Binoy Debnath, Zahra Pourfarash, Bhairavsingh Ghorpade
et al.
Reverse engineering (RE) is increasingly recognized as a vital methodology for reconstructing mechanical components, particularly in high-value sectors such as aerospace, transportation, and energy, where technical documentation is often missing or outdated. This study presents a systematic review that investigates the application, challenges, and future directions of RE in mechanical component reconstruction. Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 68 peer-reviewed studies were identified, screened, and synthesized. The review highlights RE applications in restoration, redesign, internal geometry modeling, and simulation-driven performance assessment, leveraging technologies such as 3D scanning, CAD modeling, and finite element analysis. However, persistent challenges remain across five domains: product complexity, tolerance and dimensional variations, scanning limitations, integration barriers, and human-material-process dependencies, which hinder automation, accuracy, and manufacturability. Future research opportunities include the automated conversion of point cloud data into editable boundary representation (B-rep) models and AI-driven approaches for feature recognition, geometry reconstruction, and the generation of simulation-ready models. Additionally, advancements in scanning techniques to capture hidden or internal features more effectively are crucial. Overall, this review provides a comprehensive synthesis of current practices and challenges while proposing pathways to advance RE in industrial applications, fostering greater automation, accuracy, and integration in digital manufacturing workflows.
Abstract Psycholinguistic research methods like self‐paced reading and eye‐tracking during reading yield precise data in milliseconds that indicate how much time participants spend looking at and reading specific words in a stimulus sentence. Collecting and analyzing such time‐based data entails some special considerations and three fundamental ones are covered in this entry. First, the design of stimulus sentences is an exacting task that must address multiple extraneous variables, such as word length and frequency, which might otherwise affect the data and obscure the effects of interest to the researchers. The easiest and most efficient way to ensure experimental control is to design stimulus sentences that are nearly identical across conditions except for a single word that is related to the manipulated variable. Second, stimulus items are random rather than fixed, meaning that they are similar to participants in that they are a sample drawn from a larger population and that the goal is to generalize results to a broader population. Statistical analysis should therefore capture random variation between stimulus items. Current practice accomplishes this with mixed‐effects models that include crossed random effects for subject and item with unaveraged data. Finally, recent attention to effect sizes in applied linguistics research has generated interest in establishing specific effect size guidelines that are appropriate for research with different methods. For self‐paced reading and eye‐tracking, it appears that effect sizes tend to be small, probably because reading is a largely automatic and unconscious process and because random variation in the data is relatively high.
Abstract Cultural data analytics aims to use analytic methods to explore cultural expressions—for instance art, literature, dance, music. The common thing between cultural expressions is that they have multiple qualitatively different facets that interact with each other in non trivial and non learnable ways. To support this observation, we use the Italian music record industry from 1902 to 2024 as a case study. In this scenario, a possible research objective could be to discuss the relationships between different music genres as they are performed by different bands. Estimating genre similarity by counting the number of records each band published performing a given genre is not enough, because it assumes bands operate independently from each other. In reality, bands share members and have complex relationships. These relationships cannot be automatically learned, both because we miss the data behind their creation, but also because they are established in a serendipitous way between artists, without following consistent patterns. However, we can be map them in a complex network. We can then use the counts of band records with a given genre as a node attribute in a band network. In this paper we show how recently developed techniques for node attribute analysis are a natural choice to analyze such attributes. Alternative network analysis techniques focus on analyzing nodes, rather than node attributes, ending up either being inapplicable in this scenario, or requiring the creation of more complex n-partite high order structures that can result less intuitive. By using node attribute analysis techniques, we show that we are able to describe which music genres concentrate or spread out in this network, which time periods show a balance of exploration-versus-exploitation, which Italian regions correlate more with which music genres, and a new approach to classify clusters of coherent music genres or eras of activity by the distance on this network between genres or years.
Arthur Philipe Cândido de Magalhães, Jesús Ángel Meneses Villagrá, Ileana Maria Greca
et al.
Este trabalho tem como finalidade descrever e interpretar características das concepções prévias de 18 estudantes do 4º ano do Ensino Fundamental, no contexto dos anos iniciais acerca dos conceitos de calor e temperatura. Para esta pesquisa utilizou-se uma abordagem qualitativa com objetivo descritivo, adotando o estudo de caso como procedimento. Como instrumentos de coleta de dados, teve-se a elaboração de um mapa mental, uma entrevista semiestruturada e uma roda de conversa. O diagnóstico revela que os alunos tem suas ideias prévias essencialmente de origem sensorial, o que implica em não diferenciar os conceitos de calor e temperatura, associação do calor somente as altas temperaturas, como uma substância, uma espécie de fluido, que pertence a um corpo e pode penetrar em outros. Ademais, a temperatura é apresentada com duas dimensões, frio e quente ou como oposto ao calor, mas identificam o termômetro como instrumento para verificar a temperatura. Os resultados possibilitam estruturar uma sequência de estudos, que não será objeto de análise neste artigo, que leve em consideração o que os estudantes sabem e faça-os avançar para uma perspectiva mais científica dos conceitos.
Special aspects of education, Applied mathematics. Quantitative methods
Ginger Egberts, Ginger Egberts, Fred Vermolen
et al.
Severe burn injuries often lead to skin contraction, leading to stresses in and around the damaged skin region. If this contraction leads to impaired joint mobility, one speaks of contracture. To optimize treatment, a mathematical model, that is based on finite element methods, is developed. Since the finite element-based simulation of skin contraction can be expensive from a computational point of view, we use machine learning to replace these simulations such that we have a cheap alternative. The current study deals with a feed-forward neural network that we trained with 2D finite element simulations based on morphoelasticity. We focus on the evolution of the scar shape, wound area, and total strain energy, a measure of discomfort, over time. The results show average goodness of fit (R2) of 0.9979 and a tremendous speedup of 1815000X. Further, we illustrate the applicability of the neural network in an online medical app that takes the patient's age into account.
Laura Plaza, Lourdes Araujo, Fernando López-Ostenero
et al.
Online learning is quickly becoming a popular choice instead of traditional education. One of its key advantages lies in the flexibility it offers, allowing individuals to tailor their learning experiences to their unique schedules and commitments. Moreover, online learning enhances accessibility to education, breaking down geographical and economical boundaries. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports e-learning students by tailoring materials and reinforcement activities to students’ needs. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content. The primary contributions of this research are three. Firstly, it centers on a remarkably specialized and novel domain; secondly, it introduces an effective recommendation approach exclusively guided by the student’s query. Thirdly, the recommendations not only provide answers to immediate questions, but also encourage further learning through the recommendation of supplementary activities.
This paper is related to the study of two temperature parameter effect for the axisymmetric deformation in a two-dimensional nonlocal homogeneous isotropic thick circular plate without energy dissipation. The plate is subjected to a cubical thermal source with a unit magnitude normal force. Laplace and Hankel transforms have been used to find the analytical solutions to the problem. The expressions for physical quantities such as displacement components, stress components and conductive temperature have been obtained in the transformed domain. The resulting quantities in the physical domain have been obtained with the use of inversion technique. The numerical simulated results have been depicted graphically for studying the effect of nonlocal parameter along with two temperature on the components of displacements, stresses and conductive temperature. The results obtained in this paper can be very useful for the researchers working in the field of nonlocal theory of thermoelasticity, in the fields of geophysics and various other scientific fields.
Innocent Simbanefayi, María Luz Gandarias, Chaudry Masood Khalique
This work presents a generalized hyper-elastic rod wave (gHRW) equation from the Lie symmetry method’s standpoint. The equation illustrates dispersive waves generating in hyper-elastic rods. Using multiplier approach we find conserved vectors of the underlying equation. We subsequently obtain first integrals of the conserved vectors under the time–space group invariant u(t,x)=H(x−νt). Finally, by analysing various attainable instances of the arbitrary coefficient function g(u), we perform symmetry reductions of gHRW equation to lower order ordinary differential equations and in some instances obtain analytic solutions for special values of arbitrary constants.
We study the derivative nonlinear Schrödinger equation which has several applications, such as the propagation of circular polarized nonlinear Alfvén waves in plasmas. We present general and special solutions of this equation using first integrals. Classical Lie group theory along with power series method is also applied to obtain exact analytical solutions of this equation. Finally, conservation laws of the underlying equation are constructed through the use of the multiplier method.
The evolution of information technology and the great advances in artificial intelligence are leading to a level of automation that has never been reached before. A large part of this level of automation is due to the use of robotics, which in turn ends up both hindering and accelerating the process of Industry 4.0. Industry 4.0 is driven by innovative technologies that have an effect on production systems and business models. Although technologies are the driving motors of production within Industry 4.0, many production systems require collaboration between robotics and humans, and safety is required for both parties. Given the need for robots to collaborate with humans simultaneously or in parallel, a new generation of robots, called cobots, “Collaborative Robots”, are gaining prominence to face these challenges. With cobots, it is possible to overcome security barriers and envisage working safely side-by-side with humans. This paper presents the development and testing of a low-cost, within standards, 6-axis collaborative robot that can be used for educational purposes in different task-specific applications. The development of this collaborative robot involves the design and 3D printing of the structure (connections and parts), sizing and selection of circuits and/or electronic components, programming, and control. Furthermore, this study considers the development of a user interface application with the robotic arm. Thus, the application of technological solutions, as well as of the scientific and educational approaches used in the development of cobots can foster the wide implementation of Industry 4.0.
Maria S. Zakynthinaki, Theodoros N. Kapetanakis, Anna Lampou
et al.
Estimating the heart rate (HR) response to exercises of a given intensity without the need of direct measurement is an open problem of great interest. We propose here a model that can estimate the heart rate response to exercise of constant intensity and its subsequent recovery, based on soft computing techniques. Multilayer perceptron artificial neural networks (NN) are implemented and trained using raw HR time series data. Our model’s input and output are the beat-to-beat time intervals and the HR values, respectively. The numerical results are very encouraging, as they indicate a mean relative square error of the estimated HR values of the order of 10<sup>−4</sup> and an absolute error as low as 1.19 beats per minute, on average. Our model has also been proven to be superior when compared with existing mathematical models that predict HR values by numerical simulation. Our study concludes that our NN model can efficiently predict the HR response to any constant exercise intensity, a fact that can have many important applications, not only in the area of medicine and cardio-vascular health, but also in the areas of rehabilitation, general fitness, and competitive sport.
We consider continuous-time Markov chains on integers which allow transitions to adjacent states only, with alternating rates. This kind of processes are useful in the study of chain molecular diffusions. We give explicit formulas for probability generating functions, and also for means, variances and state probabilities of the random variables of the process. Moreover we study independent random time-changes with the inverse of the stable subordinator, the stable subordinator and the tempered stable subordinator. We also present some asymptotic results in the fashion of large deviations. These results give some generalizations of those presented in [Journal of Statistical Physics 154 (2014), 1352–1364].
Abstract The hypergraph-of-entity is a joint representation model for terms, entities and their relations, used as an indexing approach in entity-oriented search. In this work, we characterize the structure of the hypergraph, from a microscopic and macroscopic scale, as well as over time with an increasing number of documents. We use a random walk based approach to estimate shortest distances and node sampling to estimate clustering coefficients. We also propose the calculation of a general mixed hypergraph density measure based on the corresponding bipartite mixed graph. We analyze these statistics for the hypergraph-of-entity, finding that hyperedge-based node degrees are distributed as a power law, while node-based node degrees and hyperedge cardinalities are log-normally distributed. We also find that most statistics tend to converge after an initial period of accentuated growth in the number of documents. We then repeat the analysis over three extensions—materialized through synonym, context, and tf_bin hyperedges—in order to assess their structural impact in the hypergraph. Finally, we focus on the application-specific aspects of the hypergraph-of-entity, in the domain of information retrieval. We analyze the correlation between the retrieval effectiveness and the structural features of the representation model, proposing ranking and anomaly indicators, as useful guides for modifying or extending the hypergraph-of-entity.
Fahrudin Fahrudin, Netriwati Netriwati, Rizki Wahyu Yunian Putra
The purpose of this study is to determine the improvement of the ability to understand mathematical concepts with problem-solving learning modification better than the ability to understand mathematical concepts with conventional learning. This research is a quantitative research type of quasi-experiment. Hypothesis test used in this research is t-test. The result of research indicates that the improvement of students' mathematical concept comprehension ability using problem-solving learning modification is better than the ability to understand mathematical concept using conventional learning.