Search Results (1,149)

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

As a metabolism-controlling peptide, insulin affects activity of almost all tissues in human organisms, including the ones located in the central nervous system. By modifying glucose uptake and processing, as well as inducing anabolic effects, insulin alters functions of various nerve centers. Data from numerous clinical trials prove that such actions can have positive influence on cognitive processes or might be utilized as measures to control appetite, mood, and blood flow, or to prevent unfavorable mental states associated with diminished ability to maintain homeostasis. The intranasal route of administration provides an efficient and targeted delivery method, allowing insulin to be applied directly to different brain regions via the nasal mucosa. Such an approach can also reduce the risk of potential adverse effects associated with this medication, including drops in plasma glucose levels. This review gathers clinical studies’ findings on intranasal insulin’s neuromodulatory properties and its efficacy as additional treatment measure in several neuropsychiatric disease entities. Full article

Against the backdrop of intensifying global technological competition and the deepening of the national innovation-driven strategy, private technology enterprises, as the core entities of technological innovation, have their sustainable innovation dynamics profoundly influenced by the strategic interactions among multiple parties such as the government, enterprises, and users. Based on evolutionary game theory, this paper constructs a tripartite evolutionary game model involving the government, private technology enterprises, and market users in the Chinese context. Through theoretical deduction and multi-scenario numerical simulation using Matlab, it systematically analyzes the logic of strategic choices and the laws of dynamic equilibrium of the three parties in the process of sustainable innovation. The research shows that the strategic evolution of multiple entities presents multiple equilibrium states. There exist critical thresholds for the intensity of policy support, the concentration of market competition, and users’ willingness to choose innovative products; beyond these thresholds, the marginal impact on sustainable innovation dynamics increases significantly. Further research finds that the government and enterprises need to compensate for the profit gap between users’ choice of innovative products and traditional products through a subsidy mechanism to form a positive cycle of “active innovation–market recognition–profit improvement”. This study enriches the theoretical system of multi-entity innovation dynamics by incorporating user behavior and provides a decision-making reference for optimizing innovation governance and fostering the development of sustainable innovation dynamics in private enterprises in China and other similar economies. Full article

Most graph learning methods remain limited to undirected, pairwise interactions, restricting their ability to capture the multi-entity and directional relationships common in real-world systems. We propose the Directed Higher-Ordered Neural Network (HONN) framework that introduces directionality into hypergraph learning through flexible spectral Laplacian formulations. Unlike fixed-Laplacian methods such as the Generalized Directed Hypergraph Neural Network (GeDi-HNN), a tunable q-parameter in our framework balances local identity preservation with global diffusion, enabling robust and generalizable feature propagation. Experiments on five benchmark datasets show that HONN consistently matches or outperforms state-of-the-art baselines, achieving 84% on NTU-2012, 87.4% on WebKB Texas, and 86.2% on Cornell, while maintaining computational efficiency. Ablation studies confirm the crucial role of Laplacian selection, activation functions, and q-tuning in shaping model performance. By unifying directionality and higher-order reasoning, HONN provides a scalable foundation for predictive modeling in domains such as knowledge graphs, spatio-temporal networks, and recommendation systems. Full article

Inequalities in enrollment in STEM persist for those entering higher education as first-generation college students, underserved racial and ethnic groups, female and nonbinary individuals, and those from lower socioeconomic backgrounds. The current study aims to better understand the relationship students have with graduate school success factors by redistributing the Graduate Student Success Survey+ (GSSS+) at an R2 institution in the southeastern United States. Exploratory factor analysis was used to test the survey’s validity, with 242 participants. A 7-factor, 40-item model was developed, comprising the following subscales: mentor support, peer support, imposter phenomenon, financial support, microaggressions (related to race and gender), access and opportunity (for research, writing, and presentations), and resilience. Item analysis identified perceived barriers (e.g., microaggressions, imposter phenomenon, and financial stress) for underserved students (i.e., females, underserved racial and ethnic groups, and part-time students). Regression analysis on resilience revealed a positive relationship with mentor support, peer support, and financial support. A negative relationship with resilience was associated with a greater perception of imposter phenomenon. Findings from this study underscore the need for additional support from mentors and other university entities to foster a stronger sense of resilience in students, along with increased opportunities for participation in research, academic writing, and publication. 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

Ischemic stroke is now widely recognized as a disease with a strong inflammatory profile. Cerebral vascular damage is both preceded and followed by a chain of molecular events involving immune cells and inflammatory markers, irrespective of the etiology of the ischemic injury. Over time, an increasingly comprehensive understanding of these markers has led to a better insight into the mechanisms behind the vascular event and recovery following ischemic stroke. However, to date, there are still no available circulating or tissue biomarkers for early diagnosis or prognostic stratification, making ischemic stroke diagnosis contingent on clinical and instrumental investigations. However, neurological and internal medicine research is progressing in identifying markers that could potentially take on this role. This manuscript, therefore, aims to review the most recent and innovative results of medical advances, summarising the current state of the art and future perspectives. If ischaemic stroke is an inflammatory disease, it is also true that it is not just a singular condition, but a group of entities with their own neuroinflammatory features. Thus, given that, in ischemic cerebral vascular damage, “time is brain,” tracking increasingly accurate markers in the diagnosis of ischemic stroke is a valuable tool that will potentially enable earlier recognition of this disease and, hopefully, make it less disabling and more widely treated. Full article

In the knowledge economy era, optimizing R&D team size is crucial for breakthrough innovation. Breakthrough technologies rely more on knowledge restructuring and technological leaps than general technologies do. However, it remains unclear whether breakthrough technology formation follows a simple “more people, more power” logic within technological systems. This work examines 35,955 patents in recommendation system technology to propose a relationship model between collaboration scale and breakthrough technological innovation based on patent data from the recommendation system field. It aims to elucidate how collaboration scale influences breakthrough technological innovation through knowledge restructuring, thereby providing theoretical support and practical guidance for enterprises, institutions, and governments in innovation activities to advance technological innovation. The findings reveal three key points: (1) The relationship between collaboration scale and breakthrough innovation is not linear but follows an inverted U-shaped curve; (2) Knowledge recombination significantly mediates this relationship, also exhibiting an inverted U-shaped pattern with collaboration scale; (3) The inverted U-shaped effect of collaboration scale on breakthrough innovation varies by country. The optimal thresholds are 14.058 entities for China, 57.151 entities for the United States, and 4.801 entities for Russia. This work breaks through the limitations of the traditional theoretical framework and constructs a three-dimensional analysis framework of “collaboration scale → knowledge recombination → breakthrough technological innovation”. By introducing the mediating variable of knowledge recombination, this paper reveals the mechanism of R&D team size on radical innovation. It provides a theoretical basis for the construction of an innovation team and provides a theoretical basis for enterprises, governments, and institutions. Full article

The increasing use of AI systems for face, object, action, scene, and emotion recognition raises significant privacy risks, particularly when processing Personally Identifiable Information (PII). Current privacy-preserving methods lack adaptability to users’ preferences and contextual requirements, and obfuscate user faces uniformly. This research proposes a user-centric, context-aware, and ontology-driven privacy protection framework that dynamically adjusts privacy decisions based on user-defined preferences, entity sensitivity, and contextual information. The framework integrates state-of-the-art recognition models for recognising faces, objects, scenes, actions, and emotions in real time on data acquired from vision sensors (e.g., cameras). Privacy decisions are directed by a contextual ontology based in Contextual Integrity theory, which classifies entities into private, semi-private, or public categories. Adaptive privacy levels are enforced through obfuscation techniques and a multi-level privacy model that supports user-defined red lines (e.g., “always hide logos”). The framework also proposes a Re-Identifiability Index (RII) using soft biometric features such as gait, hairstyle, clothing, skin tone, age, and gender, to mitigate identity leakage and to support fallback protection when face recognition fails. The experimental evaluation relied on sensor-captured datasets, which replicate real-world image sensors such as surveillance cameras. User studies confirmed that the framework was effective, with over 85.2% of participants rating the obfuscation operations as highly effective, and the other 14.8% stating that obfuscation was adequately effective. Amongst these, 71.4% considered the balance between privacy protection and usability very satisfactory and 28% found it satisfactory. GPU acceleration was deployed to enable real-time performance of these models by reducing frame processing time from 1200 ms (CPU) to 198 ms. This ontology-driven framework employs user-defined red lines, contextual reasoning, and dual metrics (RII/IVI) to dynamically balance privacy protection with scene intelligibility. Unlike current anonymisation methods, the framework provides a real-time, user-centric, and GDPR-compliant method that operationalises privacy-by-design while preserving scene intelligibility. These features make the framework appropriate to a variety of real-world applications including healthcare, surveillance, and social media. Full article

Hydrological simulation of large, transboundary water systems like the Laurentian Great Lakes remains challenging. Although deep learning has advanced hydrologic forecasting, prior efforts are fragmented, lacking a unified basin-wide model for daily streamflow. We address this gap by developing a single Entity-Aware Long Short-Term Memory (EA-LSTM) model, an architecture that distinctly processes static catchment attributes and dynamic meteorological forcings, trained without basin-specific calibration. We compile a cross-border dataset integrating daily meteorological forcings, static catchment attributes, and observed streamflow for 975 sub-basins across the United States and Canada (1980–2023). With a temporal training/testing split, the unified EA-LSTM attains a median Nash–Sutcliffe Efficiency (NSE) of 0.685 and a median Kling–Gupta Efficiency (KGE) of 0.678 in validation, substantially exceeding a standard LSTM (median NSE 0.567, KGE 0.555) and the operational NOAA National Water Model (median NSE 0.209, KGE 0.440). Although skill is reduced in the smallest basins (median NSE 0.554) and during high-flow events (median PBIAS −29.6%), the performance is robust across diverse hydroclimatic settings. These results demonstrate that a single, calibration-free deep learning model can provide accurate, scalable streamflow prediction across an international basin, offering a practical path toward unified forecasting for the Great Lakes and a transferable framework for other large, data-sparse watersheds. Full article

This study examines the challenges and opportunities of managing electronic waste (e-waste) in the Galapagos Islands, a globally significant yet vulnerable subnational insular jurisdiction (SNIJ). Drawing on theories of Circular Economy (CE) and Political Industrial Ecology (PIE), the research investigates the status of e-waste in the archipelago, the barriers to implementing CE practices, and the institutional dynamics shaping material flows. Using a mixed-methods approach—including archival analysis, participant observation, and semi-structured interviews with key informants from government, private, and nonprofit sectors—the findings presented here demonstrate that e-waste management is hindered by limited capital, infrastructure, public awareness, and fragmented governance. While some high-capital institutions can export e-waste to mainland Ecuador, most residents and low-capital entities lack viable disposal options, leading to accumulation and improper disposal. The PIE analysis yielded findings that highlight how institutional power and financial capacity dictate the sustainability of e-waste pathways, with CE loops remaining largely incomplete. Despite national policy support for CE, implementation in Galapagos remains aspirational without targeted financial and logistical support. This case contributes to broader discussions on waste governance in island settings and underscores the need for integrated, equity-focused strategies to address e-waste in small island developing states (SIDS) and SNIJs globally. Full article

Corporate Social Responsibility (CSR) is increasingly shaping the pathways of Small Medium-sized Enterprises (SMEs). This study presents an entity-relationship diagram (ERD) approach to 184 determinants of SME internationalization success, in order to provide structured inputs for Deep Learning (DL) Recommenders that can support CSR-aligned internationalization strategies. Employing Visual Paradigm 17.2 Professional software for modeling, the research synthesizes state-of-the-art findings on foreign market entry, and export performance, into ERDs. Then the market adoption drivers for such a DL tool are explored through semi-structured interviews with twelve stakeholders. The results reveal a propensity to adopt the DL recommender, with experts highlighting essential features for engagement, pricing, and implementation. The discussion contextualizes these findings, while the conclusion addresses gaps and future directions. The study’s focus in Portugal/Germany may limit worldwide extrapolation, yet it advances knowledge by consolidating success determinants, validating platform requirements, exposing gaps, and suggesting research in both CSR, AI and SME internationalization. Full article

This paper considers how Buddhist art of the early Tang dynasty was shaped by concerns with states of consciousness and transmigrating spiritual entities. Focusing on the Medicine Buddha (Skt. Bhaiṣajyaguru) tableau in the main chamber of Mogao Cave 220, dated to 642 CE and among the earliest full wall transformation tableaux at Dunhuang, I propose that the tableau depicts a structured process centered around the transmigrating spiritual entity of spirit consciousness (shenshi 神識) and its transformations that were visually expressed by lighting devices. Other elements in the tableau, such as the dancers and bodhisattvas seated in the pond, are also part and parcel to this visual project of transformation, as indicated through the colors of their attire and the types of dance being performed. The spirit consciousness could be visualized through lighting devices in the Medicine Buddha tableau because of the associations of lamps with vital, spiritual parts of humans since the first century CE. More importantly, the central role of the spirit consciousness in the Medicine Buddha tableau shows that such Buddhist murals depicting rituals and performances situated among grand edifices could be visual expressions of states of spiritual entities and their transformations. Seemingly intangible spiritual entities in Buddhist art were thus inextricably intertwined with and visually expressed through physical objects and their representations. To this end, this study is a first step towards understanding the pictorial program of Mogao Cave 220 and similar cases through explorations of cognitive templates that informed the creation and production of Buddhist art, with the spirit consciousness as a case in point. 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

This study explores the innovative application of Artificial Intelligence (AI) in transforming data engineering practices, with a specific focus on optimizing data modeling and data warehouse automation for Business Intelligence (BI) systems. The proposed framework automates the creation of Data Vault models directly from raw source tables by leveraging the advanced capabilities of Large Language Models (LLMs). The approach involves multiple iterations and uses a set of LLMs from various providers to improve accuracy and adaptability. These models identify relevant entities, relationships, and historical attributes by analyzing the metadata, schema structures, and contextual relationships embedded within the source data. To ensure the generated models are valid and reliable, the study introduces a rigorous validation methodology that combines syntactic, structural, and semantic evaluations into a single comprehensive validity coefficient. This metric provides a quantifiable measure of model quality, facilitating both automated evaluation and human understanding. Through iterative refinement and multi-model experimentation, the system significantly reduces manual modeling efforts, enhances consistency, and accelerates the data warehouse development lifecycle. This exploration serves as a foundational step toward understanding the broader implications of AI-driven automation in advancing the state of modern Big Data warehousing and analytics. Full article