadhav H. S. 
Published: March 2024

Abstract
Fragility analysis is essential for assessing structural vulnerability during natural hazards like earthquakes and floods. It guides risk mitigation, enhances infrastructure resilience, and informs decision-making, emergency planning, and disaster recovery efforts. This manuscript aims to explore the fragility curves potential in assessing the structural systems performance, particularly those with high irregularity. The study employs a non-linear static analysis of a G+6 storey irregular structural frame designed in accordance with Indian standards using SAP2000. It demonstrates the use of fragility curves in assessing structural performance of a G+6 irregular reinforced concrete structure under earthquake forces. This manuscript underscores the importance of considering discrete damage probabilities and collapse risks in different directions for accurately assessing structural vulnerabilities. It reveals a higher collapse probability within the direction of push-y (44%) in contrast with the push x-direction (5%), indicating a greater susceptibility to damage under specific loading conditions. The study concludes that fragility curves offer a rational and consistent method for seismic performance assessment of structures with high irregularity. It also affirms the adequacy of buildings designed as per Indian standards in withstanding earthquake forces.
The Mediating Effect of Digital Literacy and Self-Regulation on the Relationship between Emotional Intelligence and Academic Stress among University Students: A Cross-Sectional Study.
Authors: Ibrahim Rasha Kadri , Sabbah Saher Al , Al-Jarrah Muhammed , Senior John , Almomani Jehad Ali , Darwish Amani , Albannay Faisal 
Published: March 2024

Abstract
Aim: To assess emotional intelligence and academic stress among healthcare students and investigate the mediating role of students’ digital literacy and self-regulation.
Background: Academic stress continues to be a prevalent issue affecting students' well-being and academic achievement in today's challenging educational environment. Furthermore, combining digital literacy and self-regulation enhances emotional intelligence, creating a holistic strategy called "digitally regulated emotional intelligence" to effectively reduce academic stress. This study emphasizes the significance of developing these abilities in educational settings to prepare students for success in a complex and technology-driven world.
Methods: A cross-sectional study was conducted to examine correlations and describe quantitative data. 240 students' data was collected in the 2023-24 academic year from Fatima college of Health sciences. Pearson's correlation coefficient has been applied to assess the associations between variables, and univariate linear regression was employed to investigate the connections between emotional intelligence and pertinent variables. The Hayes Process Model 4 macro was used to study how students' digital literacy and self-regulation mediate the relationship between emotional intelligence and academic stress.
Results: The emotional intelligence level, digital literacy, and self-regulation were high. Furthermore, the academic stress level was moderate. The mediation analysis revealed that the direct effect of emotional intelligence on academic stress in the presence of the mediators was significant (t=3.830*, p<0.001*). Hence, digital literacy partially mediated the relationship between emotional intelligence and academic stress. On the other hand, self-regulation had no mediation effect on the aforementioned relationship.
Conclusion: The study noted significant connections between study attributes, particularly showing the substantial relationship between emotional intelligence and academic stress. Higher emotional intelligence did not consistently reduce stress levels for moderately stressed students, suggesting that other factors may be influencing their stress levels. Furthermore, digital literacy was found to act as a mediator between emotional intelligence and academic stress, suggesting that proficient use of online resources could help reduce stress in students with advanced digital skills. Finally, self-regulation did not act as a mediator in the relationship between emotional intelligence and academic stress.
A Case of the Successful Application of Using a “Device to Remote Transfer of Information from a Drug to the Human body” in the Treatment of Multiple Sclerosis
Authors: Djumaeva Naylya 
Published: March 2024

Abstract
Laser therapy - is a type of physiotherapy based on the use of optical radiation, the source of which is a laser. The average powers of physiotherapeutic lasers are most often in the range of 1-100 mW, and they are used in almost all areas of modern medicine. The mechanism of action of laser therapy has not been entirely determined; however, according to the Spanish World Laser Therapeutic Association, it is recommended to use the optimal energy parameters of low-level laser radiation. We could not find data on using a “device for remote transfer of information from a drug to a human body,” where the light source is a laser device with an extremely low energy level in treating severe neurological disease - multiple sclerosis. This report describes the results of a study using a “device to remote transfer of information from a drug to the human body “ (Russian patent), where a laser with a power of up to 5 mV was used as a light source, and the drugs erythromycin and acyclovir were used to treat a 52-year-old Caucasian female musician with multiple sclerosis(MS). The applied exposure has led to the suppression of the activity of the disease, preventing the growth of neurological manifestations of the disease and reducing the appearance of demyelinating lesions in the patient’s brain with MS.
BiMass: An approximate batch-incremental clustering using mass-based dissimilarity
Authors: Bhattacharjee Panthadeep , Vidyapu Sandeep , Mitra Pinaki 
Published: March 2024

Abstract
Usage of non-incremental clustering algorithms for dynamic data may lead towards issues such as increased consumption of resources, higher latency and greater execution time. To address such bottlenecks, these algorithms need to be transformed into intelligent algorithms capable of processing the continuously changing data. MBSCAN is a robust non-incremental clustering algorithm that leverages the idea of data dependent dissimilarity to find clusters. An incremental version of MBSCAN viz. iMass was designed to handle single point insertions. However, with increase in number of updates, the iMass algorithm gradually loses its efficiency over MBSCAN due to repeated singular insertions on a larger dataset. We propose an alternative approximate incremental extension to MBSCAN known as BiMass (Batch incremental mass-based clustering to overcome such issues. Our BiMass algorithm facilitates addition of points in batch-mode while building the expensive components of MBSCAN incrementally. Experiments on real world and synthetic datasets showed that our BiMass algorithm achieved a maximum efficiency of around 5.77x over MBSCAN and 2.16x over iMass respectively at a cost of about 7.07% and 13.82% mean memory overhead.
Assessment Of Level Of Polychlorinated Biphenyls (PCBs) In Soil And Edible Crops From River Ala Floodplain In Akure
Authors: Abe Taiwo , Aiyesanmi Ademola Festus , Ojonukpe Omotoyosi Blessing 
Published: March 2024

Abstract
Flooding is one significant process that contributes to the movement of soil and sediments with the pollutants present in it along the river bank. With higher velocities of water, flooding can take contamination downstream. The soil, edible crops (rice and vegetables) samples were collected from Ala river floodplain in Ondo State and analyzed for their physico-chemical characteristics and level of Polychlorinated biphenyls present. The concentrations of nineteen (19) polychlorinated biphenyls (PCBs) were analyzed in soils collected at nine different sites along the floodplain and edible crops collected from six of the sites/ location using Gas Chromatography Mass Spectrometer (GC-MS) to determine the level of contamination of the floodplain and potential risks to the ecosystem and humans. The Ʃ-19 PCB concentrations in the floodplain soils varied between not detected (nd) -60 µg kg −1, n.d – 60 µg kg −1 in vegetables and not detected to 80 µg kg −1 in rice across different locations. Higher concentration values were recorded in rice which suggests plants uptake and bioaccumulation potentials.The concentration of di to tetra homologues are more predominant in the samples. The concentration is lower than the value of permissible limit 2mg kg-1 recommended by Canadian Soil Quality Guideline (CSQG) and United States Environmental Protection Agency (US EPA). Dioxin-like PCBs were not detected
Efficiency in Language Understanding and Generation: An Evaluation of Four Open-Source Large Language Models
Authors: Wong Siu Ming , Leung Ho , Wong Ka Yan 
Published: March 2024

Abstract
This study provides a comprehensive evaluation of the efficiency of Large Language Models (LLMs) in performing diverse language understanding and generation tasks. Through a systematic comparison of open-source models including GPT-Neo, Bloom, FLAN-T5, and Mistral-7B, the research explores their performance across widely recognized benchmarks such as GLUE, SuperGLUE, LAMBADA, and SQuAD. Our findings reveal significant variations in model accuracy, computational efficiency, scalability, and adaptability, underscoring the influence of model architecture and training paradigms on performance outcomes. The study identifies key factors contributing to the models' efficiency and offers insights into potential optimization strategies for enhancing their applicability in real-world NLP applications. By highlighting the strengths and limitations of current LLMs, this research contributes to the ongoing development of more effective, efficient, and adaptable language models, paving the way for future advancements in the field of natural language processing.
Post-Traumatic Growth in Survivors within a Close Radius to the Epicenter of the Beirut Blast
Authors: Chabbouh Alfred , Fahd Ghina , Chahoud Michele , Haddad Ramzi , Naja Wadih 
Published: March 2024

Abstract
Objectives: On August 4th, 2020, one of the biggest ammonium nitrate explosions took place in Beirut. This study is the first of its kind to examine post-traumatic growth and its relation to post-traumatic stress disorder after 6 months of the Beirut Blast.
Methods: This study targeted survivors defined as adults who were in areas affected by infrastructural damage at the time of the blast (within a 4.1 km radius from the epicenter). A total of 322 participants were recruited. Validated Arabic versions of the Post-Traumatic Growth Inventory (Short Form), Patient Health Questionnaire-9, and Post-Traumatic Stress Disorder Checklist Civilian version were employed.
Results: Of the participants, 75.8% had post-traumatic stress disorder and 79.8% had depression. Almost all survivors showed a degree of growth. Individuals with PTSD had 6 PTG degrees higher compared to the group without PTSD. Appreciation for life was the highest-scored PTG domain. There was no association between PTG and depression. Almost 5 degrees of higher PTG was seen in females, participants who had no children, and those who were <1.6 km at the time of the blast.
Discussion: Our findings are consistent with available literature on other disasters around the world. Unlike resilience, PTG is highly related to the development of PTSD. Possible common underpinnings for the development of PTSD and PTG are discussed. PTG may be a useful target for psychotherapeutic approaches.
Conclusion: Beirut blast survivors have an extremely high burden of psychiatric disorders that were left unaddressed after 6 months of the event. While most people had a degree of post-traumatic growth, this was especially higher in those with PTSD. Future efforts should focus on the clinical and social implications of PTG in Lebanon.