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Enhancing anomaly-based zero-day attack detection framework using CNN-driven feature extraction and OC-SVM
Plagiarism awareness in post-graduate students: case study
Abstract Plagiarism in academic contexts presents a substantial ethical challenge within the field of publishing, impacting a diverse array of individuals, including faculty members and students. The present study employs a descriptive approach and was conducted using a survey method. The research sample consisted of 291 post-graduate students, selected through a stratified random sampling technique. The research instrument was a questionnaire. The collected data were analyzed using an independent samples t-test, and correlation coefficients. The collected data were analyzed using independent samples t-test, ANOVA, and correlation coefficients within the SPSS software environment (version 26). The findings demonstrated that the overall average awareness of academic plagiarism is higher among male participants than female participants; however, this difference was not statistically significant. Additionally, the average awareness of academic plagiarism in PhD students was significantly higher than Master’s students. The analysis revealed a significant difference in the average awareness of academic plagiarism among students from their first to fourth year, showing that awareness increased with each subsequent academic year. Furthermore, a positive and significant relationship was identified between the average awareness of academic plagiarism and academic level. The study revealed that as students progressed through their academic years, their awareness of the concepts and instances of academic plagiarism increased.
Named entity recognition for coal mining machine assembly based on domain large language model
How game score shapes anticipatory swing decisions in baseball players: a functional MRI study
Comparative mechanical properties of polyester and polyethylene composites reinforced with sunflower seed hull fibers
Multifunctional cobalt ferrite nanoparticles with optimized cobalt doping for enhanced photo-Fenton catalysis, energy storage, and antifungal applications
Abstract Designing cost-effective, eco-friendly, and high-performance nanomaterials remains crucial for advancing next-generation technologies in environmental remediation, energy conversion, and pathogen control. Here, we report the glucose-assisted synthesis of cobalt ferrite (CoFe) nanocomposites (NCs) with tunable Co concentrations (0.025–0.15 M), and utilized for multifunctional applications. XRD confirmed the phase-pure cubic spinel ferrite, and FESEM showed a morphological transition from irregular aggregates to well-defined hexagonal shapes, at the optimal dopant concentration. Among the four batches, CoFe-3 (0.1 M Co) exhibited the highest surface area (74.98 m²/g) with the pore size (16.17 nm), contributing to versatility. CoFe-3 achieved 91.44% efficiency for crystal violet (CV) degradation within 40 min under photo-Fenton conditions and retained 81.73% efficiency after five cycles. Electrochemical studies displayed a high specific capacitance of 614 F/g at 1 A/g with stable cycling over 3000 GCD cycles, indicating battery-type behaviour with promising energy (15.04 Wh/kg) and power densities (217.16 W/kg). Moreover, CoFe-3 displayed significant antifungal activity against Fusarium solani , inducing morphological deformities in hyphae. These results highlight the potential of glucose-engineered CoFe NCs as robust materials for environmental remediation, energy storage, and antifungal applications.
Digit-tracking reveals curiosity-driven visual attention in macaque monkeys
Abstract We used digit-tracking, a touch-based method for assessing visual attention, to investigate spontaneous exploration in macaque monkeys. By engaging with degraded images on a touch-sensitive display, monkeys could uncover high-resolution portions through finger movements, allowing for natural and unrestricted interaction. Monkeys received juice rewards after touching a predetermined number of pixels, but no specific regions were targeted. Attention maps were generated from their interactions, along with data from human digit-tracking and monkey eye-tracking experiments. Direct comparisons across recording methods revealed that monkey digit-tracking attention maps were significantly correlated with both monkey eye-tracking and human digit-tracking maps, indicating shared patterns of visual exploration across species and devices. We further applied a saliency model and a Convolutional Neural Network (CNN) model to predict the empirical explorations. The correlation between model prediction maps and empirical attention maps indicated that monkeys focused non-randomly on information-rich regions, with the CNN model providing the most accurate predictions. These findings suggest that exploration was driven by intrinsic curiosity, beyond the extrinsic rewards for interaction. Digit-tracking offers a minimally invasive, portable alternative to eye-tracking, expanding research opportunities in visual cognition within ecologically valid settings.
Parasitically coupled 16-port massive MIMO antenna for mmWave applications
Abstract This article presents a 16-port compact (26.2 $$\times$$ 79 $$\times$$ 0.508 mm $$^3$$ ) massive MIMO antenna for mmWave applications. The proposed antenna consists of five triangular-shaped parasitic elements, one triangular-shaped feed-line fed element, and impedance matching network on the radiating plane, whereas defected ground and stubs are used on the ground plane. A systematic study is performed to select an optimal single element antenna (design) among design-1, design-2, design-3 an design-4. Thereafter, a 16-port massive MIMO antenna is implemented using a replica of a single element with modified ground plane structure. The proposed antenna resonates at 40 GHz frequency in mmWave band (37.5–49 GHz) with a 40 dB peak return loss. According to 3GPP bands n259 (39.5–43.5 GHz), n260 (37–40 GHz) and n262 (47.2–48.2 GHz), the resonating band (37.5–49 GHz) of the proposed antenna is part of the upper band of the mmWave spectrum and contains some of the most crucial licensed 5G and beyond wireless communications bands such as 39 GHz, 41 GHz and 47 GHz. Moreover, the proposed antenna achieves a high antenna gain of 17.9 dB and 17.2 dB at 39 GHz and 41 GHz, respectively. In addition, the specific absorption rate analysis of the proposed antenna is conducted by placing the antenna on the right hand of a human male model and 0.045 W/kg of SAR value at 40 GHz is achieved for the proposed antenna. An equivalent circuit of the intended antenna is proposed and simulated using the ADS tool and validated with the results simulated using HFSS.
Urbanization drives phenotypic differences but not asymmetry in the damselfly Ischnura elegans
Abstract Urbanization is a challenging environment and has a profound impact on organisms. Cities may be the source of important phenotypic changes and lead to developmental instability. To explore the effects of urbanization (percentage of impervious surface) on organisms, we collected ~ 800 adult damselflies Ischnura elegans across 22 ponds along an urbanization gradient. These ponds were also characterized by other environmental parameters: percentage of crops and of forested area, pH, salinity and water temperature. Adults were screened for traits related to body size and dispersion (wing morphology). We also investigated fluctuating asymmetry between right and left wings, a measure of developmental instability. We found that along an urbanization gradient, adult size and thorax length decreased, with urban individuals being smaller. The effects of urbanization were also sex-specific for femur length, with a stronger decrease in urban males. Wing shape was affected by urbanization in a sex-specific manner, with more important shape variation observed in females along the urbanization gradient. Urbanization did not increase fluctuating asymmetry, which was rather pond-specific. Our results provide evidence that urbanization shapes adult phenotype and may ultimately influence dispersal ability. Urbanization did not increase developmental instability, suggesting that some organisms may overcome some city stress.
Riccati-based analytical framework for solving the potential Korteweg-de Vries equation
Combined impact of jet stream and turbulence on long-term trans-oceanic flight routes over North Atlantic Ocean using ERA5 reanalysis
Anthelmintic medication with focus on first-trimester exposure: an evaluation of pregnancy outcomes based on the Embryotox cohort
Abstract The present study aims to contribute to the available experience on the systemic use of five different anthelmintics during pregnancy (albendazole, mebendazole, praziquantel, pyrantel and pyrvinium). Particular focus was placed on the occurrence of major birth defects and pregnancy loss following first-trimester exposure. As the assessed agents differ considerably in terms of their chemical structure and mechanism of action, they were analyzed separately. A total of 282 pregnancies with exposure to the study medication were recorded in the Embryotox database (January 1, 2000 – February 28, 2023) and analyzed descriptively. Among 75 live-born infants with first-trimester exposure to mebendazole, five major malformations were reported, three of which were heart defects. Of the 21 live-born infants exposed to pyrantel in the first trimester, two were affected by aplasia cutis congenita of the scalp, one of whom was co-exposed to thiamazole. Among 47 live-born children with first-trimester pyrvinium exposure, one major birth defect was reported. No major malformations were observed among 4 and 10 live-born children exposed to praziquantel and albendazole, respectively. Given the limitations, the findings should be interpreted as descriptive observations and possible signals only. Further investigations with larger cohorts are required.
A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer’s disease
Abstract We previously proposed an MRI-based machine learning model to describe the mesoscopic architecture of the human brain to aid in classifying subjects as having non-AD related pathology (nADrp) or AD related pathology (ADrp), including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The method, developed on data from patients scanned at 1.5T showed high performance, but did not generalise well to scans obtained from 3T MRI. In the current work we overcome the problem and extend the approach to patients scanned longitudinally, and at different field strengths. Retrospective T1-MRI data from 1592 subjects scanned at 3T were included to develop the machine learning models. Three additional longitudinal datasets (n = 211) at different magnetic field strengths—1.5 and 3T—were adopted to evaluate the models. Radiomic features were extracted from each brain region. A logistic regression method with least absolute shrinkage and selection operator (LASSO) model selection was employed to classify nADrp from ADrp (classifier 1) or MCI from AD (classifier 2). Classifier 1 that discriminates nADrp from ADrp achieves high performance, with area under the curve (AUC) of the receiver operating characteristics (ROC) of 0.84 in the independent hold-out cross-sectional dataset. High performance was also seen in external testing datasets for classifier 1 (AUC of 0.70 to 0.96). Classifier 2 that discriminates MCI from AD achieves AUC of 0.79 in the independent hold-out dataset and moderate to good performance in the external testing datasets (AUC of 0.56 to 0.93). The new data analysis methods, trained on 3T data, demonstrate potential for aiding AD early detection and disease progression on both 3T and 1.5T scanners.
Construct validation of a portable virtual reality simulator for $$30^\circ$$ laparoscopic camera navigation via machine learning and latent behavioral modeling
Association between methylated fatty acids and early pregnancy outcomes in dairy cows: a plasma GC‒MS assay
Symptom-linked disruption of the vaginal Lactobacillus-IL-2/IFN-γ axis in genitourinary syndrome of menopause related symptoms
Syphilis epidemiology in Jordan: prevalence, incidence, and seroreversion from 15 years of laboratory-based data
Investigation of the molecular network underlying PET-MPs-induced inflammatory bowel disease via integrated machine learning and molecular docking approaches
Abstract Polyethylene terephthalate microplastics (PET-MPs), as environmental contaminants, have raised significant concerns due to their potential toxicity, leading to serious environmental pollution and health issues. However, their impact on inflammatory bowel disease (IBD) remains poorly elucidated. This study aims to investigate the potential molecular mechanisms linking PET-MPs to IBD pathogenesis. Multiple datasets were employed to identify Crohn’s disease (CD)- and ulcerative colitis (UC)-associated targets. Multi-machine learning approaches were combined with molecular docking of a PET-related compound to evaluate potential binding interactions with the identified hub targets. This study delineated 9 putative targets associated with PET-MPs-related CD pathogenesis and 17 potential targets associated with UC pathogenesis. For CD, GBM showed the best performance among all models, with a mean ROC-AUC of 0.862; for UC, RF performed best, with a mean ROC-AUC of 0.806, based on both training and validation sets. Multi-machine learning analysis identified 14 hub genes as candidate key regulatory factors. SHAP analysis highlighted their significant contributions to the model predictions. Molecular docking simulations suggested favorable binding affinities between a PET-related compound and the hub targets. This study suggests that PET-MPs may contribute to IBD pathogenesis by potentially interacting with 14 machine learning-prioritized hub genes. Molecular docking analyses indicated predicted high-affinity binding between a PET-related compound and these targets. Collectively, these findings provide a hypothesis-generating framework for investigating the potential role of PET-MPs in IBD progression.
Lip reading systems for Urdu alphabets in diverse environments
Abstract Lip reading technology has potential use across various fields, significantly enhancing communication for the deaf, aiding in noisy settings, and supporting information security through silent password entry. Although, notable progress has been made in constructing datasets of different types like digits, alphabets, words, phrases, and sentences levels for lip reading in various languages. However, developing a robust Urdu lip reading model remains a challenge due to the lack of a suitable dataset. Moreover, difficulties in adapting previous models, such as the LipNet model to Urdu. To address these barriers, we present the ULRA (Urdu lip reading alphabets) dataset, leverage advanced data augmentation techniques, and evaluate three cutting-edge DNN models: a LipNet-based 2D-CNN model, a Hybrid 2D_3D-CNN model, and a baseline 3D-CNN model. Each model undergoes rigorous testing in diverse environments, with both familiar and unfamiliar data. The results reveal that the LipNet-based 2D-CNN model achieves an impressive 81.97% accuracy on unknown data across diverse environments, while the Hybrid model excels in generalization, reaching 69.45% accuracy on unfamiliar data, thanks to its superior spatiotemporal feature extraction capabilities. Additionally, precision, recall, and F1-Score values of LipNet-Based 2D CNN are 0.83, 0.82, and 0.82 respectively. All three values of this model are also higher than the other two models. These findings underscore the strengths of various DNN architectures and the critical advancements made possible by the ULRA dataset, paving the way for future breakthroughs in Urdu lip reading research.