Search Results (978)

Currently, high-tech assistive technologies (ATs), particularly Socially Assistive Robots (SARs), virtual reality (VR) and conversational AI (ConvAI), are considered very useful in supporting professionals in Speech and Language Therapy (SLT) for children with communication disorders. However, despite a positive public perception, therapists face difficulties when integrating these technologies into practice due to technical challenges and a lack of user-friendly interfaces. To address this gap, a design-based research approach has been employed to streamline the integration of SARs, VR and ConvAI in SLT, and a new software platform called “ATLog” has been developed for designing interactive and playful learning scenarios with ATs. ATLog’s main features include visual-based programming with graphical interface, enabling therapists to intuitively create personalized interactive scenarios without advanced programming skills. The platform follows a subprocess-oriented design, breaking down SAR skills and VR scenarios into microskills represented by pre-programmed graphical blocks, tailored to specific treatment domains, therapy goals, and language skill levels. The ATLog platform was evaluated by 27 SLT experts using the Technology Acceptance Model (TAM) and System Usability Scale (SUS) questionnaires, extended with additional questions specifically focused on ATLog structure and functionalities. According to the SUS results, most of the experts (74%) evaluated ATLog with grades over 70, indicating high acceptance of its usability. Over half (52%) of the experts rated the additional questions focused on ATLog’s structure and functionalities in the A range (90–100), while 26% rated them in the B range (80–89), showing strong acceptance of the platform for creating and running personalized interactive scenarios with ATs. According to the TAM results, experts gave high grades for both perceived usefulness (44% in the A range) and perceived ease of use (63% in the A range). Full article

In a previous article, an estimation procedure for calculating the liquidus and solidus lines of binary equilibrium phase diagrams was presented. In this article, keeping the thermodynamic basics, the estimation method for the approximate calculation of the liquidus and solidus surfaces of ternary phase diagrams was further developed. It is shown that the procedure has a hierarchical structure, and the ternary functions contain the binary functions. The applicability of the method is checked by calculating the liquidus and solidus surfaces of the Ag-Au-Pd isomorphous ternary equilibrium phase diagram. The application of each level of the developed four-level procedure depends on the data available and the aim. It is shown that in the case of a concentration range close to the base alloy pure element, the liquidus and solidus surfaces of the ternary equilibrium phase diagram can be calculated from the liquidus and solidus functions of the binary equilibrium phase diagrams with a few K errors, which is 0.2 at% at 10 K/at% slope. The equilibrium phase diagrams were available in graphical form, so the data obtained via digitalisation of the diagrams for the calculations was used. The functions describe the slope of the surfaces, and the approximate method developed for the calculation of the partition ratios is also shown. Full article

This study introduces a new metric structure called the Graphical Fuzzy Cone Metric Space (GFCMS) and explores its essential properties in detail. We examine its topological aspects in detail and introduce the notion of Hausdorff distance within this setting—an advancement not previously explored in any graphical structure. Furthermore, a fixed-point result is proven within the framework of GFCMS, accompanied by examples that demonstrate the applicability of the theoretical results. As a significant application, we construct fractals within GFCMS, marking the first instance of fractal generation in a graphical structure. This pioneering work opens new avenues for research in graph theory, fuzzy metric spaces, topology, and fractal geometry, with promising implications for diverse scientific and computational domains. Full article

Civil structures carry significant service loads over long times but are prone to deterioration due to various natural impacts. Traditionally, these structures are inspected in situ by qualified engineers, a method that is high-cost, risky, time-consuming, and prone to error. Recently, researchers have explored innovative practices by using virtual reality (VR) technologies as inspection platforms. Despite such efforts, a critical question remains: can VR models accurately reflect real-world structural conditions? This study presents a comprehensive framework for assessing the visual fidelity of VR models for structural inspection. To make it viable, we first introduce a novel workflow that integrates UAV-based photogrammetry, computer graphics, and web-based VR editing to establish interactive VR user interfaces. We then propose a visual fidelity assessment methodology that quantitatively evaluates the accuracy of the VR models through image alignment, histogram matching, and pixel-level deviation mapping between rendered images from the VR models and UAV-captured images under matched viewpoints. The proposed frameworks are validated using two case studies: a historic stone arch bridge and a campus steel building. Overall, this study contributes to the growing body of knowledge on VR-based structural inspections, providing a foundation for our peers for their further research in this field. Full article

We present a link-centric methodology for analyzing the formation of networks in the transportation and logistics industry, advancing beyond prior research based primarily on node centrality. We graphically represent the input–output table (I/O table) indicating inter-industry transactions and propose a methodology for identifying critical factors and major industries within the transportation and logistics industry by assuming the inter-industry transaction volume as the length of a link and analyzing the shortest distance between industries. Through this, we analyze the change factors within an industry and the significance of related industries. The connectivity between industries within transportation and logistics is evaluated based on the shortest distance, and the primary, secondary, and tertiary industries are classified through cluster analysis of the evaluation results. Based on an analysis of Korea’s input–output table, we derived potential industries linked to the transportation and logistics industry that were previously not identified in the results of existing node centrality indices. Additionally, our findings demonstrate that link-based network analysis offers a comparative advantage over node centrality analysis in examining the network structure of the transportation and logistics industry. We propose a new approach to understanding industrial ecosystems by presenting a methodology for industrial structure analysis based on links rather than nodes. Full article

Corrosion is a critical issue in civil structures, significantly affecting their durability and functionality. Detecting corrosion at an early stage is essential to prevent structural failures and ensure safety. This study proposes an expert system based on a novel methodology for corrosion detection using vibration signal analysis. The approach employs graphical empirical mode decomposition (GEMD) to decompose vibration signals into their intrinsic mode functions, extracting relevant structural features. These features are then transformed into grayscale images and classified using a Convolutional Neural Network (CNN) to automatically differentiate between a healthy structure and one affected by corrosion. To enhance the computational efficiency of the method without compromising accuracy, different CNN architectures and image sizes are tested to propose a low-complexity model. The proposed approach is validated using a 3D nine-bay truss-type bridge model encountered in the Vibrations Laboratory at the Autonomous University of Querétaro, Mexico. The evaluation considers three different corrosion levels: (1) incipient, (2) moderate, and (3) severe, along with a healthy condition. The combination of GEMD and CNN provides a highly accurate corrosion detection framework that achieves 100% classification accuracy while remaining effective regardless of the damage location and severity, making it a reliable tool for early-stage corrosion assessment that enables timely maintenance and enhances structural health monitoring to improve the long life and safety of civil structures. Full article

This study investigates the influence of multiplicative noise—modeled by a Wiener process—and spatial-fractional derivatives on the dynamics of the space-fractional stochastic Regularized Long Wave equation. By employing a complete discriminant polynomial system, we derive novel classes of fractional stochastic solutions that capture the complex interplay between stochasticity and nonlocality. Additionally, the variational principle, derived by He’s semi-inverse method, is utilized, yielding additional exact solutions that are bright solitons, bright-like solitons, kinky bright solitons, and periodic structures. Graphical analyses are presented to clarify how variations in the fractional order and noise intensity affect essential solution features, such as amplitude, width, and smoothness, offering deeper insight into the behavior of such nonlinear stochastic systems. Full article

The Bayesian network is a directed, acyclic graphical model that can offer a structured description for probabilistic dependencies among random variables. As powerful tools for classification tasks, Bayesian classifiers often require computing joint probability distributions, which can be computationally intractable due to potential full dependencies among feature variables. On the other hand, Naïve Bayes, which presumes zero dependencies among features, trades accuracy for efficiency and often comes with underperformance. As a result, non-zero dependency structures, such as trees, are often used as more feasible probabilistic graph approximations; in particular, Tree Augmented Naïve Bayes (TAN) has been demonstrated to outperform Naïve Bayes and has become a popular choice. For applications where a variable is strongly influenced by multiple other features, TAN has been further extended to the k-dependency Bayesian classifier (KDB), where one feature can depend on up to k other features (for a given $k\ge 2$). In such cases, however, the selection of the k parent features for each variable is often made through heuristic search methods (such as sorting), which do not guarantee an optimal approximation of network topology. In this paper, the novel notion of k-tree Augmented Naïve Bayes (k-TAN) is introduced to augment Naïve Bayesian classifiers with k-tree topology as an approximation of Bayesian networks. It is proved that, under the Kullback–Leibler divergence measurement, k-tree topology approximation of Bayesian classifiers loses the minimum information with the topology of a maximum spanning k-tree, where the edge weights of the graph are mutual information between random variables conditional upon the class label. In addition, while in general finding a maximum spanning k-tree is NP-hard for fixed $k\ge 2$, this work shows that the approximation problem can be solved in time $O\left(n^{k+1}\right)$ if the spanning k-tree also desires to retain a given Hamiltonian path in the graph. Therefore, this algorithm can be employed to ensure efficient approximation of Bayesian networks with k-tree augmented Naïve Bayesian classifiers of the guaranteed minimum loss of information. Full article

Urogenital cannulation is a traditional method used in aquaculture to achieve sexual differentiation, but it is considered invasive. Ultrasonography is a valuable non-invasive tool for determining sex and gonadal development in fish species like mullet (Mugil liza) and Brazilian sardine (Sardinella brasiliensis) that lack sexual dimorphism. The methodology involves emitting high-frequency sound waves (20 MHz to 20,000 MHz) above the human hearing range. These waves interact with the tissues of the body, producing echoes that are detected by a transducer. The echoes are then processed by computer graphics to generate detailed images of the internal structures of the organism. This allows for the determination of the sex of fish based on the sonographic features of the tissues. For instance, in male fish, hypoechogenic structures reflect fewer sound waves, leading to darker images. Conversely, in female fish, hyperechogenic tissues reflect more sound waves, resulting in lighter images. It is possible to classify the gonadal maturation stage based on differences in image texture. This non-invasive method eliminates the need for specimen dissection. It is especially valuable when the goal is to preserve the spawners’ life and integrity. This review emphasizes the application of this technology in aquaculture, specifically targeting fish from the Clupeidae and Mugilidae families. Full article

The incorporation of renewable energy sources in power grids has necessitated innovative solutions for effective energy management. Smart grids have emerged as transformative systems which integrate consumer, generator and dual-role entities to deliver secure, sustainable and economical electricity supplies. This review explores the important role of artificial intelligence and machine learning approaches in managing the developing stability characteristics of smart grids. This work starts with a discussion of the smart grid’s dynamic structures and subsequently transitions into an overview of machine learning approaches that explore various algorithms and their applications to enhance smart grid operations. A comprehensive analysis of frameworks illustrates how machine learning and artificial intelligence solve issues related to distributed energy supplies, load management and contingency planning. This review includes general pseudocode and schematic architectures of artificial intelligence and machine learning methods which are categorized into supervised, semi-supervised, unsupervised and reinforcement learning. It includes support vector machines, decision trees, artificial neural networks, extreme learning machines and probabilistic graphical models, as well as reinforcement strategies like dynamic programming, Monte Carlo methods, temporal difference learning and Deep Q-networks, etc. Examination extends to stability, voltage and frequency regulation along with fault detection methods that highlight their applications in increasing smart grid operational boundaries. The review underlines the various arrays of machine learning algorithms that emphasize the integration of reinforcement learning as a pivotal enhancement in intelligent decision-making within smart grid environments. As a resource this review offers insights for researchers, practitioners and policymakers by providing a roadmap for leveraging intelligent technologies in smart grid control and stability analysis. Full article

The acquisition of new knowledge by students represents a significant area of interest for universities, which seek to facilitate this process to enhance educational experience. There are two principal categories of learning approaches: surface and deep. The prevalence of a particular approach is contingent upon a number of individual and contextual factors. The aim of this study is to determine whether there are discernible differences in learning styles based on the geographical area of origin of the student. To this end, a multivariate analysis will be employed to compare the predominant learning approaches of health science university students using the Biggs R-SPQ-2F scale. A sample of 464 students was subjected to a multivariate analysis, specifically a Manova-Biplot, with the objective of facilitating the graphical representation of the relationships between the two learning approaches. A confirmatory factor analysis was conducted on the sample to corroborate the factor structure of the R-SPQ-2F. The findings indicated that the majority of students demonstrated proclivity towards deep learning, although their profiles exhibited heterogeneity related to their geographical context. The results may prove valuable in the characterization of the predominant learning approaches in a university community and the design of teaching strategies. Full article

Operational modal analysis (OMA) is widely used for its simplicity and reliance on ambient noise. While commercial OMA software exists, they often limit user control. Some researchers develop their own tools, but independent software tools remain scarce. The number of such independent software is limited, and the development of new ones with enhanced features, better performance, and varied user interfaces would be beneficial to spread the informed use of dynamic identification techniques, leading to more reliable and valuable results for structural engineering applications. This work introduces the new DYMOS software for OMA from ambient vibration test recordings. DYMOS includes various state-of-art algorithms and tools for vibration-based modal identification and for optimal sensor placement (OSP), allowing for customization of analysis parameters and procedures with the aim of reducing the gap between the needs of professional practice and research. Additionally, a new graphical tool is introduced for visualizing results in both buildings and bridges. By using CAD drawings as input, it streamlines model construction, making the process faster, more intuitive, and efficient. The article aims to describe DYMOS and to demonstrate its potential for OMA and OSP in civil engineering through the application on two real case studies dynamically tested. Full article

In this study, we define the $k$-Cullen, $k$-Cullen–Lucas, and Modified $k$-Cullen sequences, and certain terms in these sequences are given. Then, we obtain the Binet formulas, generating functions, summation formulas, etc. In addition, we examine the relations among the terms of the $k$-Cullen, $k$-Cullen–Lucas, Modified $k$-Cullen, Cullen, Cullen–Lucas, Modified Cullen, $k$-Woodall, $k$-Woodall–Lucas, Modified $k$-Woodall, Woodall, Woodall–Lucas, and Modified Woodall sequences. The generating functions were derived and analyzed, especially for cases where Fibonacci numbers were assigned to parameter $k.$ Graphical representations of the generating functions and their logarithmic transformations revealed interesting growth trends and convergence behavior. Further, by multiplying the generating functions with exponential expressions such as $e^k$, we explored the self-similar nature and mirrored dynamics among the sequences. Specifically, it was observed that the Modified Cullen sequence exhibited a symmetric and inverse-like resemblance to the Cullen and Cullen–Lucas sequences, suggesting the presence of deeper structural dualities. Additionally, indefinite integrals of the generating functions were computed and visualized over a range of Fibonacci-indexed k values. These integral-based graphs further reinforced the phenomenon of symmetry and self-similarity, particularly in the Modified Cullen sequence. A key insight of this study is the discovery of a structural duality between the Modified Cullen and standard Cullen-type sequences, supported both algebraically and graphically. This duality suggests new avenues for analyzing generalized recursive sequences through generating function transformations. This observation provides new insight into the structural behavior of generalized Cullen-type sequences. Full article

Introduction: The health education curricula should explicitly recognize, define, and address the unique needs and health disparities faced by Black and LGBTQIA+ populations, as a means of ensuring that healthcare for these populations is both comprehensive and inclusive. Aim: To map scientific evidence and identify knowledge gaps regarding the health of Black and LGBTQIA+ populations within the global context of health education. Methods: A scoping review will be conducted following the JBI methodology. The articles will be retrieved from Scopus, Web of Science, PubMed, Embase, MEDLINE, BVS, CINAHL, ERIC, Cochrane, BDTD, PQDT, EBSCO, and NDLTD. The search will be conducted without language or time restrictions. Two independent reviewers will screen the studies and extract data using a form specifically developed for this purpose. The concepts, definitions, structures, results, and applications of professional health education worldwide for the healthcare of Black and LGBTQIA+ populations will be summarized and discussed. Inclusion Criteria: Studies related to professional health training at both undergraduate and graduate levels, as well as other educational modalities that address the provision of healthcare for these populations, will be included. The results will be presented in both tabular and graphical formats, accompanied by a narrative summary. Protocol registered in the Open Science Framework (OSF). Full article

This study presents, for the first time, a new class of interval-valued superquadratic stochastic processes and examines their core properties through the lens of the center-radius total order relation on intervals. These processes serve as a powerful tool for modeling uncertainty in stochastic systems involving interval-valued data. By utilizing their intrinsic structure, we derive sharpened versions of Jensen-type and Hermite–Hadamard-type inequalities, along with their fractional extensions, within the framework of mean-square stochastic Riemann–Liouville fractional integrals. The theoretical findings are validated through extensive graphical representations and numerical simulations. Moreover, the applicability of the proposed processes is demonstrated in the domain of information theory by constructing novel stochastic divergence measures and Shannon’s entropy grounded in interval calculus. The outcomes of this work lay a solid foundation for further exploration in stochastic analysis, particularly in advancing generalized integral inequalities and formulating new stochastic models under uncertainty. Full article