Matthew T. Singh, David M. Austin, Stephanie C. Mullennix
et al.
Introduction: Throughout training, an emergency medicine (EM) resident is required to increase efficiency and productivity to ensure safe practice after graduation. Multitasking is one of the 22 Accreditation Council for Graduate Medical Education (ACGME) EM milestones and is often measured through evaluations and observation. Providing quantitative data to both residents and residency administration on patients seen per hour (PPH) and efficiency could improve a resident experience and training in many ways. Our study was designed to analyze various throughput metrics and productivity trends using applied mathematics and a robust dataset. Our goals were to define the curve of resident PPH over time, adjust for relevant confounders, and analyze additional efficiency metrics related to throughput such as door-to-decision time (DTDT). Methods: We used a retrospective, observational design in a single, tertiary-care center emergency department (ED) that sees approximately 110,000 adult patients per year; our study spanned the period July 1, 2019–December 31, 2021. A total of 42 residents from an ACGME-accredited three-year residency were included in the analysis. We excluded patients <18 years of age. Data was collected using a secure data vendor, and we created an exponential regression model to assess resident PPH data. Additional models were created accounting for patient covariates. Results: We analyzed a total of 79,232 patients over 30 months. Using an exponential equation and adjusting for patient covariates, median PPH started at 0.898 and ended at 1.425 PPH. The median PPH by postgraduate (PGY) year were 1.13 for PGY 1; 1.38 for PGY 2; and 1.38 for PGY 3. Median DTDT in minutes was as follows: 185 minutes for PGY 1; 171 for PGY 2; and 166 for PGY 3. Conclusion: Productivity and efficiency metrics such as PPH and DTDT data are an essential part of working in an ED. Our study shows that residents improve with number of patients seen per hour over three years but tend to plateau in their second year. Door-to-decision time continued to improve throughout their three years of training.
Medicine, Medical emergencies. Critical care. Intensive care. First aid
Antonio Santos da Silva, Kevin Herman Muraro Gularte, Giovanni Almeida Santos
et al.
Autonomous vehicles (AVs) are transforming transportation by improving safety, efficiency, and intelligence through integrated sensing, computing, and communication technologies. However, their growing reliance on Vehicle-to-Everything (V2X) communication exposes them to cybersecurity vulnerabilities, particularly at the physical layer. Among these, jamming attacks represent a critical threat by disrupting wireless channels and compromising message delivery, severely impacting vehicle coordination and safety. This work investigates the robustness of New Radio (NR)-V2X-enabled vehicular systems under jamming conditions through a dual-methodology approach. First, two Cooperative Intelligent Transport System (C-ITS) scenarios standardized by 3GPP—Do Not Pass Warning (DNPW) and Intersection Movement Assist (IMA)—are implemented in the OMNeT++ simulation environment using Simu5G, Veins, and SUMO. The simulations incorporate four types of jamming strategies and evaluate their impact on key metrics such as packet loss, signal quality, inter-vehicle spacing, and collision risk. Second, a complementary laboratory experiment is conducted using AnaPico vector signal generators (a Keysight Technologies brand) and an Anritsu multi-channel spectrum receiver, replicating controlled wireless conditions to validate the degradation effects observed in the simulation. The findings reveal that jamming severely undermines communication reliability in NR-V2X systems, both in simulation and in practice. These findings highlight the urgent need for resilient NR-V2X protocols and countermeasures to ensure the integrity of cooperative autonomous systems in adversarial environments.
Raúl Guinovart-Díaz, Julián Bravo-Castillero, Manuel E. Cruz
et al.
A two-dimensional three-phase conducting composite with coated circular inclusions, periodically distributed in a parallelogram, is studied. The phases are assumed to be isotropic, and perfect contact conditions at the interfaces are considered. The effective behavior is determined by combining the asymptotic homogenization method with elements of the analytic function theory. The solution to local problems is sought as a series of Weierstrass elliptic functions and their derivatives with complex undetermined coefficients. The effective coefficients depend on the residue of such a solution, which in turn depends on products of vectors and matrices of infinite order. Systematic truncation of these vectors and matrices provides unified analytical formulas for the effective coefficients for any parallelogram periodic cell. The corresponding formulas for the particular cases of two-phase fibrous composites with perfect and imperfect contact at the interface are also explicitly provided. The results were applied to derive the critical normalized interfacial layer thickness and to analyze the enhancement of thermal conductivity in fibrous composites with annular cross sections. Furthermore, using a reiterated homogenization method, the analytical approach allows us to study the gains in the effective thermal conductivity tensor with thermal barriers and parallelogram cells. Numerical examples and comparisons validate the model. A simple and validated algorithm is provided that allows the calculation of effective coefficients for any parallelogram, any truncation order, and high fiber volume fractions very close to percolation. The programs created for validation are available in a freely accessible repository.
In this study, a biosensor based on an electrode that has been chemically altered is represented mathematically. The homotopy perturbation method analytically solves the model, and simulation results are verified against analytical solutions for various physical parameter values. The biosensor's mathematical model comprises three layers: an exterior diffusion layer, a dialysis membrane, and an enzyme layer. An effective diffusion coefficient is added to the mathematical model to integrate the diffusion layer with the dialysis membrane. Then, the problem is reduced to the two-layer model, which can effectively replace the three-layer model. The homotopy perturbation method (HPM) is used to solve the non-linear system of chemically modified electrode equations into an approximate analytical formulation. These formulations will be compared with MATLAB-based numerical simulations. Furthermore, an alternative to the current density in steady-state and the biosensor's sensitivity, resistance are explored.
Sylvain Fontaine, Floriana Gargiulo, Michel Dubois
et al.
Abstract In recent years, Artificial Intelligence (AI) shows a spectacular ability of insertion inside a variety of disciplines which use it for scientific advancements and which sometimes improve it for their conceptual and methodological needs. According to the transverse science framework originally conceived by Shinn and Joerges, AI can be seen as an instrument which is progressively acquiring a universal character through its diffusion across science. In this paper we address empirically one aspect of this diffusion, namely the penetration of AI into a specific field of research. Taking neuroscience as a case study, we conduct a scientometric analysis of the development of AI in this field. We especially study the temporal egocentric citation network around the articles included in this literature, their represented journals and their authors linked together by a temporal collaboration network. We find that AI is driving the constitution of a particular disciplinary ecosystem in neuroscience which is distinct from other subfields, and which is gathering atypical scientific profiles who are coming from neuroscience or outside it. Moreover we observe that this AI community in neuroscience is socially confined in a specific subspace of the neuroscience collaboration network, which also publishes in a small set of dedicated journals that are mostly active in AI research. According to these results, the diffusion of AI in a discipline such as neuroscience didn’t really challenge its disciplinary orientations but rather induced the constitution of a dedicated socio-cognitive environment inside this field.
X-ray photoelectron spectroscopy (XPS) remains a fundamental technique in materials science, offering invaluable insights into the chemical states and electronic structure of a material. However, the interpretation of XPS spectra can be complex, requiring deep expertise and often sophisticated curve-fitting methods. In this study, we present a novel approach to the analysis of XPS data, integrating the utilization of large language models (LLMs), specifically OpenAI’s GPT-3.5/4 Turbo to provide insightful guidance during the data analysis process. Working in the framework of the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility where data are obtained using ambient pressure X-ray photoelectron spectroscopy (APXPS), we implement robust curve-fitting techniques on APXPS spectra, highlighting complex cases including overlapping peaks, diverse chemical states, and noise presence. Post curve fitting, we engage the LLM to facilitate the interpretation of the fitted parameters, leaning on its extensive training data to simulate an interaction corresponding to expert consultation. The manuscript presents also a real use case utilizing GPT-4 and Meta’s LLaMA-2 and describes the integration of the functionality into the TANGO control system. Our methodology not only offers a fresh perspective on XPS data analysis, but also introduces a new dimension of artificial intelligence (AI) integration into scientific research. It showcases the power of LLMs in enhancing the interpretative process, particularly in scenarios wherein expert knowledge may not be immediately available. Despite the inherent limitations of LLMs, their potential in the realm of materials science research is promising, opening doors to a future wherein AI assists in the transformation of raw data into meaningful scientific knowledge.
Ginevra Lautizi, Alain Studer, Marie-Christine Zdora
et al.
We present a versatile method for full-field, X-ray scattering tensor tomography that is based on energy conservation and is applicable to data obtained using different wavefront modulators. Using this algorithm, we pave the way for speckle-based tensor tomography. The proposed model relies on a mathematical approach that allows tuning spatial resolution and signal sensitivity. We present the application of the algorithm to three different imaging modalities and demonstrate its potential for applications of X-ray directional dark-field imaging.
Muhammad Luthfi Shahab, Hadi Susanto, Haralampos Hatzikirou
Solving the three-dimensional (3D) Bratu equation is highly challenging due to the presence of multiple and sharp solutions. Research on this equation began in the late 1990s, but there are no satisfactory results to date. To address this issue, we introduce a symmetric finite difference method (SFDM) which embeds the symmetry properties of the solutions into a finite difference method (FDM). This SFDM is primarily used to obtain more accurate solutions and bifurcation diagrams for the 3D Bratu equation. Additionally, we propose modifying the Bratu equation by incorporating a new constraint that facilitates the construction of bifurcation diagrams and simplifies handling the turning points. The proposed method, combined with the use of sparse matrix representation, successfully solves the 3D Bratu equation on grids of up to $301^3$ points. The results demonstrate that SFDM outperforms all previously employed methods for the 3D Bratu equation. Furthermore, we provide bifurcation diagrams for the 1D, 2D, 4D, and 5D cases, and accurately identify the first turning points in all dimensions. All simulations indicate that the bifurcation diagrams of the Bratu equation on the cube domains closely resemble the well-established behavior on the ball domains described by Joseph and Lundgren [1]. Furthermore, when SFDM is applied to linear stability analysis, it yields the same largest real eigenvalue as the standard FDM despite having fewer equations and variables in the nonlinear system.
Maria Gisele dos Santos, Cinthia Lopes da Silva, Lilia Aparecida Kanan
et al.
A educação superior requer inovações e um esforço dos docentes responsáveis pelas disciplinas para que os novos conhecimentos sejam acessíveis aos graduandos, para que consigam produzir múltiplos sentidos aos temas trabalhados. A interdisciplinaridade é também fundamental nesse contexto e sua correspondência na Educação Básica, já que tradicionalmente há uma divisão entre as ciências e os conhecimentos produzidos. Assim, este trabalho, a partir dos pressupostos qualitativos, apresenta uma investigação baseada em experiência pedagógica e tem como objetivos: (i) viabilizar aos futuros professores de Educação Física de uma instituição pública do sul do Brasil, o acesso ao conhecimento interdisciplinar no ensino do conteúdo bioenergética, e (ii) analisar a experiência realizada. A experiência pedagógica é guiada pelas diferentes linguagens relacionadas à arte e ao corpo, resultando na produção de planos de ensino voltados ao Ensino Fundamental da Educação Básica. Como resultados, verificamos que a busca pelo conhecimento interdisciplinar, aliada à mobilização das emoções, é uma abordagem pedagógica poderosa que não apenas enriquece a jornada de aprendizado dos estudantes, mas também, capacita-os a responder às demandas da vida contemporânea a partir de um horizonte teórico ampliado e potencializado holisticamente. Tal abordagem é um investimento valioso no desenvolvimento de futuros professores que serão agentes de transformação na sociedade e terão expertise para mediar conhecimentos relacionados ao corpo e às manifestações corporais.
Special aspects of education, Applied mathematics. Quantitative methods
Elio Chiodo, Fabio De Angelis, Bassel Diban
et al.
In the present paper, the process of estimating the important statistical properties of extreme wind loads on structures is investigated by considering the effect of large variability. In fact, for the safety design and operating conditions of structures such as the ones characterizing tall buildings, wind towers, and offshore structures, it is of interest to obtain the best possible estimates of extreme wind loads on structures, the recurrence frequency, the return periods, and other stochastic properties, given the available statistical data. In this paper, a Bayes estimation of extreme load values is investigated in the framework of structural safety analysis. The evaluation of extreme values of the wind loads on the structures is performed via a combined employment of a Poisson process model for the peak-over-threshold characterization and an adequate characterization of the parent distribution which generates the base wind load values. In particular, the present investigation is based upon a key parameter for assessing the safety of structures, i.e., a proper safety index referred to a given extreme value of wind speed. The attention is focused upon the estimation process, for which the presented procedure proposes an adequate Bayesian approach based upon prior assumptions regarding (1) the Weibull probability that wind speed is higher than a prefixed threshold value, and (2) the frequency of the Poisson process of gusts. In the last part of the investigation, a large set of numerical simulations is analyzed to evaluate the feasibility and efficiency of the above estimation method and with the objective to analyze and compare the presented approach with the classical Maximum Likelihood method. Moreover, the robustness of the proposed Bayes estimation is also investigated with successful results, both with respect to the assumed parameter prior distributions and with respect to the Weibull distribution of the wind speed values.
We introduce a new approach to the the asymptotic iteration method (AIM) by means of which we establish the standard AIM connection with the continued fractions technique and we develop a novel termination condition in terms of the approximants. With the help of this alternative termination condition and certain properties of continuous fractions, we derive a closed formula for the asymptotic function $α$ of the AIM technique in terms of an infinite series. Furthermore, we show that such a series converges pointwise to $α$ which, in turn, can be interpreted as a specific term of the minimal solution of a certain recurrence relation. We also investigate some conditions ensuring the existence of a minimal solution and hence, of the function $α$ itself.
Since the early twentieth century, it has been understood that mathematical definitions and proofs can be represented in formal systems systems with precise grammars and rules of use. Building on such foundations, computational proof assistants now make it possible to encode mathematical knowledge in digital form. This article enumerates some of the ways that these and related technologies can help us do mathematics.
Ariadne da Costa Peres , Licurgo Peixoto de Brito (In Memorian), Marcos Augusto Carvalho Pereira
O estado da arte tem a função de situar o pesquisador acerca da produção, pesquisas e posicionamentos sobre o seu o objeto de estudos, ação que norteia as intenções de pesquisa. Nesse sentido, investigar as pesquisas sobre a educação ambiental em parques ambientais, relacionadas a educação científica, no campo da Ciência Tecnologia Sociedade e Meio ambiente, tem relevância para construção de uma educação ligada ao meio ambiente que fomente o debate crítico, a discussão do contraditório e o posicionamento diante dos avanços científicos e os desastres ambientais em nosso planeta. Para isso, fez-se uma busca em três bibliotecas virtuais: a primeira, “Biblioteca Digital de teses e dissertações” (BDTD); a segunda, “Banco de Teses e Dissertações CAPES” e, por último, no “Portal de periódicos CAPES/MEC”. Das 325 obras recuperadas, apenas duas se relacionam com o objeto de estudos selecionado - uma tratando de uma experiência pedagógica na relação parque ambiental-escola e outra tematizando a formação de professores para atuação em parques ambientais. Nessas pesquisas, temos a concepção crítica de educação que fomenta o debate sobre a ciência, tecnologia e as questões socioambientais, emergindo ainda da análise e apontamentos para a formação de professores. Com isso, a pesquisa contribui para o debate crítico acerca da educação ambiental e nos lega a necessidade de mais pesquisas relativas a educação científica, relacionando escolas e parques ambientais.
Special aspects of education, Applied mathematics. Quantitative methods
In this paper, we show a way to characterize the R-automorphisms of formal power series on several indeterminates and with coefficients over a commutative ring with identity, R. We show this characterization, as an extension of existing result for the R-automorphisms of the formal power series in an indeterminate, given by O’Malley, M. and Wood, C. in [12].
Mitchell J. Feldmann, Michael A. Hardigan, Randi A. Famula
et al.
Abstract Background Shape is a critical element of the visual appeal of strawberry fruit and is influenced by both genetic and non-genetic determinants. Current fruit phenotyping approaches for external characteristics in strawberry often rely on the human eye to make categorical assessments. However, fruit shape is an inherently multi-dimensional, continuously variable trait and not adequately described by a single categorical or quantitative feature. Morphometric approaches enable the study of complex, multi-dimensional forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the Principal Progression of k Clusters (PPKC). We use these human-recognizable shape categories to select quantitative features extracted from multiple morphometric analyses that are best fit for genetic dissection and analysis. Results We transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered 4 principal shape categories, and inferred progression using PPKC. We extracted 68 quantitative features from digital images of strawberries using a suite of morphometric analyses and multivariate statistical approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68% to 99% for the newly created phenotypic variables for describing a shape. Conclusions Our results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches that we applied in strawberry should apply to other fruits, vegetables, and specialty crops.
Robin Camarasa, Daniel Bos, Jeroen Hendrikse
et al.
Uncertainty assessment has gained rapid interest in medical image analysis. A popular technique to compute epistemic uncertainty is the Monte-Carlo (MC) dropout technique. From a network with MC dropout and a single input, multiple outputs can be sampled. Various methods can be used to obtain epistemic uncertainty maps from those multiple outputs. In the case of multi-class segmentation, the number of methods is even larger as epistemic uncertainty can be computed voxelwise per class or voxelwise per image. This paper highlights a systematic approach to define and quantitatively compare those methods in two different contexts: class-specific epistemic uncertainty maps (one value per image, voxel and class) and combined epistemic uncertainty maps (one value per image and voxel). We applied this quantitative analysis to a multi-class segmentation of the carotid artery lumen and vessel wall, on a multi-center, multi-scanner, multi-sequence dataset of (MR) images. We validated our analysis over 144 sets of hyperparameters of a model. Our main analysis considers the relationship between the order of the voxels sorted according to their epistemic uncertainty values and the misclassification of the prediction. Under this consideration, the comparison of combined uncertainty maps reveals that the multi-class entropy and the multi-class mutual information statistically out-perform the other combined uncertainty maps under study. In a class-specific scenario, the one-versus-all entropy statistically out-performs the class-wise entropy, the class-wise variance and the one versus all mutual information. The class-wise entropy statistically out-performs the other class-specific uncertainty maps in terms of calibration. We made a python package available to reproduce our analysis on different data and tasks.