Search Results (3,256)

Screw propulsors have attracted increasing attention for their potential applications in amphibious vehicles and benthic robots, owing to their ability to perform both terrestrial and underwater locomotion. To investigate their hydrodynamic characteristics, a two-stage numerical analysis was carried out. In the first stage, steady-state simulations under various advance coefficients were conducted to evaluate the influence of key geometric parameters on propulsion performance. Based on these results, a representative configuration was then selected for transient analysis to capture unsteady flow features. In the second stage, a Detached Eddy Simulation approach was employed to capture unsteady flow features under three rotational speeds. The flow field information was analyzed, and the mechanisms of vortex generation, instability, and dissipation were comprehensively studied. The results reveal that the propulsion process is dominated by the formation and evolution of tip vortices, root vortices, and cylindrical wake vortices. As rotation speed increases, vortex structures exhibit a transition from ordered spiral wakes to chaotic turbulence, primarily driven by centrifugal instability and nonlinear vortex interactions. Vortex breakdown and energy dissipation are intensified downstream, especially under high-speed conditions, where vortex integrity is rapidly lost due to strong shear and radial expansion. This hydrodynamic behavior highlights the fundamental difference from conventional propellers, and these findings provide theoretical insight into the flow mechanisms of screw propulsion. Full article

The genus Curcuma (Zingiberaceae) is a diverse group of plants widely distributed across tropical Asia, with several new species recently described in Thailand. This study documents and clarifies the taxonomic status of a new species, Curcuma nivea Saensouk, P.Saensouk & Boonma sp. nov., discovered in eastern Thailand, and evaluates its horticultural potential. Morphological comparisons were conducted with closely related species in the Curcuma subgenus Hitcheniopsis (Baker) K. Schum., focusing on diagnostic vegetative and floral traits. Curcuma nivea is characterized by its compact habit and white flowers marked with two reddish lines at the base of the labellum, lacking the yellow blotch typical of related species. Additionally, it shows the absence of both epigynous glands and anther spurs, consistent with subgeneric features. Its distinctive morphology and attractive floral display have led to its cultivation as an ornamental pot plant. The discovery of C. nivea contributes to the growing documentation of Curcuma diversity in Thailand and underscores the significance of ongoing botanical exploration and conservation. Furthermore, its compact form and unique floral traits highlight its promise for use in ornamental horticulture and breeding programs. Full article

As augmented reality (AR) advertising becomes increasingly prevalent across digital platforms, understanding how its unique features influence consumer responses is critical for both theory and practice. Based on the elaboration likelihood model (ELM), this study develops and validates a dual-dimension content–dual-route processing model to investigate how different features of AR advertising influence consumer engagement. Specifically, it examines how product-related attributes (attractiveness, informativeness) and technology-related attributes (interactivity, augmentation) shape attitudes toward the ad and purchase intentions through cognitive (information credibility) and affective (enjoyment) pathways. Using data from an online survey (N = 299), the study applies partial least squares structural equation modeling (PLS-SEM) to test the proposed model. The results show that informativeness and augmentation significantly enhance information credibility, while attractiveness primarily influences emotional responses. Interactivity and augmentation positively influence cognitive and affective responses. Mediation analysis confirms the simultaneous activation of central and peripheral processing routes, with flow experience emerging as a significant moderator in selected pathways. By introducing a structured framework for AR advertising content, this study extends the applicability of the ELM in immersive media contexts. It underscores the combined impact of rational evaluation and emotional engagement in shaping consumer behavior and offers practical insights for designing effective AR advertising strategies. Full article

Background: Pain is a complex phenomenon characterized by unpleasant experiences with profound heterogeneity influenced by biological, psychological, and social factors. According to the National Health Interview Survey, 50.2 million U.S. adults (20.5%) experience pain on most days, with the annual cost of prescription medication for pain reaching approximately USD 17.8 billion. Theranostic pain nanomedicine therefore emerges as an attractive analgesic strategy with the potential for increased efficacy, reduced side-effects, and treatment personalization. Theranostic nanomedicine combines drug delivery and diagnostic features, allowing for real-time monitoring of analgesic efficacy in vivo using molecular imaging. However, clinical translation of these nanomedicines are challenging due to complex manufacturing methodologies, lack of standardized quality control, and potentially high costs. Quality by Design (QbD) can navigate these challenges and lead to the development of an optimal pain nanomedicine. Our lab previously reported a macrophage-targeted perfluorocarbon nanoemulsion (PFC NE) that demonstrated analgesic efficacy across multiple rodent pain models in both sexes. Here, we report PFC-free, biphasic nanoemulsions formulated with a biocompatible and non-immunogenic plant-based coconut oil loaded with a COX-2 inhibitor and a clinical-grade, indocyanine green (ICG) near-infrared fluorescent (NIRF) dye for parenteral theranostic analgesic nanomedicine. Methods: Critical process parameters and material attributes were identified through the FMECA (Failure, Modes, Effects, and Criticality Analysis) method and optimized using a 3 × 2 full-factorial design of experiments. We investigated the impact of the oil-to-surfactant ratio (w/w) with three different surfactant systems on the colloidal properties of NE. Small-scale (100 mL) batches were manufactured using sonication and microfluidization, and the final formulation was scaled up to 500 mL with microfluidization. The colloidal stability of NE was assessed using dynamic light scattering (DLS) and drug quantification was conducted through reverse-phase HPLC. An in vitro drug release study was conducted using the dialysis bag method, accompanied by HPLC quantification. The formulation was further evaluated for cell viability, cellular uptake, and COX-2 inhibition in the RAW 264.7 macrophage cell line. Results: Nanoemulsion droplet size increased with a higher oil-to-surfactant ratio (w/w) but was no significant impact by the type of surfactant system used. Thermal cycling and serum stability studies confirmed NE colloidal stability upon exposure to high and low temperatures and biological fluids. We also demonstrated the necessity of a solubilizer for long-term fluorescence stability of ICG. The nanoemulsion showed no cellular toxicity and effectively inhibited PGE2 in activated macrophages. Conclusions: To our knowledge, this is the first instance of a celecoxib-loaded theranostic platform developed using a plant-derived hydrocarbon oil, applying the QbD approach that demonstrated COX-2 inhibition. Full article

Sarcopenia, characterized by progressive skeletal muscle loss and functional decline, represents a major public heath challenge in aging populations. Despite increasing awareness, current management strategies—primarily resistance exercise and nutritional support—remain limited by accessibility, adherence, and inconsistent outcomes. This underscores the urgent need for novel, effective, and scalable therapeutics. Flavonoids, a diverse class of plant-derived polyphenolic compounds, have attracted attention for their muti-targeted biological activities, including anti-inflammatory, antioxidant, metabolic, and myogenic effects. This review aims to evaluate the anti-sarcopenic potential of selected flavonoids—quercetin, rutin, kaempferol glycosides, baicalin, genkwanin, isoschaftoside, naringin, eriocitrin, and puerarin—based on recent preclinical findings and mechanistic insights. These compounds modulate key pathways involved in muscle homeostasis, such as NF-κB and Nrf2 signaling, AMPK and PI3K/Akt activation, mitochondrial biogenesis, proteosomal degradation, and satellite cell function. Importantly, since muscle wasting also features prominently in cancer cachexia—a distinct but overlapping syndrome—understanding flavonoid action may offer broader therapeutic relevance. By targeting shared molecular axes, flavonoids may provide a promising, biologically grounded approach to mitigating sarcopenia and the related muscle-wasting conditions. Further translational studies and clinical trials are warranted to assess their efficacy and safety in human populations. Full article

Macrocyclic organic nanostructures have emerged as crucial components of functional supramolecular materials owing to their unique structural and chemical features, such as their distinctive “infinite” cyclic topology and tunable topology-dependent properties, attracting significant recent attention. However, the controlled synthesis of macrocyclic compounds with well-defined compositions and geometries remains a formidable challenge. On-surface synthesis, capable of constructing nanostructures with atomic precision on various substrates, has become a frontier technique for exploring novel macrocyclic architectures. This review summarizes the recent advances in the on-surface synthesis of macrocycles. It focuses on analyzing the synthetic mechanisms and conformational characterization of macrocycles formed through diverse bonding interactions, including both covalent and non-covalent linkages. This review elucidates the intricate interplay between the thermodynamic and kinetic factors governing macrocyclic structure formation across these bonding types and clarifies the critical influence of the reaction temperature and external conditions on the cyclization efficiency. Ultimately, this study offers design strategies for the precise on-surface synthesis of larger and more flexible macrocyclic compounds. Full article

The 2025 avian influenza A(H5N1) outbreak has highlighted the urgent need for rapidly generated health communication materials during public health emergencies. Artificial intelligence (AI) systems offer transformative potential to accelerate content development pipelines while maintaining scientific accuracy and impact. We evaluated an AI-generated health communication material on bird flu precautions among 100 U.S. adults. The material was developed using ChatGPT for text generation based on CDC guidelines and Leonardo.AI for illustrations. Participants rated perceived message effectiveness, quality, realism, relevance, attractiveness, and visual informativeness. The AI-generated health communication material received favorable ratings across all dimensions: perceived message effectiveness (3.83/5, 77%), perceived message quality (3.84/5, 77%), realism (3.72/5, 74%), relevance (3.68/5, 74%), attractiveness (3.62/5, 74%), and visual informativeness (3.35/5 67%). Linear regression analysis revealed that all features significantly predicted perceived message effectiveness in unadjusted and adjusted models (p < 0.0001), e.g., multivariate analysis of outcome on perceived visual informativeness showed β = 0.51, 95% CI: 0.37–0.66, p < 0.0001. Also, mediation analysis revealed that visual informativeness accounted for 23.8% of the relationship between material attractiveness and perceived effectiveness. AI tools can enable real-time adaptation of prevention guidance during epidemiological emergencies while maintaining effective risk communication. Full article

Graphene oxide (GO), a derivative of graphene, has attracted significant attention in tribological applications due to its unique structural, mechanical, and chemical properties. This review highlights the influence of GO and its composites on friction and wear performance across various engineering systems. The paper explores GO’s key properties, such as its high surface area, layered morphology, and abundant functional groups. These features contribute to reduced shear resistance, tribofilm formation, and improved load-bearing capacity. A detailed analysis of GO-based composites, including polymer, metal, and ceramic matrices, reveals those small additions of GO (typically 0.1–2 wt%) result in substantial reductions in coefficient of friction and wear rate, with improvements ranging between 30–70%, depending on the application. The tribological mechanisms, including self-lubrication, dispersion, thermal stability, and interface interactions, are discussed to provide insights into performance enhancement. Furthermore, the effects of electrochemical environment, functional group modifications, and external loading conditions on GO’s tribological behavior are examined. Despite these advantages, challenges such as scalability, agglomeration, and material compatibility persist. Overall, the paper demonstrates that GO is a promising additive for advanced tribological systems, while also identifying key limitations and future research directions. Full article

Semantically enriched trajectories have attracted growing interest in recent research, driven by the need for more expressive and context-aware movement data analysis. Two primary approaches have emerged for the storage and management of such data: moving object databases, which operate at the transactional or operational level, and trajectory data warehouses (TDWs), which support analytical processing within decision support systems. Conventional TDW methodologies typically model semantic aspects of trajectories by introducing new dimensions into the data warehouse schema. However, this approach often requires structural modifications to the schema in order to accommodate additional semantic attributes, potentially resulting in significant disruptions to the architecture and maintenance of the underlying decision support systems. To overcome this limitation, we propose a novel TDW model that supports dynamic and extensible integration of semantic aspects, without necessitating changes to the schema. This design enhances flexibility and promotes seamless adaptability to domain-specific requirements. To enable such extensibility, we propose an innovative approach to representing semantic trajectories by leveraging natural language processing (NLP) techniques. without relying on traditional spatiotemporal features. This enables the analysis of semantic movement patterns purely through textual context. Finally, we present a comprehensive framework that implements the proposed model in real-world application scenarios, demonstrating its practical extensibility. Full article

With the widespread use of sensors in applications such as autonomous driving and intelligent security, stable and efficient target tracking from diverse sensor data has become increasingly important. Self-supervised visual tracking has attracted increasing attention due to its potential to eliminate reliance on costly manual annotations; however, existing methods often train on incomplete object representations, resulting in inaccurate localization during inference. In addition, current methods typically struggle when applied to deep networks. To address these limitations, we propose a novel self-supervised tracking framework based on image synthesis and domain adversarial learning. We first construct a large-scale database of real-world target objects, then synthesize training video pairs by randomly inserting these targets into background frames while applying geometric and appearance transformations to simulate realistic variations. To reduce domain shift introduced by synthetic content, we incorporate a domain classification branch after feature extraction and adopt domain adversarial training to encourage feature alignment between real and synthetic domains. Experimental results on five standard tracking benchmarks demonstrate that our method significantly enhances tracking accuracy compared to existing self-supervised approaches without introducing any additional labeling cost. The proposed framework not only ensures complete target coverage during training but also shows strong scalability to deeper network architectures, offering a practical and effective solution for real-world tracking applications. Full article

Introduction: Brain tumor, marked by abnormal and rapid cell growth, poses severe health risks and requires accurate diagnosis for effective treatment. Classifying brain tumors using deep learning techniques applied to Magnetic Resonance Imaging (MRI) images has attracted the attention of many researchers, and specifically, reducing the bias of models and enhancing robustness is still a very pertinent active topic of attention. Methods: For capturing diverse information from different feature sets, we propose a Features Concatenation-based Brain Tumor Classification (FCBTC) Framework using Hybrid Deep Learning Models. For this, we have chosen three pretrained models—ResNet50; VGG16; and DensetNet121—as the baseline models. Our proposed hybrid models are built by the fusion of feature vectors. Results: The testing phase results show that, for the FCBTC Model-3, values for Precision, Recall, F1-score, and Accuracy are 98.33%, 98.26%, 98.27%, and 98.40%, respectively. This reinforces our idea that feature diversity does improve the classifier’s performance. Conclusions: Comparative performance evaluation of our work shows that, the proposed hybrid FCBTC Models have performed better than other proposed baseline models. Full article

The CRISPR/Cas system has attracted increasing attention in accurate nucleic acid detection. Herein, we reported a CRISPR/Cas12a-chemiluminescence cascaded bioassay (CCCB) for the amplification-free and sensitive detection of human papillomavirus type 16 (HPV-16) and parvovirus B19 (PB-19). A magnetic bead (MB)-linking single-stranded DNA (LssDNA)-alkaline phosphatase (ALP) complex was constructed as the core component of the bioassay. During the detection process, the single-stranded target DNA was captured and enriched by LssDNA and then activated the trans-cleavage activity of Cas12a. Due to the Cas12a-mediated cleavage of LssDNA, ALP was released from the MB, subsequently catalyzing the substrate to generate a chemiluminescence (CL) signal. Given the cascade combination of CRISPR/Cas12a with the CL technique, the limits of detection for HPV-16 and PB-19 DNA were determined as 0.14 pM and 0.37 pM, respectively, and the whole detection could be completed within 60 min. The practicality and reliability of the platform were validated through target-spiked clinical specimens, and the recovery rate was 93.4–103.5%. This dual-amplification strategy—operating without target pre-amplification—featured high specificity, low contamination risk, facile preparation, and robust stability. It provides a novel approach for sensitive nucleic acid detection, with the potential for rapid extension to the diagnosis of various infectious diseases. Full article

The Diamond Sutra (Vajracchedikā-Prajñāpāramitā-Sūtra) translated by the Sui Dynasty monk Dharmagupta is the fourth Chinese rendition of the Diamond Sutra. Characterized by unprecedented linguistic opacity and syntactic complexity within the history of Buddhist textual transmission, this translation’s distinctive features have attracted significant scholarly attention. This study synthesizes existing academic perspectives and employs Sanskrit–Chinese textual criticism and comparative analysis of parallel translations to conduct a granular examination of Dharmagupta’s retranslation. Our findings reveal that this text fundamentally deviates from conventional sutras designed for religious dissemination or liturgical recitation. Its defining traits, including morphological calquing of Sanskrit structures, simplified pronominal systems, and etymologically prioritized equivalence, collectively reflect a pedagogical focus characteristic of language instructional texts. Dharmagupta’s approach epitomizes a translation-as-pedagogy paradigm, with the text’s deviations from conventional norms resulting from the interplay of religious development, historical context, and translator agency. We argue that the Diamond Sutra retranslation constitutes a radical experimental paradigm in translation history, warranting re-evaluation of its significance within the broader trajectory of Buddhist textual practice. Full article

The presence of people has a complex relationship with public safety—while it is often associated with increased natural surveillance, it can also attract specific types of crime under certain urban conditions. This exploratory study examines this dual relationship by integrating Jane Jacobs’s urban theories and the principles derived from them in Quito, Ecuador. Anchored in Jacobs’s concept of “eyes on the street,” this research assesses four morphological dimensions—density, land use mixture, contact opportunity, and accessibility through nine specific indicators. A binary logistic regression model is used to examine how these features relate to the incidence of street robberies against individuals. The findings indicate that urban form characteristics that foster “eyes on the street”—such as higher population density and a mix of commercial and residential uses—show statistically significant associations with lower rates of street robbery. However, other indicators of “eyes on the street”—such as larger block sizes, proximity to public transport stations, greater street lighting, and a higher balance between residential and non-residential land uses—correlate with increased crime rates. Some indicators, such as population density, block size, and distance to public transport stations, show statistically significant relationships, though the practical effect size compared to residential/non-residential balance, commercial and facility mix, and street lighting is modest. These results underscore the importance of contextualizing Jacobs’s frameworks and offer a novel contribution to the literature by empirically testing morphological indicators promoting the presence of people against actual crime data. Full article

Optimization problems often feature local optima with a significant difference in the basin of attraction (BoA), making evolutionary computation methods prone to discarding solutions located in less-attractive BoAs, thereby posing challenges to the search for optima in these BoAs. To enhance the ability to find these optima, various niching methods have been proposed to restrict the competition scope of individuals to their specific neighborhoods. However, redundant searches in more-attractive BoAs as well as necessary searches in less-attractive BoAs can only be promoted simultaneously by these methods. To address this issue, we propose a general framework for niching methods named niching-driven divide-and-conquer hill exploration (NDDCHE). Through gradually learning BoAs from the search results of a niching method and dividing the problem into subproblems with a much smaller number of optima, NDDCHE aims to bring a more balanced distribution of searches in the BoAs of optima found so far, and thus enhance the niching method’s ability to find optima in less-attractive BoAs. Through experiments where niching methods with different categories of niching techniques are integrated with NDDCHE and tested on problems with significant differences in the size of the BoA, the effectiveness and the generalization ability of NDDCHE are proven. Full article