Search Results (224,497)

This study examines how the Galápagos Islands can better meet the needs of the rapidly expanding senior travel market. Using a sequential exploratory mixed-method design, the study combined qualitative input from senior tourists and tourism companies with quantitative survey data from 877 senior tourists and 30 tourism firms. PLS-SEM and MGA identify accessibility as the system’s primary structural catalyst, associated with safety perceptions, experiential engagement, and overall destination attractiveness. Results highlight a cognitive disconnect: seniors prioritize emotional reassurance, social connection, and low-effort experiences, while companies focus on infrastructure and operations. The study calls for accessibility-driven redesign that integrates emotional and social factors. Future research should explore how digital navigability and trust cues are associated with senior decision-making in remote island contexts. Full article

Automated system architecture generation is a key task in Model-Based Systems Engineering (MBSE), where requirement and functional models need to be transformed into a physical architecture composed of selected components and port-level connections. However, this task remains challenging because functional information is often semi-structured and distributed, and is represented at a different level of abstraction from physical architectures. Existing knowledge-driven methods provide systematic and traceable reasoning but require labor-intensive formal modeling and rule definition, whereas direct Large Language Model (LLM)-based generation improves automation but may suffer from component hallucination and low interpretability. To address these limitations, this study proposes a retrieval-augmented system architecture generation approach using Task-Oriented Functional Semantics (TOFS). TOFS is constructed from SysML requirements and functional models through an LLM-based process, and represents system goals, functions, logical entities, ports, and engineering constraints in a unified representation. Based on TOFS, a hybrid component retrieval method is proposed, which integrates interface matching, parameter constraint matching, and semantic matching to retrieve candidate components from an engineering component library. TOFS and the retrieved component sets are then jointly organized as the generation context to guide LLM-based architecture generation. A hydronic heating control system is used as a case study, and scenario-based comparative experiments demonstrate improved component retrieval performance, functional completeness, and engineering validity. Full article

Path planning for safflower filament harvesting is a critical component for ensuring the efficient operation of robotic harvesting systems. This paper proposes a path planning algorithm named SA-TD3-RRT, which integrates the Rapidly-exploring Random Tree (RRT), Twin Delayed Deep Deterministic Policy Gradient (TD3), and a spatial attention mechanism to achieve hierarchical planning of safflower filament harvesting paths. In the RRT component, techniques including adaptive step size, goal-biased sampling, and Douglas-Peucker path simplification are introduced. Specifically, the adaptive step size strategy converts the fixed step size into a dynamically adjustable extension step, effectively improving both the search real-time nature and path quality of the algorithm. The purely random sampling is replaced with goal-biased sampling, guiding the search tree growth toward the goal while suppressing redundant nodes, thereby reducing path planning time. Post-processing techniques such as Douglas-Peucker are employed to significantly reduce path complexity and improve path quality. In the reinforcement learning component, a spatial attention module is added, enabling dynamic perception of obstacle distribution. A spatial awareness term is introduced into the reward function to obtain more favorable spatial attention rewards. Additionally, the smoothed RRT path is injected into the experience replay buffer as expert demonstrations, realizing policy guidance based on imitation learning. Simulation results show that under favorable operating conditions, the proposed algorithm reduces the planned path length by 85.64%, 82.69%, and 92.55% compared to TD3, DDPG, and PPO algorithms, respectively, and shortens the path planning time by 17.68% relative to the original TD3 algorithm. In a three-dimensional environment, the planned path length is reduced by 8.29% compared to TD3, and the planning time is decreased by 81.24%, reaching 4.72 s. Although the current results are based on rigorous evaluation and demonstration in a high-fidelity simulation environment that abstracts from unstructured planting conditions, the proposed SA-TD3-RRT algorithm consistently achieved significant performance gains over the baseline. This simulation-based feasibility study provides a solid algorithmic foundation and key parameter benchmarks for future deployment on physical harvesting platforms, effectively bridging the gap between algorithmic innovation and practical agricultural robotics. Full article

Traditional ant colony optimization (ACO) often suffers from insufficient initial pheromone information, premature convergence, and limited adaptability across problem scales when solving the traveling salesman problem (TSP). This study proposes a multi-strategy adaptive ant colony optimization algorithm, termed MSA-ACO. Unlike hybrid ACO methods whose added strategies are governed by separate or predetermined rules, MSA-ACO uses smoothed normalized pheromone entropy as a shared internal search-state signal. This signal coordinates search-stage switching, exploitation preference, pheromone evaporation intensity, heuristic-information dependence, and the adaptive 3-opt budget under scale-dependent control profiles. A rank-weighted multi-elite initialization method and candidate-restricted hierarchical 2-opt/3-opt search are further incorporated to improve early guidance and solution refinement. Experiments on 35 TSPLIB instances show that MSA-ACO attains the best-known solution on 22 instances and achieves an average best-solution deviation of 0.17%, compared with 3.87% for ACS and 5.27% for MMAS. Its maximum deviation is 1.22%, and the deviation remains within 1% on 33 instances. Nonparametric tests confirm significantly better overall solution quality than ACS and MMAS. Under equal distance-query budgets, MSA-ACO obtains the lowest mean tour length on st70, kroC100, and kroA150, with significant improvements over ACS-based local search on kroC100 and kroA150. Component-wise ablation and sequential timing results reveal instance-dependent component effects and identify 3-opt as a major source of computational cost as well as an important refinement mechanism on the larger instance. Moreover, compared with the fixed 1000-iteration setting, the scale-segmented iteration budget reduces average running time by 28.06–83.75% across four representative instances, while still attaining the BKS on eil76 and ts225 and yielding mean deviations of approximately 0.30% and 0.20% on ch150 and kroA200, respectively. Overall, MSA-ACO provides an effective scale-aware optimization framework for symmetric TSPs, combining consistently high solution quality with controlled computational effort across different instance scales. Full article

Adolescent anxiety presents a growing challenge for schools, with emotional difficulties affecting educational engagement, attendance and wellbeing before some pupils meet thresholds for specialist mental health support. This creates a need to investigate accessible and non-stigmatising forms of early support within schools. Emerging TTRPG research has described experiences involving social connection, confidence, self-expression and emotional exploration, but direct evidence concerning secondary-aged pupils experiencing emerging or subthreshold anxiety remains limited. This opinion paper brings together findings from existing TTRPG research, mechanisms identified in broader psychological theory and related narrative approaches, and propositions concerning how these may operate through school-based TTRPG participation. Its contribution is a conceptual model linking components of TTRPG participation with five overlapping proposed mechanisms: narrative distance; psychological safety and social connectedness; intrinsic motivation and agency; identity exploration and self-efficacy; and cognitive flexibility and emotional regulation. The paper also outlines an illustrative school-based delivery model and a staged agenda for future research. These pathways are theoretically plausible rather than empirically established, and TTRPG participation should not be assumed to reduce anxiety or replace specialist assessment or treatment. Initial research should establish feasibility, acceptability and safety, including potential adverse experiences, before psychological or educational outcomes are evaluated. Full article

Porous materials represent a versatile solution for several applications. Indeed, the recent development of a new material, named 3D graphene, which combines the exceptional characteristics of graphene with a three-dimensional structure, opens perspectives for applications where a high surface-to-volume ratio is beneficial. Here, we explore the functionalization of 3D graphene with nickel (Ni) nanoparticles (diameter <15 nm) as a strategy to enhance hydrogen storage capabilities and oxidation resilience. Samples have been hydrogenated by supplying molecular or atomic hydrogen, and hydrogen storage performance was assessed through thermal desorption spectroscopy. Optimal sample performance is achieved after annealing at temperatures of 900–950 °C. Sample functionalization with Ni NPs enhances the hydrogen uptake by about 70%. Afterward, oxidation effects were systematically studied by exposing the samples to atmospheric oxygen, followed by further hydrogenation experiments. We observed that the adsorption peak at 350 °C is oxidation-resilient and increases up to 85% compared with before exposure to air. Moreover, the oxidation impact is fully recovered after successive annealing at 900 °C. The morphology and structural properties of the pristine and Ni-functionalized samples were characterized using scanning electron microscopy. Additionally, X-ray photoelectron spectroscopy was employed to analyze the surface chemical composition of the functionalized samples. Our results indicate that Ni functionalization influences both hydrogen adsorption and oxidation behavior, with potential implications for improving the stability of the material, especially for hydrogen storage applications. Full article

On-load tap changers (OLTCs) are critical to reliable transformer operation, yet mechanical fault samples are difficult to obtain in practice, limiting the feature representation and diagnostic performance of conventional data-driven methods under limited-sample conditions. To address this issue, this paper proposes a Semantic-Enhanced Fuzzy Prototypical Network (SFPN) for few-shot OLTC fault diagnosis. Three parallel encoders are constructed in the time, frequency, and time–frequency domains to extract complementary vibration features. Fault descriptions are encoded using a pretrained text embedding model and fused with vibration features through cross-modal attention. Fuzzy prototypes are then constructed according to the class memberships of support samples to reduce the influence of uncertain samples on prototype estimation. Experiments are conducted using vibration signals collected from a self-built OLTC mechanical fault simulation platform. Under a closed-set 4-way 5-shot episodic setting, where the fault classes are observed during meta-training and 5-shot denotes five support samples per class in each episode, SFPN achieves an average diagnostic accuracy of 97.24 ± 0.48%. SFPN also maintains superior performance across different support-sample sizes and noise levels, demonstrating its effectiveness for closed-set OLTC fault diagnosis under limited-support conditions. Full article

Helical milling can produce Ti-6Al-4V holes, but the combined effects of cutting conditions on wall surface roughness ( $R_a$ ), within-hole variability, and burr height remain unclear. A replicated 2⁴ full-factorial experiment examined tangential feed, axial helical pitch, fluid supply, and axial ultrasonic vibration across 48 holes. Roughness was quantified from 1536 readings in four axial zones; burr height was obtained from 12 circumferential measurements at the entrance of each hole. Factorial and mixed-effects analyses were supplemented by conservative paired-block inference for the fluid main effect and grouped validation of five regression models against a mean baseline. Within the fixed fluid–tool run structure, flood machining was associated with 12.94% lower hole-mean $R_a$ (2.355 to 2.050 µm; p = 0.0285) and 25.10% lower mean within-zone $R_a$ SD. Both associations had nominal paired-block support but did not survive FDR correction across six responses; fluid was confounded with tool identity and run position. Axial variability reductions were descriptive and unsupported by the conservative test, and no systematic top-to-bottom roughness gradient was detected. Ultrasonic vibration was unrelated to wall roughness but was associated with 26.3% higher burr height, whereas higher tangential feed was associated with 44.6% lower burr height. Burr height was the most predictable individual-hole response; hole-mean $R_a$ showed moderate predictability, and axial $R_a$ CV transferred poorly across replicates. Wide and incompletely calibrated Gaussian process intervals preclude individual-hole tolerance certification. The findings support multi-response assessment and response-specific validation of hole quality. Full article

(1) Background: The integration of generative artificial intelligence (AI) into academic reading raises the question of whether such tools improve genuine comprehension or merely alter readers’ perception of it. (2) Objective: This study examined the association between generative AI use and reading performance and cognitive experience among university students reading a conceptually dense regulatory text. (3) Method: In a quasi-experimental, post-test-only design, with a single cross-sectional measurement per participant, 531 students from two Spanish higher education institutions read a Spanish-language regulatory text and were assigned to six conditions combining two systems (Microsoft Copilot Chat and ChatGPT 4 and 4.5) with varying pedagogical mediation, from unassisted reading to guided use with prior training. Literal comprehension and conceptual interpretation scores were assessed alongside perceptual measures of interest, attention, perceived learning and explanatory confidence. (4) Results: Literal comprehension did not differ across conditions (F(5, 525) = 0.85, p = 0.518, η² = 0.008), whereas conceptual interpretation did (F(5, 525) = 3.55, p = 0.004, η² = 0.033), although no AI condition outperformed unassisted reading. The clearest effect emerged for explanatory confidence (F(5, 525) = 12.58, p < 0.001, η² = 0.107), followed by attention (p < 0.001, η² = 0.027). Prior AI training was associated with the lowest interpretation and confidence scores. (5) Conclusions: Generative AI use was associated with changes in the subjective experience of reading, particularly explanatory confidence, more consistently than in actual performance. The observed pattern suggests that how students interact with generative AI may warrant at least as much attention as mere tool availability, although the present design does not permit this effect to be isolated from institutional and phase-related confounders. Full article

Biochar materials exhibit promising prospects in adsorption–separation owing to their large specific surface area and well-developed pore structures. Herein, a series of manganese-oxide-modified porous carbon adsorbents (PEC-MnO_x) were fabricated via the pyrolysis method, using Ulva prolifera as the carbon source and KMnO₄ as the activator. XRD, SEM, TEM, N₂ adsorption–desorption, FT-IR and XPS were employed to characterize the morphology, crystal phase, pore structure and surface chemical properties of the as-prepared materials. The results demonstrated that KMnO₄ activation effectively optimized pore architecture, increased specific surface area, and generated abundant unsaturated -Mn-OH sites and oxygen vacancies. Batch adsorption experiments were carried out to evaluate Cd(II) removal performance. PEC-MnO_x-6 displayed the optimal adsorption capability with an equilibrium adsorption uptake of 152.4 mg/g at 298.15 K. Kinetic fitting suggested that the pseudo-first-order model better described the adsorption behavior. Intraparticle diffusion analysis confirmed the multi-step mass-transfer nature, and liquid-film diffusion acted as the primary rate-limiting step. Both Langmuir and Freundlich isotherm equations obtained high correlation coefficients, indicating the coexistence of homogeneous active sites and heterogeneous pores/defects on the carbon matrix. Combined with characterization results, Cd(II) sequestration was realized through synergistic effects mainly including surface complexation and ion exchange, with the assistance of electrostatic interaction. This work provides a reference for high-value utilization of Ulva prolifera biomass and remediation of Cd(II)-contaminated wastewater. Full article

Background: Although diabetes is a major public health concern worldwide, evidence on formal diabetes education programs for community pharmacists in Palestine remains limited, despite their important role as accessible healthcare providers. Objectives: To evaluate immediate changes in community pharmacists’ diabetes-related knowledge and attitudes toward patient-centered care following participation in a structured diabetes education program and to assess their satisfaction with the educational experience. Methods: This non-randomized controlled pretest–posttest study enrolled 200 community pharmacists in the West Bank, Palestine. Knowledge and attitudes were assessed using modified versions of the Diabetes Knowledge Test and Diabetes Attitude Scale (DAS-3), respectively. Satisfaction with the educational experience was assessed after the educational period using an adapted questionnaire. General linear models provided adjusted post-intervention comparisons and between-group comparisons of pre–post changes. Results: At the pooled baseline knowledge mean, the adjusted post-intervention knowledge score was higher in the intervention group (adjusted mean difference = 6.73, 95% CI [6.13, 7.33], p < 0.001), and the adjusted change in knowledge from pre- to post-intervention was greater in the intervention group (adjusted mean difference in change = 7.04, 95% CI [6.37, 7.71], p < 0.001). At the pooled baseline attitude mean, the adjusted post-intervention attitude score was higher in the intervention group (adjusted mean difference = 0.579, 95% CI [0.393, 0.765], p < 0.001). The adjusted between-group difference in change in attitude was small and not statistically significant (adjusted mean difference in change = 0.091, 95% CI [−0.061, 0.242], p = 0.238). The adjusted satisfaction score was likewise higher in the intervention group (adjusted difference = 0.304, 95% CI [0.122, 0.487], p = 0.001). Conclusion: Participation in the structured educational program was associated with greater immediate knowledge gains and higher satisfaction than receipt of standard written materials. However, the adjusted between-group difference in attitude change was not statistically significant. The non-randomized design limits causal inference, and longer-term studies are needed to assess sustained educational benefits and changes in practice. Full article

Conversational agents built on large language models increasingly converse like humans, expressing emotions and disclosing their own experiences. Human-likeness, however, has been studied largely as a matter of appearance—how human an agent looks. Language now also lets an agent display internal human-likeness by conveying an inner life, raising the question of how deeply an AI should express its own experiences—and whether that depends on its appearance. In a 2 (appearance: machine-like vs. human-like) × 3 (self-disclosure: none vs. low vs. high) between-subjects experiment, 193 university students completed a single, brief, fully scripted counseling session with an AI counselor prototype and evaluated it on six measures, controlling for prior mind perception. The effects of disclosure depth depended on appearance: with the machine-like counselor, intimacy and social presence peaked at low and declined at high disclosure, with trust and perceived authenticity showing the same direction—an uncanny valley-like pattern—whereas with the human-like counselor evaluations generally rose with depth. Deeper disclosure was not judged more inappropriate, and moderated mediation analyses showed that deepening disclosure from low to high reduced anthropomorphism—and, through it, intimacy and trust—only for the machine-like counselor. Internal and external human-likeness were read together: how deeply an AI should speak about itself cannot be decided apart from how it looks. Full article

Aspirin is one of the most widely used pharmaceuticals, yet its long-term use is associated with some side effects, including iron-deficiency anemia, which has been mainly linked to the iron-chelating properties of its major metabolite, salicyluric acid (SUA). In this study, the chelating and redox properties of SUA in the presence of iron (Fe³⁺), copper (Cu²⁺), and aluminum (Al³⁺), under neutral pH conditions were investigated using UV-Vis spectrophotometry, Job plot analysis, and ¹H NMR spectroscopy. The stability constants determined for SUA complexes with Cu²⁺ and Al³⁺ were K = 9.9 × 10⁶ and 1.40 × 10⁴ M⁻¹, respectively, classifying SUA as a moderate-affinity chelator. No detectable complexes of SUA with Fe²⁺ or Cu⁺ were observed, indicating selectivity of SUA for higher oxidation states. Competition experiments with the chelating drug deferiprone (L1) confirmed the quantitative displacement of SUA from metal coordination spheres. In a Fenton-initiated lipid peroxidation model using linoleic acid micelles, SUA–copper and iron metal complexes accelerated lipid oxidation, demonstrating pro-oxidant activity. However, the combination of SUA and vitamin C resulted in a net decrease in the lipid peroxidation rate compared to the system containing SUA alone, suggesting a complex interplay between chelation and redox modulation. Full article

Space-to-ground collaborative remote sensing has become a promising mode for global-scale earth observation. However, the open satellite-ground communication link faces severe security threats, especially when unauthorized eavesdroppers deploy gradient or feature inversion attacks based on deep learning to reconstruct sensitive geographic targets. Traditional differential privacy methods apply uniform noise across the entire image space, which inevitably damages the geometric structure of the non-sensitive areas and the performance of downstream tasks. This paper proposes Fed-SMDP, an end-to-end Federated Spatially Modulated Differential Privacy framework for secure satellite-ground collaborative sensing. Specifically, an on-board self-supervised masked autoencoder is deployed to extract robust latent feature representations. To maintain geographical sovereignty, a lightweight spatial modulation mechanism is designed to identify sensitive regions dynamically, where non-uniform noise is injected directly into the corresponding neural activations. Therefore, the transmitted feature representations become visually ambiguous to potential eavesdroppers while preserving essential semantics for downstream applications. At the ground station, a weak-supervised alignment protocol is established to fine-tune task-specific heads using historical labeled datasets, periodically feedback the lightweight model parameter increments to the satellites, and complete the closed-loop federated evolution. The theoretical analysis establishes a rate-distortion-based lower bound on the expected reconstruction distortion of sensitive regions under the considered threat model. Extensive experiments on public datasets demonstrate that Fed-SMDP substantially degrades reconstruction quality under the evaluated generative inversion attacks while maintaining negligible accuracy degradation for global land-cover monitoring tasks. Full article

This paper is a fundamental study that theoretically estimates the acoustic properties of a simplified structural model of a sound-absorbing foam material, which is porous. This study adopts a simplified foam-structure model consisting of continuous spherical pores arranged as a simple cubic lattice. This model is termed a negative simple cubic lattice, and fundamental research was conducted to estimate its acoustic properties theoretically. An experiment was performed by 3D-printing samples and measuring the normal-incidence sound absorption coefficient using a two-microphone impedance measurement tube. In this theoretical analysis, the void region, which consists of the negative simple cubic lattice, was elementally divided perpendicular to the direction of sound incidence and modeled as a gap enclosed by two parallel planes. The calculated characteristic impedance and propagation constant were substituted into a transfer matrix for sound pressure and volume velocity based on the 1D wave equation, and the normal-incidence sound absorption coefficient was derived using the transfer matrix method. Furthermore, the theoretical values that consider Helmholtz resonance and tortuosity closely follow the trend of the experimental values. In addition, theoretical values were derived from mathematical models whose dimensions were approximated to those of an actual sound-absorbing foam material. Full article