Exemplary Application Research Articles

Application of machine learning in predicting frailty syndrome in patients with heart failure.

Prevention and diagnosis of frailty syndrome (FS) in patients with heart failure (HF) require innovative systems to help medical personnel tailor and optimize their treatment and care. Traditional methods of diagnosing FS in patients could be more satisfactory. Healthcare personnel in clinical settings use a combination of tests and self-reporting to diagnose patients and those at risk of frailty, which is time-consuming and costly. Modern medicine uses artificial intelligence (AI) to study the physical and psychosocial domains of frailty in cardiac patients with HF. This paper aims to present the potential of using the AI approach, emphasizing machine learning (ML) in predicting frailty in patients with HF. Our team reviewed the literature on ML applications for FS and reviewed frailty measurements applied to modern clinical practice. Our approach analysis resulted in recommendations of ML algorithms for predicting frailty in patients. We also present the exemplary application of ML for FS in patients with HF based on the Tilburg Frailty Indicator (TFI) questionnaire, taking into account psychosocial variables.

Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis

Natural hazards can present a significant risk to road infrastructure. This infrastructure is a fundamental component of the transportation infrastructure, with significant importance. During emergencies, society heavily relies on the functionality of the road infrastructure to facilitate evacuation and access to emergency facilities. This study introduces a versatile, multi-scale framework designed to analyze accessibility within road networks during natural hazard scenarios. The first module of the framework focuses on assessing the influence of natural hazards on road infrastructure to identify damaged or blocked road segments and intersections. It relies on near real-time information, often provided by citizen science through Volunteered Geographic Information (VGI) data and Natural Language Processing (NLP) of VGI texts. The second module conducts network analysis based on freely available Open Street Map (OSM) data, differentiating between intact and degraded road networks. Four accessibility measures are employed: betweenness centrality, closeness centrality, a free-flow assumption index, and a novel alternative routing assumption measure considering congestion scenarios. The study showcases its framework through an exemplary application in California, the United States, considering different hazard scenarios, where degraded roads and connected roads impacted by the hazard can be identified. The road extraction methodology allows the extraction of 75% to 100% of the impacted roads mentioned in VGI text messages for the respective case studies. In addition to the directly extracted impacted roads, constructing the degraded network also involves finding road segments that overlap with hazard impact zones, as these are at risk of being impacted. Conducting the network analysis with the four different measures on the intact and degraded network, changes in network accessibility due to the impacts of hazards can be identified. The results show that using each measure is justified, as each measure could demonstrate the accessibility change. However, their combination and comparison provide valuable insights. In conclusion, this study successfully addresses the challenges of developing a generic, complete framework from impact extraction to network analysis independently of the scale and characteristics of road network types.

Describing the digital competencies of mathematics teachers: theoretical and empirical considerations on the importance of experience and reflection

This article presents an extension of the well-known TPACK model to describe the professional digital competencies of mathematics teachers. The extension leads to what we want to call MPC-model (Media–Pedagogy–Content) in the following. It additionally includes (1) the consideration of competencies instead of knowledge for a holistic description, (2) the integration of professional digital competencies in a broader context of professional media competencies (including explicitly analog and digital teaching media), (3) the description of concrete individual experiences with digital technology in context-bound subjective domains of experience, and (4) the cross-linking of concrete individual experiences about (digital) technology in specific (subjective) domains of experience. In this article, we first present a motivating literature overview leading to the research question: How can the TPACK model be extended to enable a qualitative description of professional digital competencies of mathematics teachers against the background of situated experiences? This extended framework is developed and presented in a detailed theoretical background. In the empirical part of the article, an exemplary application of the MPC-model is carried out in an explicative case study dealing with the reflections of a mathematics teacher on a planned lesson using virtual reality technology in a guided interview. The qualitative data is interpreted according to the systematic-extensional analysis method. The case study illustrates the importance of taking into account concrete situated experiences opening up a new reflective level analyzing the development of professional mathematics-specific digital competencies.

A parametric additive hazard model for time-to-event analysis

BackgroundIn recent years, the use of non- and semi-parametric models which estimate hazard ratios for analysing time-to-event outcomes is continuously criticized in terms of interpretation, technical implementation, and flexibility. Hazard ratios in particular are critically discussed for their misleading interpretation as relative risks and their non-collapsibility. Additive hazard models do not have these drawbacks but are rarely used because they assume a non- or semi-parametric additive hazard which renders computation and interpretation complicated.MethodsAs a remedy, we propose a new parametric additive hazard model that allows results to be reported on the original time rather than on the hazard scale. Being an essentially parametric model, survival, hazard and probability density functions are directly available. Parameter estimation is straightforward by maximizing the log-likelihood function.ResultsApplying the model to different parametric distributions in a simulation study and in an exemplary application using data from a study investigating medical care to lung cancer patients, we show that the approach works well in practice.ConclusionsOur proposed parametric additive hazard model can serve as a powerful tool to analyze time-to-event outcomes due to its simple interpretation, flexibility and facilitated parameter estimation.

Human Robot Interaction with Social Humanoid Robots

The widespread use of robots in social areas has revealed the necessity of having social features. Seamless integration of robots into human life should be achieved through a natural interaction. In order to establish this interaction efficiently, robot applications should be user-friendly. In the pertinent literature, it has been observed that users experienced serious difficulties in this regard, and it has not been recognized widely. In addition to this, it is seen that human-robot interaction is not efficient in several cases. In this study, the problems encountered during utilization of Pepper Robot, which is defined as the first social humanoid robot, in the process of human-robot interaction were revealed and an exemplary application was developed to resolve these problems. In this application, it was seen that it is necessary for social humanoid robots to be the initiator of the interaction in order to socialize with humans in a natural way.

Photo-induced microfluidic production of ultrasmall platinum nanoparticles.

We describe here the synthesis of ultrasmall Pt nanoparticles (NPs) obtained by a robust and reliable protocol using UV-Vis photoreduction of a platinum salt precursor, under continuous flow conditions. These ligand-free Pt NPs were rapidly dispersed onto a solid support or stabilized towards aggregation as a colloidal solution by the addition of an appropriate ligand in the reaction mixture. The proposed protocol exploits a microfluidic platform where the Pt4+ precursor is photo-reduced to small Pt0 NPs (1.3 nm) at room temperature in the presence of ethanol, without any additional reducing agent. We apply the protocol to prepare Pt NPs highly dispersed on carbon support (Pt/C) proven to be a very efficient heterogeneous catalyst for both the hydrosilylation of terminal alkynes and hydrogenation of nitroaromatic compounds, selected as model reactions. Furthermore, we exploit the versatility of this microfluidic approach to produce stabilized aqueous/ethanol colloidal solutions of Pt NPs, employing a ligand of choice (e.g., PVP or a thiol-ligand). These colloids offer long-term storage and further ligand modification. We showcase the synthesis of biocompatible glycol-stabilized Pt nanoparticles as an exemplary application.

A rapid lithium-ion cathode discovery pipeline and its exemplary application

A new computational screening pipeline accelerates the process of identifying potential Li-ion cathodes, with a demonstration case upon its preliminary application.

Curvature estimation for point cloud 2-manifolds based on the heat kernel

<p>The geometry processing of a point cloud 2-manifold (or point cloud surface) heavily depends on the discretization of differential geometry properties such as Gaussian curvature, mean curvature, principal curvature, and principal directions. Most of the existing algorithms indirectly compute these differential geometry properties by seeking a local approximation surface or fitting point clouds with certain polynomial functions and then applying the curvature formulas in classical differential geometry. This paper initially proposed a new discretized Laplace-Beltrami operator by applying an inherent distance parameter, which acts as the foundation for precisely estimating the mean curvature. Subsequently, the estimated mean curvature was taken as a strong constraint condition for estimating the Gaussian curvatures, principal curvatures, and principal directions by determining an optimal ellipse. The proposed methods are mainly based on the heat kernel function and do not require local surface reconstruction, thus belonging to truly mesh-free methods. We demonstrated the correctness of the estimated curvatures in both analytic and non-analytic models. Various experiments indicated that the proposed methods have high accuracy. As an exemplary application, we utilized the mean curvature for detecting features of point clouds.</p>

Learning to manage the unexpected: applying Weick and Sutcliffe’s HRO principles to oil tanker accidents

PurposeWeick and Sutcliffe identified five principles that enable high-reliability organizations (HROs) to address environmental complexity and manage unexpected events. The current study aims to adopt this sensemaking perspective to analyze accidents within a typical HRO sector, namely maritime transport.Design/methodology/approachThrough a retrospective case study analysis, this study focused on seven oil tanker accidents, using them as illustrative examples.FindingsFindings show how the five principles contributed to the accidents' occurrence, explaining how failures in sensemaking affected the crew's capability to both prevent errors and cope with their consequences, thus leading to disasters.Research limitations/implicationsOverall, the study offers an applicative contribution showing how this model may provide a reliable framework for analyzing the psychosocial factors affecting an accident. This approach deepens the understanding of how latent factors are enacted and how the prevention and error management phases interrelate within a comprehensive flow of the entire accident sequence. Furthermore, the study emphasizes consistent patterns that emerge across multiple accidents within the same sector, in order to learn valuable lessons to improve safety measures in the future.Originality/valueThis study constitutes an exemplary application in support of how Weick and Sutcliffe’s model is valuable for investigating HROs. It offers a second-order interpretative framework to understand accidents and underscores the interplay among these factors during the dynamic development of an accident.

An examplary application of mathematics teaching based on formative assessment

Mathematics, by nature, has a spiral structure. The fact that the students fail to acquire an achievement in the mathematics course negatively affects other achievements. Therefore, students' mathematics failure continues exponentially. In this context, it is important to identify and eliminate students’ weaknesses, if any, with the formative assessments in the learning process. The present study aimed to improve the success level of the students with the formative assessment-based teaching practice about circle and circular region in secondary school seventh-grade mathematics teaching and implement an exemplary application for formative assessment-based teaching. Since the study sought to evaluate the learning process of the students and eliminate the weaknesses identified in the process, an action research design, one of the methods of qualitative research, was used. The study was carried out with 34 seventh-grade secondary school students. Data were collected and analyzed descriptively through observation, interview and various formative assessment tools used in the process. At the end of the three-week implementation period, substantial improvement was observed in the achievements of low and medium-achieving students, and the overall success of the students changed positively at the end of the process.

Jeszcze szybsza lampa błyskowa czyli Nagroda Nobla z fizyki 2023

Nobel Prize in Physics in 2023 was awarded for generation of attosecond (1 as=10−18 s) pulses of electromagnetic radiation and application of attosecond pulses in studies of electrons in condensedmatter. I show basic physicalmechanisms leading to generation of such pulses and methods to measure them. I also show an exemplary application of attosecond pulses - studies dynamic of photo effect.

Robust incorporation of historical information with known type I error rate inflation.

Bayesian clinical trials can benefit from available historical information through the specification of informative prior distributions. Concerns are however often raised about the potential for prior-data conflict and the impact of Bayes test decisions on frequentist operating characteristics, with particular attention being assigned to inflation of type I error (TIE) rates. This motivates the development of principled borrowing mechanisms, that strike a balance between frequentist and Bayesian decisions. Ideally, the trust assigned to historical information defines the degree of robustness to prior-data conflict one is willing to sacrifice. However, such relationship is often not directly available when explicitly considering inflation of TIE rates. We build on available literature relating frequentist and Bayesian test decisions, and investigate a rationale for inflation of TIE rate which explicitly and linearly relates the amount of borrowing and the amount of TIE rate inflation in one-arm studies. A novel dynamic borrowing mechanism tailored to hypothesis testing is additionally proposed. We show that, while dynamic borrowing prevents the possibility to obtain a simple closed-form TIE rate computation, an explicit upper bound can still be enforced. Connections with the robust mixture prior approach, particularly in relation to the choice of the mixture weight and robust component, are made. Simulations are performed to show the properties of the approach for normal and binomial outcomes, and an exemplary application is demonstrated in a casestudy.

Reduced order modeling of structural problems with damage and plasticity

AbstractModel order reduction (MOR) techniques are used across the engineering sciences to reduce the computational complexity of high‐fidelity simulations. MOR methods reduce the computation time by representing the problem using a lower number of degrees of freedom (DOF). The use of reduced order models (ROM) in the analysis of structural problems with damage and plasticity has the potential to significantly reduce computational time and increase efficiency. Of course, the approximation of a problem in a lower dimensional space introduces an approximation error that needs to be kept small enough so that the results of the ROM maintain their validity. One well‐known reduced order modeling approach is the proper orthogonal decomposition (POD). POD is used to extract the dominant modes of the structure, which are then used to solve a problem in the smaller dimensional subspace. To overcome the limitations of the POD regarding nonlinear problems, the discrete empirical interpolation method (DEIM) is employed. An exemplary uncertainty quantification application is used to investigate the methodology. The investigation shows that the POD‐based DEIM can significantly reduce the computational effort of highly nonlinear structural simulations incorporating damage while maintaining a high approximation accuracy.

Trustworthy Digital Representations of Analog Information—An Application-Guided Analysis of a Fundamental Theoretical Problem in Digital Twinning

This article compares two methods of algorithmically processing bandlimited time-continuous signals in light of the general problem of finding “suitable” representations of analog information on digital hardware. Albeit abstract, we argue that this problem is fundamental in digital twinning, a signal-processing paradigm the upcoming 6G communication-technology standard relies on heavily. Using computable analysis, we formalize a general framework of machine-readable descriptions for representing analytic objects on Turing machines. Subsequently, we apply this framework to sampling and interpolation theory, providing a thoroughly formalized method for digitally processing the information carried by bandlimited analog signals. We investigate discrete-time descriptions, which form the implicit quasi-standard in digital signal processing, and establish continuous-time descriptions that take the signal’s continuous-time behavior into account. Motivated by an exemplary application of digital twinning, we analyze a textbook model of digital communication systems accordingly. We show that technologically fundamental properties, such as a signal’s (Banach-space) norm, can be computed from continuous-time, but not from discrete-time descriptions of the signal. Given the high trustworthiness requirements within 6G, e.g., employed software must satisfy assessment criteria in a provable manner, we conclude that the problem of “trustworthy” digital representations of analog information is indeed essential to near-future information technology.

Levels of policy drivers: Introducing and demonstrating an analytical framework for education policy spread

A plethora of scholars investigate how policies spread globally. This paper establishes an analytical framework to structure empirical macro-quantitative research on global education policy spread and enable this analysis to be more comprehensive. Different theories and concepts of drivers for education policy are structured into three distinct levels of origin: the domestic, and the horizontal and vertical interrelational level of policy drivers (LoPD). Analyzing education policy spread considering these levels, it will be argued, enriches analysis normally focusing only on one single level. An exemplary empirical application on the spread of Adult Basic Education policies suggests the merits of this framework. While not contradicting existing theories, it allows a more thorough analysis of mechanisms of policy spread. Hence, the LoPD framework can serve as a springboard to analyze policy spread in a more structured and holistic way. Thus, it can focus macro-quantitative research on education policy spread on optimizing measures.

Discrete Modeling Approach for Cluster-Based Excess Gibbs-Energy of Molecular Liquids.

The excess Gibbs-energy of a two-component liquid molecular mixture is modeled based on discrete clusters of molecules. These clusters preserve the three-dimensional geometric information about local molecule neighborhoods that inform the interaction energies of the clusters. In terms of a discrete Markov-chain, the clusters are used to hypothetically construct the mixture using sequential insertion steps. Each insertion step and, therefore, cluster is assigned a probability of occurring in an equilibrium system that is determined via the constrained minimization of the Helmholtz free energy. For this, informational Shannon entropy based on these probabilities is used synonymously with thermodynamic entropy. A first approach for coupling the model to real molecules is introduced in the form of a molecular sampling algorithm, which utilizes a force-field approach to determine the energetic interactions within a cluster. An exemplary application to four mixtures shows promising results regarding the description of a variety of excess Gibbs-energy curves, including the ability to distinguish between structural isomers.

Learning coarse-grained force fields for fibrogenesis modeling

Fine-grained molecular dynamics (FGMD) can examine the dynamical and structural details of many complex systems including those found in biology, and can mitigate the many deficiencies of in vitro experiments. However these advantages come at the cost of prohibitive computing expense. Coarsening molecular structure and dynamics can dramatically diminish the computing complexity of simulations, while retaining modeling accuracies, and have become the state-of-the-art alternative to FGMD studies. Here we formulate the coarsening operator for application to fine-grained coordinates and develop the corresponding coarsening operator for forces to mathematically facilitate the transformation. In an exemplary application to studying the formation of fibrin networks of a few hundreds of soluble protein fibrinogens, we leverage multiscale molecular dynamics and machine learning to develop a physics-informed parameter learning (PIPL) framework with the learnable force parameters in physics priors as the learning target. While its adaptivity or performance on more general macromolecules remains untested, we show its performances, measured in terms of speed and accuracy, and demonstrate that simulations with the learned CGMD force field achieve higher accuracies and higher speeds than with existing methods. The resulting predictions of our model, additionally, are validated by in vitro observations.

INTERNALIZING DISCIPLINE CHARACTER TO SENIOR HIGH SCHOOLS’ STUDENTS

The internalization of discipline is a necessary character value to follow the many regulations of a school. Students at SMA Negeri 1 Halmahera and SMA Negeri 7 in the South Halmahera Regency were surveyed for this study with the hope of encouraging them to adopt a more disciplined mindset. Qualitative research techniques were used for this analysis. Using an approach based on habituation of disciplinary values, experience, and exercises to strengthen discipline values, and exemplary application by exemplary teachers and students as role models. The results showed that SMA Negeri 1 and SMA Negeri 7 in South Halmahera Regency, North Maluku Province successfully internalized disciplinary values. Students who 1) arrive at school promptly at 7.00 WIT; 2) read a short letter for 15 minutes before beginning the lesson; and 3) stay for 5 minutes after the end of class to sing the national anthem are more likely to internalize the discipline. These three activities are performed daily. The cultivation of discipline in students, particularly through adherence to established norms and programs, enables them to effectively incorporate these principles into their daily lives. Keywords: values internalization; character; students’ discipline; senior high school

Open‐Cell Robust COF‐Nanowire Network Sponges as Sustainable Adsorbent and Filter

AbstractConstructing crystalline covalent organic frameworks (COF) robust 3D reusable macroscopic objects exposing more adsorption sites with high water flux for use as a filter is an unresolved challenge. A simple scalable procedure is shown for making a robust, highly compressible 3D crystalline COF nanowire interconnected porous open‐cell sponge. The compressive strength and Young's modulus (80% strain) of the sponge are 175 and 238 kPa, respectively. The sponge can withstand multiple compression‐release cycles and a load of 2800 times its weight without collapsing. As an exemplary application, the use of a COF sponge in the selective removal and separation of cationic model dye from a mixture of dyes in water by adsorption and filtration with >99% efficiency is shown. Depending on the dye concentration, the dye removal time can be as short as 2 min, and dye adsorption efficiency can be as high as 653 mg g−1 (COF in the sponge). During filtration, the sponges as filters show a high water flux of 2355 L h−1 m−2 under ambient conditions and maintain their performance for many cycles. The lightweight, reusability, and efficiency make present sponges sustainable materials as adsorbents and filters.

A Scalar Product for Computing Fundamental Quantities in Matter

We introduce a systematic way to obtain expressions for computing the amount of fundamental quantities such as helicity and angular momentum contained in static matter, given its charge and magnetization densities. The method is based on a scalar product that we put forward which is invariant under the ten-parameter conformal group in three-dimensional Euclidean space. This group is obtained as the static restriction (frequency ω=0) of the symmetry group of Maxwell equations: The fifteen-parameter conformal group in 3+1 Minkowski spacetime. In an exemplary application, we compute the helicity and angular momentum squared stored in a magnetic Hopfion.