Search Results (371)

Autonomous vehicle (AV) accidents introduce uncertainty in liability attribution, as responsibility is divided between humans and automated systems. The 2018 Arizona crash highlighted growing societal concerns about accountability. To address these issues, prior studies proposed investigation processes considering perception sensors, driving control systems, communication infrastructure, and cybersecurity. However, conducting such investigations requires integrating large-scale data from multiple sources, including vehicle sensors, onboard recorders, V2X communications, and road infrastructure. Raw data often lack descriptive information, limiting their use in real investigations. This study establishes a structured mapping framework linking investigation procedures, responsible entities, items, and data across accident phases. With this backdrop, an autonomous driving–specific metadata schema extending DCAT was designed, comprising 10 Classes and 76 Properties. To demonstrate its applicability, a prototype data catalog user interface (UI) was conceptualized with data discovery and visualization examples. The proposed schema strengthens accountability and interoperability by explicitly aligning responsibilities and data relationships. It enables precise event localization and effective linkage of heterogeneous data. Future work will refine the schema by incorporating DSSAD, V2X, and security log data, and develop a user-tested UI prototype as a practical support tool for AV accident investigation. Full article

Humans typically describe spatial location using names, hierarchies, and relative positions (e.g., east of, inside), yet mainstream GIS represents places primarily through geometric coordinates, rendering qualitative spatial queries computationally challenging. We introduce the Qualitative Place Model (QPM), a GIS-native framework that transforms standard boundary datasets and place layers into structured knowledge bases of Qualitative Place Description (QPD). QPM provides a homogeneous representation whereby administrative units and physical places are treated uniformly as Place entities. The model materializes a compact set of local relations, hierarchical containment, directional neighbourhood, and optional proximity, that support rich inferences through sound logical operations (inverse relationships and per-predicate transitive closure). We implement QPM as an ArcGIS Pro toolbox that computes and persists QPDs within a geodatabase, with optional RDF export for SPARQL querying. This implementation enables natural-language-style spatial queries such as “Where is x?” or “Which places are north of x?” within standard GIS workflows. Evaluation on Wales (UK) administrative, postal, and electoral hierarchies plus a comprehensive place layer demonstrates robust performance: QPM generated 95.8% of expected basic-place statements (52,821 places) and achieved 89.7–96.5% coverage across administrative hierarchies. All QPDs proved unique under our deterministic signature. Despite compact storage requirements, directional relations expand by more than an order of magnitude (10.6× overall expansion) under logical closure, demonstrating substantial inferential power from a minimal stored representation. QPM complements geometric GIS with an explainable qualitative layer that aligns with human spatial cognition while remaining fully operational within standard GIS environments. Full article

This article analyzes the construction of the Digital Extended Self of digital influencers from the travel channel Travel Channel, drawing on R. W. Belk’s theory. The study employs a qualitative exploratory–descriptive approach, using a case study as its methodological strategy. Data collection involved analyzing five podcast interviews with the channel’s founders, along with videos published between 2022 and 2024. In addition, viewer comments on these videos were extracted and examined. All materials were analyzed using Bardin’s content analysis. The results reveal a strong presence of the Extended Self dimensions, co-construction, and sharing, showing that interaction with the audience actively shapes the influencers’ identity and content. The dimensions of dematerialization (e.g., cloud storage) and distributed memory (the use of digital records as extensions of memory) were also evident. Reincarnation (the use of avatars or personas) was the least observed dimension, a finding attributed to the influencers’ authentic style and focus on real-life experiences. Overall, the Digital Extended Self of the Travel Channel emerges as a genuine and organically constructed entity, resulting in an aggregated Self that reflects a strong connection with its audience. This research provides valuable insights into how Belk’s theory can be applied to the in-depth analysis of digital materials. Full article

This study analyses disclosures on materiality in non-financial information (NFI) reporting by examining their likely explanatory factors, including entities’ financial or structural characteristics, governance features, and contextual factors, grounded in a set of relevant theories. Based on archival research and content analysis, this study uses consolidated NFI reports from 2021 of entities listed in the main Euronext indices. The descriptive analysis reveals that while 71% of companies present a materiality matrix, only about half (50%) meet all eight criteria of materiality disclosure, with double materiality being addressed by just 16%. The regression results show that the level of materiality disclosure is significantly and positively associated only with the size of the board of directors, whereas other expected relationships, such as those with firm size, profitability, or debt, were not statistically significant, challenging traditional assumptions from stakeholders, agency, and positive accounting theories. These findings suggest that governance structures may play a more decisive role in transparency regarding materiality than the entities’ financial or structural characteristics. This research contributes to both the academic literature and practice by identifying explanatory factors and empirical patterns in materiality disclosure in NFI reporting, which may be relevant for standard-setting bodies, regulators, auditors, and stakeholders. Full article

Background/Objectives: KRAS mutations in renal cell carcinoma (RCC) are uncommon and most frequently described in papillary renal neoplasm with reverse polarity (PRNRP). Beyond this entity, the broader clinicopathologic and molecular features of KRAS-mutated RCC remain insufficiently characterized. This study aimed to provide a descriptive assessment of KRAS-mutated RCC. Methods: KRAS-mutant RCC patients were identified from the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) and The Cancer Genome Atlas Kidney Renal Papillary Cell Carcinoma (TCGA-KIRP) cohorts. Copy-number alterations were evaluated using Fraction and allele-specific copy number estimates from tumor sequencing (FACETS). Available samples were used for immunohistochemistry and RNA-sequencing analysis. Results: Seventeen patients were included. Three distinct KRAS-mutant RCC subtypes were identified: KRAS-mutant PRCC (35%), KRAS-mutant URCC (35%), and PRNRP (29%). Seven patients (41%) had metastatic disease; none were PRNRP. RNA-based deconvolution analysis revealed that PRNRP had enrichment in distal nephron components, whereas KRAS-mutant PRCC was enriched in proximal tubule cells (p = 0.02). IHC staining of L1CAM was positive in PRNRP but negative in KRAS-mutant PRCC, supporting their distinct cell-of-origin phenotypes. This study is limited by its cohort size, which influences the availability of tissue samples. Conclusions: PRNRP represents a distinct KRAS-mutant RCC subtype with unique metabolic and genomic features linked to its distal nephron origin. This contrasts with the genomic complexity and aggressive clinical behavior observed in KRAS-mutant PRCC and URCC, highlighting the need for subtype-specific diagnostic criteria and therapeutic strategies. Full article

Knowledge graphs (KGs) have emerged as fundamental infrastructures for organizing structured information across a wide range of AI applications. Practically, KGs are often incomplete, which limits their effectiveness. Knowledge Graph Completion (KGC) has become a critical research problem. Existing methods of KGC primarily rely on graph structure or textual descriptions independently, often failing to capture the complex interplay between structural topology and rich semantic context. Recent advances in Large Language Models (LLMs) offer promising capabilities in understanding and generating human-like semantic representations. However, effectively integrating such models with structured graph information remains a challenging and underexplored area. In this work, we propose an enhanced KGC framework that leverages a structure-aware and semantic fusion mechanisms driven by the representational power of LLMs. Our method jointly encodes the topological structure of the graph and the textual semantics of entities and relations, allowing for more informed and context-rich KGC. The experimental results of benchmark datasets demonstrate that our approach outperforms existing baselines, particularly in scenarios with sparse graph connectivity or limited textual information. In particular, on the WN18RR dataset, the model demonstrates a 12.4% increase in Hits@3 and an 11.7% increase in Hits@10. Full article

Since the early twentieth century, quantum mechanics has sought an interpretation that offers a consistent worldview. In the course of that, many proposals were advanced, but all of them introduce, at some point, interpretation elements (semantics) that find no correlate in the formalism (syntactics). This distance from semantics and syntactics is one of the major reasons for finding so abstruse and diverse interpretations of the formalism. To overcome this issue, we propose an alternative stochastic interpretation, based exclusively on the formal structure of the Schrödinger equation, without resorting to external assumptions such as the collapse of the wave function or the role of the observer. We present four (mathematically equivalent) mathematical derivations of the Schrödinger equation based on four constructs: characteristic function, Boltzmann entropy, Central Limit Theorem (CLT), and Langevin equation. All of them resort to axioms already interpreted and offer complementary perspectives to the quantum formalism. The results show the possibility of deriving the Schrödinger equation from well-defined probabilistic principles and that the wave function represents a probability amplitude in the configuration space, with dispersions linked to the CLT. It is concluded that quantum mechanics has a stochastic support, originating from the separation between particle and field subsystems, allowing an objective description of quantum behavior as a mean-field theory, analogous, but not equal, to Brownian motion, without the need for arbitrary ontological entities. Full article

Background/Objectives: Job satisfaction is an essential element for organizational functions. Working entities would not effectively operate without employee contentment. This study aimed to determine the level of job satisfaction among pharmacists and investigate its correlation with demographic variables and professional personal experience. Methods: A cross-sectional online survey targeting registered pharmacists in Saudi Arabia was conducted from September to November 2024 using an IRB-approved structured questionnaire adapted from validated instruments. Reliability and validity were confirmed (Cronbach’s α = 0.8), and a target sample of 380 was calculated to ensure representativeness. Data were analyzed using descriptive statistics, chi-squared tests, and univariate and multivariate logistic regression analyses utilizing SPSS v28, with significance set at p < 0.05. Results: A total of 330 pharmacists responded to the survey, representing 86.8% of the calculated sample size. Of those, 57% were male and 68.5% were staffing pharmacists. More than half of participants had professional experience of ≤5 years (57.3%), while 31.8% had 5 to 15 years of experience. Approximately 60% of participants worked in shift systems and reported dissatisfaction with their pay (70%) and lack of benefits (66.7%). Of all participants, only 26.4% confirmed satisfaction with their job and no intention to quit, while 23% clearly reported job dissatisfaction and an intention to quit; the rest of the participants were undecided (50.6%). Significant correlations were found between job satisfaction and variables such as education, current position, organization type, monthly income, and professional experience. Additionally, most of the items assessing professional personal experience such as working in a shift system, working as a team member, gaining financial benefits, and having accomplishments or growth opportunities at work were significantly correlated with job satisfaction. Opportunities for professional development, promotion, and a positive work environment were also frequently selected as factors contributing to job satisfaction (60.6%, 75.2% and 75.5%, respectively). Interestingly, motivation showed minimal impact on participants’ opinions regarding job satisfaction and decisions over whether to quit their jobs. Finally, occupation and age were found to significantly influence work environments, promotions, and opportunities, which consequently impact participants’ satisfaction towards their jobs. Conclusions: Our findings indicate that Saudi pharmacists experience low-to-moderate job dissatisfaction, with a significant percentage considering quitting form their jobs. Improving monetary rewards, recognition, and career advancement opportunities could improve job satisfaction and retention in this crucial workforce. Full article

The cross-scale fusion and consistent representation of cross-source heterogeneous vector polygon data are fundamental tasks in the field of GIS, and they play an important role in areas such as the refined management of natural resources, territorial spatial planning, and the urban emergency response. However, the existing methods suffer from two key limitations: the insufficient utilization of semantic information, especially non-standardized attributes, and the lack of differentiated modeling for 1:1, 1:M, and M:N matching relationships. To address these issues, this study proposes a geometric–attribute collaborative matching method for multi-scale polygonal entities. First, matching relationships are classified into 1:1, 1:M, and M:N based on the intersection of polygons. Second, geometric similarities including spatial overlap, size, shape, and orientation are computed for each relationship type. Third, semantic similarity is enhanced by fine-tuning the pre-trained Sentence-BERT model, which effectively captures the complex semantic information from non-standardized descriptions. Finally, a three-branch attention network is constructed to specifically handle the three matching relationships, with adaptive feature weighting via attention mechanisms. The experimental results on datasets from Tunxi District, Huangshan City, China show that the proposed method outperforms the existing approaches including geometry–attribute fusion and BPNNs in precision, recall, and F1-score, with improvements of 3.38%, 1.32%, and 2.41% compared to the geometry–attribute method, and 2.91%, 0.27%, and 1.66% compared to BPNNs, respectively. A generalization experiment on Hefei City data further validates its robustness. This method effectively enhances the accuracy and adaptability of multi-scale polygonal entity matching, providing a valuable tool for multi-source GIS database integration. Full article

Digital twins are increasingly utilized across all lifecycle stages of physical entities. Augmented reality (AR) offers real-time immersion into three-dimensional (3D) data, which provides an immersive experience with dynamic, high-quality, and multi-dimensional digital twins. A robust and customizable data platform is essential to create scalable 3D digital twins; Universal Scene Description (USD) provides these necessary qualities. Given the potential for integrating immersive AR and 3D digital twins, we developed a software application to bridge the gap between multi-modal AR immersion and USD-based digital twins. Our application provides real-time, multi-user AR immersion into USD-based digital twins, making it suitable for time-critical tasks and workflows. AR digital twin software is currently being tested and evaluated in an application we are developing to train astronauts. Our work demonstrates the feasibility of integrating immersive AR with dynamic 3D digital twins. AR-enabled digital twins have the potential to be adopted in various real-time, time-critical, multi-user, and multi-modal workflows. Full article

Quantitative Geometrical Thermodynamics (QGT) exploits the entropic Lagrangian–Hamiltonian canonical equations of state as applied to entities obeying the holographic principle and exhibiting Shannon information, the creation of which measures the (validly defined) “entropic purpose” of the system. QGT provides a physical description for what we might consider the true “atoms” of physical science and has also recently enabled a number of significant advances: accounting ab initio for the chirality of DNA and the stability of Buckminsterfullerene; the size of the alpha particle (and other nuclear entities) and the lifetime of the free neutron; and the shape, structure, and stability of the Milky Way galaxy. All these entities, ranging in size over more than 38 orders of magnitude, can each be considered to be an “atom”; in particular, the size of the alpha is calculated from QGT by assuming that the alpha is a “unitary entity” (that is, than which exists no simpler). The surprising conclusion is that clearly compound entities may also be physically treated as unitary (“uncuttable”) according to a principle of scale relativity, where a characteristic size for such an entity must be specified. Since QGT is entropic, and is therefore described using a logarithmic metric (involving hyperbolic space), it is not surprising that the length scale must be specified in order to account for unitary properties and for an entity to be appropriately considered an “atom”. The contribution to physics made by QGT is reviewed in the context of the related work of others. Full article

The forecasting of Drug-Drug Interactions (DDIs) is essential in pharmacology and clinical practice to prevent adverse drug reactions. Existing approaches, often based on neural networks and knowledge graph embedding, face limitations in modeling correlations among drug features and in handling complex BioKG relations, such as one-to-many, hierarchical, and composite interactions. To address these issues, we propose Rot4Cap, a novel framework that embeds drug entity pairs and BioKG relationships into a four-dimensional vector space, enabling effective modeling of diverse mapping properties and hierarchical structures. In addition, our method integrates molecular structures and drug descriptions with BioKG entities, and it employs capsule network–based attention routing to capture feature correlations. Experiments on three benchmark BioKG datasets demonstrate that Rot4Cap outperforms state-of-the-art baselines, highlighting its effectiveness and robustness. Full article

Background: Evolutionary game theory (EGT), originating from Darwinian competition studies, offers a powerful framework for understanding complex healthcare interactions where multiple stakeholders with conflicting interests evolve strategies over time. Unlike traditional game theory, EGT accounts for bounded rationality and strategic evolution through imitation and selection. Aims and objectives: In our study, we use Synthetic Knowledge Synthesis (SKS) that integrates descriptive bibliometrics and bibliometric mapping to systematically analyze the application of EGT in healthcare. The SKS aimed to identify prolific research topics, suitable publishing venues, and productive institutions/countries for collaboration and funding. Data was harvested from the Scopus bibliographic database, encompassing 539 publications from 2000 to June 2025, Results: Production dynamics is revealing an exponential growth in scholarly output since 2019, with peak productivity in 2024. Descriptive bibliometrics showed China as the most prolific country (376 publications), followed by the United States and the United Kingdom. Key institutions are predominantly Chinese, and top journals include PLoS One and Frontiers in Public Health. Funding is primarily from Chinese entities like the National Natural Science Foundation of China. Bibliometric mapping identified five key research themes: game theory in cancer research, evolution game-based simulation of supply management, evolutionary game theory in epidemics, evolutionary games in trustworthy connected public health, and evolutionary games in collaborative governance. Conclusions: Despite EGT’s utility, significant research gaps exist in methodological robustness, data availability, contextual modelling, and interdisciplinary translation. Future research should focus on integrating machine learning, longitudinal data, and explicit ethical frameworks to enhance EGT’s practical application in adaptive, patient-centred healthcare systems. Full article

Objectives: We aimed to determine the frequency and factors related to oropharyngeal dysphagia in adults in a health center in Colombia evaluated by videofluroscopy of swallowing. Methods: We reviewed the records of 144 patients evaluated through videofluroscopy of swallowing. In order to analyze the results, descriptive, bivariate statistical analysis, and multivariate regression were used. Results: This investigation revealed that 23.6% of adults had oropharyngeal dysphagia. Older adults had a higher percentage of occurrence, and the factors associated with this symptom were having a history of cerebral stroke and being medicated with anticholinergic drugs. Conclusions: These findings strongly suggest that older adults with other comorbidities have a high percentage of presenting oropharyngeal dysphagia. Further research is needed to characterize the entity in other populations. Full article

Named entity recognition (NER) is one of the fundamental tasks in building knowledge graphs. For some domain-specific corpora, the text descriptions exhibit limited standardization, and some entity structures have entity nesting. The existing entity recognition methods have problems such as word matching noise interference and difficulty in distinguishing different entity labels for the same character in sequence label prediction. This paper proposes a span-based feature reuse stacked bidirectional long short term memory network (BiLSTM) nested named entity recognition (SFRSN) model, which transforms the entity recognition of sequence prediction into the problem of entity span suffix category classification. Firstly, character feature embedding is generated through bidirectional encoder representation of transformers (BERT). Secondly, a feature reuse stacked BiLSTM is proposed to obtain deep context features while alleviating the problem of deep network degradation. Thirdly, the span feature is obtained through the dilated convolution neural network (DCNN), and at the same time, a single-tail selection function is introduced to obtain the classification feature of the entity span suffix, with the aim of reducing the training parameters. Fourthly, a global feature gated attention mechanism is proposed, integrating span features and span suffix classification features to achieve span suffix classification. The experimental results on four Chinese-specific domain datasets demonstrate the effectiveness of our approach: SFRSN achieves micro-F1 scores of 83.34% on ontonotes, 73.27% on weibo, 96.90% on resume, and 86.77% on the supply chain management dataset. This represents a maximum improvement of 1.55%, 4.94%, 2.48%, and 3.47% over state-of-the-art baselines, respectively. The experimental results demonstrate the effectiveness of the model in addressing nested entities and entity label ambiguity issues. Full article