Test Examples Research Articles

Motor neuron diseases are not exclusively motor; the SSR paradigm.

Motor Neuron Diseases (MNDs), familial and sporadic, are progressive neurodegenerative disorders that, for an extended period in the past, were considered purely motor disorders. During the course of the disease, however, some patients exhibit concomitant non-motor signs; thus, MNDs are currently perceived as multisystem disorders. Assessment of non-motor symptoms is usually performed clinically, although laboratory tests can also be routinely used to objectively evaluate these symptoms. Sympathetic Skin Response (SSR) is an example of a neurophysiological test that has been used in cases of Amyotrophic Lateral Sclerosis, Spinal Muscular Atrophy, and Monomelic Atrophy, mostly to assess Autonomic Nervous System (ANS) disorders. Dysautonomia affects quality of life and life expectancy, as it is involved in cardiovascular events and incidents of sudden death. SSR abnormalities are present even in subclinical involvement of the ANS in MNDs. In this review, we present published research examining SSR findings in various MNDs, and discuss the correlation of SSR findings with clinical symptoms and disease severity, as well as the potential sources of abnormal findings. The aim of this study is to raise clinician awareness of autonomic dysfunction in MNDs and present the benefits of SSR examination in patient care.

An Efficient Implementation of Interior-Point Methods for a Class of Nonsymmetric Cones

We present an implementation of interior-point methods for generalized power cones, power mean cones and relative entropy cones, by exploiting underlying low-rank and sparsity properties of the Hessians of their logarithmically homogeneous self-concordant barrier functions. We prove that the augmented linear system in our interior-point method can be sparse and quasidefinite after adding a static regularization term, enabling the use of sparse LDL factorization for nonsymmetric cones. Numerical results show that our implementation can exploit sparsity in our test examples.

A Computationally Efficient Technique for Dynamic Sensitivity Filtering in Topology Optimization

ABSTRACTIn this paper, we present a novel approach: a dynamic sensitivity filtering technique designed for density‐based topology optimization. Sensitivity filtering methods have found extensive utility in mitigating numerical instabilities. Nevertheless, conventional sensitivity filtering can introduce numerous gray elements along the topology's boundaries, thereby impeding practical manufacturing within real‐world engineering applications. To address this challenge, we propose a dynamic sensitivity filter that adjusts the sensitivity at each iterative step based on the optimization outcomes of the preceding iteration. Through diverse test examples, our method demonstrates the capacity to effectively solve the numerical instability problem while concurrently achieving a nearly pure black and white design, characterized by significantly reduced computational expense and improved structural stiffness.

Drosophila: the century-long flight from the wild to the patient

Evolutionary conservation of key biological pathways between the fruit fly Drosophila melanogaster and humans and reduced genetic redundancy have long made flies a valuable genetic model organism. Thanks to the arsenal of sophisticated genetic tools developed and refined by the fly community, the use of Drosophila has expanded well beyond basic research. From the fundamental notion that genes are located on chromosomes to modeling human complex diseases such as cancer and neurological disorders, to designing fly “avatar” lines that precisely reproduce the specific mutations found in single cancer patients for personalized medicine, Drosophila continues to fuel biomedical advances. Numerous examples of drug testing in flies have yielded novel drug candidates, new uses for approved drugs, and applications for rapid drug optimization in modern approaches combining biology with medicinal chemistry. Thanks to the effectiveness of “fly pharmacology” approaches, Drosophila is also proficiently used to study the mechanism of action of environmental pollutants, that represent a serious concern to human health. This review traces the history of some of the main advances in Drosophila biomedical and cancer research.

High-Accuracy Solutions to the Time-Fractional KdV–Burgers Equation Using Rational Non-Polynomial Splines

A non-polynomial spline is a technique that utilizes information from symmetric functions to solve mathematical or physical models numerically. This paper introduces a novel non-polynomial spline construct incorporating a rational function term to develop an efficient numerical scheme for solving time-fractional differential equations. The proposed method is specifically applied to the time-fractional KdV–Burgers (TFKdV) equation. and time-fractional differential equations are crucial in physics as they provide a more accurate description of various complex processes, such as anomalous diffusion and wave propagation, by capturing memory effects and non-local interactions. Using Taylor expansion and truncation error analysis, the convergence order of the numerical scheme is derived. Stability is analyzed through the Fourier stability criterion, confirming its conditional stability. The accuracy and efficiency of the rational non-polynomial spline (RNPS) method are validated by comparing numerical results from a test example with analytical and previous solutions, using norm errors. Results are presented in 2D and 3D graphical formats, accompanied by tables highlighting performance metrics. Furthermore, the influences of time and the fractional derivative are examined through graphical analysis. Overall, the RNPS method has demonstrated to be a reliable and effective approach for solving time-fractional differential equations.

Digital Footprint as a Source of Big Data in Education

The purpose of this study is to consider the prospects and problems of using big data in education.Materials and methods. The research methods include analysis, systematization and structuring of information in the field of big data application in education, as well as modeling and software implementation of a test model for processing big data by using the Apache Spark framework.Results. The article considers the key aspects of using big data in education. In particular, the sources of big data in the form of a digital footprint of learning, methods of analysis and areas of application of big data are considered. At the same time, the following sources of big data in education were identified: electronic educational environment and electronic library of the university; mobile applications for learning; university website; social networks and forums; feedback data, requests and surveys; personal data, including psychometric characteristics of students; data from scientific smart laboratories; data from video surveillance and access control systems; data on the career path and success of graduates. The use of big data in education includes the following points: personalization of e-learning, issuance of personalized recommendations; data analytics; assessment and feedback; predicting student success; monitoring the quality of education; creation of a learner model; development of curricula based on employer requests; development of new educational programs; emergence of new learning models; improvement of university management processes; improvement of the work of the admissions office; modernization of software and hardware teaching aids; optimization of the teaching staff. The following problems are considered as problems of using big data in education: protection of personal data, the need for new methodologies and technologies for analyzing big data, the need for significant modernization of the technical means available in the education system, the need for qualified personnel. The article also provides a test example of analyzing a log file (event log) of an e-course using Spark SQL big data processing technologies. The example shows the potential and practical applicability of big data processing technologies to the tasks of analyzing the digital footprint of learning.Conclusion. Big data in education can provide unique opportunities for analyzing and optimizing the educational process, helping to identify trends, predict student success and adapt educational programs to the individual needs of students. However, the use of big data in the educational sphere also implies certain risks and challenges related to ethical aspects, protection of personal data and the need for personnel modernization of the existing education system. For the successful integration of data analytics into educational practice, it is necessary to develop not only technical resources, but also the level of digital security and ethics in the use of personal data.

Bidimensional Gegenbauer Polynomials for Variable‐Order Time‐Fractional Integro‐Partial Differential Equation With a Weakly Singular Kernel

ABSTRACTIn this paper, a pseudo‐operational collocation method based on Gegenbauer polynomials is presented to solve a category of variable‐order time‐fractional integro‐partial differential equations with singular kernels. The applications of these functional equations can be revealed in the theory of elasticity, hydrodynamics, heat conduction, and nuclear reactor theory. The pseudo‐operational matrices are constructed utilizing bivariate Gegenbauer polynomials to approximate the solution of the mentioned equation. Then, using the collocation method and resultant matrices, the main equation is converted into a system of algebraic equations that can be solved by Newton's iteration method. Besides presenting a fast and accurate method, an error bound is determined in a Gegenbauer‐weighted space for the residual function obtained from the proposed approach. Finally, several test examples are performed to confirm the reliability and efficiency of the proposed method.

Numerical solution of time-dependent two-parameter singularly perturbed problems via Trigonometric Quintic B-spline collocation technique

Abstract This paper introduces a novel algorithm for solving time-dependent two-parameter singularly perturbed parabolic convection–diffusion-reaction equations with Dirichlet boundary conditions. The algorithm is formulated using the Crank-Nicolson (CN) scheme for the temporal derivative discretization. Then, the Trigonometric Quintic B-spline (TQBS) is applied to approximate the state variable and its spatial derivatives on nonuniform collocation points. We conducted a comprehensive convergence analysis and stability of the proposed method and proved that the scheme achieved a parameter-uniform convergence of approximately fourth order in space and second order in time. To make additional evidence to support the theoretical findings and further assess the proposed method, we implemented the numerical algorithm to solve three test examples. Furthermore, using these test examples, we demonstrated the parameter-uniform convergence of the proposed numerical scheme.

Stochastic evolution of projectile motion during artillery exterior ballistic

Abstract In order to analyze the variation law of the flight distance error of the unpowered projectile with time, the probability density function (PDF) of projectile flight distance is calculated by the probability density evolution method (PDEM) based on 6 degrees of freedom rigid ballistic equation with random parameters of the projectile. A test example shows that the calculated results of PDEM are consistent with Monte Carlo (MC) method, which further verifies the effectiveness. In addition, the evolution of projectile flight distance and its error is analyzed within the given ranges of projectile parameter error, which provides a reference for the general design of artillery.

Degradation Testing of Thermoelectrics Due to Temperature

Abstract In today's global environment, accelerated life testing (ALT) is becoming a competitive advantage when time spent from the conceptual stage to final product development needs to be minimized. Using ALT techniques for semiconductors and structural applications has substantial challenges when defining the thermal fatigue life-stress relationship to represent actual field performance. Helpful strategies to address such common problems using ALT are presented for faster ALT planning. Test examples from the refrigeration industry are used for this demonstration. The tests and analyses performed effectively increased the degree of reliability improvement and reduced the total number of test hours, resulting in a shorter design cycle.

An enhanced guided stochastic search with repair deceleration mechanism for very high-dimensional optimization problems of steel double-layer grids

Finding reasonably good solutions using a fewer number of objective function evaluations has long been recognized as a good attribute of an optimization algorithm. This becomes more important, especially when dealing with very high-dimensional optimization problems, since contemporary algorithms often need a high number of iterations to converge. Furthermore, the excessive computational effort required to handle the large number of design variables involved in the optimization of large-scale steel double-layer grids with complex configurations is perceived as the main challenge for contemporary structural optimization techniques. This paper aims to enhance the convergence properties of the standard guided stochastic search (GSS) algorithm to handle computationally expensive and very high-dimensional optimization problems of steel double-layer grids. To this end, a repair deceleration mechanism (RDM) is proposed, and its efficiency is evaluated through challenging test examples of steel double-layer grids. First, parameter tuning based on rigorous analyses of two preliminary test instances is performed. Next, the usefulness of the proposed RDM is further investigated through two very high-dimensional instances of steel double-layer grids, namely a 21,212-member free-form double-layer grid, and a 25,514-member double-layer multi-dome, with 21,212 and 25,514 design variables, respectively. The obtained numerical results indicate that the proposed RDM can significantly enhance the convergence rate of the GSS algorithm, rendering it an efficient tool to handle very high-dimensional sizing optimization problems.

Energy Decomposition Analysis Method Based on the Constrained Active Space Unrestricted Mean-Field Method.

An energy decomposition analysis method based on the constrained unrestricted mean-field (CUHF) theory, called GKS-EDA(CU), is presented for intermolecular interactions involving radicals. This method is able to take into account the spin contamination in both open-shell singlet states and high-spin states. By using GKS-EDA(CU), the total interaction energy can be divided into the terms of electrostatic, exchange-repulsion, polarization, and correlation. Test examples illustrate that GKS-EDA(CU) not only overcomes the spin contamination in the analysis for radical-radical interactions but also provides insights into the variation of the magnetic coupling constant.

Eloquent numerical approach for solving generalized time fractional convection-diffusion-reaction problems

Abstract This paper attempts to develop a spectral method based on derivatives of orthogonal polynomials to solve the time fractional convection–diffusion-reaction equations. The method utilizes derivatives of fractional order orthogonal functions to approximate derivatives involved in the fractional differential equations. Specifically, the derivatives of fractional order Legendre functions and fractional order Chebyshev functions are used to represent both integer and non-integer derivatives of the solutions. These derivative representations are achieved through the use of operational matrices, which are matrices that encode the operations performed on the polynomials. An attempt is made to derive the operational matrix of Vieta-Fibonacci-like polynomials and used to solve the fractional differential equations. The derived operational matrix provides a systematic way to manipulate and work with these polynomials, facilitating their application in various mathematical and engineering problems. By employing these operational matrices, the original fractional convection–diffusion-reaction equation is transformed into a system of linear or nonlinear algebraic equations. However, if the system is nonlinear, a Newton-like solver is applied, which is capable of handling nonlinear systems. The estimation of error bounds of numerical solutions is also given. The numerical experiments have been performed over a few test examples to validate the proposed numerical method. The use of fractional order functions highlights their ability to solve fractional differential equations with non-smooth solutions accurately.

A fast calculation method for dynamic topology optimization based on hybrid spectral element method

In this study, a Hybrid Spectral Element Method (HSEM) integrated with Equivalent Static Load (ESL) in the frequency domain is suggested. This integration aims to enhance the computational efficiency of dynamic topology optimization. In comparison with existing techniques, the proposed HSEM transforms the governing equation of dynamic analysis into a spectral element equation within the frequency domain by utilizing the Fast Fourier Transform (FFT) algorithm. This approach enables the representation of both structural displacements and external loads in spectral forms, potentially leading to a reduction in the number of dimensions compared to traditional time-interval-based methods. By using spectral representation, a low-dimensional ESL set can be constructed in the frequency domain for model reduction. To validate the effectiveness of the suggested proposed method, extensive analyses and comparisons using various two-dimensional (2D) and three-dimensional (3D) examples are carried out. The obtained results demonstrate a substantial improvement in computational efficiency, both during the dynamic analysis phase and the quasi-static topology optimization phase, while maintaining high levels of accuracy. Moreover, even as the scale of the model increases, our method maintains its advantage in computational efficiency. In the test examples, a maximum speedup ratio of up to 6.54 times was observed, indicating the significant potential of the proposed HSEM-ESL approach in enhancing the performance of dynamic topology optimization tasks.

Chrysalis: decoding tissue compartments in spatial transcriptomics with archetypal analysis

Dissecting tissue compartments in spatial transcriptomics (ST) remains challenging due to limited spatial resolution and dependence on single-cell reference data. We present Chrysalis, a computational method that rapidly uncovers tissue compartments through spatially variable gene (SVG) detection and archetypal analysis without requiring external reference data. Additionally, it offers a unique visualisation approach for swift tissue characterisation and provides access to the underlying gene expression signatures, enabling the identification of spatially and functionally distinct cellular niches. Chrysalis was evaluated through various benchmarks and validated against deconvolution, independently obtained cell type abundance data, and histopathological annotations, demonstrating superior performance compared to other algorithms on both in silico and real-world test examples. Furthermore, we showcased its versatility across different technologies, such as Visium, Visium HD, Slide-seq, and Stereo-seq.

Numerical Investigation of the Nonlinear Drill String Dynamics Under Stick–Slip Vibration

This paper presents a comprehensive analysis of the influence of rotary table velocity, weight-on-bit, and viscous damping on the drill string stick–slip vibration. The analysis allows for studying the qualitative and quantitative variation of the dynamic response of the drill pipes and drill collars/bit. To achieve this goal, a robust and practical finite element (FE) model of the full-scaled drill string was developed based on a velocity-weakening formulation of the nonlinear bit–rock interaction. A detailed investigation of damping parameters was carried out. The performance of the developed model was verified through comparisons with a lumped-parameter model and a field test example. Parametric studies on the stick–slip response of the entire drill string under different field operational conditions were conducted. The dynamical time series of the system response were analyzed in terms of the phase planes, response spectra, and descriptive statistics of the drill pipes and drill collars. The findings of the study revealed that for a realistic drill string geometry, the angular velocity (i.e., mean, peak-to-peak amplitude, and standard deviation) and dominant frequency of self-excited torsional stick–slip oscillations along the drill pipes and drill collars/bit are mainly governed by the rotary table velocity. Furthermore, it was shown that the contribution of higher harmonics in the torsional stick–slip response of the drill pipes is more substantial than the drill collars/bit.

Reliable Computational Method for Systems of Fractional Differential Equations Endowed with ψ-Caputo Fractional Derivative

This study develops a highly convergent computational method, the ψ-Laplace Adomian Decomposition Method (ψ-LADM), for solving coupled systems ofψ-Caputo Fractional Differential Equations (FDEs). The effectiveness of the proposed method has been assessed using various numerical test examples, including a real-world application for atmospheric convection models utilizing Lorenz chaotic dynamical systems. Notably, the method consistently produced solutions that matched the true solutions of the governing models. In the case of the Lorenz chaotic system, the obtained solutions accurately portrayed the characteristic phase portraits of a true chaotic system.

Identification of Boundary Conditions in a Spherical Heat Conduction Transmission Problem

Although numerous analytical and numerical methods have been developed for inverse heat conduction problems in single-layer materials, few methods address such problems in composite materials. The following paper studies inverse interface problems with unknown boundary conditions by using interior point observations for heat equations with spherical symmetry. The zero degeneracy at the left interval 0<r<R1 leads to solution difficulties in the one-dimensional interface problem. So, we first investigate the well-posedness of the direct (forward) problem in special weighted Sobolev spaces. Then, we formulate three groups of unknown boundary conditions and inverse problems upon internal point measurements for the heat equation with spherical symmetry. Second-order finite difference scheme approaches for direct and inverse problems are developed. Computational test examples illustrate the theoretical statements proposed.

Machine learning-based predictive model for temperature and comfort parameters in indoor enviroment using experimantal data

The research introduces an artificial neural network model that predicts temperature and assesses thermal comfort metrics for a cooling room, demonstrating how machine learning advancements can enhance thermal efficiency and cost-effectiveness in building design. The study utilized the Levenberg-Marquardt (LM) artificial neural network (ANN) approach to derive the average temperature and thermal comfort metrics collected under actual operating settings. The Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD)values were measured at three distinct sites and then compared to the trial findings. The model uses a dataset of 205 observations, with 143 cases used for training and 31 examples for testing and validation. The ANN model demonstrated effective training, with negligible errors in estimated error values. The mean squared error values for average temperature and thermal comfort parameters were 0.0342, 0.0376, 0.0571, 0.0029, and 0.2296. The R values for temperature measurements are 0.9947 and 0.9923, 0.9847 and 0.9437, and 0.9737, demonstrating a highly effective engineering method. The ANN model provided precise predictions for temperature and thermal comfort metrics, such as PMV and PPD in a cooling chamber, with a tolerance of ± 15 %. The LM approach, a machine learning methodology, produced excellent outcomes, particularly at lower temperatures, with 15 % of the data exceeding this range.

Evaluating What Others Say: The Effect of Accuracy Assessment in Shaping Mental Models of AI Systems

Forming accurate mental models that align with the actual behavior of an AI system is critical for successful user experience and interactions. One way to develop mental models is through information shared by other users. However, this social information can be inaccurate and there is a lack of research examining whether inaccurate social information influences the development of accurate mental models. To address this gap, our study investigates the impact of social information accuracy on mental models, as well as whether prompting users to validate the social information can mitigate the impact. We conducted a between-subject experiment with 39 crowdworkers where each participant interacted with our AI system that automates a workflow given a natural language sentence. We compared participants' mental models between those exposed to social information of how the AI system worked, both correct and incorrect, versus those who formed mental models through their own usage of the system. Specifically, we designed three experimental conditions: 1) validation condition that presented the social information followed by an opportunity to validate its accuracy through testing example utterances, 2) social information condition that presented the social information only, without the validation opportunity, and 3) control condition that allowed users to interact with the system without any social information. Our results revealed that the inclusion of the validation process had a positive impact on the development of accurate mental models, especially around the knowledge distribution aspect of mental models. Furthermore, participants were more willing to share comments with others when they had the chance to validate the social information. The impact of inaccurate social information on altering user mental models was found to be non-significant, while 69.23% of participants incorrectly judged the social information accuracy at least once. We discuss the implications of these findings for designing tools that support the validation of social information and thereby improve human-AI interactions.