Search Results (15,080)

Severe COVID-19 and bacterial sepsis share clinical manifestations of systemic inflammation and organ dysfunction. Yet, early differentiation between these conditions and timely identification of patients at risk of deterioration remain major clinical challenges. Extracellular vesicle (EV)-associated microRNAs (miRNAs) have emerged as promising biomarkers of host immune dysregulation. In our study, we have characterized circulating EV-miRNAs in patients with COVID-19, bacterial sepsis, localized bacterial infections, and healthy subjects to assess their diagnostic and prognostic utility. After EV isolation from plasma and characterization by nanoparticle tracking analysis and flow cytometry, a panel of 12 inflammation-related miRNAs were individually quantified by qRT-PCR. Four EV-miRNAs—miR-28-5p, miR-199a-5p, miR-200a-3p, and miR-369-3p—were significantly elevated in COVID-19 patients, with higher levels in those with poor prognosis. miR-199a-5p and miR-200a-3p were increased in bacterial sepsis compared with COVID-19, enabling discrimination between viral and bacterial sepsis. Three EV-miRNAs—miR-28-5p, miR-199a-5p, and miR-200a-3p—were markedly higher in bacterial sepsis than localized infections, and ROC analysis showed a strong diagnostic performance, particularly for miR-199a-5p, alone or in combination with other EV-miRNAs. The increased expression of selected EV-miRNAs was associated with higher SOFA scores and in-hospital mortality. These findings indicate that EV-miRNAs reflect pathogen-specific and severity-related immune responses, supporting their potential as minimally invasive biomarkers for early diagnosis and risk stratification in severe infections. Full article

Temporomandibular joint (TMJ) disorders represent chronic degenerative musculoskeletal conditions with a high prevalence in the general population and limited regenerative treatment options. Owing to the insufficient efficacy of current conservative and surgical therapies, there is a growing clinical need for biologically based regenerative approaches. Tissue engineering (TE), particularly scaffold-based strategies, has emerged as a promising avenue for TMJ regeneration. This systematic review analyzed preclinical in vivo studies investigating scaffold-based interventions for TMJ disc and osteochondral repair. A structured literature search of PubMed and Scopus databases identified 39 eligible studies. Extracted data included scaffold composition, use of cellular and bioactive components, animal models, and reported histological, radiological, and functional outcomes. Natural scaffolds, such as decellularized extracellular matrix and collagen-based hydrogels, demonstrated favorable biocompatibility and support for fibrocartilaginous regeneration, whereas synthetic materials including polycaprolactone, poly (lactic-co-glycolic acid), and polyvinyl alcohol provided superior mechanical stability and structural tunability. Cells were used in 17/39 studies (43%); quantitative improvements were variably reported across these studies. Bioactive molecule delivery, including transforming growth factor-β, histatin-1, and platelet-rich plasma, further enhanced tissue regeneration, while emerging drug- and gene-delivery approaches showed potential for modulating local inflammation. Despite encouraging results, the reviewed studies exhibited substantial heterogeneity in experimental design, outcome measures, and animal models, limiting direct comparison and translational interpretation. Scaffold-based approaches show preclinical promise but heterogeneity in design and incomplete quantitative reporting limit definitive conclusions. Future research should emphasize standardized methodologies, long-term functional evaluation, and the use of clinically relevant large-animal models to facilitate translation toward clinical application. However, functional and biomechanical outcomes were inconsistently reported and rarely standardized, preventing robust conclusions regarding the relationship between structural regeneration and restoration of TMJ function. Full article

This study presents a comparative assessment of the Johnson–Cook (J-C) and Ductile Damage (DD) material models, evaluating their capability to replicate the tensile behavior and fracture development in ductile metals. Numerical models of AL6063-T4 aluminium and A36 steel dog-bone specimens with two different thicknesses were developed in ABAQUS to assess force–displacement response, stress–strain characteristics, and crack evolution under quasi-static loading. Results showed that specimen thickness directly doubled load capacity, while both models captured the overall elastic and plastic behavior of the materials. A key finding is that the DD model provided yield stresses closely matching the reference material values, whereas the J-C model exhibited higher apparent yields due to its intrinsic strain-rate sensitivity. Differences in damage behavior were also pronounced: the DD model better reproduced the gradual, inclined fracture path in aluminium, while the J-C model more accurately captured the strong necking-localization response characteristic of steel. Comparisons with experimentally tested specimens further supported these fracture tendencies. By analysing both materials under identical conditions, this work highlights the relative strengths and limitations of the two fracture formulations. The originality of the study lies in its systematic comparison across materials and thicknesses, providing clear guidance for selecting appropriate constitutive models in structural and computational mechanics research. Full article

Within the cloud manufacturing paradigm, Cloud Manufacturing Service Composition (CMSC) is a core technology for intelligent resource orchestration in Cloud Manufacturing Platforms (CMP). However, existing research faces critical limitations in real-world CMP operations: single-task-centric optimization ignores resource sharing/competition among coexisting manufacturing tasks (MTs), causing performance degradation and resource “starvation”; traditional heuristics require full re-execution for new scenarios, failing to support real-time online decision-making; single-agent reinforcement learning (RL) lacks mechanisms to balance global efficiency and inter-task fairness, suffering from inherent fairness defects. To address these challenges, this paper proposes a fairness-aware multi-task CMSC method based on Multi-Agent Reinforcement Learning (MARL) under the Centralized Training with Decentralized Execution (CTDE) framework, targeting the symmetry-breaking issue of uneven resource allocation among MTs and aiming to achieve symmetry restoration by restoring relative balance in resource acquisition. The method constructs a multi-task CMSC model that captures real-world resource sharing/competition among concurrent MTs, and integrates a centralized global coordination agent into the MARL framework (with independent task agents per MT) to dynamically regulate resource selection probabilities, overcoming single-agent fairness defects while preserving distributed autonomy. Additionally, a two-layer attention mechanism is introduced—task-level self-attention for intra-task subtask correlations and global state self-attention for critical resource features—enabling precise synergy between local task characteristics and global resource states. Experiments verify that the proposed method significantly enhances inter-task fairness while maintaining superior global Quality of Service (QoS), demonstrating its effectiveness in balancing efficiency and fairness for dynamic multi-task CMSC. Full article

The increasing penetration of renewable energy leads to a continuous reduction in system inertia, for which conventional synchronization criteria based solely on frequency consistency can no longer accurately capture the coupled dynamics of frequency and voltage during transients. To address this issue, this paper employs the concept of complex frequency and develops an analysis framework that integrates theory, indices, and simulation for assessing synchronization stability in low-inertia power systems. Firstly, the basic concepts and mathematical formulation of complex frequency and complex frequency synchronization are introduced. Then, dynamic criteria for local and global complex synchronization are established, upon which a complex inertia index is proposed. This index unifies the supporting role of traditional frequency inertia and the voltage support capability associated with voltage inertia, enabling the quantitative evaluation of the strength of coordinated frequency–voltage support and disturbance rejection within a region. Finally, transient simulations on a modified WSCC nine-bus system are carried out to validate the proposed method. The results show that the method can clearly reveal the synchronization relationships between subnetworks and the overall system, providing a useful theoretical reference for stability analysis and control strategy design in low-inertia power systems. Full article

Accurate indoor localization is essential for navigation, monitoring, and industrial applications, especially in environments with Non-line of sight (NLOS) conditions. An indoor positioning system consists of fixed physical nodes, referred to as anchors, which serve as reference nodes with known locations, and entities that could be persons or objects that are also equipped with a node, referred to as targets, whose positions are estimated based on signal measurements exchanged with the surrounding anchors. Although RSSI is widely used due to hardware simplicity, its performance is often affected by signal degradation, multipath propagation, and environmental interference. To address this limitation, this work aims to develop an indoor positioning system, especially in wide areas with a minimal number of physical anchors, while maintaining high positioning accuracy and low latency. The proposed approach integrates VA, RSSI-based multilateration, and ML as a tool to refine and improve positioning accuracy, where ML models are used to predict the VA features and subsequently predict the corresponding distances. In addition, the system relies on energy-efficient WuRx nodes, which ensure a low power consumption and support on-demand communication. The study area covers two distinct floors with a total area of 366.9 m², covered using only four physical anchors. Two studies were performed, the offline and the online, in order to evaluate the proposed system under both the theoretical performance and real implementation conditions. In the offline phase, hexagonal and rectangular grid architectures were compared using multiple machine learning models under varying numbers of virtual anchors. By comparing different architectures and machine learning models, the rectangular grid with 10 virtual anchors combined with the XGBoost model achieved the best performance, resulting in an RMSE of $1.49\mathrm{m}$ with a processing time of approximately $0.15\mathrm{s}$. The online evaluation confirmed the performance of the proposed system, achieving an RMSE of $2.48\mathrm{m}$. Full article

This study assesses the innovation performance and convergence dynamics across 237 European regions (NUTS 2 level) from 2016 to 2023, explicitly accounting for the structural and behavioural changes triggered by the COVID-19 pandemic. The article provides a novel regional-level assessment of how an unprecedented external shock reshaped innovation trajectories before and after the pandemic. To this end, the analysis combines Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), sigma-convergence measures, and a Difference-in-Differences (DiD) framework within an integrated multi-method empirical approach to evaluate shifts in regional innovation patterns over time. The results reveal a highly uneven distribution of innovation activities, with increasing polarization in the post-pandemic period. Northern and Western European regions strengthened their competitive advantage through robust digital infrastructure, strong human capital, and substantial R&D investments. In contrast, many Southern and Eastern European regions faced heightened structural barriers, leading to a widening innovation gap. Nevertheless, several regions exhibited notable resilience and achieved significant innovation catch-up, providing new empirical evidence on heterogeneous regional adaptive dynamics supported by targeted regional policies and improved local capabilities. The sigma-convergence analysis indicates a general increase in overall disparities, as reflected by rising dispersion in the Regional Innovation Index (RII) during 2020–2023. However, according to the DiD estimation, regions most severely affected by COVID-19 experienced a statistically significant relative increase (approximately 2.17%) in innovation performance, highlighting the pandemic’s role as a catalyst for accelerated digital transformation and innovation adjustment at the regional level. Full article

Intimal hyperplasia (IH) at vascular anastomosis sites arises from endothelial injury, thrombin activation, and the subsequent proliferation and phenotypic modulation of vascular smooth muscle cells (VSMCs). Existing clinically used systemic pharmacologic regimens (e.g., antiplatelet/anticoagulant therapy) and reported local material-based strategies in the literature (e.g., drug-eluting sutures, hydrogels, or coatings) largely rely on drug release, which can result in burst kinetics, finite duration, and off-target/systemic exposure. We developed a covalently immobilized, non-releasing biointerface in which mitomycin C (MMC) is stably anchored onto polypropylene sutures via low-pressure, non-thermal acetic-acid plasma (AAP) activation. AAP functionalization introduced reactive oxygen-containing groups on polypropylene, enabling amide-bond immobilization of MMC while preserving suture mechanics. Anchored: MMC exhibited potent contact-mediated regulation of VSMC fate, reducing metabolic activity to 81% of control, suppressing G₂/M progression, and inducing a dominant sub-G1 apoptotic population (66.3%), consistent with MMC-induced DNA crosslinking, p21 upregulation, and cyclin B1–CDK1 inhibition. In vivo, in a rat infrarenal aortic anastomosis model (male Wistar rats, 10–12 weeks, 300–350 g), MMC-anchored sutures markedly reduced arterial wall thickening and α-SMA and PCNA accumulation at 4 and 12 weeks, without overt evidence of systemic toxicity. Notably, no measurable MMC release was detected under the tested conditions, supporting that the observed bioactivity is consistent with an interface-confined mechanism rather than bulk diffusion. This work establishes a non-releasing suture-based platform that delivers sustained molecular regulation of vascular healing through interface-confined control of VSMC behavior. Covalent drug anchoring transforms a clinically used suture into an active therapeutic interface, providing a promising strategy to prevent pathological vascular remodeling and anastomotic IH. Full article

Visual cues in urban street environments shape residents’ perceived safety, and these perceptions often differ across social groups. Using Shanghai as a case study, this research focuses on two vulnerable populations: older adults and migrants. In the context of rapid urban transformation and increasingly fine-grained governance, perceived safety not only reflects environmental experience but also relates to whether different social groups can receive equitable perceptual support and access to opportunities for public-space use. We trained a deep learning model and rated perceived safety using over 160,000 street-level images, integrated with demographic census data at the neighborhood level, to systematically examine inequalities in visual environment perception and underlying group-specific mechanisms. However, existing studies have largely relied on small-sample surveys or average-effect analyses, and systematic evidence remains limited that can simultaneously characterize city-scale inequalities in perceived safety, disparities in group exposure, and group-specific mechanisms, while translating findings into actionable guidance for targeted governance. Firstly, we quantified spatial inequality in perceived safety using the Gini coefficient and the Theil T index. Decomposition results indicate that the remaining disparity is primarily associated with between-group differences linked to social structure. Nonparametric tests and multiple linear regression further identified significant interactions between demographic characteristics (the share of older adults and the migrant proportion) and visual environmental features, confirming group-differentiated responses to comparable streetscape conditions. In addition, we developed a priority governance index that combines perceived safety scores with vulnerability indicators to spatially identify neighborhoods requiring targeted interventions. Results suggest relatively low overall spatial inequality in perceived safety at the city scale, while decomposition analyses reveal clear group-structured disparities between central and peripheral areas and between local residents and migrants. Migrants are more frequently concentrated in neighborhoods with lower perceived safety. Priority intervention areas are primarily older-adult communities in central districts and migrant settlements in peripheral areas, characterized by lower perceived safety and higher demographic vulnerability. These findings underscore the need to shift urban renewal from uniform improvements toward differentiated strategies that account for perceptual equity and social identity. Our main contribution is not the development of a new network architecture but the alignment of image-based perception estimates with demographic vulnerability at the neighborhood scale. By combining inequality decomposition with tests of interaction mechanisms, we provide governance-relevant evidence for identifying priority intervention areas and advancing fine-grained renewal decisions oriented toward visual justice. Full article

Generative Artificial Intelligence (GenAI) is rapidly reshaping public-sector operations, yet its adoption in developing countries remains poorly understood. Existing research focuses largely on traditional AI in developed contexts, leaving unanswered questions about how GenAI interacts with institutional, organizational, and governance constraints in resource-limited settings. This study examines the organizational factors shaping GenAI adoption in Vietnamese local government using 25 semi-structured interviews analyzed through the Technology–Organization–Environment (TOE) framework. Findings reveal three central dynamics: (1) the emergence of informal, voluntary, and bottom-up experimentation with GenAI among civil servants; (2) significant institutional capacity constraints—including absent strategies, limited budgets, weak integration, and inadequate training—that prevent formal adoption; and (3) an “AI accountability vacuum” characterized by data security concerns, regulatory ambiguity, and unclear responsibility for AI-generated errors. Together, these factors create a state of governance paralysis in which GenAI is simultaneously encouraged and discouraged. The study contributes to theory by extending the TOE framework with an environment-specific construct—the AI accountability vacuum—and by reframing resistance as a rational response to structural gaps rather than technophobia. Practical implications highlight the need for capacity-building, regulatory guidance, accountable governance structures, and leadership-driven institutional support to enable safe and effective GenAI adoption in developing-country public sectors. Full article

This article presents an ontology-driven Digital Twin (DT) for hotel front-desk operations that fuses two real-time data streams: (i) physiological and activity signals from wrist-worn wearables assigned to staff, and (ii) 3D people-positioning and occupancy events captured by reception-area cameras using a proprietary implementation of Optical Camera Communication (OCC). Building on a previously proposed front-desk ontology, the semantic model is extended with positional events, zone semantics, and wearable-derived workload indices to estimate queue state, staff workload, and service demand in real time. A vendor-agnostic, property-based REST API specifies the DT interface in terms of observable properties, including authentication and authorization, idempotent ingestion, timestamp conventions, version negotiation, integrity protection for signed webhooks, rate limiting and backoff, pagination and filtering, and privacy-preserving identifiers, enabling any compliant backend to implement the specification. The proposed layered architecture connects ingestion, spatial reasoning, and decision services to dashboards and key performance indicators (KPIs). This article details the positioning pipeline (calibration, normalized 3D coordinates, zone mapping, and confidence handling), the wearable workload pipeline, and an evaluation protocol covering localization error, zone classification, queue-length estimation, and workload accuracy. The results indicate that a spatially aware, ontology-based DT can support more balanced staff allocation and improved guest experience while remaining technology-agnostic and privacy-conscious. Full article

The integration of sustainable development into biology education has been a growing area of interest. Biology education for sustainability is considered through competencies and skills, taking different dimensions of knowledge into account. Solving problems requires not only knowledge but also communicative and strategic activity. Thus, biology education must emphasize the main visions of scientific literacy proposed in the literature, supporting students to understand society and everyday socioscientific challenges at the local as well as at the global level, and to deal with differing scientific results and uncertain information. However, there are very few studies from a holistic didactic viewpoint on the implementation of sustainable education (SE) in biology education in the context of teacher education and school education for promoting a sustainable future. This study addresses this gap via a bibliometric and content analysis of the literature (n = 165 and 131, respectively) based on the categories of the sustainable development goals (SDGs), subject aims, learning objectives, content knowledge, teaching methods, competencies and skills, and assessment methods. The literature analyzed emphasizes the environmental and social SDGs, the development of students’ factual and conceptual knowledge and learning, interactive teaching and learning methods, critical thinking and reflection, and summative and formative assessment methods. There is much less attention on economic and institutional SDGs, scientific skills, environmental attitudes, knowledge creation, strategic thinking and empathy, and diagnostic assessment methods. Compared to earlier studies performed in the 2010s, teaching and learning methods have become more diverse in contrast to the earlier focus on teacher-centered methods. Overall, the conclusion is that biology education must evolve beyond content mastery to integrate ethical, technological, and transdisciplinary dimensions—empowering learners not only to understand life but to sustain it—aligned with quality education (SDG 4), good health and well-being (SDG 3), and life on land (SDG 15). The findings also suggest that powerful knowledge needs to be emphasized for providing essential insights into ecosystems, biodiversity, and the processes that sustain life on Earth. They also highlight the importance of regular evaluations of teaching and learning for detecting how pedagogical and didactic approaches and strategies have supported students’ learning focused on sustainable development. Full article

Background: Whole-slide imaging and algorithmic advances have moved computational pathology from research to routine consideration. Despite notable successes, real-world deployment remains limited by generalization, validation gaps, and human-factor risks, which can be amplified in resource-constrained settings. Content/Scope: This narrative review and implementation perspective summarizes clinically proximate AI capabilities in cancer pathology, including lesion detection, metastasis triage, mitosis counting, immunomarker quantification, and prediction of selected molecular alterations from routine histology. We also summarize recurring failure modes, dataset leakage, stain/batch/site shifts, misleading explanation overlays, calibration errors, and automation bias, and distinguish applications supported by external retrospective validation, prospective reader-assistance or real-world studies, and regulatory-cleared use. We translate these evidence patterns into a practical checklist covering dataset design, external and temporal validation, robustness testing, calibration and uncertainty handling, explainability sanity checks, and workflow-safety design. Equity Focus: We propose a stepwise adoption pathway for low- and middle-income countries: prioritize narrow, high-impact use cases; match compute and storage requirements to local infrastructure; standardize pre-analytics; pool validation cohorts; and embed quality management, privacy protections, and audit trails. Conclusions: AI can already serve as a reliable second reader for selected tasks, reducing variance and freeing expert time. Safe, equitable deployment requires disciplined validation, calibrated uncertainty, and guardrails against human-factor failure. With pragmatic scoping and shared infrastructure, pathology programs can realize benefits while preserving trust and accountability. Full article

Mushrooms are valued for their nutritional qualities and have been used in traditional medicine since the Neolithic era. They exhibit various bioactivities, including antioxidant, hypocholesterolemic, immunomodulatory, and anticancer effects. The anticancer effects arise via direct action on tumor cells and indirect modulation of the immune system; the latter is the predominant mechanism. Numerous studies indicate that various mushroom species are potent immunostimulants because their cell wall polysaccharides and proteoglycans are recognized by intestinal immune cells. This enhances antitumor immunity through multiple molecular pathways. However, their direct effects on cancer cells are of questionable physiological relevance due to bioavailability constraints. Nevertheless, we hypothesize that the accumulation of non-absorbed polysaccharides in the gastrointestinal tract positions mushrooms as dual-action agents with the potential to treat colorectal cancer by providing indirect immunomodulation and direct local tumor suppression. Conversely, the direct anticancer effects of mushrooms are generally attributed to bioactive secondary metabolites that influence essential cellular processes, including signaling pathways, cell cycle regulation, apoptosis, autophagy, cellular migration, invasion, and cancer stem cell characteristics. Beyond these anticancer effects, clinical evidence suggests that certain mushroom-derived substances can improve survival outcomes for cancer patients and provide supportive care benefits in oncology, thereby improving quality of life. Specifically, mushrooms may mitigate the side effects of chemotherapy and radiotherapy, bolster immune function often suppressed by cancer treatments, and enhance overall well-being. In this review, we discuss the therapeutic benefits of dietary and medicinal mushrooms in cancer care, as well as unresolved challenges and future research directions. Full article

At 300 km/h, an end-to-end vision delay of 100 ms corresponds to 8.3 m of unobserved travel; therefore, real-time anomaly monitoring must balance sensitivity with strict tail-latency constraints at the edge. We propose a hybrid cache–retrieval inference architecture for visual anomaly detection in high-speed motorsport that exploits lap-to-lap spatiotemporal redundancy while reserving local similarity retrieval for genuinely uncertain events. The system combines a hierarchical visual encoder (a lightweight backbone with selective refinement via a Nested U-Net for texture-level cues) and an uncertainty-driven router that selects between two memory pathways: (i) a static cache of precomputed scene embeddings for track/background context and (ii) local similarity retrieval over historical telemetry–vision patterns to ground ambiguous frames, improve interpretability, and stabilize decisions under high uncertainty. Routing is governed by an entropy signal computed from prediction and embedding uncertainty: low-entropy frames follow a cache-first path, whereas high-entropy frames trigger retrieval and refinement to preserve decision stability without sacrificing latency. On a high-fidelity closed-circuit benchmark with synchronized onboard video and telemetry and controlled anomaly injections (tire degradation, suspension chatter, and illumination shifts), the proposed approach reduces mean end-to-end latency to 21.7 ms versus 48.6 ms for a retrieval-only baseline (55.3% reduction) while achieving Macro-F1 = 0.89 at safety-oriented operating points. The framework is designed for passive monitoring and decision support, producing advisory outputs without actuating ECU control strategies. Full article