Search Results (1,863)

Wound healing dysregulation often results in visible scarring with significant psychological impact, particularly affecting individuals with darker skin phototypes. This study investigates the efficacy of an aqueous extract of Australian Jellybush honey (Leptospermum polygalifolium) in improving scar appearance across diverse skin tones. Our research demonstrated the extract reduced IL-8 production by 42%, decreased reactive oxygen species by 14.6%, and increased pro-collagen I synthesis by 28%. Ex vivo experiments revealed a 44% increase in neo-epidermal tongue length after 10 days while gene expression analysis showed coordinated wound healing through timed modulation of key markers such as filaggrin, matrix metalloproteases, keratins. In a double-blind clinical study involving 21 volunteers (phototypes II–VI) with acne scars, 8-week application of 2% Jellybush honey extract improved skin homogeneity by 15%, reduced scar surface area by 3% and decreased atrophic and hypertrophic scars dimensions by 9% and 16%, respectively, versus placebo. These improvements were consistent across all phototypes, including V-VI. Jellybush honey extract offers a promising approach for scar management through balanced modulation of wound healing pathways across all skin phototypes. Full article

Neonicotinoid insecticides (NEOs), one of the most widely used pesticide classes worldwide, have raised concerns due to potential neurotoxic effects. Yet evidence on human exposure and health outcomes, particularly in preschool children, remains limited. In this study, 506 children aged 3–6 years from Shenzhen, China, were assessed. Neurobehavioral development was evaluated with the Strengths and Difficulties Questionnaire (SDQ), and urinary concentrations of 11 NEOs were measured, including imidacloprid (IMI), clothianidin (CLO), thiamethoxam (THM), dinotefuran (DNT), nitenpyram (NIT), sulfoxaflor (SFX), acetamiprid (ACE), thiacloprid (THD), flonicamid (FLO), 6-chloronicotinic acid (6-CINA), N-desmethyl-acetamiprid (NACE), and N-desmethyl-thiamethoxam (NTHM). Seven compounds showed high detection rates, including IMI (97.4%), CLO (100%), THM (100%), DNT (99.8%), NIT (99.8%), NACE (100%), and NTHM (99.8%). The mean urinary concentration was 234.145 μg/g creatinine, exceeding levels in earlier studies and indicating widespread exposure. IMI, NTHM, and NACE showed significant positive dose–response relationships with emotional symptoms, hyperactivity, and total difficulties and were major contributors in mixture models; sex-stratified analyses suggested effect modification for NTHM and NACE. These findings provide new epidemiological evidence to inform public health risk assessment and regulatory action on NEOs. Full article

The rapid evolution of UAV technology has increased the demand for lightweight airborne perception systems. This study introduces ADG-YOLO, an optimized model for real-time target detection and ranging on UAV platforms. Building on YOLOv11n, we integrate C3Ghost modules for efficient feature fusion and ADown layers for detail-preserving downsampling, reducing the model’s parameters to 1.77 M and computation to 5.7 GFLOPs. The Extended Kalman Filter (EKF) tracking improves positional stability in dynamic environments. Monocular ranging is achieved using similarity triangle theory with known target widths. Evaluations on a custom dataset, consisting of 5343 images from three drone types in complex environments, show that ADG-YOLO achieves 98.4% mAP_0.5 and 85.2% mAP_0.5:0.95 at 27 FPS when deployed on Lubancat4 edge devices. Distance measurement tests indicate an average error of 4.18% in the 0.5–5 m range for the DJI NEO model, and an average error of 2.40% in the 2–50 m range for the DJI 3TD model. These results suggest that the proposed model provides a practical trade-off between detection accuracy and computational efficiency for resource-constrained UAV applications. Full article

Rectal cancer has become a significant health concern in current years, but there are very effective current neo-adjuvant treatment modalities which can result in the complete disappearance of the disease without surgery, which is often associated with severe post-surgical sequelae. Therefore, a significant effort has been made to identify the subset of patients who can avoid surgery and to investigate the long-term oncologic and functional results associated with the Non-Operative Management of such a disease. Full article

Background: Ongoing challenges such as geopolitical conflicts, trade disruptions, economic sanctions, and political instability have underscored the urgent need for large manufacturing enterprises to improve resilience and reduce dependence on global supply chains. Integrating regional and local Small- and Medium-Sized Enterprises (SMEs) has been proposed as a strategic approach to enhance supply chain localization, yet barriers such as limited visibility, qualification hurdles, and integration difficulties persist. Methods: This study proposes a comprehensive knowledge graph driven framework for representing and discovering SMEs, implemented as a proof-of-concept in the U.S. BioPharma sector. The framework constructs a curated knowledge graph in Neo4j, converts it to Resource Description Framework (RDF) format, and aligns it with the Schema.org vocabulary to enable semantic interoperability and enhance the discoverability of SMEs. Results: The developed knowledge graph, consisting of 488 nodes and 11,520 edges, enabled accurate multi-hop SME discovery with query response times under 10 milliseconds. RDF serialization produced 16,086 triples, validated across platforms to confirm interoperability and semantic consistency. Conclusions: The proposed framework provides a scalable, adaptable, and generalizable solution for SME discovery and supply chain localization, offering a practical pathway to strengthen resilience in diverse manufacturing industries. Full article

Digital currencies, led by Bitcoin and USDT, are characterized by decentralization and anonymity, which obscure the identities of traders and create a conducive environment for illicit activities such as drug trafficking, money laundering, cyber fraud, and terrorism financing. Focusing on the USDT-TRC20 token on the Tron blockchain, we propose a two-layer transaction network-based approach for virtual currency address identity recognition for digging out hidden relationships and encrypted assets. Specifically, a two-layer transaction network is constructed: Layer A describes the flow of USDT-TRC20 between on-chain addresses over time, while Layer B represents the flow of TRX between on-chain addresses over time. Subsequently, an identity metric is proposed to determine whether a pair of addresses belongs to the same user or group. Furthermore, transaction records are systematically acquired through blockchain explorers, and the efficacy of the proposed recognition method is empirically validated using dataset from the Key Laboratory of Digital Forensics. Finally, the transaction topology is visualized using Neo4j, providing a comprehensive and intuitive representation of the traced transaction pathways. Full article

Background and objectives: Large language models (LLMs) show promise in automating open-ended evaluation tasks, yet their reliability in rubric-based assessment remains uncertain. Variability in scoring, feedback, and rubric adherence raises concerns about transparency and pedagogical validity in educational contexts. This study introduces CourseEvalAI, a framework designed to enhance consistency and fidelity in rubric-guided evaluation by fine-tuning a general-purpose LLM with authentic university-level instructional content. Methods: The framework employs supervised fine-tuning with Low-Rank Adaptation (LoRA) on rubric-annotated answers and explanations drawn from undergraduate computer science exams. Responses generated by both the base and fine-tuned models were independently evaluated by two human raters and two LLM judges, applying dual-layer rubrics for answers (technical or argumentative) and explanations. Inter-rater reliability was reported as intraclass correlation coefficient (ICC(2,1)), Krippendorff’s α, and quadratic-weighted Cohen’s κ (QWK), and statistical analyses included Welch’s t tests with Holm–Bonferroni correction, Hedges’ g with bootstrap confidence intervals, and Levene’s tests. All responses, scores, feedback, and metadata were stored in a Neo4j graph database for structured exploration. Results: The fine-tuned model consistently outperformed the base version across all rubric dimensions, achieving higher scores for both answers and explanations. After multiple-testing correction, only the Generative Pre-trained Transformer (GPT-4)—judged Technical Answer contrast remains statistically significant; other contrasts show positive trends without passing the adjusted threshold, and no additional significance is claimed for explanation-level results. Variance in scoring decreased, inter-model agreement increased, and evaluator feedback for fine-tuned outputs contained fewer vague or critical remarks, indicating stronger rubric alignment and greater pedagogical coherence. Inter-rater reliability analyses indicated moderate human–human agreement and weaker alignment of LLM judges to the human mean. Originality: CourseEvalAI integrates rubric-guided fine-tuning, dual-layer evaluation, and graph-based storage into a unified framework. This combination provides a replicable and interpretable methodology that enhances the consistency, transparency, and pedagogical value of LLM-based evaluators in higher education and beyond. Full article

Vibocracy refers to societal conditions in which public life and decision-making are shaped by affective resonance, performative legitimacy, and unstable epistemic frames, often amplified by algorithmic media and neo-oral communication environments. Unlike wicked problems, which presuppose shared intelligibility, and post-truth politics, which emphasize the erosion of factual authority, vibocracy designates contexts where problems themselves are enacted and sustained through affective circulation. Recent years have seen the emergence of societal challenges where public life and decision-making are shaped less by shared evidence and deliberative reasoning than by affective resonance and performative legitimacy. This entry introduces the concept of vibocracy to describe these conditions and distinguishes it from existing categories such as wicked problems and messes. The analysis is based on a conceptual synthesis of scholarship from planning, organizational studies, media theory, and political science, combined with illustrative examples from recent societal controversies. The main finding is that vibocratic problems resist not only solutions but stable framing itself, creating volatile, performative arenas where legitimacy is enacted rather than negotiated. The entry concludes by proposing vibocracy as a distinct conceptual lens for understanding emerging societal challenges and outlines methodological implications for researchers and practitioners. Full article

We consider an axysimmetric cylindrical vessel grown by surface deposition at the inner boundary. The residual stress in the vessel can vary, e.g., depending on the loading history during growth. Can we represent and characterize a stress-free material (namely, reference) configuration for the vessel? Extending an idea initially proposed for surface growth occurring on a fixed boundary, the material configuration is introduced as a two-dimensional manifold immersed in a three-dimensional space. The problem is first formulated in fairly general terms for an incompressible neo-Hookean material in plane strain and then specialized to material configurations represented by ruled surfaces. An illustrative example using geometric and material parameters of carotid arteries shows the characterization of different material configurations based on their three-dimensional slope and computes the corresponding residual stress fields. Finally, such a slope is shown to be in a one to one relationship with the customary measure of residual stress in arteries, i.e., the opening angle in response to a cut. The present work introduces a novel framework for residual stress and shows its applicability in a special setting. Several generalizations and extensions are certainly necessary in the following sections to further test and assess the proposed method. Full article

Background/Objectives: In early-stage oncology clinical trials, the use of endpoints beyond overall survival (OS), including recurrence-free survival (RFS) or event-free survival (EFS), is becoming more common. To understand whether these outcomes are important to patients, this study explored the perceived value of non-OS endpoints among Canadians treated for early-stage cancer or with curative intent. Methods: Canadians treated for early-stage breast, lung, or gastrointestinal cancer participated in semi-structured interviews. Participants provided perspectives on OS, RFS, disease-free survival (DFS), EFS, and pathological complete response (pCR) endpoints. Reflexive thematic analysis was used to explore patterns in responses and alignment of trial endpoints with patient treatment goals, priorities and preferences. Results: The mean age of the 33 participants was 54.8 years, and 21 were female; 28 reported prior surgery, and 21 were also treated with chemotherapy (11 specified as neo-adjuvant; 9 specified adjuvant). All participants valued OS, and most viewed non-OS endpoints as reflective of their treatment priorities, including maintaining health-related quality of life and getting back to ‘normal’. They also valued timely and equitable treatment access and equated having access to new treatments with better options. While participants considered efficacy data from clinical trials provided by non-OS endpoints sufficient to want access to new treatments, the relative importance of being disease- or recurrence-free versus maximizing length of life differed according to recurrence status, prognosis, cancer type and life stage. Conclusions: These findings support the relevance and importance of non-OS endpoints to Canadians with early-stage cancer and highlight participants’ desire for rapid approval of treatments with demonstrated improvements in non-OS endpoints. Full article

Retinal diseases are among the leading causes of vision impairment worldwide, and early detection is essential for enabling personalized treatments and preventing irreversible vision loss. In this paper, we propose a method aimed to identify and localize retinal conditions, i.e., Age-Related Macular Degeneration, Diabetic Retinopathy, and Choroidal Neovascularization, using explainable deep learning. For this purpose, we consider seven fine-tuned convolutional neural networks: MobileNet, LeNet, StandardCNN, CustomCNN, DenseNet, Inception, and EfficientNet. Moreover, we develop a novel architecture i.e., NeoNet, specifically designed for the detection of retinal diseases, achieving an accuracy of 99.5%. Furthermore, with the aim to provide explaianability behind the model decision, we highlight the most critical regions within retinal images influencing the predictions of the model. The obtained results show the ability of the model to detect pathological features, thereby supporting earlier and more accurate diagnosis of retinal diseases. Full article

To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source heterogeneous data and subsequently utilizing text mining and natural language processing techniques to extract design themes and method elements. A “theme–stage–attribute” three-dimensional mapping model is established to achieve semantic coupling of knowledge. The BERT-BiLSTM-CRF (Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field) model is employed for entity recognition and relation extraction, while the Sentence-BERT (Sentence Bidirectional Encoder Representations from Transformers) model is used to perform multi-source knowledge fusion. The Neo4j graph database facilitates knowledge storage, visualization, and querying, forming the basis for developing a prototype of a design method recommendation system. The framework’s effectiveness was validated through experiments on extraction performance and knowledge graph quality. The results demonstrate that the framework achieves an F1 score of 91.2% for knowledge extraction, and an 8.44% improvement over the baseline. The resulting graph’s node and relation coverage reached 94.1% and 91.2%, respectively. In complex semantic query tasks, the framework shows a significant advantage over traditional classification systems, achieving a maximum F1 score of 0.97. It can effectively integrate dispersed knowledge in the field of design methods and support method matching throughout the entire design process. This research is of significant value for advancing knowledge management and application in innovative product design. Full article

Global modern renewable energy based on geothermal, wind, solar, and marine resources has grown rapidly over the last decades despite low energy density, intermittent supply, and other qualities inferior to those of fossil fuels. What is the explanation for this growth? The main drivers of growth are assessed using economic theories and verified with statistical data. From the neo-classic viewpoint that focuses on price substitutions, the growth can be explained by the shift from energy-intensive agriculture and industry to labour-intensive services. However, the energy resources complemented rather than substituted for each other. In the evolutionary idea, investments supported by policies enabled cost-reducing technological change. Still, policies alone are insufficient to generate the growth of modern renewable energy as they are inconsistent across countries and in time. From the behavioural perspective that is preoccupied with innovative entrepreneurs, the value addition of electrification can explain the introduction of modern renewable energy in market niches, but not its fast growth. Instead of these mono-causalities, the growth of modern renewable energy is explained by technology diffusion during the pioneering, growth, and maturation phases. Possibilities that postpone the maturation are pinpointed. Full article

Background/Objectives: Natural products, especially plant metabolites, play a crucial role in drug development and are widely used in medicine, cosmetics, and nutrition. The present review aims to provide a comprehensive overview of the pharmacological profile of Glycyrrhizin (GL), with a specific focus on its molecular targets. Methods: Scientific literature was thoroughly retrieved from reputable databases, including Scopus, Web of Science, and PubMed, up to 30 July 2025. The keywords “glycyrrhizin” and “glycyrrhizic acid” were used to identify relevant references, with a focus on pharmacological applications. Studies on synthetic analogs, non-English publications, non-pharmacological applications, and GL containing crude extracts were largely excluded. Results: Glycyrrhizin, the major bioactive constituent of Glycyrrhiza glabra, exhibits diverse pharmacological activities, including anti-inflammatory, antiviral, hepatoprotective, antitumor, neuroprotective, and immunomodulatory effects. These actions are primarily mediated through the inhibition of high-mobility group box 1 (HMGB1) and the modulation of key signaling pathways, including nuclear factor kappa B (NF-κB), mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), and various cytokine networks. As a result of its therapeutic potential, GL-based formulations, including Stronger Neo-Minophagen C, and GL-rich extracts of G. glabra are commercially available as pharmaceutical preparations and food additives. Conclusions: Despite its therapeutic potential, the clinical application of GL is limited by poor oral bioavailability, metabolic variability, and adverse effects such as pseudoaldosteronism. Hence, careful consideration of pharmacokinetics and safety is essential for translating its therapeutic potential into clinical practice. Full article

Polyvinyl chloride (PVC)-dibutyl adipate (DBA) gels are a fascinating dielectric elastomer actuator showing promise in soft robotics. When actuated with high voltages, the gel deforms towards the anode. A recent application of PVC gels in electrohydraulic actuators motivates elastic and hyperelastic constitutive relationships for tensile loading modes. PVC gels with plasticizer-to-polymer weight ratios of 2:1, 4:1, 6:1, and 8:1 w/w were evaluated. PVC gels exhibit a linear elastic region up to 25% strain. The elastic modulus decreased with increasing plasticizer content from 288.8 kPa, 56.1 kPa, 24.7 kPa, to 11 kPa. Poisson’s ratio also decreased with increasing plasticizer content from 0.42, 0.43, 0.39, to 0.35. We suggest that the decrease in polymer concentration facilitates a weakly interconnected polymer network susceptible to chain slippage that hinders the network response, thus lowering Poisson’s ratio. Our work suggests that PVC gels can be treated as isotropic and incompressible for large strains and hyperelastic modeling; however, highly plasticized gels tend to act less incompressible at small strains. The power scaling law between the elastic modulus and plasticizer weight ratio showed high agreement, making the elastic modulus deterministic for any plasticizer content. The Neo–Hookean, Mooney–Rivlin, Yeoh, Gent, Ogden, and extended tube hyperelastic constitutive models are investigated. The Yeoh model shows the highest feasibility when evaluated up to 3.5 stretch, showing a maximum normalized root-mean-square-error of 6.85%. Together, these findings establish a constitutive basis for PVC-DBA gels, incorporating small strain elasticity, large strain non-linear behavior, and network analysis while providing suggestive insight into the network structure required for accurately modeling the EPIC. Full article