Search Results (7,453)

In this study, a new approach to stabilizing polypropylene (PP) melts using the bio-based antioxidant propyl gallate (PG) is considered. Since PG is extensively used in the food and cosmetics fields and its low doses do not cause reactions in humans, producing PP melts with PG is an interesting approach. Furthermore, PG is already actively used in the processing of natural polymers, for example, in the NMMO process. It is shown that introducing up to 0.2 wt.% PG into the system is sufficient to significantly reduce the decrease in melt viscosity during repeated PP processing. After five processing cycles, the viscosity of systems with PG decreases by less than an order of magnitude, while for melts without the antioxidant the viscosity drops by almost three orders of magnitude to 10 Pa s. For melts with antioxidant additives, the crossover point position in the frequency dependences remains virtually unchanged, indicating the preservation of the system’s elastic properties. Macrofibers were spun from the resulting melts, which can then be used for concrete reinforcement. For the spun fibers with PG, the strength decreased to 68.1 MPa after five passes, whereas for the PP fibers, the values did not exceed 31 MPa. The structure and properties of the fibers were studied using X-ray diffraction and IR spectroscopy, and contact angles were determined. Full article

This article argues that gifted education is at a critical turning point, shaped by persistent conceptual fragmentation and the limitations of identification-centered systems. Despite decades of refinement, identification practices remain inconsistent, inequitable, and weakly aligned with the educational experiences they are intended to support. At the same time, the field continues to be divided between competing paradigms, most notably the Gifted Child Paradigm and the Talent Development Paradigm, resulting in incoherence in research, policy, and practice. Drawing on recent empirical and theoretical work, this article contends that these dual challenges undermine the effectiveness and equity of gifted and creative programming. In response, this article advances a reconceptualization of gifted education grounded in the talent development framework, which views ability as dynamic and emergent rather than fixed. Central to this vision is the Three Legs of Talent Development model, which emphasizes (a) access to advanced content, (b) intellectually stimulating instruction characterized by depth and complexity, and (c) interest-based authentic learning opportunities. Importantly, this framework situates the majority of gifted education services within the general classroom, positioning classroom instruction as the primary site for talent development rather than relying on separate or pull-out programs. Together, these elements form an integrated, system-level approach that shifts the focus from identifying gifted individuals to designing opportunity-rich, classroom-based environments that cultivate talent broadly. Full article

Data Envelopment Analysis (DEA) has emerged as a major non-parametric technique for measuring efficiency in sustainability and environmental economics because it can handle multiple inputs and outputs without making explicit functional assumptions. DEA allows the simultaneous consideration of economic performance, resource utilization, environmental impacts, and recycling results in the evaluation of the circular economy (CE). This review investigates the evolution of DEA models in the last years and the variables used to measure the CE performance. We analyze 209 peer-reviewed articles to systematically explore the evolution of DEA applications from conventional single-stage efficiency models to advanced network-based structures that better reflect the intricacy of circular systems. The review discusses the most advanced DEA approaches to date in the literature on CE assessment and uncovers specific factors that affect the choice of models in empirical studies. Finally, it points to promising directions of future research by showing interest in the development of comprehensive DEA models adapted to the specificity of the CE systems. Full article

Background: Anxiety disorders are prevalent and debilitating. Given challenges in maintaining mindfulness at one-month remission, integrative approaches targeting anxiety and psychological resources (especially mindfulness) have gained interest. This study aimed to assess the effectiveness of adding biofeedback to the treatment of anxiety disorders. Methods: Inpatients with anxiety disorder (ICD10 F40/F41) were randomized to three groups: (1) biofeedback for enhancing mindfulness (BFB, n = 76); (2) medication therapy (MT, escitalopram 10 mg/day, n = 46); and (3) combined therapy (BFB + MT, CT, n = 66). Per-protocol analysis: 180 completers; ITT: all 188 randomized patients. The primary outcomes were changes in the Hamilton Anxiety Rating Scale (HAM-A) total score and the Five Facet Mindfulness Questionnaire total score, assessed at baseline, after 10 days of treatment, and at one-month follow-up. The HAM-A assessor was blinded to group allocation. Results: In the per-protocol analysis, the BFB and CT groups showed a significantly greater reduction in the HAM-A score than the MT group did. For example, post-treatment, the mean difference between BFB and MT groups was –6.30 points (95% CI: –9.12 to –3.48, p = 0.00046). The BFB and CT groups did not differ significantly. For mindfulness, the BFB and CT groups showed an increase, whereas the MT group showed a decrease (BFB vs. MT: mean difference 30.28 points, 95% CI: 23.54–37.02, p < 0.000001). Intention-to-treat (ITT) analysis (n = 188) confirmed the robustness of these findings. Conclusions: BFB appears to be a promising approach for reducing anxiety and fostering mindfulness skills; these findings suggest it may be worth exploring further as a potential component of comprehensive therapy for anxiety disorders. Clinical Trial Registration: ClinicalTrials.gov NCT07628153 (retrospectively registered). The trial was retrospectively registered, and the primary analysis was conducted on a per-protocol population. Full article

Background: Equitable and adequate access to primary care services is essential for reducing healthcare disparities and advancing social justice. In developing countries like China, achieving a balanced primary care provision across urban–rural divides, regions, and population groups represents a critical strategy for improving public health outcomes. Methods: This study integrates high-resolution population data, nationwide point of interest (POI) data, and aggregated individual survey data to analyze the spatial distribution of primary care facilities in China, evaluate their accessibility and equity, and examine the relationships among primary care accessibility, socioeconomic factors, and public health outcomes using geographic analysis and machine-learning methods. Results: (1) Primary care facilities in China exhibit significant spatial clustering and pronounced urban–rural disparities, with 23% of the urban population having access within walking distance; (2) while horizontal equity in primary care accessibility is relatively well-maintained for China’s aging population, vertical equity requires substantial improvement; and (3) primary care accessibility demonstrates significant but nonlinear associations with key socioeconomic indicators, including urban population size, GDP, built-up area, health insurance coverage, and public expenditure. Conclusions: These findings provide valuable insights for health resource allocation and urban planning policies aimed at achieving equitable primary care access. Full article

Fine-scale identification of urban building functions is essential for understanding urban spatial structure, socioeconomic organization, and sustainable urban development. However, large-scale building function mapping remains constrained by reliance on proprietary data, insufficient representation of geographical context, and limited cross-city generalization. To address these challenges, this study proposes a multi-source geographical knowledge fusion framework for fine-scale building function classification using exclusively open-source data. In addition to conventional morphological, POI-based, and spectral features, the framework systematically integrates open-source geographical environmental features to characterize accessibility, infrastructure relationships, ecological surroundings, and environmental conditions at the building level. Beijing is selected as the training area, while Tianjin is used for independent cross-city validation. Ten representative models, including deep learning, ensemble learning, and traditional machine learning methods, are systematically evaluated for classifying six building function types. Results show that the systematic integration of geographical environmental features improves classification performance and interpretability. Deep learning and ensemble models outperform traditional methods, with CNN achieving the highest accuracy of 87.07% in Beijing and 69.83% in Tianjin. Feature contribution analysis further indicates that geographical environmental features play a dominant role in functional discrimination, while POI features provide important socio-semantic information. Overall, this study provides a reproducible open-data framework for urban building function mapping, supporting sustainable urban planning, land-use optimization, infrastructure allocation, and smart city governance. Full article

The greatest challenge of location recommendation is data sparsity. Some works introduce auxiliary information such as social links and semantics to enrich the representation. However, there are still two main limitations. Firstly, existing models only investigate how a person chooses POIs (Point-of-Interests), highlighting the user interest in locations from a human perspective, limited by users’ sparse trajectory. Secondly, they recommend POIs by one’s trajectory independently. However, it is hard to uncover specific behavior patterns or daily routines individually for each user. To address these problems, we develop a novel method for the next location recommendation via bi-directional inference and similarity-aware enhancement (SimBIN). Specifically, it considers the location recommendation task from both the human and location view, conducting the next POI inference and the next visitor inference synchronously. In particular, we employ the adaptive learning strategy to refine the model based on the conformity of two inference parts. Moreover, for the POI inference, we take the highly similar users to approximate a user’s behavior for enriching the next POI candidates. It is similar for the user inference. Experiments show that the proposed SimBIN significantly outperforms the state-of-the-art approaches. Furthermore, each key component can be refined and generally used in other frameworks. By establishing a bidirectional probabilistic collaboration framework, this study not only advances the theoretical understanding of mutual regularization in spatial-temporal modeling, but also provides a possible solution for personalized location-aware services and urban trajectory planning. Full article

The coronavirus disease 2019 (COVID-19) pandemic highlighted the need for rapid, accurate, and point-of-care diagnostic technologies, accelerating interest in biosensors as next-generation analytical platforms. However, biosensor performance is governed by a connected sequence of processes, including bioprobe–target recognition, sensor fabrication, structural optimization, and signal interpretation. Because these processes involve multiple interacting variables, conventional empirical approaches often have limitations in efficiently optimizing biosensor performance and interpreting complex analytical signals. Artificial intelligence (AI) and machine learning (ML) provide tools to model these relationships and support prediction-guided biosensor development. This review discusses recent progress in AI-assisted biosensor development in three sequential stages. First, AI-assisted bioprobe design is reviewed, including in silico aptamer discovery, smart-SELEX-based aptamer screening, and peptide receptor design for improving molecular recognition. Second, AI-driven sensor fabrication and structural optimization are discussed, focusing on electrochemical feature extraction, paper-based microfluidic device optimization, and optical biosensor parameter prediction. Third, ML-based signal analysis is examined as a strategy for converting complex electrochemical, colorimetric, and optical responses into quantitative analytical outputs. By organizing these examples as a connected workflow rather than as separate applications, this review highlights how AI can link molecular design, device engineering, and signal interpretation to accelerate the development of next-generation biosensors. Full article

We study a two degrees of freedom Hamiltonian system describing the motion of a particle in a potential field in the form of a $S^1$ symmetric double well, namely $V=-(x_1^2+x_2^2)+{(x_1^2+x_2^2)}^2$, known also as a champagne bottle potential. This system is completely integrable. The champagne bottle is the simplest member of a class of integrable systems that have no global action variables due to a non-trivial monodromy. Beyond that, the geometric and dynamical properties of the system near the equilibrium are of primary interest. We calculate the Birkhoff normal form and the nontrivial action near the focus–focus singularity and obtain the semi-global symplectic invariant near the focus–focus point, which is introduced by Vũ Ngọc. Examples of such calculations are still few. We compare our result with the semi-global symplectic invariant of the spherical pendulum. Full article

Fragmented point-based conservation limits the integrated protection and adaptive reuse of timber arch covered bridges. This study develops a quantitative decision-support workflow for identifying and evaluating heritage corridor networks, using 99 timber arch covered bridges in Northeastern Fujian, China, as the regional sample and three representative bridges for bridge-scale interpretation. The workflow combines spatial pattern analysis, Maximum Entropy (MaxEnt) suitability modeling, XGBoost–SHAP-assisted resistance analysis, circuit theory corridor extraction, complex network analysis, and maturity assessment. The results show that the bridges form a significant clustered pattern with a Shouning primary core and a Pingnan secondary core. MaxEnt identified high-suitability areas of 1.857 × 10⁵ ha, accounting for 3.65% of the study area, mainly in the Shouning–Pingnan–Zhenghe border zone. The XGBoost–SHAP procedure was used as a secondary modeling step to translate suitability patterns into a composite resistance surface; it indicated that road proximity, elevation, nighttime light intensity, and distance to points of interest (POIs) were the main variables shaping the model output. Based on 27 selected heritage source sites, 75 primary corridors totaling 4078.22 km were identified, together with 33 pinch points and 27 barrier points. A corridor protection width of 7 km was determined as optimal. Network analysis identified Luanfeng Bridge, Longtan Bridge, and Denglong Bridge as important structural nodes and divided all 99 bridges into five river basin-related communities. Maturity assessment revealed a pattern of “one core, two belts, and multiple nodes” but also considerable functional disparity. The three representative cases are reframed as preliminary interpretations of model results: Luanfeng Bridge emphasizes landscape-integrity and visitor-pressure assessment, Denglong Bridge emphasizes route continuity and low-intervention interpretation, and Qiancheng Bridge emphasizes livelihood coordination and incremental rural tourism. This study provides decision-making support for advancing the integrated conservation of timber arch covered bridges and the synergistic development of rural tourism. Full article

Background/Objectives: Distal radius and carpal fractures are common injuries in emergency radiology, where accurate lateral wrist positioning is essential for diagnostic image quality. Maintaining a true lateral position can be challenging, often requiring manual support and increasing occupational radiation exposure. This pilot phantom study aimed to design, fabricate, and evaluate a novel 3D-printed wrist immobilization device and compare its performance with adhesive tape and sandbag stabilization. Methods: A wrist immobilization device was designed using computer-aided design software and fabricated by fused deposition modeling with polylactic acid. A wrist phantom was imaged using three stabilization techniques: the 3D-printed device, adhesive tape, and sandbag support. Image quality was independently assessed by two blinded observers using an eight-point scoring system. Inter-rater reliability was evaluated using the intraclass correlation coefficient (ICC). Positioning time was recorded, and radiopacity was assessed using pixel intensity measurements within predefined regions of interest. Results: No significant differences in image quality were observed among the three stabilization techniques (p = 0.263). Inter-rater reliability was excellent (ICC = 0.877). Positioning time differed significantly among techniques (p = 0.036), with sandbag stabilization requiring the shortest time. Radiopacity analysis demonstrated minimal attenuation (8.6–20.8%) in regions adjacent to the anatomical area of interest, while higher attenuation was limited to non-diagnostic regions. Conclusions: The 3D-printed wrist immobilization device produced image quality comparable to conventional stabilization methods with minimal radiographic interference. These findings support its feasibility as a low-cost positioning aid and warrant further evaluation in larger clinical studies. Full article

Propolis has gained widespread interest due to its multiple applications in the food and pharmaceutical industries. Therefore, the objective of this study was to determine the sensory differences among propolis samples collected in the Mexican Comarca Lagunera region and to identify the sensory attributes highest-rated by a trained sensory panel. The methodology involved collecting ten propolis samples following the guidelines of the Mexican Official Standard NOM-003-SAG/GAN-2017. A panel of 24 panelists was established, evaluating the samples using an attribute-specific categorical scale and a nine-point hedonic Likert scale. The attributes considered were color, aroma, taste, consistency, and overall acceptance. Data were analyzed using descriptive statistics, Spearman correlation coefficients, hierarchical cluster analysis, Principal Component Analysis (PCA), and the non-parametric Kruskal–Wallis test. Results indicated that taste and aroma were the most relevant sensory attributes, predominantly characterized by a bitter taste and intense aroma. Both attributes showed statistically significant differences. Samples TLA1 and SB1 obtained the highest scores in these parameters. Conversely, SB2 and SB3 showed lower acceptability across all evaluated attributes. This study highlights the need for continued multidisciplinary research aimed at the sensory characterization of propolis and at strengthening its regional documentation. Such efforts will contribute to its commercial valuation, geographical differentiation, and consolidation as a bee product with high functional potential. Full article

Snacking has become a routine part of how people eat today, with real potential to shape overall diet quality, food choices, and daily nutrient intake. This cross-sectional study aimed to investigate snack purchasing and consumption habits among Greek adults, examine consumers’ perceptions of the nutritional value of snacks, and assess their attitudes toward nutrition labelling, nutrition claims, and innovative snack products. A structured questionnaire was sent out electronically to 1039 Greek adults. Participants provided information on their sociodemographic background, health and lifestyle habits, snack consumption and purchasing behavior, perceptions of snack products, nutrition labelling, and interest in innovative and functional snacks. The data were analyzed using descriptive statistics and Chi-square tests of independence. The most common packaged snack for the average person in the study was a cereal bar, while the least popular non-packaged snack was a bakery cheese pie. Consumers viewed the appearance of the product’s packaging as a secondary consideration at the point of purchase, and the most prominent label elements that attracted consumer attention were nutrition, calories, and fat. The claims that consumers found most appealing were “no preservatives” and “sugar-free/no added sugars.” A clear preference was shown for snack products that relied mainly on naturally occurring nutrients rather than fortified ingredients, as well as a greater willingness to try new savory snack options that used familiar/demonstrable Greek ingredients, such as certain olives, nuts, and fruit. Statistically significant relationships have been identified between certain snacking behaviors, attitudes, and labelling preferences, with respect to age, gender, education level, employment status, Body Mass Index (BMI), health status, physical activity, and place of residence. Within this relatively highly educated and predominantly urban sample, sociodemographic, education level and lifestyle all have an influence on how Greek adult consumers view and use snacks. Interest in nutrition information varies widely between different types of consumers. These findings may be useful in guiding the future development of snacks that meet the nutritional requirements of the Mediterranean diet, as well as in creating more targeted nutrition information and consumer education programs. Full article

The relationship between athletes and equipment in the sport of speed skating is critical. A speed skater’s equipment, namely their skates, is an integral part of a dynamic system that facilitates the translation of human motion to on-ice racing. Although it is common practice to customize the setup of long track speed skates, empirical evidence supporting best practices is relatively undocumented. The exploratory nature of this investigation was intended to address two purposes: (i) profiling skate metrics in a competitive cohort of long track speed skaters and (ii) exploring the association between skate metrics and on-ice race times. Two databases were populated for the purpose of analysis: one for skate metrics and another for on-ice race times. Data were linked to the skates of thirty-one provincial-level long track speed skaters (male n = 19; female n = 12). The skate metrics database was populated by a single equipment technician, trained using measurement protocols consistent with the industry’s standards, and the metrics were grouped into three categories: (i) boot dimensions (n = 4), (ii) blade dimensions (n = 7), and (iii) skate setup (n = 5). The on-ice race time database was populated and collated using a secondary data source and included an aggregate time based on the mean of the three fastest race times per athlete by distance (500 m, 1000 m, and 1500 m) collected from the 2019–2023 seasons. Statistical analyses were conducted within and across the databases to (i) determine the variation in skate metrics and race times across athletes, and (ii) explore the association between skate metrics and race times. Analysis of the skate metrics database revealed coefficients of variance (CVs) for all metrics including the following: boot dimensions (6.95–8.69%), blade dimensions (0.00–14.24%), and skate setup metrics (8.63–17.05%). Of significant interest were large CVs for pivot point position (14.54%) and blade offset (8.63–17.05%), suggesting inconsistency and a potential lack of understanding of the impact of skate setup on performance. No significant correlations were revealed between skate setup metrics and race times. Across the three race distances, regression models were not statistically significant and explained only a small proportion of variance, highlighting the limited understanding between skate setup metrics and race times in practice. Profiling skate metrics and understanding their relationship with race times provides equipment technicians, coaches, and athletes with a baseline to inform decisions when customizing skate setup. Full article

With the continuous embedding of small- and medium-sized manufacturing enterprises (SMMEs) into manufacturing platform ecosystems, selecting the optimal resource reconstruction model has become a critical decision for both platforms and SMMEs. However, the existing literature provides limited insight into how different resource types compare in terms of their efficiency in enabling capability reconstruction. Addressing this gap, this paper constructs asymmetric Stackelberg game-theoretic models for the platform–SMME collaborative system and systematically investigates efficiency differentiation and strategy selection under three resource models: the fund resource model, the knowledge resource model, and the fund and knowledge hybrid model. Drawing on dynamic capability theory, the models formalize the transmission paths through which heterogeneous resources affect SMMEs’ capability reconstruction, and derive the equilibrium conditions governing resource input, pricing, revenue-sharing, and payoff outcomes. The findings reveal three key insights. (1) As dynamic capability matures and price sensitivity rises, the platform’s optimal strategy shifts systematically from fund resource model through hybrid model to knowledge resource model, requiring dynamic model switching aligned with the SMME’s capability path and market conditions. (2) Fund-driven improvement operates through two paths, while knowledge-driven improvement operates through four paths and the upstream paths, creating a win-win zone and downstream paths generating diverging interests. (3) Dynamic capability, particularly conversion efficiency of “information-management,” reduces resource input while increasing both parties’ payoffs, shifting the strategy from a fund resource model to a knowledge resource model. The findings point to a shift from resource provision to capability-driven collaboration, requiring dynamic model switching by platforms, precision matching by SMMEs, and shared investment in dynamic capabilities for sustainable mutual benefit. Full article