Classical Methods Research Articles

Comparison of bone age assessment methods using a hand radiography in patients with active growth plate and anteromedial knee instability

Background Bone age is essential for pediatric patients with active growth zones and anteromedial instability to facilitate optimal treatment strategy and minimize postoperative complications. However, many people are unaware of various tools for determining bone age, including classical methods and modern machine learning techniques.The objective was to show and compare different methods for calculating bone age and determining surgical strategy for patients with anteromedial instability of the knee joint.Material and methods All-Inside anterior cruciate ligament reconstruction was performed for 20 patients. Wrist radiographs were performed for bone age assessment using the "point scoring system" of Tanner and Whitehouse and the "atlas matching" method of Greulich and Pyle. Machine learning programs were used in addition to standard bone age assessments.Results The findings showed an average difference of 21 months (80 %) in a group of 20 individuals with bone age ahead of the passport age and an average difference of 18 months (20 %) in patients with retarded bone age.Discussion The findings showed the difference between chronological and bone age and could be encountered in scientific articles on endocrinology and pediatrics. No scientific studies on the use of the methods could be found in the specialty “trauma and orthopaedics”.Conclusion Bone age assessment, prediction of children's target height are essential for surgical treatment of patients with open growth plates.

A Classical Interpretation of the Quantum Universe Extrapolated from the Fine Structure Constant

The Fine Structure Constant (FSC) model of the universe is a conceptual perspective based on a classical method for analyzing the Fine Structure Constant (α). A Python algorithm calculates prime number property sets where the sum of the elements equals the whole number values for αequal to 137 and 139. A hybrid coupling of these property sets produces a U{137/139}twin prime metaverse where α=137.036, an almost exact match to the observed value. The FSC Model projects that the Fractional Coupling Constant (α137= 0.036)is a more accurate measure of its relative electromagnetic (EM) strength. This same calculation is used to determine the Fractional Coupling Constants (αm) for the twin prime metaverses (U{2/3}, U{3/5}, U{5/7}through U{197/199}) to estimate their respective electromagnetic forces. The results suggest that this model mirrors our observable universe and offers an abstract landscape into the quantum nature of electromagnetic forces, including Baryonic Matter, Dark Matter, Dark Energy, and a possible variable speed of light.

Target Detection Adapting to Spectral Variability in Multi-Temporal Hyperspectral Images Using Implicit Contrastive Learning

Hyperspectral target detection (HTD) is a crucial aspect of remote sensing applications, aiming to identify targets in hyperspectral images (HSIs) based on their known prior spectral signatures. However, the spectral variability resulting from various imaging conditions in multi-temporal hyperspectral images poses a challenge to both classical and deep learning (DL) methods. To overcome the limitations imposed by spectral variability, an implicit contrastive learning-based target detector (ICLTD) is proposed to exploit in-scene spectra in an unsupervised way. First, only prior spectra are utilized for explicit supervision, while an implicit contrastive learning module (ICLM) is designed to normalize the feature distributions of prior and in-scene spectra. This paper theoretically demonstrates that the ICLM can transfer the gradients from prior spectral features to those of in-scene spectra based on their feature similarities and differences. Because of transferred gradient signals, the ICLTD is regularized to extract similar representations for the prior and in-scene target spectra, while augmenting feature differences between the target and background spectra. Additionally, a local spectral similarity constraint (LSSC) is proposed to enhance the capability of scene adaptation by leveraging the spectral similarities among in-scene targets. To validate the performance of the ICLTD under spectral variability, multi-temporal HSIs captured under various imaging conditions are collected to generate prior spectra and in-scene spectra. Comparative evaluations against several DL detectors and classical methods reveal the superior performance of the ICLTD in achieving a balance between target detectability and background suppressibility under spectral variability.

QUANTUM CRYPTOGRAPHY AND U.S. DIGITAL SECURITY: A COMPREHENSIVE REVIEW: INVESTIGATING THE POTENTIAL OF QUANTUM TECHNOLOGIES IN CREATING UNBREAKABLE ENCRYPTION AND THEIR FUTURE IN NATIONAL SECURITY

This study provides a comprehensive review of quantum cryptography and its implications for U.S. national security in the face of emerging quantum technologies. The primary objective is to investigate the potential of quantum cryptographic methods in creating unbreakable encryption and their future role in enhancing digital security. Employing a systematic literature review and content analysis, the study draws on recent peer-reviewed articles, institutional reports, and academic journals from 2013 to 2023. The methodology focuses on evaluating the evolution, current state, and challenges of quantum cryptography, along with its integration into existing security frameworks. Key findings reveal that Quantum Key Distribution (QKD) and post-quantum cryptography (PQC) offer promising solutions against the threats posed by quantum computing to classical encryption methods. However, the practical implementation of these technologies faces significant challenges, including technological limitations and the need for global standardization. The study underscores the urgency for U.S. national security policy to prioritize the development and integration of quantum-resistant cryptographic technologies and to foster international collaboration for standardization. Finally, the study highlights the transformative potential of quantum cryptography in digital security, emphasizing the need for continued research and collaboration to overcome implementation challenges. Future research directions include the development of efficient quantum cryptographic protocols and ethical considerations surrounding the deployment of quantum technologies. This study contributes to the discourse on securing national interests in the face of advancing quantum computing capabilities.
 Keywords: Quantum Cryptography, Digital Security, Post-Quantum Cryptography, Quantum Key Distribution.

Innovative vs classical methods for drying heterotrophic Chlorella vulgaris: Impact on protein quality and sensory properties

Drying is a necessary step in the microalgae production chain to reduce microbial load and oxidative degradation of the end product. Depending on the differences in applied temperature and treatment time, the process of drying can have a substantial impact on protein quality and aroma, important characteristics determining the incorporation potential in food products. In this study, we compared the drying of heterotrophic Chorella vulgaris with both innovative (agitated thin film drying (ATFD), pulse combustion drying (PCD) and solar drying (SolD)) and commonly used drying techniques (spray drying (SprD) and freeze drying (FD)). To evaluate the impact on protein quality, we evaluated techno-functional properties, in vitro digestibility (INFOGEST) as well as protein denaturation using differential scanning calorimetry (DSC). A sensory analysis was performed by a trained expert panel, combined with headspace solid-phase microextraction (HS-SPME) - gas chromatography-mass spectrometry (GC–MS) to determine volatile organic compounds (VOCs). ATFD was found to increase techno-functional properties such as gelling, water holding and solubility as well as in vitro protein digestibility. These observations could be related to induced cell disruption and protein denaturation by ATFD. Sensory analysis indicated an increased earthy off-flavor after ATFD. Interestingly, the high-temperature PCD led to an increase in cacao odor while low-temperature FD resulted in lower flavor, odors and VOCs. These results demonstrate that protein quality and sensorial properties of C. vulgaris can be steered through the type of drying, which could help in the selection of application-specific drying methods. Overall, this work could promote the incorporation of microalgal single cell proteins in different innovative food products.

Moving Object Detection Method Based on the Fusion of Online Moving Window Robust Principal Component Analysis and Frame Difference Method

The accuracy of moving object detection has a great impact on the accuracy of extracting the shape center coordinates of moving workpieces. The classic inter-frame difference method has "cavity" and "double shadow" issues for workpieces with comparable internal colors. In order to solve this problem, a moving object detection algorithm combining the three-frame difference method and Online Moving Window Robust Principal Component Analysis (OMWRPCA) is proposed. By using the OMWRPCA to extract the background image in the current frame and comparing it to the previous and current frames, the "cavity" and "double shadow" problems are avoided as well as the effects of background pixels. This paper presents a case study of a visual sorting experiment bench in an "intelligent manufacturing production demonstration line". The experiments show that the workpiece shape center coordinates obtained by the improved moving object detection algorithm are closer to the actual value than those obtained by the traditional algorithm, and the F-measure scores are above 0.8, which are more accurate than the other two algorithms. It is compared with the traditional algorithm of the frame difference method and the Online Mixture of Gaussian Matrix Factorization (OMoGMF).

Machine learning methods for service placement: a systematic review

With the growth of real-time and latency-sensitive applications in the Internet of Everything (IoE), service placement cannot rely on cloud computing alone. In response to this need, several computing paradigms, such as Mobile Edge Computing (MEC), Ultra-dense Edge Computing (UDEC), and Fog Computing (FC), have emerged. These paradigms aim to bring computing resources closer to the end user, reducing delay and wasted backhaul bandwidth. One of the major challenges of these new paradigms is the limitation of edge resources and the dependencies between different service parts. Some solutions, such as microservice architecture, allow different parts of an application to be processed simultaneously. However, due to the ever-increasing number of devices and incoming tasks, the problem of service placement cannot be solved today by relying on rule-based deterministic solutions. In such a dynamic and complex environment, many factors can influence the solution. Optimization and Machine Learning (ML) are two well-known tools that have been used most for service placement. Both methods typically use a cost function. Optimization is usually a way to define the difference between the predicted and actual value, while ML aims to minimize the cost function. In simpler terms, ML aims to minimize the gap between prediction and reality based on historical data. Instead of relying on explicit rules, ML uses prediction based on historical data. Due to the NP-hard nature of the service placement problem, classical optimization methods are not sufficient. Instead, metaheuristic and heuristic methods are widely used. In addition, the ever-changing big data in IoE environments requires the use of specific ML methods. In this systematic review, we present a taxonomy of ML methods for the service placement problem. Our findings show that 96% of applications use a distributed microservice architecture. Also, 51% of the studies are based on on-demand resource estimation methods and 81% are multi-objective. This article also outlines open questions and future research trends. Our literature review shows that one of the most important trends in ML is reinforcement learning, with a 56% share of research.

Effect of hydrocephalus in an unsteady cerebrospinal fluid influencing diffusivity

The present study proposes a mathematical framework based on in vivo inputs to compare the dynamic interactions of cerebrospinal fluid (CSF) through an asymmetric channel due to hydrocephalus. This study’s primary goal is to determine the significance of diffusivity in cerebrospinal fluid concerning the rate of fluid velocity for the cardiac cycle. The model of governing equations was simulated by applying a suitable number of dimensionless parameters. The dimensionless governing equations are solved through the classical Laplace transformation method. The impacts of numerous variables over velocity, temperature, and concentration are examined. These variables comprise the porosity parameter, resistance parameter, buoyancy force, and Schmidt number. These plots are sketched for the considerable magnitude of these variables through MATLAB software, and the plots are discussed in detail. The present study shows a decent justification for the neurological papers showing an increase in pressure reflects the velocity for an increase in resistance, porosity, and the Schmidt number shows an inverse in diffusivity, as elaborated in the tabular outline. It was justified that rigorous cerebrospinal fluid velocity for hydrocephalus proved more than the result in ref. [1]. That is, according to ref. [1] the velocity of hydrocephalic patient is 10 mm/s; we proved it is more than 10 mm/s using analytical Laplace transform method.

Rubisco in high Arctic tidewater glacier‐marine systems: A new window into phytoplankton dynamics

AbstractThe hundreds of tidewater glaciers found in the Canadian Arctic Archipelago have the potential to enhance delivery of nutrients and other material to the surface ocean. Despite this, their influence on marine ecosystems, specifically phytoplankton, is poorly characterized. Here we developed and applied a quantitative mass spectrometry‐based approach to measure phytoplankton ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco) concentrations to examine differences in productivity in glacierized and non‐glacierized marine systems in Jones Sound, Nunavut, within Inuit Nunangat. Comparisons to chloroplast 16S rRNA gene amplicon sequencing data suggested that these measurements detect the majority of Rubisco produced in Jones Sound. Because Rubisco catalyzes carbon fixation, we used these measurements to estimate total and group‐specific primary production potential, which were within the range of historical primary production measurements made using classical methods in this region. Our measurements also revealed that up to 2% of total protein in the water column is Rubisco, and that Rubisco concentrations are correlated with chlorophyll fluorescence, with maxima near the nitracline. Rubisco produced by diatom genera Chaetoceros and Thalassiosira were higher in marine regions influenced by glaciers, while Rubisco from Micromonas (Chlorophyta) was greater in non‐glacierized regions. This suggests that future climate scenarios may favor smaller phytoplankton groups, like Micromonas, with consequences for food webs and carbon cycling. This study broadens our understanding of how tidewater glaciers will impact phytoplankton communities, now and in a warmer future, and lays the foundation for using this mass spectrometry‐based approach to quantify phytoplankton group‐specific carbon fixation potential in other marine regions.

Extraction frequent patterns in trauma dataset based on automatic generation of minimum support and feature weighting

PurposeData mining has been used to help discover Frequent patterns in health data. it is widely used to diagnose and prevent various diseases and to obtain the causes and factors affecting diseases. Therefore, the aim of the present study is to discover frequent patterns in the data of the Kashan Trauma Registry based on a new method.MethodsWe utilized real data from the Kashan Trauma Registry. After pre-processing, frequent patterns and rules were extracted based on the classical Apriori algorithm and the new method. The new method based on the weight of variables and the harmonic mean was presented for the automatic calculation of minimum support with the Python.ResultsThe results showed that the minimum support generation based on the weighting features is done dynamically and level by level, while in the classic Apriori algorithm considering that only one value is considered for the minimum support manually by the user. Also, the performance of the new method was better compared to the classical Apriori method based on the amount of memory consumption, execution time, the number of frequent patterns found and the generated rules.ConclusionsThis study found that manually determining the minimal support increases execution time and memory usage, which is not cost-effective, especially when the user does not know the dataset's content. In trauma registries and massive healthcare datasets, its ability to uncover common item groups and association rules provides valuable insights. Also, based on the patterns produced in the trauma data, the care of the elderly by their families, education to the general public about encountering patients who have an accident and how to transport them to the hospital, education to motorcyclists to observe safety points in Recommended when using a motorcycle.

Graded mesh modified backward finite difference method for two parameters singularly perturbed second-order boundary value problems

The existence of boundary layers in the solutions of two-parameter singularly perturbed boundary value problems makes classical numerical methods insufficient in providing accurate approximations. Consequently, the development of layer-adapted mesh methods that achieve parameter uniform convergence and are specifically designed to accurately handle these layers becomes highly significant. The aim of this paper is to construct and examine a numerical approach for obtaining approximate solutions to a specific class of two-parameter singularly perturbed second order boundary value problems whose solutions exhibit boundary layers at both ends of the domain. The problem is discretized by employing a modified backward finite difference method on a mesh that is graded. To validate the theoretical findings, well known test problems from the existing literature are utilized. Moreover, the efficiency of the proposed method is demonstrated by comparing it to other existing methods in the literature. The stability and uniform convergence with respect to the parameters of the proposed method have been verified, revealing that it attains second-order convergence in the maximum norm. The numerical outcomes illustrate that the proposed method offers remarkably accurate approximations of the solution. Theoretical findings are in agreement with the experimental results.

Continuous light-guide control of melts temperature in induction furnaces

The article is devoted to the question of the most effective for the full use of the induction furnaces technological flexibility continuous temperature control. The aim of the work is to create a light-guide technology for continuous temperature control of the processes of induction melting, treatment and pouring of liquid metal in metallurgy of machine building. The investigations of crucible and channel, melting, holding and pouring induction furnaces from the standpoint of light-guide thermometry have been developed. Materials, designs, as well as technologies of manufacturing, mounting and safe operation of the light-guide and auxiliary devices have also been investigated. Using the results of complex studies, the base light-guide thermometry system, general and particular methods of the light-guide temperature measurements have been developed. On the base of the Huygens construction, as well as the laws of geometric and crystal optics, techniques for calculation of the optical characteristics of the light-guide and focusing devices, as well as schemes of their optical joint have been developed. These techniques increase the metrological characteristics of the light-guide thermometry. Standard construction of the secondary part of thermometry system requires classical pyrometry methods application. These methods are acceptable for continuously operated metallurgical aggregates, where stable emissivity of the light-guide operating end takes place. The secondary part of thermometry system has been modernized in order to widen application field of light-guide thermometry on periodically operated metallurgical aggregates where emissivity of light-guide immersion end randomly changes. Modernized secondary part is purposed for spectral (multicolor) pyrometry methods realization. These methods minimize influence of the instability of emissivity of light-guide immersion end on methodical errors of temperature measurements. Research and industrial exploitation, at domestic and foreign enterprises, have shown obvious metrological advantages of the light-guide thermometry technology in comparison with known solutions. Implementation of the light-guide thermometry systems on induction furnaces has high technical-economic efficiency, including by reduction the spoilage of metal products and resource costs for production. Keywords: induction furnace, continuous temperature control, light-guide thermometry, amorphous, poly- and single-crystalline materials, measurement error, Huygens construction, technical-economic efficiency.

On-water Catalyst-free Synthesis of 3-Alkenyl Oxindoles

3-Alkenyl oxindoles are key structural motifs in organic chemistry with a wide range of applications mainly in the pharmaceutical industry, due to excellent biological activities such as anti-inflammatory, antiviral, antibacterial, and anticancer properties. With the discovery of the importance of oxindole derivatives in the pharmaceutical industry, demand for sustainable protocols for the synthesis increased as most classical methods have their limitations and flaws. In this study on-water, catalyst-free, atom-economical green approach was developed for the synthesis of 3- alkenyl oxindole derivatives using oxindole and aldehyde as starting materials. The reaction between oxindole and benzaldehyde was conducted on water under catalyst-free conditions. Interestingly, 100% conversion was observed when the reaction was carried out for 24 hours at 100° C. As the next step, the applicability of the developed approach was proven for different aldehydes. Furaldehyde, cinnamaldehyde, and vanillin were used as the aldehydes to synthesize 3- alkenyl oxindole derivatives, and the isolated yields were obtained as 77%, 54%, and 51% respectively. The desired product formation was confirmed by comparing the Rf value of the product with that of the previously synthesized well-characterized compounds; 3- benzylideneindolin-2-one, 3-(furan-3-methylene)indolin-2-one, 3-(3-phenylallylidene)indolin-2- one, and 3-(4-hydroxy-3-methoxybenzylidene)indolin-2-one using Thin Layer Chromatography. Further characterization was done by Fourier-transform infrared (FT-IR), Ultraviolet-visible (UVVis), and Nuclear Magnetic Resonance (NMR) spectroscopic techniques. In further optimization steps, the reaction was performed using surfactants as the catalyst. By adhering to the green chemistry principles different sources of biosurfactants were used including Acacia concinna pods, Sapindus emarginatus fruit pericarp, Dillenia retusa fruit pericarp, and Trigonella foenumgraecum seeds. In order to compare the effect of biosurfactants, a synthetic surfactant, sodium dodecyl sulfate (SDS) was used as the catalyst. When the reaction was carried out for 21 hours at 100 °C using fruit pericarp of Sapindus emarginatus as the source of biosurfactant, 100% conversion was observed. Using water as the solvent, readily available starting materials, higher yield, and eco-friendliness and catalyst-free conditions suggest the possible use of the developed method for large-scale preparations of 3-alkenyl oxindole derivatives.
 Keywords: Green protocol, Oxindole derivatives, Surfactant, On water synthesis, Catalyst-free

Optical structural and radiation shielding efficiency of vanadium-doped LZBS glass

By using the classic melt-quench method, a glass system including silicon-doped vanadium ions, lithium, zinc, and boron was fabricated with the following formula, 27SiO2-2ZnF2-53B2O3- xV2O5-(18-x) Li2O- with x = 0.0, 0.25, 0.5, 1, and 2 mol%. XRD was used to confirm amorphous status. Density, molar volume, oxygen packed densities and Inter-nuclear distance were determined. The optical characteristics were investigated. The energy optical-gap declines with increased vanadate’s content. It is 3.25 eV for pristine glass and reduced to 1.91 eV for glass doped with 2 mol% of vanadium ions. Refractive index, absorption coefficient, dielectric constant, optical and electrically conductive, basicity and oxide polarizability have been estimated. On the other hand, the glass samples were simulated using MCNP-5 code to set their radiation shielding ability. The results of the coefficients of attenuation μm, μL, HVL, MFP, Zeff, Zeq, Ne, RPE and FNRCS were calculated. The comparison between results obtained by the MCNP5 code, PHYX-PSD package and XCOM at photons flux of 0.015 MeV to 15 MeV for all glass samples were in decent accord. All findings of the parameters refer to the good shielding abilities of our glass system when increasing the concentration of (V2O5) at the expense of (Li2O) values at the different energies.

Extended Dynamic Mode Decomposition with Invertible Dictionary Learning

The Koopman operator has received attention for providing a potentially global linearization representation of the nonlinear dynamical system. To estimate or control the original system, the invertibility problem is introduced into the data-driven modeling, i.e., the observables are required to be reconstructed the original system’s states. Existing methods cannot solve this problem perfectly. Only linear or nonlinear but lossy reconstruction can be achieved. This paper proposed a novel data-driven modeling approach, denoted as the Extended Dynamic Mode Decomposition with Invertible Dictionary Learning (EDMD-IDL) to address this issue, which can be interpreted as a further extension of the classical Extended Dynamic Mode Decomposition (EDMD). The Invertible Neural Network (INN) is introduced in the proposed method, where its inverse process provides the explicit inverse on the dictionary functions, thus allowing the nonlinear and lossless reconstruction. An iterative algorithm is designed to solve the extended optimization problem defined by the Koopman operator and INN by combining the optimization algorithm based on the gradient descent and the classical EDMD method, making the method successfully obtain the finite-dimensional approximation of the Koopman operator. The method is tested on various canonical nonlinear dynamical systems and is shown that the predictions obtained in a linear fashion and the ground truth match well over the long-term, where only the initial status is provided. Comparison experiments highlight the superiority of the proposed method over the other EDMD-based methods. Notably, a typical example in fluid dynamics, cylinder wake, illustrates the potential of the method to be further extended to the high-dimensional system with tens of thousands of states. By combining the Proper Orthogonal Decomposition technique, nontrivial Kármán vortex sheet phenomenon is perfectly reconstructed. Our proposed method provides a new paradigm for solving the finite-dimensional approximation of the Koopman operator and applying it to data-driven modeling.

Synthesis of Eu3+-doped β-Ca2SiO4 phosphors and its utility as an indicator to visualize the distribution of grouting material

Grouting materials have been widely used in the engineering fields for filling the cracks and increasing the strength of concrete structures, etc. However, tracing the distribution of grouting material is still laborious and lack of efficient tools. In this study, a grouting material that could be used as an indicator to visualize the distribution of grouting material was proposed. Serial Eu3+-doped β-Ca2SiO4 phosphors were synthesized through a classical solid-state reaction method. The phase and crystallographic properties of the samples were investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The hydration performance of the samples was studied by measuring the isothermal calorimetry and compressive strength. The luminescence properties were explored through photoluminescence spectroscopy and the visualizing of the distribution of grouting materials was realized in simulated grouting experiments. The results demonstrated that the inherent luminescence property of Eu3+-doped β-Ca2SiO4 endowed its capability as an indicator during grouting process and it could be adopted as a candidate to verify the accuracy of numerical simulation.

Methods for the Determination of Fire Blight on Pome Fruit Crop Plants in the Western Part of Azerbaijan

Fire blight caused by Erwinia amylovora (Burrill, 1882) Winslow et al., 1920 causes significant production losses in the world’s leading pome fruit crop producing countries. The main route of infection of many fruit crops, including apple and pear trees, is through newly blooming flowers. The bacterium overwinters in the bark tissue along the edges of cankers formed on branches and stems as a result of infection in the previous year and becomes a source of infection in the next year. Determining the prevalence of the disease in areas with a large number of orchards is important from the point of view of disease control. Therefore, the purpose of the work is to study the prevalence of the disease in the Ganja-Dashkesan and Gazakh-Tovuz Economic Regions, as well as the accurate diagnosis of the obtained E. amylovora isolates using classical and molecular methods. For this purpose, trees in the studied apple and pear orchards were examined for the disease, the pathogen was isolated from infected plants, and morphological, biochemical, physiological and molecular analyzes were carried out. Among 36 bacterial isolates obtained from 27 samples of infected plants, Erwinia amylovora was identified in 11 as a result of morphological, biochemical, physiological and molecular tests.

Linear mixed models and latent growth curve models for group comparison studies contaminated by outliers.

The linear mixed model (LMM) and latent growth model (LGM) are frequently applied to within-subject two-group comparison studies to investigate group differences in the time effect, supposedly due to differential group treatments. Yet, research about LMM and LGM in the presence of outliers (defined as observations with a very low probability of occurrence if assumed from a given distribution) is scarce. Moreover, when such research exists, it focuses on estimation properties (bias and efficiency), neglecting inferential characteristics (e.g., power and type-I error). We study power and type-I error rates of Wald-type and bootstrap confidence intervals (CIs), as well as coverage and length of CIs and mean absolute error (MAE) of estimates, associated with classical and robust estimations of LMM and LGM, applied to a within-subject two-group comparison design. We conduct a Monte Carlo simulation experiment to compare CIs and MAEs under different conditions: data (a) without contamination, (b) contaminated by within-subject outliers, (c) contaminated by between-subject outliers, and (d) both contaminated by within- and between-subject outliers. Results show that without contamination, methods perform similarly, except CIs based on S, a robust LMM estimator, which are slightly less close to nominal values in their coverage. However, in the presence of both within- and between-subject outliers, CIs based on robust estimators, especially S, performed better than those of classical methods. In particular, the percentile CI with the wild bootstrap applied to the robust LMM estimators outperformed all other methods, especially with between-subject outliers, when we found the classical Wald-type CI based on the t statistic with Satterthwaite approximation for LMM to be highly misleading. We provide R code to compute all methods presented here. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Whole-Genome sequencing in routine Mycobacterium bovis epidemiology - scoping the potential.

Mycobacterium bovis the main agent of bovine tuberculosis (bTB), presents as a series of spatially-localised micro-epidemics across landscapes. Classical molecular typing methods applied to these micro-epidemics, based on genotyping a few variable loci, have significantly improved our understanding of potential epidemiological links between outbreaks. However, they have limited utility owing to low resolution. Conversely, whole-genome sequencing (WGS) provides the highest resolution data available for molecular epidemiology, producing richer outbreak tracing, insights into phylogeography and epidemic evolutionary history. We illustrate these advantages by focusing on a common single lineage of M. bovis (1.140) from Northern Ireland. Specifically, we investigate the spatial sub-structure of 20 years of herd-level multi locus VNTR analysis (MLVA) surveillance data and WGS data from a down sampled subset of isolates of this MLVA type over the same time frame. We mapped 2108 isolate locations of MLVA type 1.140 over the years 2000-2022. We also mapped the locations of 148 contemporary WGS isolates from this lineage, over a similar geographic range, stratifying by single nucleotide polymorphism (SNP) relatedness cut-offs of 15 SNPs. We determined a putative core range for the 1.140 MLVA type and SNP-defined sequence clusters using a 50 % kernel density estimate, using cattle movement data to inform on likely sources of WGS isolates found outside of core ranges. Finally, we applied Bayesian phylogenetic methods to investigate past population history and reproductive number of the 1.140 M. bovis lineage. We demonstrate that WGS SNP-defined clusters exhibit smaller core ranges than the established MLVA type - facilitating superior disease tracing. We also demonstrate the superior functionality of WGS data in determining how this lineage was disseminated across the landscape, likely via cattle movement and to infer how its effective population size and reproductive number has been in flux since its emergence. These initial findings highlight the potential of WGS data for routine monitoring of bTB outbreaks.

Efficient numerical methods for semilinear one dimensional parabolic singularly perturbed convection-diffusion systems

In this work we deal with the numerical solution of one dimensional semilinear parabolic singularly perturbed systems of convection-diffusion type. We assume that the coupling in the convection terms is weak and also that the coupling reaction terms are nonlinear. In the case of considering different small diffusion parameters at each equation with different orders of magnitude, the exact solution usually shows overlapping boundary layers on the outflow of the spatial domain. To approximate it, we construct a numerical scheme which combines the upwind finite difference scheme, defined on a piecewise uniform Shishkin mesh, to discretize in space and a linearized version of the fractional implicit Euler method together with an appropriate splitting by components to discretize in time. Then, the fully discrete method is uniformly convergent with respect to both diffusion parameters, having first order in time and almost first order in space. The choice of this time integrator provokes that only tridiagonal linear systems must be solved at each time step; in this way, the computational cost of the algorithm is considerably lower than this one associated to classical implicit methods. The numerical results obtained for different test problems corroborate in practice the good performance of the numerical algorithm.