Search Results (1,044)

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

pH is a critical parameter requiring precise monitoring across scientific, industrial, and biological domains. Fluorescent pH probes offer a powerful alternative to traditional methods (e.g., electrodes, indicators), overcoming limitations in miniaturization, long-term stability, and electromagnetic interference. By utilizing photophysical mechanisms—including intramolecular charge transfer (ICT), photoinduced electron transfer (PET), and fluorescence resonance energy transfer (FRET)—these probes enable high-sensitivity, reusable, and biocompatible sensing. This review systematically details recent advances, categorizing probes by operational pH range: strongly acidic (0–3), weakly acidic (3–7), strongly alkaline (>12), weakly alkaline (7–11), near-neutral (6–8), and wide-dynamic range. Innovations such as ratiometric detection, organelle-specific targeting (lysosomes, mitochondria), smartphone colorimetry, and dual-analyte response (e.g., pH + Al³⁺/CN⁻) are highlighted. Applications span real-time cellular imaging (HeLa cells, zebrafish, mice), food quality assessment, environmental monitoring, and industrial diagnostics (e.g., concrete pH). Persistent challenges include extreme-pH sensing (notably alkalinity), photobleaching, dye leakage, and environmental resilience. Future research should prioritize broadening functional pH ranges, enhancing probe stability, and developing wide-range sensing strategies to advance deployment in commercial and industrial online monitoring platforms. Full article

Metasurfaces have drawn extensive research attention for their unique optical properties and vast application potential. Among the various resonant modes induced in metasurfaces, BIC and electric anapole modes stand out as particularly interesting due to their distinctive physical characteristics. In this work, we designed and investigated novel dimeric dumbbell-shaped metasurfaces incorporating two independently tunable asymmetric parameters. This structural innovation enables the simultaneous excitation of both electric anapole and QBIC modes under normally incident MIR illumination. More importantly, by adjusting these two asymmetric parameters, one can independently tune the resonance peaks of the two modes, thereby overcoming the performance limits of conventional single-peak modulation. This metasurface design demonstrates outstanding performance for dielectric environment-sensing applications. We conducted a comprehensive investigation of the sensing sensitivity for dumbbell-shaped metasurfaces of various geometries. Our simulation results show that the circular-shaped configuration achieved high sensitivity, reaching 20,930 GHz/RIU. This work offers a novel design paradigm for multi-mode control and functionalization of metasurface structures. Full article

To explore the potential seed compounds from natural products as anticancer agents against small-cell lung cancer (SCLC), the underground parts of Agapanthus africanus, a plant commonly used for ornamental purposes, were investigated. Three spirostan-type steroidal glycosides (1–3) were isolated and identified by nuclear magnetic resonance spectral analysis. Compounds 1–3 exhibited cytotoxicity against SBC-3 human SCLC cells, with IC₅₀ values of 0.56, 1.4, and 7.4 µM, respectively. Compound 1, also known an agapanthussaponin A, demonstrated the most potent cytotoxicity among the isolated compounds and was evaluated for its apoptosis- and ferroptosis-inducing activities. Compound 1 arrested the cell cycle of SBC-3 cells in the G₂/M phase and induced apoptosis primarily via the mitochondrial pathway, characterized by caspases-3 and -9 activation, loss of mitochondrial membrane potential, and overproduction of reactive oxygen species. Additionally, 1 triggered ferroptosis via a dual mechanism consisting of enhanced cellular iron uptake through upregulation of transferrin and transferrin receptor 1 expression and impaired glutathione synthesis via downregulation of both xCT and glutathione peroxidase 4 expression. Compound 1 induces cell death via the apoptosis and ferroptosis pathways, suggesting its promise as a seed compound for the development of anticancer therapeutics against SCLC. Full article

A dual-polarization energy selective surface (ESS) with low insertion loss (IL) and high shielding effectiveness (SE) based on a dual-resonant equivalent circuit topology was proposed for high-intensity radiation field (HIRF) protection in this paper. The design principle was elucidated through an equivalent circuit model and translated into a physical ESS implementation. It consists of two resonant rings, vertically arranged and loaded with diodes, along with two lumped capacitors. Simulation and measurement results demonstrate that the IL is less than 3 dB when in the OFF state in a working frequency band, and the SE exceeds 20 dB when in the ON state. Moreover, the ESS’s dual-polarization, low cost, and easy-to-design characteristics hold great promise for broad applications in protecting communication and radar systems in complex electromagnetic environments. Full article

Based on the theory of optical sensing, we propose a high-precision plasmonic refractive index nanosensor, which consists of a symmetric rectangular waveguide and a circular ring containing a rectangular cavity. The designed novel tunable micro-resonant circular cavity filter based on surface plasmon excitations is able to confine light to sub-wavelength dimensions. The data show that different geometrical factors have different effects on sensing, with the geometry of the rectangular cavity and the radius of the circular ring being the key factors affecting the Fano resonance. Furthermore, the resonance bifurcation enables the structure to achieve a tunable dual Fano resonance system. The structure was tuned to obtain optimal sensitivity (S) and figure of merit values up to 3066 nm/RIU and 78. The designed structure has excellent sensing performance with sensitivities of 0.4767 nm·(mg/d${\mathrm{L}}^{-1}$) and 0.6 nm·(mg/d${\mathrm{L}}^{-1}$) in detecting Na⁺ and K⁺ concentrations in the electrolyte solution, respectively, and can be easily achieved by the spectrometer. The wavelength accuracy of 0.001 nm can be easily achieved by a spectrum analyzer, which has a broad application prospect in the field of optical integration. Full article

Background/Objectives: Gut-derived tryptophan (Trp) metabolites play important roles in metabolic and cardiovascular regulation. Although animal studies suggest their protective effects against metabolic dysfunction, data in adolescents, particularly those with obesity, remain limited. The objective of this study was to evaluate associations between circulating gut-derived Trp metabolites and markers of cardiometabolic, vascular, and platelet health in adolescents with obesity. Methods: Data were analyzed from 28 adolescents (ages 13–18; mean BMI = 36 ± 6.4 kg/m²). Fasting blood was collected to assess lipid profiles using a clinical analyzer and insulin resistance using the homeostatic model assessment for insulin resistance (HOMA-IR). Gut-derived Trp metabolites were measured by UPLC–mass spectrometry, peak oxygen uptake (VO_{2 peak}) by gas exchange during an incremental cycle ergometer test, and body composition by dual-energy X-ray absorptiometry. Platelet spare respiratory capacity (SRC), endothelial function, and liver fat were measured using high-resolution respirometry, flow-mediated dilation (FMD) of the brachial artery, and magnetic resonance imaging respectively. Results: Indole-3-propionic acid was inversely associated with diastolic blood pressure (rho = −0.39, p = 0.047), total cholesterol (rho = −0.55, p = 0.002), and LDL-C (rho = −0.57, p = 0.0014), independent of sex and obesity severity. Indoxyl sulfate was positively correlated with fasting glucose (rho = 0.47, p = 0.012), and adolescents with impaired fasting glucose had 1.6-fold higher IS levels. Indole-3-acetaldehyde declined with age (rho = −0.50, p = 0.007), and Indole-3-acetic acid and indole were higher in Hispanics vs. non-Hispanics. No significant associations were observed between Trp metabolites and FMD, VO₂ peak, or SRC. Conclusions: Gut-derived Trp metabolites, particularly indole-3-propionic and indoxyl sulfate, are associated with markers of cardiometabolic risk in adolescents with obesity. These findings support their potential relevance in early-onset cardiovascular disease risk. Full article

This study designed and validated a dual-component beat-frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) gas detection system utilizing time-division multiplexing (TDM). By applying TDM to drive distributed feedback lasers, the system achieved the simultaneous detection of acetylene and methane. Its key innovation lies in exploiting the transient response of the quartz tuning fork (QTF) to acquire gas concentrations while concurrently capturing the QTF resonant frequency and quality factor in real-time. Owing to the short beat period and rapid system response, this approach significantly reduces time-delay constraints in time-division measurements, eliminating the need for periodic calibration inherent in conventional methods and preventing detection interruptions. The experimental results demonstrate minimum detection limits of 5.69 ppm for methane and 0.60 ppm for acetylene. Both gases exhibited excellent linear responses over the concentration range of 200 ppm to 4000 ppm, with the R² value for methane being 0.996 and for acetylene being 0.997. The system presents a viable solution for the real-time, calibration-free monitoring of dissolved gases in transformer oil. Full article

It is difficult to simultaneously achieve surface-enhanced Raman scattering (SERS) and surface-enhanced fluorescence (SEF) for noble metals. Herein, a composite substrate is demonstrated based on the rational construction of Ag nanoparticles (Ag NPs) and inverse opal polydimethylsiloxane (PDMS) for surface Raman fluorescence dual enhancement. The well-designed Ag nanoparticle (Ag NP)-decorated inverse opal PDMS (AIOP) composite substrate is fabricated using the polystyrene (PS) photonic crystal method and the sensitization reduction technique. The inverse opal PDMS enhances the electromagnetic (EM) field by increasing the loading of Ag NPs and plasmonic coupling of Ag NPs, leading to SERS activity. The thin shell layer of polyvinyl pyrrolidone (PVP) in core–shell Ag NPs isolates the detected molecule from the Ag core to prevent the fluorescence resonance energy transfer and charge transfer to eliminate fluorescence quenching and enable SEF performance. Based on the blockage of the core–shell structure and the enhanced EM field originating from the inverse opal structure, the as-fabricated AIOP composite substrate shows dual enhancement in surface Raman fluorescence. The AIOP composite substrate in this work, which combines improved SERS activity and SEF performance, not only promotes the development of surface-enhanced spectroscopy but also shows promise for applications in flexible sensors. Full article

This study presents a neural-network-based method for on-demand design of terahertz metasurface sensors, aimed at detecting plant endogenous and exogenous molecules. The approach uses target performance indicators (constructed via fingerprint peaks) as inputs and structural parameters as outputs, employing a neural network to map the complex relationship between them. Two single-resonant-peak metasurface sensors were developed to detect abscisic acid and gibberellic acid. The abscisic acid metasurface sensor achieved an average MSE of 5.66 × 10⁻⁶ and R_ER of 0.167%, while the gibberellic acid metasurface sensor had an average MSE of 8 × 10⁻⁷ and R_ER of 0.086%. Their resonant peaks highly matched the substance fingerprint peaks, enabling specific detection. Metasurface sensors’ sensitivities were effectively controlled using correlation analysis and neural networks, achieving remarkable levels of 156.7 and 150.1 GHz/RIU, allowing trace detection. Three dual-resonant-peak metasurface sensors were designed to improve the detection specificity for chlorophyll and folpet and to detect chlorophyll and folpet simultaneously. These metasurface sensors exhibited average MSEs of 1.4 × 10⁻⁵, 1.6 × 10⁻⁶, 1.35 × 10⁻⁵ and R_ERs of 0.27%, 0.088%, 0.20%. The model also worked for four other plant-related molecules, proving its strong generalization ability. Overall, for different application scenarios of exogenous and endogenous molecules in plants, the on-demand design methodology offers a whole new set of ideas for quickly designing and widely applying metasurface sensors with suitable performance indicators. Full article

In the context of the rapid digital transformation of the catering industry, the design of ordering interfaces—key hubs of human–computer interaction—has become critical to user service quality and brand competitiveness, especially in terms of usability, visual appeal, and emotional resonance. Based on a human–computer interaction design framework, this study proposes a dual-path optimization model integrating user demand mapping and conflict resolution to synergize explicit need translation with innovative design problem solving. The model employs KE to capture implicit user needs, applies AHP to construct a weighted design element system, and uses QFD to establish a matrix linking user needs with technical attributes. To address contradictions among design elements, TRIZ is introduced to resolve conflicts between functional redundancy and interaction efficiency. Additionally, generative AI tools such as MidJourney are incorporated to accelerate concept generation and improve innovation. Based on user evaluations and controlled experiments, the optimized design demonstrates measurable improvements in task efficiency and visual appeal. Overall, the dual-path approach effectively bridges the gap between vague user needs and concrete design solutions, achieving a balanced integration of functionality, aesthetics, and interactivity. The proposed model overcomes the limitations of experience-driven design by offering a systematic methodology encompassing demand analysis, technological transformation, conflict resolution, and intelligent generation, with practical value for enhancing the user experience of digital service touchpoints in the catering sector. Full article

This study analyzes the dynamic response of a floating dual vertical-axis tidal turbine platform under extreme environmental loads, focusing on two different mooring systems as follows: taut and catenary. The analysis assumes a non-operational turbine state where power generation is stopped, and the vertical turbines are lifted for structural protection. Using time-domain simulations via OrcaFlex 11.4, the floating platform’s motion and mooring line effective tensions are evaluated under high waves, strong wind, and current loads. The results reveal that sway and heave motions are significantly influenced by wave excitation, with the catenary system exhibiting larger responses due to mooring system features, while the taut system experiences higher mooring effective tension but shows more restrained motion. Notably, in the roll direction, both systems exhibit peak frequencies unrelated to the wave spectrum, attributed instead to resonance with the system’s natural frequencies—0.12438 Hz for taut and 0.07332 Hz for catenary. Additionally, the failure scenario of ML02 (Mooring Line 02) and the application of dynamic power cables to the floating platform are analyzed. The results demonstrate that under non-operational and extreme load conditions, mooring system type plays a main role in determining platform stability and structural safety. This comparative analysis offers valuable insights for selecting and designing mooring configurations optimized for reliability in extreme environmental conditions. Full article

In this study, we present a novel surface-enhanced Raman scattering (SERS) substrate based on porous silicon microcavities (PSiMCs) decorated with silver nanoparticles (AgNPs) for ultra-sensitive molecule detection. This substrate utilizes a dual enhancement mechanism: the localized surface plasmon resonance (LSPR) of AgNPs and the optical resonance of the PSiMC structure, which together create intense electromagnetic hot spots and prolong photon–molecule interactions. The porous architecture provides a large surface area for uniform nanoparticle distribution and efficient analyte adsorption. The AgNP/PSiMC substrate demonstrates an impressive detection limit of 1.0 × 10⁻¹³ M for rhodamine101 and 1.0 × 10⁻¹⁰ M for methyl parathion, outperforming many previously reported SERS platforms. Furthermore, the substrate exhibits excellent signal uniformity (RSD ≈ 6.14%) and long-term stability, retaining over 50% signal intensity after 28 days. These results underscore the potential of AgNP/PSiMCs as highly efficient, reproducible, and scalable SERS platforms for trace-level chemical and environmental sensing applications. Full article

This study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model was evaluated in two distinct applications. In the first, using multivariate datasets from 70 real-world electric vehicle (EV) trips, TS-MSDA U-Net achieved a mean absolute error below 1% across key parameters, including battery state of charge, voltage, acceleration, and torque—representing a two-fold improvement over the baseline TS-p2pGAN. While dual-attention modules provided only modest gains over the basic U-Net, the multiscale design enhanced overall performance. In the second application, the model was used to reconstruct high-resolution signals from low-speed analog-to-digital converter data in a prototype resonant CLLC half-bridge converter. TS-MSDA U-Net successfully learned nonlinear mappings and improved signal resolution by a factor of 36, outperforming the basic U-Net, which failed to recover essential waveform details. These results underscore the effectiveness of transformer-inspired U-Net architectures for high-fidelity multivariate time series modeling in both EV analytics and power electronics. Full article

High-quality reconstruction of magnetic resonance imaging (MRI) data from undersampled k-space remains a significant challenge in medical imaging. While the integration of compressed sensing and deep learning has notably improved the performance of MRI reconstruction, existing convolutional neural network-based methods are limited by their small receptive fields, which hinders the exploration of global image features. Meanwhile, Swin-Transformer-based approaches struggle with inter-window information interaction and global feature extraction and perform poorly when dealing with complex repetitive structures and similar texture features under undersampling conditions, resulting in suboptimal reconstruction quality. To address these issues, we propose a Symmetry-based Cascaded Dual-Domain Hybrid Attention Network (SCDDHAN). Leveraging the inherent symmetry of medical images, the network combines channel and self-attention to improve global context modeling and local detail restoration. The overlapping window self-attention module is designed with symmetry in mind to improve cross-window information interaction by overlapping adjacent windows and directly linking neighboring regions. This facilitates more accurate detail recovery. The concept of symmetry is deeply embedded in the network design, guiding the model to better capture regular patterns and balanced structures within MRI images. Experimental results demonstrate that under 5× and 10× undersampling conditions, SCDDHAN outperforms existing methods in artifact suppression, achieving more natural edge transitions, clearer complex textures and superior overall performance. This study highlights the potential of integrating symmetry concepts into hybrid attention modules for accelerating MRI reconstruction and offers an efficient, innovative solution for future research in this area. Full article