Search Results (19,981)

Background/Objectives: Although many studies have examined the determinants and management of dental anxiety (DA), its broader placement as a potential bottleneck along oral–systemic health pathways, from the determinants of DA to consequences through dental avoidance, oral outcomes, psychosocial impacts, and possible systemic health outcomes, has not been mapped across the review literature. This review aimed to conceptually map how existing DA reviews are distributed across this pathway, whether this broad framing changed across 5-year periods, and how systemic health outcomes were framed. Methods: We conducted a conceptual mapping review of DA-focused review articles published between 2005 and 2025. PubMed and Scopus were searched for English-language narrative, systematic, scoping and umbrella reviews and meta-analyses addressing the determinants or consequences of DA. One reviewer screened records, extracted review characteristics, and classified each review into predefined domains using binary framed/not framed coding rules. A structured AI-assisted prompt was used only to support full-text evaluation across domains; all final coding decisions were made by the reviewer. Results: The search identified 851 records; after removing 426 duplicates, 425 unique records were screened, and 39 reviews met the inclusion criteria. Framing concentrated on environmental and psychological determinants and on the pathway from DA to avoidance and poor oral health, whereas broader consequences, including shame, OHRQoL, and systemic health outcomes, were less consistently framed. Across 5-year periods, the broad pattern of framing remained relatively stable. Systemic health outcomes were framed in only a minority of reviews. Conclusions: Future research should test hypothesized pathways from DA to broader health consequences using clearly specified bridge mechanisms and appropriate temporal designs. Full article

Corporate reputation teams rely on media monitoring and qualitative research, both limited in speed and coverage when digital narratives form rapidly. This paper proposes SAPIENT (Sentinel-Augmented Population Intelligence for Emerging Narrative Tracking), a multi-agent system that links a sentinel layer over public text streams with a simulation layer that runs moderated, repeatable in silico focus-group sessions. The sentinel layer ingests social media, news, and forum text to produce a compact signal state (topics, sentiment, anomaly scores, risk labels), which conditions the simulation layer through an orchestrator. Persona agents and a moderator follow an Agentic Focus Group (AFG) protocol with repeated runs, variance reporting, and human review gates. We describe four sustainability communication scenarios: greenwashing backlash prediction, greenhushing risk assessment, campaign pre-testing, and crisis communication simulation. Nine experiments span 280 AFG runs across 20 conditions, three LLM backends (Claude Sonnet 4, GPT-4o, and Gemini 2.5 Flash), and a preregistered pilot human validation study with 54 participants. Signal conditioning improved simulation specificity ($p=0.012$). Cross-lingual sessions revealed a sentiment asymmetry between English and Turkish ($p=0.001$) with preserved persona rank ordering ($r=0.81$, $p=0.015$). Cross-model comparison showed consistent persona differentiation across all three backends (Pearson $r>0.92$, $p<0.002$ for all pairs). Sentiment was robust to prompt paraphrasing ($p=0.061$, n.s.), though credibility was sensitive to prompt wording ($p<0.001$). All significant results from Experiments 1–8 survived Benjamini–Hochberg correction. A preregistered pilot with 54 human participants on Prolific replicated the predicted credibility ranking across framing variants ($p=0.004$) but not the sentiment ranking, identifying a specific calibration target for future work. Full article

This study examines institutional processes in digital justice through a mixed conceptual approach that integrates bibliometric analysis and technology-adoption modeling, incorporating artificial intelligence (AI) as a projected component rather than an implemented system. A corpus of approximately 200 Scopus-indexed documents (2003–2024) was analyzed, identifying five dominant thematic clusters: advanced technologies, institutional justice, digital government, judicial information management, and digital criminal justice. The results reveal persistent gaps in the literature, particularly in rural and underserved communities, where connectivity barriers and the limited application of adoption models hinder inclusive digital transformation. As an institutional contribution, the study presents the conceptual design of the digital solution “Travel Permits—Accessible Justice”, developed under a Service-Oriented Architecture (SOA) and projected for future integration with AI-supported components to automate judicial authorizations through biometric validation, electronic signatures, and digital delivery. To evaluate its potential acceptance, the Technology Acceptance Model (TAM) is analytically adapted and extended to the community-based judicial context, framing institutional learning processes as a prospective form of learning analytics focused on user interaction, perceived usefulness, perceived ease of use, and behavioral intention. Taken together, the integration of bibliometric evidence with an extended TAM, along with the projected incorporation of AI-supported institutional learning processes, offers a coherent foundation for future studies on inclusive digital innovation in justice environments. Full article

This article investigates the housing precarity of EU migrant workers in the Dutch–German border region, focusing on the Venlo Greenport area. Drawing on documentary analysis, 28 interviews, field observations, and stakeholder engagement, it explores how local governance, market dynamics, and framing practices shape housing outcomes. While EU law guarantees free movement, housing remains excluded from the EU rights frameworks, leaving workers dependent on employer-linked or agency-controlled short-stay facilities. These arrangements—often overcrowded, surveilled, and formally temporary—become long-term solutions, producing what we term short-stay sedentarism: prolonged residence in housing designed to deny permanence. The study conceptualises the local “battleground” where municipalities, employers, housing providers, NGOs, and residents negotiate competing interests. Seven interpretive frames—nuisance/disorder, cowboys, human rights, NIMBY, shadow power, integration, and unwanted accumulation—structure these debates, legitimising certain strategies while obscuring structural deficiencies. Findings reveal that certification and enforcement, while intended to improve standards, often entrench precariousness by sustaining the short-stay model. Emerging integration-oriented policies signal a shift but remain fragile amid economic imperatives and spatial constraints. The paper argues that addressing housing precarity requires structural reforms: expanding access to regular housing, reducing employer dependency, and recognising migrant workers as long-term residents rather than temporary labour inputs. Full article

This paper reviews recent work on dynamic virtual power plants (DVPPs) using an Energy–Information–Market framework. It addresses the important problem of how DVPPs can support low-inertia power system operation and feeder-level stability under high renewable penetration. First, system-level studies on low-inertia operation and frequency control are used to frame quantitative requirements on rate of change of frequency, nadir, and quasi-steady-state limits. Second, energy-layer models are surveyed, including participation-factor-based DVPP controllers, grid-forming architectures, model-free frequency regulation, and robust frequency-constrained scheduling for allocating virtual inertia and fast frequency response (FFR) across distributed energy resource fleets. Third, information-layer and market-layer models are reviewed, covering stochastic and robust bidding, distribution locational marginal price-based clearing, peer-to-peer and community markets, privacy-preserving coordination, and emerging governance and cybersecurity schemes for DVPP participation. Across these strands, much of the literature remains centred on steady-state active and reactive power dispatch, with dynamic security enforced as constraints rather than formulated as verifiable and tradable services. This review identifies gaps in dynamic metrics and benchmarks, forecasting of available inertia and FFR capacity, market-physics co-design, multi-aggregator interaction, and experimentally validated DVPP implementations. These findings suggest that DVPPs can “sell stability” at the feeder level only through co-designed control, information, and market mechanisms and outline a research roadmap for this purpose. Full article

The rapid electrification of transportation and proliferation of distributed energy resources (DERs) are transforming distribution grids into highly dynamic, data-intensive, and cyber-physical systems. While reinforcement learning (RL), multi-agent coordination, and edge computing offer powerful tools for adaptive control, their deployment in safety-critical utility environments raises concerns regarding stability, certification compatibility, cyber-resilience, and regulatory acceptance. This paper presents an architecture-centric framework for edge-intelligent and cyber-resilient coordination of electric vehicles (EVs) and DERs that reconciles adaptive learning with deterministic safety guarantees. The proposed hierarchical edge–cloud architecture integrates multi-agent system (MAS) coordination, constraint-invariant reinforcement learning, and embedded cybersecurity mechanisms within a structured control hierarchy. Learning-enabled edge agents operate exclusively within standards-compliant safety envelopes enforced through supervisory constraint projection, control barrier functions, and Lyapunov-consistent stability safeguards. Protection-critical functions remain deterministic and isolated from adaptive layers, preserving compatibility with IEEE 1547 and existing utility protection schemes. The framework further incorporates anomaly triggered policy freezing, fail-safe fallback modes, and communication-aware resilience mechanisms to prevent unsafe transient behavior in non-stationary, distributed environments. Unlike simulation-only learning approaches, the architecture embeds progressive validation through software-in-the-loop (SIL), hardware-in-the-loop (HIL), and power hardware-in-the-loop (PHIL) testing to empirically verify transient stability, constraint compliance, and cyber-resilience under realistic timing and disturbance conditions. Beyond technical performance, the paper situates edge intelligence within standards evolution, governance structures, workforce transformation, techno-economic assessment, and equitable deployment pathways. By framing adaptive control as a bounded, auditable augmentation layer rather than a disruptive replacement for certified infrastructure, the proposed architecture provides a pragmatic roadmap for evolutionary modernization of distribution systems. Full article

As automated vehicles move from controlled environments to unpredictable real-world roads, scenario-based testing has become the cornerstone of safety validation. In recent years, substantial progress has been made in scenario representation standards and generation methodologies. However, integrating scenario generation, standards-aligned packaging, validation, curation, and structured querying into a reproducible end-to-end lifecycle remains challenging in practice. This work presents a reproducible reference architecture for Scenario Databases (SCDBs) that treats scenario collections as lifecycle-governed data systems rather than static repositories. The proposed architecture unifies the scenario lifecycle within a single workflow. It integrates scenario generation and ingestion, validation and curation, immutable storage, semantic and value-based querying, and reproducible export. Scenario semantics are represented using ASAM OpenX formats (OpenDRIVE and OpenSCENARIO), together with ASAM OpenLABEL metadata, enabling standards-aligned interoperability. Querying is performed over categorical and value-carrying metadata without requiring inspection of raw scenario artifacts at query time. The reference implementation is deployed using Infrastructure-as-Code, supporting reproducibility and low operational overhead. Execution-based metric enrichment is supported as an optional extension, enabling scenarios to be augmented with execution-derived measurements and trace metadata. The contribution is not a centralized database, but a reference architecture and deployment blueprint that supports interoperable and federated scenario ecosystems. By framing SCDBs as reproducible lifecycle systems, this work supports scalable scenario reuse and more transparent safety validation workflows. Full article

Underwater multi-sensor passive tracking faces two critical challenges: the strong nonlinearity of Doppler–bearing measurements and underwater acoustic propagation delays. To address these issues, this paper proposes a distributed fusion filtering method based on extended measurement space modeling and delay compensation. First, an extended measurement space comprising range, Doppler frequency, bearing, and bearing rate is constructed to transform the nonlinear measurements into a linear framework. Within this space, linear prediction equations for constant velocity (CV) motion are derived to facilitate linearized local filtering. Furthermore, a closed-form linear solution for propagation delay is established within the constructed state space. To resolve the incompatibility of multi-node estimates caused by local coordinate frame discrepancies, a distributed architecture based on the Unscented Transform (UT) is designed. In this architecture, local states are transformed into a unified Cartesian coordinate system for temporal compensation and fast Covariance Intersection (FCI) fusion, followed by an inverse mapping back to the local space. Simulation results demonstrate that, compared with traditional nonlinear methods based on mixed coordinate systems, the proposed method significantly reduces nonlinear approximation errors, thereby enhancing tracking accuracy and robustness. Full article

Armillaria species hold significant ecological and economic importance and they play a vital role in the growth of traditional Chinese medicine Gastrodia elata (G. elata). In this study, we assembled and compared the mitochondrial genomes (mitogenomes) of six Armillaria mellea (Vahl) P. Kumm. (A. mellea) strains isolated from the main G. elata-producing region of Hanzhong, China. The internal transcribed spacer (ITS) sequencing confirmed that all six strains form a monophyletic clade. Their mitogenomes (120,775 to 120,839 bp) exhibit a highly conserved architecture, each containing 16 protein-coding genes (PCGs), 23 open reading frames (ORFs), 27 tRNAs, and two rRNAs. Codon usage and amino acid frequency were strikingly similar among the six strains, with a strong AT bias. In contrast, comparisons with other Armillaria species revealed marked differences in gene order, repeat structures, and selection pressures. Phylogenetic analyses based on PCGs further resolved the close relationship among the six strains while highlighting distinct molecular variation across species. On the whole, these findings demonstrate that A. mellea strains co-evolving with G. elata maintain a highly uniform mitochondrial genome architecture, suggesting strong purifying selection or recent divergence within this symbiotic population. The pronounced differences from other Armillaria species at the levels of gene arrangement and selection pressure imply that mitochondrial gene rearrangement may have accompanied species diversification in the genus. By providing the first complete mitogenomes of A. mellea from a major G. elata cultivation area, this study not only expands the genomic resources for Armillaria but also establishes a foundation for understanding how mitochondrial variation might influence fungal growth, adaptation, and symbiotic efficiency with G. elata. Full article

Extreme class imbalance is a persistent obstacle for machine learning-driven intrusion detection, as rare but high-impact cyberattacks occur far less frequently than benign traffic in training data. In many real-world cybersecurity datasets, this imbalance becomes extreme, with certain attack types containing a handful of samples, effectively placing the problem in a few-shot learning regime. This paper presents a controlled benchmarking study of Generative Adversarial Network (GAN) objectives for synthesizing minority-class cyberattack data. Using the UWF-ZeekData22 network traffic dataset, each MITRE ATT&CK tactic is framed as a separate binary detection task, and tactic-specific GANs are trained solely on minority samples to generate synthetic attack records. Four widely used GAN variants—Vanilla GAN, Conditional GAN (cGAN), Wasserstein GAN (WGAN), and Wasserstein GAN with Gradient Penalty (WGAN-GP)—are compared under unified training steps and fixed augmentation conditions. The utility of generated data is assessed by evaluating downstream detection performance using five traditional classifiers: Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Decision Tree, and Random Forest. The results indicate that GAN augmentation generally strengthens minority-class detection across tactics and models, reducing false negatives and improving recall consistency, while not systematically harming majority-class performance. However, the effectiveness of each GAN objective varies significantly with data sparsity. Specifically, simpler adversarial objectives often outperform more complex architectures by preserving discriminative feature structure, while heavily regularized models may overly smooth minority-class distributions and reduce separability. Wasserstein-based objectives provide improved training stability, but additional regularization does not consistently translate to better detection performance. Overall, the results demonstrate that in extreme-imbalance settings, GAN effectiveness is governed more by data sparsity and structure preservation than by architectural complexity. These findings establish class-specific generative augmentation as a practical strategy for intrusion detection and provide empirical guidance for selecting appropriate GAN objectives for tabular cybersecurity data under highly imbalanced conditions. Full article

The Composite Cycle Engine (CCE) enhances the conventional Joule/Brayton cycle by replacing the high-pressure compressor with a high-quality piston-based gas generator that enables extremely high compression, combustion, and expansion of the working fluid before entering the classic Joule burner. This piston-based topping cycle unlocks much more efficient fuel utilization. This paper studies a CCE concept featuring a system of free double piston (FDP) units for a potential long-range (LR) application in 2045, benchmarked against an advanced turbofan engine representative of the same time frame. In-house-developed simulation tools for the piston system and the overall power plant, as well as aircraft non-linear trade factor analysis, are used for different levels of conceptual assessment. First, the cooling demand inside the FDP system is determined. An engine cycle parametric study is then performed for the design point top-of-climb (ToC). Off-design performance is further studied, demonstrating a 9.3% improvement in thrust-specific fuel consumption (TSFC) in cruise relative to the baseline engine. After incorporating the engine weight and nacelle geometry effects, the engine reaches a total mission fuel burn reduction of around 14.7% compared to the baseline engine. The concept evaluation shows the fuel burn potential of the CCE in the future LR aviation sector and lays the foundation for further climate impact analysis. Full article

Lactiplantibacillus plantarum MS11, isolated from traditionally fermented yak milk in the high-altitude Gannan region of the eastern Tibetan Plateau, was investigated for its technological and functional potential in food applications. Using whole-genome sequencing combined with targeted experimental verification, this study clarified the genetic determinants and metabolic capacity associated with its production of folate, lactic acid, bacteriocin, and exopolysaccharides (EPS). The MS11 genome consists of one circular chromosome and three plasmids, totaling 3,318,231 bp with a GC content of 44.48%, and encodes 3155 predicted open reading frames. Complete biosynthetic gene clusters were identified for folate (7 genes), L-lactic acid (13 genes), bacteriocin (14 genes), and EPS (17 genes). Phenotypic assays confirmed the strain’s high metabolite productivity, including folate (0.6043 μg/mL), L-lactic acid (76.24 mg/mL), and EPS (544.2 mg/L). The cell-free fermented supernatant exhibited strong antibacterial activity against Escherichia coli, supporting the functional relevance of its bacteriocin-associated gene cluster. To the best of our knowledge, this is the integrated genomic and experimental characterization demonstrating that a L. plantarum strain originating from a unique high-altitude fermented dairy niche can concurrently synthesize high levels of folate together with multiple beneficial metabolites. The multifunctional attributes of MS11—including nutrient fortification, acidification capacity, EPS formation, and antimicrobial activity—indicate substantial promise for its application as a composite starter culture, natural bio-preservative, and nutritionally enhanced probiotic in fermented food systems. Full article

Tongue squamous cell carcinoma (TSCC) is clinically heterogeneous, and patients with a similar TNM stage can experience markedly different outcomes. We systematically reviewed omics-driven studies to identify prognostic TSCC biomarkers. Although fundamentally prognostic, we discussed their theoretical translational relevance regarding future clinical decisions—such as treatment stratification or surveillance intensity—while strictly framing them as preliminary, hypothesis-generating targets. PubMed, Scopus, Web of Science, and Cochrane were searched for original human studies published between 2014 and 2024 using high-throughput genomic or transcriptomic profiling. Study selection followed referred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), data were extracted with a structured workbook, and risk of bias was assessed using QUIPS and PROBAST, with reporting completeness appraised using REMARK. Seventeen studies were included, identifying 85 distinct biomarkers. Across biomarkers supported by multivariable overall survival analyses, higher-risk associations were reported for NELL2, PDE4D, CTTN, HBEGF, and CA9, whereas lower-risk associations were reported for AC139530.1, LINC01711, CCDC96, CYP2J2, and SPAG16. Recurrent biological themes included IL-17 signaling, ECM-receptor interaction, and focal adhesion. CA9 was the only biomarker reported in more than one included study, supporting its prioritization for validation. Although the evidence remains heterogeneous and largely hypothesis-generating, these markers may support the future validation of response-oriented therapeutic stratification in TSCC. Full article

This paper explores the ethical dimensions of violence in sporting contexts, proposing that violence can be a virtue when characterized by controlled physicality. While society often views violence negatively, the paper argues that within rule-governed sports, certain forms of violence are morally permissible, strategically valuable, and essential to upholding the integrity of the game. Drawing on Suitsian terms and Kantian ethics, the paper develops a theory of lusory violence, distinguishing it from uncontrolled physicality or unmitigated violence. By examining the roles of enforcers in hockey, the development of MMA, and the ethics of sport jiu-jitsu, the paper suggests that violence is acceptable within a lusory framework only when it is purposive, strategically relevant, and constrained by rules that prioritize technical skill over raw damage. Ultimately, the paper argues that the ability to modulate violent behaviour represents a form of moral development, framing virtuous violence as a necessary tool for maintaining natural justice and personal excellence within specific sporting environments. Yet, virtuous violence is subordinate to technique, justice, and other defining elements of sports. Full article

We developed a dual-arm motion recognition system designed for real-time upper-limb movement analysis using video input. The system integrates MediaPipe Hands for skeletal critical point detection, a feature extraction pipeline that encodes spatial and temporal characteristics from upper-limb joints, and a three-layer long short-term memory network for temporal modeling and classification. By computing directional vectors from the shoulder to the elbow and wrist, a 168-dimensional feature vector is generated per frame. Sequences of 90 frames are used to capture full motion patterns. The system effectively supports multi-class recognition of coordinated dual-arm gestures, offering applications in rehabilitation, gesture control, and human–computer interaction. Full article