Search Results (1,197)

Many physical systems contain elements with distributed and lumped parameters; this paper proposes modeling these systems using a bond graph approach. A junction structure is proposed in which the relationships between the distributed and lumped parameter elements are indicated; from this structure, the state space mathematical model of the system is obtained. Thus, a symmetry between the graphical model and the mathematical model is determined. Traditionally, the distributed parameters in the bond graph approach have been modeled by fields. However, when these fields may be subject to external disturbances or parametric uncertainties, their analysis is complicated to carry out because all the information is in a compact form. Therefore, this paper presents a methodology for changing a field in an element model; these fields can be storage fields in an integral or derivative causality assignment or dissipation fields in both cases for any number of field ports. Likewise, there is another symmetry in bond graph from a model with fields to a model with elements. As a case study, a wind turbine containing fields and elements in bond graph is modeled. The state space mathematical model of the turbine is obtained from the bond graph structure of the model with fields in bond graph. Another model of the turbine in bond graph with elements only, applying the field decomposition procedure to elements, is presented. Thus, an external disturbance is introduced into the turbine model with elements showing the objective of obtaining this symmetrical model of the turbine. Simulation results of bond graphs with fields and elements are obtained by checking the symmetry of the models. Likewise, the behavior under conditions of an external disturbance applied to the turbine is presented. Full article

Adaptive learning at scale requires explicit handling of uncertainty and information flow across diverse educational technologies. This paper proposes a TOGAF-conformant enterprise architecture for the University of Tabuk (UT) that embeds entropy- and uncertainty-aware requirements from the outset and aligns them with institutional goals in teaching, research, and administration. Using the Architecture Development Method (ADM), we map information-theoretic requirements to architectural artifacts across the architecture vision, business, information systems, and technology domains; formally specify core entropy-informed observables, including predictive entropy, expected information gain, workflow variability entropy, and uncertainty hot-spot severity; and define semantic and metadata standards for their near-real-time computation. These indicators are positioned explicitly across the TOGAF domains: business architecture identifies where uncertainty matters, information systems architecture defines the computable data and application representations, technology architecture operationalizes secure and scalable computation, and later ADM phases use the resulting metrics for prioritization and governance. The architecture also establishes governance that ranks initiatives by their expected uncertainty reduction through Architecture Review Board (ARB) decision gates. We address three research questions: (R.Q.1) how to design a TOGAF-conformant architecture for UT that natively encodes uncertainty-aware requirements and aligns with institutional needs; (R.Q.2) how to integrate dispersed data, achieve semantic harmonization, and deliver analytics-ready streams that support information-theoretic indicators for personalization without delay; and (R.Q.3) how to embed IT demand planning in opportunities and solutions and migration planning using uncertainty reduction and expected information gain as prioritization criteria. The resulting architecture offers a university-wide foundation for adaptive learning: it unifies learner and system interaction data under governed schemas, supports low-latency analytics, and formalizes decision processes that treat uncertainty as a primary metric. Though learner-level operational validation is future work, the design establishes the technical and organizational foundations for responsible, large-scale deployment of entropy-driven learner modeling, content sequencing, and feedback optimization. Full article

Multimodal named entity recognition (MNER) leverages both textual and visual information to improve entity recognition, particularly in unstructured scenarios such as social media. While existing approaches predominantly rely on raster images (e.g., JPEG, PNG), scalable vector graphics (SVG) offer unique advantages in resolution independence and structured semantic representation—an underexplored potential in multimodal learning. To fill this gap, we propose MNER-SVG, the first framework that incorporates SVG as a visual modality and enhances it with ChatGPT-generated auxiliary knowledge. Specifically, we introduce a Multimodal Similar Instance Perception Module that retrieves semantically relevant examples and prompts ChatGPT to generate contextual explanations. We further construct a Full-Text Graph and a Multimodal Interaction Graph, which are processed via Graph Attention Networks (GATs) to achieve fine-grained cross-modal alignment and feature fusion. Finally, a Conditional Random Field (CRF) layer is employed for structured decoding. To support evaluation, we present SvgNER, the first MNER dataset annotated with SVG-specific visual content. Extensive experiments demonstrate that MNER-SVG achieves state-of-the-art performance with an F1 score of 82.23%, significantly outperforming both text-only and existing multimodal baselines. This work validates the feasibility and potential of integrating vector graphics and large language model-generated knowledge into multimodal NER, opening a new research direction for structured visual semantics in fine-grained multimodal understanding. Full article

The integration of outdoor camera images with three-dimensional (3D) geographic information on the observed scene is of interest for many video acquisition applications. To solve this data fusion problem, camera images have to be matched with the 3D geometry provided by a geographic information system (GIS). Considering a camera with a known geographical position, this paper proposes the use of a dense local azimuth–elevation map (LAEM) derived from a gridded digital elevation model (DEM) to represent the data and thus facilitate the matching of GIS and image data. To each regularly sampled azimuth and elevation angle pair, this map assigns the geographic point derived from the DEM viewed in this direction. The problem of computing the LAEM from the DEM is closely related to that of surface rendering, for which solutions exist in computer graphics. However, rendering software cannot be used directly in this case, since their view directions are constrained by the pinhole camera model and the apparent colour, rather than the position of the viewed point, is assigned to the viewing direction. Therefore, this paper also proposes a specific algorithm for the computation of the LAEM from the DEM. A MATLAB^® implementation of the algorithm is also provided, which is tailored to process the DEM dataset swissALTI3D from the Swiss Federal Office of Topography swisstopo. Full article

Urban flood resilience has emerged as a holistic citywide approach for mitigating flood hazards and navigating the impacts of extreme weather patterns induced by climate change. This is particularly pertinent for high-risk, low-elevation coastal cities like Georgetown, Guyana. However, while the literature on Georgetown includes assessments, analyses, modeling, vulnerability, and the socio-political history of flooding, we found no evidence of flood resilience assessment for the city. Therefore, this study presents a data-driven evaluation of flood resilience at the sub-district level in Georgetown. To accomplish this, we constructed flood resilience indices (FRIs) using the aggregated weighted mean index approach and census-based indicators across physical, social, and economic dimensions. Principal component analysis (PCA) was employed to generate these weights and, subsequently, to perform dimensionality reduction and determine a linear regression model for the FRI values. To evaluate the stability of the constructed indices, robustness tests were conducted using alternative normalization and weighting schemes to demonstrate the consistency of resilience rankings across specifications. The results show that (a) economic resilience is lowest, (b) there is notable clustering and sharp disparities in the physical and social dimensions, and (c) the social dimension has the strongest correlation with the total FRI, which is generally heterogeneous. PCA-derived principal components explained 77.347% of the variation in the FRI values, enabling dimensionality reduction and three-dimensional graphical presentations. Our findings provide urban planners with insights into the distribution of flood resilience needs across the city. This study enables informed decision-making, serving as a pathway to achieve equitable resource allocation and build the city’s resilience. Full article

Efficient aero-acoustic regression is critical for unmanned aerial vehicle (UAV) design and urban air mobility operations, where noise mitigation is essential for regulatory compliance and public acceptance. While data-driven fuzzy Takagi–Sugeno–Kang (TSK) systems have shown potential for modeling complex aero-acoustic behaviors in UAV applications, their performance is strongly affected by input dimensionality and rule-base complexity. This work extends previous research on dimensionality reduction for genetic algorithm-optimized fuzzy systems by conducting a comparative benchmark on an aero-acoustic database regression task relevant to drone propulsion noise prediction. Several TSK architectures are evaluated, including zero- and first-order models, different membership function granularities, and clustering-based rule-generation strategies. In addition, a physics-based heuristic TSK rule system incorporating aero-acoustic knowledge is introduced and compared against data-driven fuzzy configurations. Model performance is primarily assessed through graphical regression analysis and optimization convergence behavior, with a focus on computational efficiency, structural complexity, and qualitative prediction trends—critical considerations for onboard UAV systems and real-time acoustic monitoring. The results highlight the trade-offs between data-driven learning and physics-informed rule construction, demonstrating that physics-based heuristics can reduce optimization complexity while preserving physically consistent behavior. This study provides practical insights into the design of interpretable and efficient fuzzy regression models for UAV aero-acoustic applications, supporting next-generation drone acoustic signature management. Full article

Background: Despite the widespread implementation of treat-to-target strategies and modern disease-modifying antirheumatic drugs, a substantial proportion of patients with rheumatoid arthritis (RA) continue to report unmet needs (UNs), defined as a mismatch between patient expectations and symptom burden on the one hand and outcomes achieved with current care on the other. Patient–physician discordance in global assessments may reflect multidimensional influences, including pain mechanisms, psychosocial factors, functional impairment, and communication gaps, extending beyond inflammatory disease activity. Methods: In this cross-sectional study, 133 patients with RA and 57 healthy controls were included. UNs were operationalized as the signed difference between patient global assessment and physician global assessment (ΔPGA–PhGA). Clinical variables, patient-reported outcomes, and Short Form-36 (SF-36) domains were incorporated into two regularized partial correlation network models estimated using the extended Bayesian information criterion graphical least absolute shrinkage and selection operator (EBICglasso). Node centrality indices (strength, signed strength, betweenness, and closeness) were calculated. Network stability was evaluated using 2000 bootstrap resamples and correlation stability (CS) coefficients. Results: In the clinical network, pain intensity demonstrated the highest strength centrality and the strongest direct association with UNs. In contrast, Disease Activity Score in 28 joints with C-reactive protein (DAS28-CRP) showed no direct association with UNs after accounting for shared variance. In the SF-36-based quality-of-life network, UNs exhibited inverse associations, particularly with perceived health change and role–emotional functioning. Stability analyses indicated acceptable to good robustness (clinical network: CS = 0.59 for edge weights and 0.44 for strength; SF-36 network: CS = 0.59), supporting the reliability of the estimated network structures. Conclusions: UNs in RA are not solely determined by inflammatory disease activity but are embedded within interconnected clinical and psychosocial domains. Pain occupies a structurally central position in the clinical network, whereas perceived health change and emotional role limitations characterize the quality-of-life context of UNs. These findings underscore the importance of multidimensional and patient-centered assessment strategies in RA management. Full article

This article examines the potential risk of impaired visibility for drivers caused by sunlight reflecting off the surfaces of PV panels installed within the right-of-way of motorways and expressways. A literature review was conducted to describe the current state of knowledge and identify the requirements applicable to this area. The procedures for measuring the reflective properties of PV panels using a goniophotometer and a luminance camera (imaging luminance measuring device—ILMD) were evaluated. The measurement results for three PV panels with different surface structures are presented, allowing the properties of PV panels to be determined in terms of their potential impact on driver safety. A computer application was developed to determine whether the sun’s rays will reflect off a photovoltaic panel’s surface toward a vehicle’s direction of travel. The application graphically displays information on whether the sun will be reflected in the direction of a moving vehicle and whether this reflection poses a threat to driver safety. A comprehensive procedure for assessing the risk of glare caused by sunlight reflecting off the surfaces of photovoltaic panels was developed, along with detailed requirements. This study supports sustainable development by promoting renewable energy deployment in motorway corridors while simultaneously ensuring road safety. It integrates environmental (renewable energy use), social (driver safety), and technical (quantitative glare assessment methods) dimensions of sustainability. Full article

This paper explores the use of infographics by Líbero magazine, which is a benchmark of design and the epitome of slow journalism in Spain. The aim is to pinpoint the characteristics and role of these graphic features at a time when visual data journalism is becoming crucial in sports publications. This case study is based on analysing all 52 issues published by Líbero throughout its history. The authors apply a triangulation methodology that combines several techniques: qualitative, including content analysis based on an ad hoc form, designed to formally describe the purposes of the infographics, along with semi-structured in-depth interviews; and qualitative techniques, used to address the statistical aspect. The findings indicate a regular presence of infographics in the early issues, which were complex and large, yet with a strong emphasis on international football matches. However, the last few years of the sample reflect a trend towards gradual simplification of the infographics, together with less frequent use. Data suggest infographics are used to create complex narratives with simple visual compositions in order to improve the reader’s understanding of data that accompanies a journalistic story. This is consistent with Líbero’s commitment to slow journalism, which focuses on detailed explanations and in-depth information. Full article

This paper explores the feasibility of employing small language models (SLMs) on edge devices powered by batteries in environments with limited/no internet connectivity. SLMs in fact offer significant advantages in such scenarios due to their lower resource requirements with respect to large language models. The use case in this study is maritime navigation—in particular, the documentation on Sailing Directions (Enroutd) of the World Port Index (WPI) provided by the National Geospatial-Intelligence Agency (NGA), which provides information that cannot be shown graphically on nautical charts and is not readily available elsewhere. In this environment, response immediacy is not critical, as users have sufficient time to query information while navigating and planning activities, making edge devices ideal for running these models. On the contrary, the response quality is fundamental. For this reason, given the constrained knowledge of SLMs in maritime contexts, we investigate the use of the retrieval-augmented generation (RAG) methodology, integrating external information from sailing directions. A comparative analysis is presented to evaluate the performance of various state-of-the-art SLMs, focusing on response quality, the effectiveness of the RAG component, and inference times. Full article

This study touches upon the issue of searching for tourist information in the context of preferred forms of cartographic presentation in different geographic scales. The main objective of our research was to examine the link between the type of tourist information that is searched for and the graphical level of abstraction, as well as geographic scale. We used the method of the online survey on twelve maps to study users’ preferences in two respondent groups: geographers and sociologists. Based on the map rankings obtained, we have drawn conclusions on the informative value of realistic and conventional sources of tourist information. The study has demonstrated the globalization of social behavior that significantly favors global web map services over other online sources. The most important factor in choosing a map is whether it contains the information the user is currently seeking. It is impossible to clearly indicate a preferred level of abstraction for presenting tourist information at every geographical scale. However, consistently high rankings were observed for maps using pictorial and symbolic signs. The map type preferences of geographers and sociologists were very similar, although geographers showed a slightly stronger preference for maps with conventional symbols. All respondents rated traditional hypsometric maps highly. Full article

Background. Self-congruency refers to the coherence between emotional experience (internal states) and enacted behavior (outward actions). Reduced self-congruency has been linked to vulnerability in mental health, yet its physiological correlates remain poorly characterized. Heart–brain temporal coupling may provide a candidate physiological marker of this psychological coherence. Methods. Thirty-eight healthy adults underwent resting-state functional magnetic resonance imaging while cardiac activity was simultaneously recorded using photoplethysmography to derive heart rate variability (HRV). Self-congruency was assessed using a graphic rating scale based on the spatial overlap between emotional experience and enacted behavior. Heart–brain temporal coupling between HRV and regional blood-oxygen-level-dependent (BOLD) signals was quantified using cross-covariance analysis across biologically plausible temporal shifts. Results. Heart–brain temporal coupling predominantly reflected brain-to-heart temporal ordering, particularly within regions central to the neurovisceral integration model, including the ventromedial prefrontal and anterior cingulate cortices. In contrast, higher self-congruency was associated with stronger heart-to-brain temporal coupling, notably within the right rostral middle frontal gyrus and supramarginal gyrus, regions implicated in emotion regulation and socio-emotional processing. Conclusions. While global heart–brain temporal coupling is dominated by top-down neural regulation, greater alignment between emotional experience and enacted behavior is associated with enhanced bottom-up cardiac temporal ordering on neural activity. These findings seem to identify a physiological–psychological axis that may inform original prevention-oriented approaches in mental health. Full article

Monitoring intracranial pressure (ICP) dynamics is critical for the management of traumatic brain injury, stroke, other neurosurgical conditions, and cerebral blood flow autoregulation; however, invasive ICP monitoring carries risks such as infection, hemorrhage, and sensor zero drift. Increasing evidence suggests that ICP waveform morphology provides clinically relevant information beyond mean ICP value alone. In this first-in-human prospective comparative clinical study, we evaluated the feasibility and accuracy of a novel, fully passive, non-invasive ICP pulse waveform monitoring system (Archimedes 02) based on the detection of eyeball mechanical movement. Fifteen intensive care unit patients (6 males, 9 females; mean age 57.1 ± 18.8 years) with clinically indicated invasive ICP monitoring or external ventricular drainage were enrolled. Three-minute monitoring sessions were performed to simultaneously acquire non-invasive ICP pulse waveforms, invasive ICP waveforms, and invasive radial artery blood pressure (ABP) waveforms. Averaged waveforms were derived for each patient and compared graphically and using correlation analysis. Non-invasive ICP pulse waves recorded with Archimedes 02 showed a strong correlation with invasive ICP waveforms ($\overline{\mathrm{R}}$ = 0.965). In contrast, correlations between non-invasive ICP and ABP waveforms ($\overline{\mathrm{R}}$ = 0.699), as well as between invasive ICP and ABP waveforms ($\overline{\mathrm{R}}$ = 0.749), were lower. These findings indicate that the non-invasive signal primarily reflects ICP dynamics rather than arterial blood pressure. This novel non-invasive ICP monitoring approach has the potential to enhance neurocritical care, particularly in settings where invasive monitoring is impractical or unavailable. Further validation in larger and more diverse patient populations is warranted. Full article

Traditional underground space evaluation systems often employ 2D GIS methods to represent 3D information, leading to issues such as the loss of 3D spatial data and insufficient resolution in depth. To address the practical needs and methodological steps of 3D geological suitability evaluation for underground space (3D UGEE) development, this study adopts an integrated secondary development approach to design and implement a software system capable of conducting quantitative geological suitability evaluation in three dimensions using multivariate data. The system incorporates the latest methods and achievements in 3D UGEE, featuring functional modules such as multidimensional data conversion, 3D statistical analysis, 3D spatial distance analysis, and 3D comprehensive evaluation, which enable the integration and analytical assessment of multivariate geoscientific data. In comparison with existing 3D-UGEE systems, the proposed 3D-UGEE system integrates a broader range of functional modules, conducts in-depth integration and mining of multi-source geological data, boasts robust 3D graphical display and interactive capabilities, and achieves more efficient operational performance. This study elaborates on the system’s overall architecture, development approach, and the design and implementation processes of its functional modules. Application results from a case study in Qingdao demonstrate that the system not only provides a suite of 3D spatial analysis and comprehensive evaluation tools for integrating multivariate geoscientific data but also offers robust support for enhancing 3D UGEE practices. Full article

Based on its rich information of chemical specificity, Raman spectroscopy has been widely applied for in vivo biomedical investigations. For extracting quantitative information of target constitution, it is imperative to establish a robust model for unveiling the relationship between spectral features with/without priori references. By integrating a variety of traditional machine learning and artificial neural network algorithms, an integrated Raman spectra analysis toolbox (AI-Assisted Raman Spectra Analysis Toolbox [AI-Raman] V 1.0) was developed for spectral processing, model training, and regression analysis by using MATLAB R2024a. Besides the utilization of back propagation artificial neural network and convolutional neural network algorithms, classical machine learning algorithms, such as partial least squares regression and support vector regression, were also compacted as the supporting functions of presented toolbox. A spectral dataset obtained from nailfold from different subjects was utilized to evaluated the feasibility and performance of the developed software, which demonstrated that the analysis software can predict glucose concentrations by in vivo Raman spectral measurement. With a friendly graphics interface, the analytical model can be customized and optimized for accomplishing the desired objectives, which will benefit many Raman-based inventions, especially for biomedical transformations. Full article