Search Results (13,025)

The gradual developments in mathematical analysis have increased the demand for improving the efficiency of constraints and their validation, which have a significant contribution to resolving many real-world problems. There are many techniques that are used to modify the fractional inequalities, but all approaches are analytical. The adoption of Machine learning (ML) models is one practical approach to optimize the bounds of inequalities and their numerical validations. In this paper, we implement ML models to modify the bounds of Hermite–Hadamard-type inequalities, which consider the h-Godunova–Levin as a weight function. The classical development of inequalities by generalized fractional operators was never systematically studied to determine which value of the weight gives the best bound possible. We prove a sharp lower bound for the Hermite–Hadamard gap uniform over all admissible weights, show that it is achieved exactly by the classical convex weight, and characterize the optimal weights. To complement this analytical result, a parameterization of the weight function by a feedforward neural network is introduced, which is assumed to be admissible in the h-Godunova–Levin framework, and the validity of the resulting inequalities is proved. A Lipschitz-type error analysis is used to relate the approximation accuracy of the network to the tightness of the bound, and it is shown in numerical experiments that the network learns the optimal weight without any knowledge of the analytical weight’s form. The framework thus ensures the rigor of fractional convexity theory while offering a data-driven method of determining the optimal weight functions in cases where they are not explicitly known. The inherent symmetry properties of the fractional operators and the symmetric structure of the Hermite-Hadamard inequalities are preserved, while the neural network framework introduces a symmetry-breaking mechanism that enables the discovery of optimal weights. This dual perspective on symmetry—both preserving and breaking—provides a comprehensive understanding of the underlying mathematical structures and aligns perfectly with the scope of the journal Symmetry. This practical approach opens a new horizon for researchers and yields better results in the field of analysis. Full article

Oral diseases represent a major global health burden, and non-coding RNAs (ncRNAs) have emerged as pivotal regulators in their pathogenesis, offering new avenues for diagnosis and therapy. This review synthesizes current knowledge on ncRNAs—including miRNAs, lncRNAs, and circRNAs—across a spectrum of oral conditions, encompassing periodontitis, oral squamous cell carcinoma, oral submucous fibrosis, oral lichen planus, oral leukoplakia, and chronic orofacial pain. Mechanistically, ncRNAs function through competing endogenous RNA networks, immune–inflammatory cascades, metabolic reprogramming, and fibroblast activation; notably, recent discoveries have revealed that certain circRNAs and lncRNAs encode functional micropeptides, adding an additional layer of regulatory complexity in oral pathology. On the translational front, salivary and circulating ncRNAs have shown promise as non-invasive biomarkers. However, major bottlenecks remain, including insufficient longitudinal validation, marked technical heterogeneity across studies, and delivery challenges specific to the oral microenvironment. By critically evaluating mechanistic insights alongside these translational barriers, this review identifies critical knowledge gaps and proposes prioritized strategies to facilitate the clinical integration of ncRNA-based precision dentistry. Full article

Ecological civilization education can advance learners from knowledge acquisition to behavioral internalization, yet a persistent “knowing–doing gap” remains rooted in the disconnection between cognition and action. This study proposes an Institution–Conception–Behavior (ICB) model and examines how institutional support is associated with university students’ pro-environmental behavior, mediated by ecological civilization conception. A dual-path model integrating association and bidirectional association pathways was tested using structural equation modeling with bootstrap analysis (N = 500). Data were collected from two universities in Inner Mongolia, a region designated as China’s northern ecological security barrier. The results indicate that institutional support is positively associated with pro-environmental behavior through the indirect role of ecological civilization conception (indirect effect = 0.309, 95% CI [0.296, 0.506]), while its direct association is non-significant. Pro-environmental behavior (β = 0.278, p < 0.001) and ecological civilization conception (β = 0.417, p < 0.001) are positively associated with institutional perception orientation. These findings are consistent with the view that ecological civilization conception may serve as an associational bridge between institutions and behavior, suggesting a pattern of mutual associations in which institutions are associated with conceptions, conceptions relate to behavior, and behavior is associated with institutional perception. This study provides empirical evidence for advancing ecological civilization education through institutional support and conceptual cultivation. Full article

Nitrogen cycling is a key determinant of ecosystem functioning, productivity, and carbon sequestration during forest development. This review synthesizes ecological evidence on nitrogen cycling in young forest ecosystems established through natural regeneration, afforestation, reforestation, or major disturbance across boreal, temperate, and tropical biomes. The review evaluates how successional stage, plant community composition, soil properties, microbial processes, climate, and disturbance influence nitrogen transformations, retention, and losses during early forest development. The literature indicates that young forests can exhibit rapid nitrogen turnover while maintaining effective nutrient retention, although the balance between conservation and loss varies with biome, site conditions, and disturbance intensity. Boreal forests tend to show more conservative nitrogen cycling, whereas tropical forests generally exhibit faster turnover and greater nitrogen fluxes. The synthesis also highlights important knowledge gaps, particularly the limited availability of long-term studies, insufficient integration of aboveground–belowground interactions, underrepresentation of tropical and Southern Hemisphere forests, and limited assessment of climate-change effects on plant–soil–microbial feedbacks. Addressing these gaps will improve understanding of nitrogen dynamics during forest development and strengthen ecosystem modelling, forest restoration, and climate-change mitigation strategies. Full article

Drawing on a design-based intervention in active European citizenship education, this article explores the extent to which participatory citizenship education equips primary pupils with the necessary democratic resilience in the digital age. It argues that teachers’ constant search for consensus in the primary classroom has become a democratic liability in a digital public era of polarisation. My Big Friendly Guide to the EU which was implemented across five Irish primary schools in 2018–2019 with 458 pupils aged five to twelve, examines what participatory European citizenship education looks like in practice. Data shows that knowledge gains were substantial, ranging from 16 to 66 percentage points across identity, institutional, and national indicator domains. Yet qualitative observation reveals a consistent finding that teachers, who largely felt they had insufficient substantive knowledge of the EU, pushed towards consensus when discussions generated unresolved disagreement. The article discusses how the piloting of this educational programme in participatory citizenship education highlights gaps in pupils’ democratic resilience. It argues that a solution could be integrating Mouffe’s theory of agonistic democracy and Ruitenberg’s application to pedagogy. This would ensure that pupils are equipped to resist platform polarisation and algorithmic selection. It further contends that this dual approach cannot be achieved without structured teacher training in powerful knowledge on the European Union and agonism, which a digitally aware civic education requires. Full article

Liver fibrosis is the common pathway through which chronic liver injury progresses to cirrhosis, portal hypertension, liver failure, hepatocellular carcinoma, and systemic complications. Its burden is increasing worldwide, driven mainly by metabolic dysfunction-associated steatotic liver disease, alcohol-related liver disease, viral hepatitis, and cardiometabolic comorbidity. Current evidence supports a clinically practical approach centered on early risk recognition, non-invasive fibrosis assessment, etiologic treatment, lifestyle and metabolic risk reduction, and timely referral of patients with suspected advanced fibrosis. Although advanced cirrhosis may remain only partly reversible, fibrosis can regress when the injurious stimulus is controlled, making prevention of progression a realistic therapeutic goal. This review provides a clinically actionable framework for the assessment, management, and prevention of liver fibrosis, integrating current insights into epidemiological trends, etiopathogenesis, non-invasive and portal-hypertension assessment, sex-specific effects, hepatic and extrahepatic outcomes, and treatment strategies. It highlights the potential for fibrosis regression when the underlying etiologic factor is controlled and emphasizes the stages (F0–F2) at which reversibility is most achievable. Additionally, the paper outlines key research priorities to address current knowledge gaps in biomarker discovery, precision medicine, and artificial intelligence-assisted risk stratification, while defining priorities for personalized screening, multidisciplinary care, and combination antifibrotic research. Full article

Cnidaria is a phylum of aquatic invertebrates that includes jellyfish, sea anemones and corals. Their venoms are among the oldest in the animal kingdom and an important source of bioactive molecules. Advances in omics technologies have revolutionized toxin discovery, yet knowledge of cnidarian toxin diversity remains fragmented across databases and individual studies. To address this gap, this review integrates information from 342 toxins curated in the ToxProt database, 76 additional toxins reported in the literature, and evidence from 81 proteomic and 31 transcriptomic studies. Toxins have been characterized from 145 species, with sea anemones accounting for the largest number of studied species (65). Cnidarian toxins are used for prey capture, predator deterrence, territorial competition and digestion. These include toxic enzymes, neurotoxins, protease inhibitors and pore-forming toxins, among others. To date, the three-dimensional structures of 37 toxins have been resolved, and 78 have reported biomedical or biotechnological potential, including antiarrhythmic, analgesic, anticancer, and insecticidal activities. According to omics studies, metalloproteinases, phospholipases, lectins, Kunitz-like peptides, venom coagulation factor and CRiSP/CAP toxins are the most widespread, whereas neurotoxins show a more restricted taxonomic distribution. The first venom-related transcriptomic analysis of three Corallimorpharia and two Antipatharia (black corals) species demonstrates putative pore-forming toxins, while black corals also harbor putative neurotoxins. Overall, this review synthesizes the current knowledge of cnidarian venoms and highlights their remarkable diversity and promise as a source of bioactive molecules. Full article

Advanced Air Mobility (AAM) is increasingly recognized as a promising approach for improving healthcare logistics, yet evidence remains fragmented across aviation, transportation, healthcare, and digital infrastructure. This study examined the operational, clinical, and institutional evidence to characterize the evolution of healthcare-focused AAM, identify dominant research themes, and determine critical knowledge gaps. The review followed PRISMA-ScR guidelines and combined bibliometric analysis, thematic synthesis, and semantic network analysis of 168 peer-reviewed studies published between 2015 and 2025 and retrieved from IEEE Xplore, ScienceDirect, Scopus, and Web of Science. The analysis identified six thematic clusters encompassing system design, healthcare logistics, biological specimen transport, emergency response, health equity, and digital infrastructure. Publication activity increased rapidly after 2020. Emergency response represented the most mature research domain, and pharmaceutical logistics, longitudinal operational validation, and health equity remained comparatively underdeveloped. These findings demonstrate that healthcare-focused AAM functions as a sociotechnical system requiring coordinated advances in technology, governance, institutional integration, and equitable access to support clinically reliable and operationally sustainable healthcare delivery. Full article

Modern methods of construction (MMC) are welcomed by authorities as opportunities to accelerate economic growth and meet their housing needs, largely due to the conjecture that MMC are economically competitive. While scholars have discussed the consequences of using individual MMC, arguing for their economic productivity, including allegedly superior time and quality performance, and marginally inferior cost performance, the published studies lack rigorous substantiation. This knowledge gap is partially addressed by offering an evaluation of the detailed cost performance of four MMC, namely one timber frame, two identical steel frame modular MMC, and one concrete and one steel frame panelized construction system, in a live house construction experiment. Unlike previous studies where the data required adjustments for, inter alia, time and location, the context of this live research experiment was a construction project that was managed by a single contractor on one construction site. The research method includes an investigation of the site conditions and residential building designs, interviews with relevant construction personnel, and analysis of cost records. The actual cost performance of the four MMC houses was poorer than previously argued in the literature, which was mainly attributable to the higher building superstructure, preliminary, and repair costs. The cost performance of the two identical MMC houses also differed. These findings are significant in the context of the questionable time and quality performance of MMC and are likely to impact their sustainability and desirability unless improvements are achieved. Full article

Fermented beverages are increasingly recognized as promising vehicles for delivering probiotics and bioactive compounds; however, their functionality cannot be explained solely by microbial properties, as it is strongly shaped by interactions between the microorganism, the food matrix, and processing conditions. This review critically synthesizes evidence across fruit-, dairy-, and cereal-based fermented beverage systems, with particular attention to the fragmented and often difficult-to-compare nature of the available literature. Although Saccharomyces cerevisiae var. boulardii (S. boulardii) demonstrates notable tolerance to gastrointestinal and processing-related stresses and has been associated with immunomodulatory and gut-health benefits, the translation of these attributes into consistent beverage functionality remains highly matrix dependent. Evidence derived from in vitro, animal, and clinical studies suggests potential health benefits; however, differences in strains, fermentation conditions, beverage composition, and analytical approaches complicate direct comparisons and may partly explain the variability observed among reported outcomes. Beyond biological performance, important challenges remain regarding stability, ethanol production, sensory acceptance, safety considerations, and industrial implementation. Current evidence indicates that beverage composition and processing conditions are major determinants of S. boulardii performance and may partly explain the variability observed among studies. The review further highlights current knowledge gaps and identifies priorities for the development of next-generation functional beverages based on S. boulardii fermentation. Full article

Large language model (LLM) agents are becoming cognitive interfaces to data lakes, enterprise knowledge bases, vector memories, browsers, files, and software tools. This shift creates a data-governance gap: an agent may reason over large private context, yet the protected resource often lacks a verifiable record of which user intent, data object, action, destination, and semantic release were authorized. This paper proposes CapAgent, a semantic data-flow governance middleware for LLM agents in big-data cognitive-computing environments. CapAgent maps human-attested task intent into signed, attenuable, and purpose-bound capability tokens that are checked by a reference monitor before sensitive tool invocation, memory retrieval, data export, and inter-agent delegation. Its policy layer combines task templates, resource labels, destination rules, caveats, semantic release modes, and audit obligations; its runtime enforces both symbolic scope checks and semantic recoverability checks over protected facts. We present formal governance semantics, a conservative intent compiler, an explainable data-flow decision workflow, and a runnable Python middleware. A reproducible trace-replay benchmark with 600 benign and adversarial traces across five data-intensive agent scenarios reports attack success, benign success, false blocking, latency, component ablations, and audit quality. In this synthetic trace-replay evaluation, the full monitor reduces measured attack success from 100.00% under ambient execution and 12.50% under scope-only authorization to 0.00% (Wilson 95% CI [0.00, 0.95]), while retaining 75.00% benign success. In addition, we conduct a 520-trial end-to-end tool-calling benchmark with representative prompt-only, task-shield-style, CaMeL-style, scope-only, and full-CapAgent configurations; a 210-task compiler gold-standard evaluation; a 240-item semantic-release calibration set; and a 12-cell BDCC-style scalability microbenchmark. In these supplemental tests, full CapAgent obtains 0.00% ASR (95% CI [0.00, 1.06]) in the tool-calling benchmark, 87.50% exact-policy compiler match with 0.00% over-authorization, 88.89% semantic-release recall with 0.00% false-block rate, and sub-millisecond in-process authorization latency up to 100,000 resources. The results support CapAgent as an auditable governance layer for cognitive LLM agents rather than as a replacement for model-level alignment or public end-to-end agent benchmarks. Full article

Surface biomass, including litter, organic matter and ground vegetation, plays a very important role in forest ecosystems for regulating water retention and soil erosion. At the global and European temperate forest scale, no bibliometric analysis has previously been conducted, even though the scientific interest has been growing. This bibliometric analysis studied 1189 global and 178 European temperate forest articles retrieved from Scopus from 2005 to 2025 using RStudio (bibliometrix) and VOSviewer. The global output grew at an annual growth rate of almost 20.83%, coinciding with the Paris Agreement 2015 and the European Green Deal 2019 that may have brought scientific attention to climate change, hydrological process, forest ecosystem services and sustainable forest management, leading to the increases, while European research accounted for only 14.9% of global output and was mainly influenced by Mediterranean fire-related research, which limits its direct applicability to central European forest conditions. China and the USA dominated the global research output. Despite being the country with 30% of its area covered with forests, no Polish institution appeared in the top 10 contributors in both the global and European datasets. Thematic analysis showed that infiltration and surface runoff are poorly integrated with forest management and climate change research. Litter and soil organic carbon appeared as an emerging topic in the European Temperate Forest dataset. Three dominant tree species of the central European temperate forest, Scots Pine (Pinus sylvestris L.), Norway Spruce (Picea abies), and European aspen (Populus tremula L.), were each mentioned just once in 178 European publications, indicating the need for species-specific research about water retention and erosion susceptibility and surface runoff. This bibliometric analysis identified the significant species-specific, institutional, and regional knowledge gaps in European temperate forest research, most importantly, for the central European ecosystem and the dominant Polish forest. The study highlighted the limited incorporation of hydrological processes with forest management and climate adaptation strategies. Future research direction should focus on long-term field research analysing biomass changes, water retention and erosion susceptibility in the European temperate forest ecosystem, while promoting stronger international research collaborations. Full article

Industrial digital transformation has become a strategic priority for manufacturing organizations, yet many initiatives still struggle to move from digital ambition to organizational execution. In Industry 4.0-oriented contexts, this gap persists because transformation depends not only on technologies, processes and investments, but also on the critical knowledge required to sustain success factors across socio-technical systems. This study develops and evaluates a socio-technical systems framework for critical knowledge mapping in industrial digital transformation. Grounded in Design Science Research, the study combines structured literature-based construct development, expert validation and application in an industrial organization. The empirical demonstration was conducted in a large Brazilian-born multinational food and beverage ingredients manufacturing organization, whose identity has been anonymized. The framework connects critical success factors, knowledge domains and knowledge vulnerabilities, enabling a more actionable system-level diagnosis of knowledge readiness, defined as the organizational capacity to identify, mobilize and protect the critical knowledge required for transformation. The application showed that strategic and governance-related factors were relatively more consolidated in the analyzed organization, while the main vulnerabilities were concentrated in human and organizational dimensions, especially knowledge sharing, talent management, workforce readiness and industrial-digital literacy. By linking Industry 4.0 readiness to human-aware and resilient transformation concerns associated with Industry 5.0, the study shows how critical knowledge mapping can support the transition from readiness assessment to socio-technical execution. Full article

Background and Objectives: Artificial intelligence (AI) has rapidly emerged as a promising tool for delivering personalized interventions in physical activity, exercise prescription, and weight management. AI technologies may facilitate individualized recommendations, behavioral support, and lifestyle modification through adaptive digital health solutions, although their effectiveness remains to be established across different populations and settings. However, the current evidence remains heterogeneous, and the practical implementation of AI in personalized training and weight management requires further evaluation. This scoping review aimed to summarize the current evidence regarding the application of artificial intelligence for the personalization of training and weight reduction, with particular emphasis on the types of AI technologies used, their reported outcomes, practical applications, and current limitations. Methods: A scoping review was conducted following a structured literature search of studies investigating AI-supported interventions related to physical activity, exercise, dietary behavior, and weight management. Eight studies involving diverse populations, intervention designs, and AI technologies were included. Data were extracted on study characteristics, AI technologies, intervention characteristics, reported outcomes, and research gaps. The literature search was subject to access-based restrictions, including the use of “Free full text” in PubMed and “Open Access” in Web of Science, as well as language restrictions. Results: The included studies investigated a wide range of AI technologies, including conversational chatbots, natural language processing systems, machine learning algorithms, computer vision applications, knowledge-based systems, and large language models. Selected studies reported favorable or modest changes in exercise adherence, physical activity participation, dietary behaviors, user engagement, and weight-related outcomes; however, the magnitude and consistency of these findings varied across studies. Personalized coaching, real-time feedback, and continuous behavioral support were common features of interventions reporting favorable outcomes. However, considerable heterogeneity existed across study designs, participant populations, intervention protocols, AI technologies, and outcome measures, and evidence regarding long-term effectiveness remains limited. The findings should be interpreted in the context of the adopted search strategy, including access-based restrictions and the inability to retrieve eight of 57 reports sought for retrieval, which may have contributed to availability bias. Conclusions: Current evidence suggests that artificial intelligence may have potential as a tool for supporting the personalization of training and weight management interventions, particularly through individualized behavioral support, feedback, and user engagement. However, the available evidence is heterogeneous and does not yet allow firm conclusions regarding the effectiveness or mechanisms of AI-supported interventions. AI should currently be viewed as a complement rather than a replacement for healthcare and exercise professionals. Future large-scale randomized controlled trials with longer follow-up periods and standardized outcome measures are needed to clarify the effectiveness, sustainability, and practical implementation of AI-supported interventions. Full article

This systematic review synthesizes theoretical, meta-analytic, and empirical evidence from 85 studies to clarify the conceptual boundaries and overlaps among five core job performance dimensions: task performance (TP), contextual performance (CP), adaptive performance (AP), creative and innovative performance (CIP), and counterproductive work behavior (CWB). In doing so, it addresses two gaps in the literature. To our knowledge, no prior study has: (1) systematically examined the distinctions and overlaps among all five dimensions simultaneously, across a broad range of occupations, rather than in isolated pairs, or (2) synthesized their antecedents into a single integrative framework. The synthesized evidence indicates that these dimensions are empirically and conceptually distinct yet correlated, and that multivariate, dimension-specific models can account for both shared and unique variance across dimensions, consistent with more targeted performance management. As the review’s central contribution, we propose a two-stage conceptual model. In the expression stage, the degree to which the work role prescribes a behavior (prescribed, discretionary, proscribed, or unscripted) determines which determinant class (ability, motivation, or opportunity) remains free to vary and hence to predict each dimension, with proximal states (e.g., task knowledge and skill, engagement, psychological capital) carrying these effects into behavior. In the evaluation stage, behavior is converted into dimension scores against standards that are themselves prescription-dependent, explaining why rater and measurement effects pattern differently across dimensions. The model yields testable propositions, most distinctively that formalizing a previously discretionary behavior should shift both which antecedents predict it and how it is scored. Full article