Search Results (5,174)

Flavonoids constitute a broad class of naturally occurring chemical compounds classified as polyphenols, widely present in various plants, fruits, and vegetables. They share a common flavone backbone, composed of two aromatic rings (A and B) connected by a three-carbon bridge forming a heterocyclic ring (C). One representative flavonoid is chrysin, a compound found in honey, propolis, and passionflower (Passiflora spp.). Chrysin exhibits a range of biological activities, including antioxidant, anti-inflammatory, anticancer, neuroprotective, and anxiolytic effects. Its biological activity is primarily attributed to the presence of hydroxyl groups, which facilitate the neutralization of free radicals and the modulation of intracellular signaling pathways. Cellular uptake of chrysin and other flavonoids occurs mainly through passive diffusion; however, certain forms may be transported via specific membrane-associated carrier proteins. Despite its therapeutic potential, chrysin’s bioavailability is significantly limited due to poor aqueous solubility and rapid metabolism in the gastrointestinal tract and liver, which reduces its systemic efficacy. Ongoing research aims to enhance chrysin’s bioavailability through the development of delivery systems such as lipid-based carriers and nanoparticles. Full article

Skeleton-based action recognition is an important subject in deep learning. Graph Convolutional Networks (GCNs) have demonstrated strong performance by modeling the human skeleton as a natural topological graph, representing the connections between joints. However, most existing methods rely on non-adaptive topologies or insufficiently expressive representations. To address these limitations, we propose a Multi-view Topology Refinement Graph Convolutional Network (MTR-GCN), which is efficient, lightweight, and delivers high performance. Specifically: (1) We propose a new spatial topology modeling approach that incorporates two views. A dynamic view fuses joint information from dual streams in a pairwise manner, while a static view encodes the shortest static paths between joints, preserving the original connectivity relationships. (2) We propose a new MultiScale Temporal Convolutional Network (MSTC), which is efficient and lightweight. (3) Furthermore, we introduce a new temporal topology strategy by modeling temporal frames as a graph, which strengthens the extraction of temporal features. By modeling the human skeleton as both a spatial and a temporal graph, we reveal a topological symmetry between space and time within the unified spatio-temporal framework. The proposed model achieves state-of-the-art performance on several benchmark datasets, including NTU RGB + D (XSub: 92.8%, XView: 96.8%), NTU RGB + D 120 (XSub: 89.6%, XSet: 90.8%), and NW-UCLA (95.7%), demonstrating the effectiveness of our GCN module, TCN module, and overall architecture. Full article

Vehicle-to-everything (V2X) communication is a fundamental technology in the development of intelligent transportation systems, encompassing vehicle-to-vehicle (V2V), infrastructure (V2I), and pedestrian (V2P) communications. This technology enables connected and autonomous vehicles (CAVs) to interact with their surroundings, significantly enhancing road safety, traffic efficiency, and driving comfort. However, as V2X communication becomes more widespread, it becomes a prime target for adversarial and persistent cyberattacks, posing significant threats to the security and privacy of CAVs. These challenges are compounded by the dynamic nature of vehicular networks and the stringent requirements for real-time data processing and decision-making. Much research is on using novel technologies such as machine learning, blockchain, and cryptography to secure V2X communications. Our survey highlights the security challenges faced by V2X communications and assesses current ML and blockchain-based solutions, revealing significant gaps and opportunities for improvement. Specifically, our survey focuses on studies integrating ML, blockchain, and multi-access edge computing (MEC) for low latency, robust, and dynamic security in V2X networks. Based on our findings, we outline a conceptual framework that synergizes ML, blockchain, and MEC to address some of the identified security challenges. This integrated framework demonstrates the potential for real-time anomaly detection, decentralized data sharing, and enhanced system scalability. The survey concludes by identifying future research directions and outlining the remaining challenges for securing V2X communications in the face of evolving threats. Full article

Climate change represents one of the main challenges of this century. The hazards generated by this process are various and involve territorial assets all over the globe. Hydrogeological risk represents one of these aspects, and the violence of rain precipitations has led experts to focus their interest on the study of geotechnical assets in relation to these dangerous weather events. At the same time, geospatial representation in 3D WebGIS based on open-source solutions led specialists to employ this kind of technology to remotely analyze and monitor territorial events considering different sources of information. This study considers the construction of a 3D WebGIS framework for the real-time management of geospatial information developed with open-source technologies applied to the dynamic mapping of precipitation in the metropolitan area of Palermo (Italy) based on real-time weather station acquisitions. The structure considered is a WebGIS platform developed with Cesium.js JavaScript libraries, the Postgres database, Geoserver and Mapserver geospatial servers, and the Anaconda Python platform for activating real-time data connections using Python scripts. This framework represents a basic geospatial digital twin structure useful to municipalities, civil protection services, and firefighters for land management and for activating any preventive operations to ensure territorial safety. Furthermore, the open-source nature of the platform favors the free diffusion of this solution, avoiding expensive applications based on property software. The components of the framework are available and shared using GitHub. Full article

Art therapy serves as a crucial intervention modality for children with autism spectrum disorder (ASD), demonstrating unique value in emotional expression, sensory integration, and social communication. However, current practice presents critical challenges, including the disconnect between design expertise and clinical needs, unclear mechanisms of environmental factors’ impact on therapeutic outcomes, and insufficient evidence-based support for technology integration. Purpose: This study aimed to construct an evidence-based theoretical framework for art therapy environment design for children with autism, clarifying the relationship between environmental design elements and therapeutic effectiveness. Methodology: Based on the Web of Science database, this study employed a dual-track approach comprising bibliometric analysis and micro-qualitative content analysis to systematically examine the knowledge structure and developmental trends. Research hotspots were identified through keyword co-occurrence network analysis using CiteSpace, while 24 representative design cases were analyzed to gain insights into design concepts, emerging technologies, and implementation principles. Key Findings: Through keyword network visualization analysis, this study identified ten primary research clusters that were systematically categorized into four core design elements: sensory feedback design, behavioral guidance design, emotional resonance design, and therapeutic support design. A responsive therapeutic environment conceptual framework was proposed, encompassing four interconnected components based on the ABC model from positive psychology: emotional, sensory, environmental, and behavioral dimensions. Evidence-based design principles were established emphasizing child-centeredness, the promotion of multisensory expression, the achievement of dynamic feedback, and appropriate technology integration. Research Contribution: This research establishes theoretical connections between environmental design elements and art therapy effectiveness, providing a systematic design guidance framework for interdisciplinary teams, including environmental designers, clinical practitioners, technology developers, and healthcare administrators. The framework positions technology as a therapeutic mediator rather than a driver, ensuring technological integration supports rather than interferes with children’s natural creative impulses. This contributes to creating more effective environmental spaces for art therapy activities for children with autism while aligning with SDG3 goals for promoting mental health and reducing inequalities in therapeutic access. Full article

Urban tourism has evolved into a complex adaptive system, where unregulated expansion disrupts the ecological balance and intensifies resource stress. Understanding the dynamic evolution and resilience mechanisms of the tourism ecological health network (TEHN) is essential for supporting sustainable urban tourism as a coupled human–natural system. Using Shanghai as a case study, we applied the “vigor–organization–resilience–services” (VORS) framework to evaluate ecosystem health, which served as a constraint for constructing the TEHN, using the minimum cumulative resistance (MCR) model for the period from 2001 to 2023. A resilience framework integrating structural and functional dimensions was further developed to assess spatiotemporal evolution and guide targeted enhancement strategies. The results indicated that as ecosystem health degraded, particularly in peripheral areas, the urban TEHN in Shanghai shifted from a dispersed to a centralized structure, with limited connectivity in the periphery. The resilience of the TEHN continued to grow, with structural resilience remaining at a high level, while functional resilience still required enhancement. Specifically, the low integration and limited choice between the tourism network and the transportation system hindered tourists from selecting routes with higher ecosystem health indices. Enhancing functional resilience, while sustaining structural resilience, is essential for transforming the TEHN into a multi-centered, multi-level system that promotes efficient connectivity, ecological sustainability, and long-term adaptability. The results contribute to a systems-level understanding of tourism–ecology interactions and support the development of adaptive strategies for balancing network efficiency and environmental integrity. Full article

Cellular senescence is closely connected with cancer progression, recurrence, and metastasis. Senotherapy aims to soothe the harmful effects of senescent cells either by inducing their apoptosis (senolytic) or by suppressing the senescence-associated secretory phenotype (SASP) (senomorphic). Fisetin, a well-studied senotherapeutic drug, was selected for this study to evaluate its efficiency when delivered in a liposomal formulation. The experiment evaluated the impact of liposome-encapsulated fisetin on senescent cells induced by doxorubicin (DOX) from two cell lines: WI-38 (normal lung fibroblasts) and A549 (lung carcinoma). Senescence was characterized by SA-β-galactosidase (SA-β-gal) activity, proliferation, morphology, and secretion of pro-inflammatory interleukin 6 (IL-6) and interleukin 8 (IL-8). Due to fisetin’s hydrophobic nature, it was encapsulated in liposomes to enhance cellular delivery. Cellular uptake studies confirmed that the liposomes were effectively internalized by both senescent cell types. Treatment with fisetin-loaded liposomes revealed a lack of senolytic effects but showed senomorphic activity, as evidenced by a significant reduction in IL-6 and IL-8 secretion in senescent cells. The liposomal formulation enhanced fisetin’s therapeutic efficacy, showing comparable results even at the lowest tested concentration. Full article

The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo. Full article

The Mayanghe National Nature Reserve, a key habitat for the endangered François’ langur (Trachypithecus francoisi), faces significant anthropogenic disturbances, including extensive distribution of croplands, roads, and settlements. These human-modified features are predominantly concentrated at elevations between 500 and 800 m and on slopes of 10–20°, which notably overlap with the core elevation range utilized by François’ langur. Spatial analysis revealed that langurs primarily occupy areas within the 500–800 m elevation band, which comprises only 33% of the reserve but hosts a high density of human infrastructure—including approximately 4468 residential buildings and the majority of cropland and road networks. Despite slopes >60° representing just 18.52% of the area, langur habitat utilization peaked in these steep regions (exceeding 85.71%), indicating a strong preference for rugged karst terrain, likely due to reduced human interference. Habitat type analysis showed a clear preference for evergreen broadleaf forests (covering 37.19% of utilized areas), followed by shrublands. Landscape pattern metrics revealed high habitat fragmentation, with 457 discrete habitat patches and broadleaf forests displaying the highest edge density and total edge length. Connectivity analyses indicated that distribution areas exhibit a more continuous and aggregated habitat configuration than control areas. These results underscore François’ langur’s reliance on steep, forested karst habitats and highlight the urgent need to mitigate human-induced fragmentation in key elevation and slope zones to ensure the species’ long-term survival. Full article

Physical activity, particularly when practiced in natural settings, has well-established benefits for overall health, sleep, and body composition. These effects are especially important for postmenopausal women, although research specifically targeting this population remains limited. The study evaluated a 16-week multicomponent outdoor exercise program (cardiorespiratory, strength, balance, coordination, and flexibility training) in postmenopausal women, consisting of three 60 min sessions per week. Participants were non-randomly assigned to an experimental group (EG, n = 55) and a control group (CG, n = 20). Measurements were taken at baseline and after 16 weeks, including body composition, sleep (duration and quality), and connection with nature. No significant differences were observed between groups at baseline. After the intervention, the EG and CG presented significant differences (p ≤ 0.01) in the rates of change in body mass, fat mass (FM; −9.26% and −1.21%, respectively), and visceral fat level (VFL; −13.46 points and −3.80 points). These differences were also observed for the sleep fragmentation index (p ≤ 0.01), but not for connection with nature. A significant interaction effect (p < 0.01) of time × group was observed for %FM, VFL, and appendicular skeletal muscle mass. Exercise duration had an effect (p = 0.043) on participants’ personal and affective identification with nature, and the time × group × medication interaction significantly influenced sleep efficiency (p = 0.034). The exercise program proved effective in reducing total and central adiposity levels; however, it did not lead to improvements in sleep duration, sleep quality, or connection with nature. Full article

Neural networks (NNs) achieve breakthroughs in computer vision and natural language processing, yet their “black box” nature persists. Traditional methods prioritise parameter optimisation and loss design, overlooking NNs’ fundamental structure as topologically organised nonlinear computational systems. This work proposes a complex network theory framework decoding structure–function coupling by mapping convolutional layers, fully connected layers, and Dropout modules into graph representations. To overcome limitations of heuristic compression techniques, we develop a topology-sensitive adaptive pruning algorithm that evaluates critical paths via node strength centrality, preserving structural–functional integrity. On CIFAR-10, our method achieves 55.5% parameter reduction with only 7.8% accuracy degradation—significantly outperforming traditional approaches. Crucially, retrained pruned networks exceed original model accuracy by up to 2.63%, demonstrating that topology optimisation unlocks latent model potential. This research establishes a paradigm shift from empirical to topologically rationalised neural architecture design, providing theoretical foundations for deep learning optimisation dynamics. Full article

In the present paper, a mathematical analysis of the Gardner equation with varying coefficients has been performed to give a more realistic model of physical phenomena, especially in regards to plasma physics. First, a Lie symmetry analysis was carried out, as a result of which a symmetry classification following the different representations of the variable coefficients was systematically derived. The reduced ordinary differential equation obtained is solved using the power-series method and solutions to the equation are represented graphically to give an idea of their dynamical behavior. Moreover, a fully connected neural network has been included as an efficient computation method to deal with the complexity of the reduced equation, by using traveling-wave transformation. The validity and correctness of the solutions provided by the neural networks have been rigorously tested and the solutions provided by the neural networks have been thoroughly compared with those generated by the Runge–Kutta method, which is a conventional and well-recognized numerical method. The impact of a variation in the loss function of different coefficients has also been discussed, and it has also been found that the dispersive coefficient affects the convergence rate of the loss contribution considerably compared to the other coefficients. The results of the current work can be used to improve knowledge on the nonlinear dynamics of waves in plasma physics. They also show how efficient it is to combine the approaches, which consists in the use of analytical and semi-analytical methods and methods based on neural networks, to solve nonlinear differential equations with variable coefficients of a complex nature. Full article

Functional foods like flax (Linum usitatissimum L.) are rich sources of specialized metabolites that contribute to their nutritional and health-promoting properties. Understanding the biosynthesis of these compounds is essential for improving their quality and potential applications. However, dissecting complex metabolic networks in plants remains challenging due to the dynamic nature and interconnectedness of biosynthetic pathways. In this study, we present a synergistic approach combining stable isotopic labeling (SIL), Candidate Substrate–Product Pair (CSPP) networks, and a time-course study with high temporal resolution to reveal the biosynthetic fluxes shaping phenylpropanoid metabolism in young flax seedlings. By feeding the seedlings with ¹³C₃-p-coumaric acid and isolating isotopically labeled metabolization products prior to the construction of CSPP networks, the biochemical validity of the connections in the network was supported by SIL, independent of spectral similarity or abundance correlation. This method, in combination with multistage mass spectrometry (MSⁿ), allowed confident structural proposals of lignans, neolignans, and hydroxycinnamic acid conjugates, including the presence of newly identified chicoric acid and related tartaric acid esters in flax. High-resolution time-course analyses revealed successive waves of metabolite formation, providing insights into distinct biosynthetic fluxes toward lignans and early lignification intermediates. No evidence was found here for the involvement of chlorogenic or caftaric acid intermediates in chicoric acid biosynthesis in flax, as has been described in other species. Instead, our findings suggest that in flax seedlings, chicoric acid is synthesized through successive hydroxylation steps of p-coumaroyl tartaric acid esters. This work demonstrates the power of combining SIL and CSPP strategies to uncover novel metabolic routes and highlights the nutritional potential of flax sprouts rich in chicoric acid. Full article

Background: Amid growing concerns about climate crisis and its psychological toll, understanding how people find meaning through their connection to nature is increasingly important. The first aim of this study is to examine the association between Nature Relatedness (NR) and Meaning in Life (MIL). The second aim is to investigate if gender moderates this association and to explore how Jewish traditions influence gender differences in this relationship. Methods: A multi-methods design was employed. Participants were recruited through purposive sampling of prominent Jewish community figures, followed by snowball sampling via informant referrals. Thirty-five participants completed the Meaning in Life Questionnaire (MLQ) and the NR Scale. Two questions provided qualitative insights into participants’ personal interpretations and culturally grounded meanings of NR and MIL in the context of climate change and Jewish traditions. Results: Hierarchical multiple regression analyses assessed the main effects of NR and gender, as well as their interaction, on MLQ subscales. NR positively correlated with the MLQ Search dimension, indicating that individuals with stronger NR actively seek meaning in life. Gender moderated this relationship: NR did not correlate with MLQ Presence overall, but higher NR was linked to greater MIL presence among female participants. Thematic analysis of qualitative responses revealed gender-based variations and emphasized the role of Jewish teachings in connecting NR to cultural and religious practices. Conclusions: The findings point to the importance of cultural, religious, and gender factors in shaping the relationship between NR and MIL in a time of climate change crisis, offering implications for positive mental health research and culturally sensitive interventions. Full article

One of the fundamental goals of contemporary mobility is to optimize transport processes in urban areas. The solution in this area seems to be the implementation of the idea of sustainable transport systems based on the Smart City concept. The article presents a case study—an assessment of the possibilities of changing mobility habits based on the idea of sustainable urban transport, taking into account the criterion of energy consumption of individual means of transport. The analyses are based on a comparison of selected means of transport occurring in the urban environment according to several key parameters for the optimization and efficiency of transport processes, i.e., cost, time, travel comfort, and impact on the natural environment, while simultaneously linking them to the criterion of energy consumption of individual means of transport. The analyzed parameters currently constitute the most important group of challenges in the area of shaping and planning optimal and sustainable urban transport. The presented research was used to indicate the connections between various areas of optimization of the transport process and the energy efficiency of individual modes of transport. Analyses have shown that the least time-consuming process of urban mobility is associated with the highest level of CO₂ emissions and, at the same time, the highest level of energy efficiency. However, combining public transport with other means of transport can meet most of the transport expectations of city residents, also in terms of energy optimization. The research results presented in the article can contribute to the creation of a strategy for the development of the transport network based on the postulates of increasing the optimization and efficiency of individual means of transport in urban areas. At the same time, recognizing the criterion of energy intensity of means of transport as leading in the development of sustainable urban mobility. Thus, confirming the important role of existing transport systems in the process of shaping and planning sustainable urban mobility in accordance with the idea of Smart City. Full article