Search Results (1,683)

This study presents a systematic review regarding the use of augmented reality and virtual reality in exergaming by analyzing studies published during 2010–2025. This study focuses on providing an overview of the field and on examining and synthesizing the findings of related studies to identify the contexts, applications, and domains in which extended reality exergames are being used and the related implications, benefits, and challenges. Based on the results, augmented reality and virtual reality exergames offer immersive, enjoyable, engaging, and personalized experiences that support physical, cognitive, and emotional well-being, while enhancing physical performance, cognitive functioning, psychological outcomes, and mental health. They promote motivation, active lifestyles, and sustainable health behaviors across diverse populations, including older adults, individuals with disabilities, and neurological groups, as well as the general adult and youth populations. Although emphasis is placed on their use in physical and cognitive rehabilitation and treatment, they also show great potential to be effectively used in different domains, including education. Among the technologies examined, the significant majority of studies focused on virtual reality exergames, a limited number of studies involved augmented reality, and only a few studies examined mixed reality, extended reality, and the metaverse. Finally, nine main topics were identified through topic modeling, providing a clear representation of the core themes within the literature. Full article

Herein, a matrix representation of the Hamilton quaternion group by 4 × 4 square matrices with entries equal to −1, 0, or 1 is defined. It is proven that this group, denoted as $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$, is a group of rotational symmetries of the four-dimensional hypercube ${\mathbb{Z}}_2^4,$ that is, a subgroup of the special orthogonal group ${\mathrm{SO}}_4\left(\mathbb{Z}\right).$ As a consequence, $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$ is a group of rotational symmetries for each of the biological hypercubes RNY, YNY, YNR, and RNR. It is also proven that $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$ is a group of permutations of the eight cubes contained in the four-dimensional hypercube ${\mathbb{Z}}_2^4.$ The latter is a novel result. It is also proven that the matrix quaternion group $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$ is a normal subgroup of ${\mathrm{SO}}_4\left(\mathbb{Z}\right)$ and that the latter is a semidirect product of $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$ with a copy of the special orthogonal group ${\mathrm{SO}}_3\left(\mathbb{Z}\right),$ also called an octahedral group because it is a group of rotational symmetries of a regular octahedron or of a three-dimensional cube. Full article

Customer sentiment analysis plays a pivotal role in the digital economy by offering comprehensive insights that inform strategic business decisions, optimize digital marketing initiatives, and improve overall customer satisfaction. We propose a large language model-based topic-level sentiment analysis framework. We employ a BERT-based model to generate contextualized vector representations of the documents, and then clustering algorithms are automatically applied to group documents into topics. Once the topics are formed, a GPT model is used to perform sentiment classification on the content related to each topic. The simulations show the effectiveness of this approach, where selecting appropriate clustering techniques yields more semantically coherent topics. Furthermore, topic-level sentiment polarization shows that 31.7% of all negative sentiment concentrates on the shopping experience, despite an overall positive sentiment trend. Full article

This study assesses the risk of geoheritage degradation along a low-lying coastal stretch Okin the municipalities of Macau, Guamaré, and Galinhos, located in the central portion of Rio Grande do Norte’s northern coastline, Brazil. Twelve geosites, inventoried based on their scientific value, susceptibility to degradation, and representation of diverse coastal processes and landforms, were numerically assessed for their degradation risk. The methodology comprised 11 sub-criteria grouped into three main criteria: natural vulnerability, anthropogenic vulnerability, and public use. The results indicate that all 12 geosites in the study area are subject to moderate to high degradation risk, with the highest levels observed in those with the most evident signs of human use and intervention. To mitigate these impacts, the implementation of access restrictions or protective measures by local authorities is recommended. Furthermore, raising awareness among local communities about the environmental consequences of their activities and the geosites’ role in promoting sustainability is essential. Given the region’s heightened vulnerability to sea level oscillations, future assessments should incorporate climate change implications into the assessment criteria. Full article

Echocardiography is the primary imaging modality for diagnosing cardiac disease in children, with quantitation largely based on nomograms. Over the past decade, significant efforts have been made to address the numerical and methodological limitations of earlier nomograms. As a result, robust and reliable pediatric echocardiographic nomograms are now available for most two-dimensional anatomical measurements, three-dimensional volumes, and strain parameters. These more recent nomograms are based on adequate sample sizes, strict inclusion and exclusion criteria, and rigorous statistical methodologies. They have demonstrated good reproducibility with minimal differences across different authors, establishing them as reliable diagnostic tools. Despite these advances, some limitations persist. Certain ethnic groups remain underrepresented, and data for preterm and low-weight infants are still limited. Most existing nomograms are derived from European and North American populations, with sparse data from Asia and very limited data from Africa and South America. Nomograms for preterm and low-weight infants are few and cover only selected cardiac structures. Although diastolic parameter nomograms are available, the data remain heterogeneous due to challenges in normalizing functional parameters according to age and body size. The accessibility of current nomograms has greatly improved with the development of online calculators and mobile applications. Ideally, integration of nomograms into echocardiographic machines and reporting systems should be pursued. Future studies are needed to develop broader, more comprehensive, and multi-ethnic nomograms, with better representation of preterm and low-weight populations, and to validate new parameters derived from emerging three- and four-dimensional echocardiographic techniques. Full article

The accurate extraction of navigational regions from images of navigational waters plays a key role in ensuring on-water safety and the automation of unmanned vessels. Nonetheless, current technological methods encounter significant challenges in addressing fluctuations in water surface illumination, reflective disturbances, and surface undulations, among other disruptions, in turn making it challenging to achieve rapid and precise boundary segmentation. To cope with these challenges, in this paper, we propose a coordinate-aware multi-scale feature network (GASF-ResNet) method for water segmentation. The method integrates the attention module Global Grouping Coordinate Attention (GGCA) in the four downsampling branches of ResNet-50, thus enhancing the model’s ability to capture target features and improving the feature representation. To expand the model’s receptive field and boost its capability in extracting features of multi-scale targets, the Avoidance Spatial Pyramid Pooling (ASPP) technique is used. Combined with multi-scale feature fusion, this effectively enhances the expression of semantic information at different scales and improves the segmentation accuracy of the model in complex water environments. The experimental results show that the average pixel accuracy (mPA) and average intersection and union ratio (mIoU) of the proposed method on the self-made dataset and on the USVInaland unmanned ship dataset are 99.31% and 98.61%, and 98.55% and 99.27%, respectively, significantly better results than those obtained for the existing mainstream models. These results are helpful in overcoming the background interference caused by water surface reflection and uneven lighting in the aquatic environment and in realizing the accurate segmentation of the water area for the safe navigation of unmanned vessels, which is of great value for the stable operation of unmanned vessels in complex environments. Full article

Here all rings have identities. Let R be a ring and let R-mod denote the additive category of left finitely generated R-modules. Note that if R is a noetherian ring, then R-mod is an abelian category and every R-module is a finite direct sum of indecomposable R-modules. Finite Group Modular Representation Theory concerns the study of left finitely generated $\mathcal{O}G$-modules where G is a finite group and $\mathcal{O}$ is a complete discrete valuation ring with $\mathcal{O}/J\left(\mathcal{O}\right)$ a field of prime characteristic p. Thus $\mathcal{O}G$ is a noetherian $\mathcal{O}$-algebra. The Green Theory in this area yields for each isomorphism type of finitely generated indecomposable (and hence for each isomorphism type of finitely generated simple $\mathcal{O}G$-module) a theory of vertices and sources invariants. The vertices are derived from the set of p-subgroups of G. As suggested by the above, in Basic Definition and Main Results for Rings Section, let $\mathrm{\Sigma }$ be a fixed subset of subrings of the ring R and we develop a theory of $\mathrm{\Sigma }$-vertices and sources for finitely generated R-modules. We conclude Basic Definition and Main Results for Rings Section with examples and show that our results are compatible with a ring isomorphic to R. For Idempotent Morita Equivalence and Virtual Vertex-Source Pairs of Modules of a Ring Section, let e be an idempotent of R such that $R=ReR$. Set $B=eRe$ so that B is a subring of R with identity e. Then, the functions $eR{\otimes }_R\ast :R-\mathrm{mod}\to B-\mathrm{mod}$ and $Re{\otimes }_B\ast :B-\mathrm{mod}\to R-\mathrm{mod}$ form a Morita Categorical Equivalence. We show, in this Section, that such a categorical equivalence is compatible with our vertex-source theory. In Two Applications with Idemptent Morita Equivalence Section, we show such compatibility for source algebras in Finite Group Block Theory and for naturally Morita Equivalent Algebras. Full article

Fagonia cretica, a medicinal herb from the Zygophyllaceae family, is traditionally utilized to treat various conditions such as hepatitis, gynecological disorders, tumors, urinary tract issues, and diabetes. The present study aimed to evaluate the therapeutic potential of Fagonia cretica in treating polycystic ovarian syndrome (PCOS) induced in female rats. PCOS, a complex hormonal disorder, was experimentally induced by administering Letrozole (1 mg/kg) in combination with a high-fat diet for 21 days. The affected rats were then treated with hydro-alcoholic extracts of Fagonia cretica at doses of 100 mg/kg, 200 mg/kg, and 300 mg/kg for 20 days. Key biochemical parameters—including serum testosterone, insulin, fasting blood glucose, insulin resistance (HOMA-IR), cholesterol, triglycerides, and lipoprotein levels—were measured. Ultrasound imaging and histopathological analysis of ovarian tissues were also performed. The data were analyzed using computer-based statistical tools, including one-way ANOVA, Cohen’s d effect size, and Tukey’s HSD test, with graphical representations generated using Python 3.10 on the Kaggle platform. Results demonstrated a significant reduction in serum testosterone, insulin, cholesterol, and triglyceride levels (p < 0.05) in treated groups, along with improved ovarian morphology. These findings support the therapeutic potential of Fagonia cretica as a natural treatment for PCOS. Full article

Let X be a complex projective variety embedded in a complex projective space. The dimensions of the secant varieties of X have an expected value, and it is important to know if they are equal or at least near to this expected value. Blomenhofer and Casarotti proved important results on the embeddings of G-varieties, G being an algebraic group, embedded in the projectivations of an irreducible G-representation, proving that no proper secant variety is a cone. In this paper, we give other conditions which assure that no proper secant varieties of X are a cone, e.g., that X is G-homogeneous. We consider the Segre product of two varieties with the product action and the case of toric varieties. We present conceptual tests for it, and discuss the information we obtained from certain linear projections of X. For the Segre–Veronese embeddings of ${\mathbb{P}}^n\times {\mathbb{P}}^n$ with respect to forms of bidegree $(1,d)$, our results are related to the simultaneous rank of degree d forms in $n+1$ variables. Full article

In this research paper, we study symmetry groups, soliton solutions, and the dynamical behavior of the Ivancevic Option Pricing Model (IOPM). First, we find the Lie symmetries of the considered model; next, we use them to determine the corresponding symmetry groups. Then, we attempt to solve IOPM by means of two methods. We provide some wave solutions and give further details of the solution using 2D and 3D graphs. These results are interpreted as important clarifications in financial mathematics and deepen our understanding of the dynamics involved during the pricing of options. Secondly, the quasi-periodic behavior of the two-dimensional dynamical system and its perturbed system are plotted using Python software (Python 3.13.5 version). Various frequencies and amplitudes are considered to confirm the quasi-periodic behavior via the Lyapunov exponent, bifurcation diagram, and multistability analysis. These findings are particularly in consonance with current research that investigates IOPM as a nonlinear wave alternate for normal models and the importance of graphical representations in the understanding of financial derivative dynamics. We, therefore, hope to fill in the gaps in the literature that currently exist about the use of multi-solution methods and their effects on financial modeling through the employment of sophisticated graphical techniques. This will be helpful in discussing matters in the field of financial mathematics and open up new directions of investigation. Full article

This study estimates the population median through stratified random sampling, which enhances accuracy by ensuring the proper representation of key population groups. The proposed class of estimators based on transformations effectively handles data variability and enhances estimation efficiency. We examine bias and mean square error expressions up to the first-order approximation for both existing and newly introduced estimators, establishing theoretical conditions for their applicability. Moreover, to assess the effectiveness of the suggested estimators, five simulated datasets derived from distinct asymmetric distributions (gamma, log-normal, Cauchy, uniform, and exponential), along with actual datasets, are used for numerical analysis. These estimators are designed to significantly enhance the precision and effectiveness of median estimation, resulting in more reliable and consistent outcomes. Comparative analysis using percent relative efficiency (PRE) reveals that the proposed estimators perform better than conventional approaches. Full article

The flowering stage is a critical phase in the growth of rapeseed crops, and non-destructive, high-throughput quantitative analysis of rape flower clusters in field environments holds significant importance for rapeseed breeding. However, detecting and counting rape flower clusters remains challenging in complex field conditions due to their small size, severe overlapping and occlusion, and the large parameter sizes of existing models. To address these challenges, this study proposes a lightweight rape flower clusters detection model, SPL-YOLOv8. First, the model introduces StarNet as a lightweight backbone network for efficient feature extraction, significantly reducing computational complexity and parameter counts. Second, a feature fusion module (C2f-Star) is integrated into the backbone to enhance the feature representation capability of the neck through expanded spatial dimensions, mitigating the impact of occluded regions on detection performance. Additionally, a lightweight Partial Group Convolution Detection Head (PGCD) is proposed, which employs Partial Convolution combined with Group Normalization to enable multi-scale feature interaction. By incorporating additional learnable parameters, the PGCD enhances the detection and localization of small targets. Finally, channel pruning based on the Layer-Adaptive Magnitude-based Pruning (LAMP) score is applied to reduce model parameters and runtime memory. Experimental results on the Rapeseed Flower-Raceme Benchmark (RFRB) demonstrate that the SPL-YOLOv8n-prune model achieves a detection accuracy of 92.2% in Average Precision (AP₅₀), comparable to SOTA methods, while reducing the giga floating point operations per second (GFLOPs) and parameters by 86.4% and 95.4%, respectively. The model size is only 0.5 MB and the real-time frame rate is 171 fps. The proposed model effectively detects rape flower clusters with minimal computational overhead, offering technical support for yield prediction and elite cultivar selection in rapeseed breeding. Full article

Background: Renal tumors, encompassing benign, malignant, and normal variants, represent a significant diagnostic challenge in radiology due to their overlapping visual characteristics on computed tomography (CT) scans. Manual interpretation is time consuming and susceptible to inter-observer variability, emphasizing the need for automated, reliable classification systems to support early and accurate diagnosis. Method and Materials: We propose KidneyNeXt, a custom convolutional neural network (CNN) architecture designed for the multi-class classification of renal tumors using CT imaging. The model integrates multi-branch convolutional pathways, grouped convolutions, and hierarchical feature extraction blocks to enhance representational capacity. Transfer learning with ImageNet 1K pretraining and fine tuning was employed to improve generalization across diverse datasets. Performance was evaluated on three CT datasets: a clinically curated retrospective dataset (3199 images), the Kaggle CT KIDNEY dataset (12,446 images), and the KAUH: Jordan dataset (7770 images). All images were preprocessed to 224 × 224 resolution without data augmentation and split into training, validation, and test subsets. Results: Across all datasets, KidneyNeXt demonstrated outstanding classification performance. On the clinical dataset, the model achieved 99.76% accuracy and a macro-averaged F1 score of 99.71%. On the Kaggle CT KIDNEY dataset, it reached 99.96% accuracy and a 99.94% F1 score. Finally, evaluation on the KAUH dataset yielded 99.74% accuracy and a 99.72% F1 score. The model showed strong robustness against class imbalance and inter-class similarity, with minimal misclassification rates and stable learning dynamics throughout training. Conclusions: The KidneyNeXt architecture offers a lightweight yet highly effective solution for the classification of renal tumors from CT images. Its consistently high performance across multiple datasets highlights its potential for real-world clinical deployment as a reliable decision support tool. Future work may explore the integration of clinical metadata and multimodal imaging to further enhance diagnostic precision and interpretability. Additionally, interpretability was addressed using Grad-CAM visualizations, which provided class-specific attention maps to highlight the regions contributing to the model’s predictions. Full article

This paper explores the potential and implications arising from the convergence of virtual reality, the metaverse, and digital twins in translating a real-world commemorative event into a virtual environment. It emphasizes how such integration influences digital transformation processes, particularly in reshaping models of social interaction. Virtual reality is conceptualized as an immersive technology, enabling advanced multisensory experiences within persistent virtual spaces, such as the metaverse. Furthermore, this study delves into the concept of digital twins—high-fidelity virtual representations of physical systems, processes, and objects—highlighting their application in simulation, analysis, forecasting, prevention, and operational enhancement. In the context of virtual events, the convergence of these technologies is examined as a means to create interactive, adaptable, and scalable environments capable of accommodating diverse social groups and facilitating global accessibility. As a practical application, a digital twin of the Ferrándiz and Carbonell buildings—the most iconic architectural ensemble on the Alcoi campus—was developed to host a virtual event commemorating the 50th anniversary of the integration of the Alcoi School of Industrial Technical Engineering into the Universitat Politècnica de València in 1972. The virtual environment was subsequently evaluated by a sample of users, including students and faculty, to assess usability and functionality, and to identify areas for improvement. The digital twin achieved a score of 88.39 out of 100 on the System Usability Scale (SUS). The findings underscore the key opportunities and challenges associated with the adoption of these emerging technologies, particularly regarding their adaptability in reconfiguring digital environments for work, social interaction, and education. Using this case study as a foundation, this paper offers insights into the strategic role of the metaverse in extending environmental perception and its transformative potential for the future digital ecosystem through the implementation of digital twins. Full article

Addressing environmental challenges requires the inclusion of local communities with relevant knowledge of the social–ecological system in which they are embedded, in addition to using transdisciplinary approaches that are critical to the co-production of successful and sustainable environmental solutions. A qualitative methodology was used to examine stakeholders’ views of decadal changes in Ria de Aveiro, a coastal lagoon on Portugal’s Atlantic coast. Seven focus groups were conducted, which included 42 stakeholders from coastal parishes, in order to obtain identical geographical representation with a study conducted a decade ago. Participants represented a diverse sample of groups interested in or affected by management options and activities in the lagoon system and were asked to reflect on the main changes that occurred over the last decade. Positive changes reflected an increase in the levels of environmental awareness, a positive trajectory of the environmental status of Ria de Aveiro, and a decrease in illegal fishing activities. Persisting concerns referred to the lack of an efficient management body for Ria de Aveiro, pressures related to changes in the hydrodynamic regime of the lagoon, the disappearance of native species and increase in invasive alien species, the abandonment of traditional activities (e.g., harvesting of seagrass and seaweed, salt production, agriculture in lagoon margins, and artisanal fishing), and the degradation and lack of maintenance of salt pans. Our findings highlight the importance of longer-term transdisciplinary and social–ecological research and illustrate how stakeholder views regarding the shortfalls of the movement towards the integrated management of ecosystems remain. Full article