Search Results (40,760)

Thyroid hormones (thyroxine, T₄, and triiodothyronine, T₃) are indispensable for sustaining vertebrate life, and their deficiency gives rise to a wide range of symptoms characteristic of hypothyroidism, affecting 5–10% of the world’s population. The precursor for thyroid hormone synthesis is thyroglobulin (Tg), a large iodoglycoprotein consisting of upstream regions I-II-III (responsible for synthesis of most T₄) and the C-terminal CholinEsterase-Like (ChEL) domain (responsible for synthesis of most T₃, which can also be generated extrathyroidally by T₄ deiodination). Using CRISPR/Cas9-mediated mutagenesis, we engineered a knock-in of secretory ChEL into the endogenous TG locus. Secretory ChEL acquires Golgi-type glycans and is properly delivered to the thyroid follicle lumen, where T₃ is first formed. Homozygous knock-in mice are capable of thyroidal T₃ synthesis but largely incompetent for T₄ synthesis such that T₄-to-T₃ conversion contributes little. Instead, T₃ production is regulated thyroidally by thyrotropin (TSH). Compared to cog/cog mice with conventional hypothyroidism (low serum T₄ and T₃), the body size of ChEL-knock-in mice is larger; although, these animals with profound T₄ deficiency did exhibit a marked elevation of serum TSH and a large goiter, despite normal circulating T₃ levels. ChEL knock-in mice exhibited a normal expression of hepatic markers of thyroid hormone action but impaired locomotor activities and increased anxiety-like behavior, highlighting tissue-specific differences in T₃ versus T₄ action, reflecting key considerations in patients receiving thyroid hormone replacement therapy. Full article

Olive tree leaf is a phenol-rich, high-potential-value biomass that can be used to formulate food additives and supplements. Leaf phenolic content varies depending on numerous factors, like cultivar, geographical origin, year, and season of harvest. The aim of this research was to evaluate the variations in phenolic profile of four major Tuscan cultivars (Frantoio, Leccio del Corno, Leccino, and Moraiolo) over four different phenological phases and across two years. All 96 olive leaf samples were harvested from trees grown in the same orchard located in Florence. After drying, their phenolic profile was characterized using HPLC-DAD-MS, and the obtained data were processed by ANOVA, GA-LDA, and RF methods. A total of 25 phenolic derivatives were analyzed, with total contents ranging 16,674.0–50,594.3 mg/kg and oleuropein (4570.0–27,547.7 mg/kg) being the predominant compound regardless of cultivar, year, and season of harvest. Oleuropein and hydroxytyrosol glucoside showed inverse proportionality and similar behavior across years in all cultivars, and therefore were highlighted as main phenolic compounds correlated with the seasonal variability in studied cultivars. Interesting behavior was also pointed out for apigenin rutinoside. Application of GA-LDA and RF methods allowed pointing out the excellent performance of leaf phenols in discriminating samples based on cultivar, harvest year, and harvesting season. Full article

This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoin’s realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of models—especially when combined with the proposed quantile-driven factor—offers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets. Full article

Structural Health Monitoring (SHM) techniques are crucial for evaluating the condition of structures, enabling early maintenance interventions, and monitoring factors that could compromise structural integrity. Modal analysis studies the dynamic response of structures when subjected to vibrations, evaluating natural frequencies and vibration modes. This study focuses on detecting and comparing the natural frequencies of a 3-DoF structure under various excitation scenarios, including ambient vibration (in healthy and damaged conditions), two types of transient excitation, and three harmonic excitation variations. Signal processing techniques, specifically Power Spectral Density (PSD) and Continuous Wavelet Transform (CWT), were employed. Each method provides valuable insights into frequency and time-frequency domain analysis. Under ambient vibration excitation, the damaged condition exhibits spectral differences in amplitude and frequency compared to the undamaged state. For the transient excitations, the scalogram images reveal localized energetic differences in frequency components over time, whereas PSD alone cannot observe these behaviors. For the harmonic excitations, PSD provides higher spectral resolution, while CWT adds insight into temporal energy evolution near resonance bands. This study discusses how these analyses provide sensitive features for damage detection applications, as well as the influence of different excitation types on the natural frequencies of the structure. Full article

The degradation process of HVOF-MCrAlY + APS-nanostructured YSZ (APS-nYSZ) thermal barrier coatings, produced using gas turbine OEM-approved MCrAlY powders, is investigated by studying the TGO growth and crack propagation behaviors in a thermal cycling environment. The TGO growth yields a parabolic mechanism on the surfaces of all HVOF-MCrAlYs, and the growth rate increases with the aluminum content in the “classical” MCrAlYs. The APS-nYSZ layer comprises micro-structured YSZ (mYSZ) and nanostructured YSZ (nYSZ) zones. Both mYSZ/mYSZ and mYSZ/nYSZ interfaces appear to be crack nucleation sites, resulting in crack propagation and consequent crack coalescence within the APS-nYSZ layer in the APS-nYSZ/HVOF-MCrAlY vicinity. Crack propagation in the TBCs can be characterized as a steady-state crack propagation stage, where crack length has a nearly linear relationship with TGO thickness, and an accelerating crack propagation stage, which is apparently a result of the coalescence of neighboring cracks. All TBCs fail in the same way as APS-/HVOF-MCrAlY + APS-conventional YSZ analogs, but the difference in thermal cycling lives is not substantial, although the HVOF-low Al-NiCrAlY encounters chemical failure in the early stage of thermal cycling. Full article

To investigate the oscillation modes of iced transmission lines, this study introduces a forcing term into the galloping equation and applies two discretization approaches: Discrete Method I (DMI), which directly transforms the partial differential equation into an ordinary differential form, and Discrete Method II (DMII), which first averages dynamic tension along the span. The finite element method is employed to validate the analytical solutions. Using a multiscale approach, amplitude-frequency responses under primary, harmonic, and internal resonance are derived. Results show that DMII yields larger galloping amplitudes and trajectories than DMI, with lower resonant frequencies and weaker geometric nonlinearities. In harmonic resonance, superharmonic and subharmonic modes (notably 1/2) are more easily excited. Under 2:1:2 internal resonance, amplitude differences in the vertical (z) direction are more sensitive to the discretization method, whereas the 1:1:1 case shows minimal variation across directions. These findings suggest that the choice of discretization significantly influences galloping behavior, with DMII offering a more conservative prediction. Full article

This study investigates how topic-specific expression by women delivery riders on digital platforms predicts their community engagement, emphasizing the mediating role of self-disclosure and the moderating influence of cognitive and emotional language features. Using unsupervised topic modeling (Top2Vec, Topical Vectors via Embeddings and Clustering) and psycholinguistic analysis (LIWC, Linguistic Inquiry and Word Count), the paper extracted eleven thematic clusters and quantified self-disclosure intensity, cognitive complexity, and emotional polarity. A moderated mediation model was constructed to estimate the indirect and conditional effects of topic probability on engagement behaviors (likes, comments, and views) via self-disclosure. The results reveal that self-disclosure significantly mediates the influence of topical content on engagement, with emotional negativity amplifying and cognitive complexity selectively enhancing this pathway. Indirect effects differ across topics, highlighting the heterogeneous behavioral salience of expressive themes. The findings support a statistically grounded, semantically interpretable framework for predicting user behavior in high-dimensional text environments. This approach offers practical implications for optimizing algorithmic content ranking and fostering equitable visibility for marginalized digital labor groups. Full article

Target tracking is a representative task in multi-agent reinforcement learning (MARL), where agents must collaborate effectively in environments with dense obstacles, evasive targets, and high-dimensional observations—conditions that often lead to local optima and training inefficiencies. To address these challenges, this paper proposes a collaborative tracking algorithm for UAVs that integrates behavior cloning with temporal difference (BCTD) and multi-agent proximal policy optimization (MAPPO). Expert trajectories are generated using the artificial potential field (APF), followed by policy pre-training via behavior cloning and TD-based value optimization. MAPPO is then employed for dynamic fine-tuning, enhancing robustness and coordination. Experiments in a simulated environment show that the proposed MAPPO+BCTD framework outperforms MAPPO, QMIX, and MADDPG in success rate, convergence speed, and tracking efficiency. The proposed method effectively alleviates the local optimization problem of APF and the training inefficiency problem of RL, offering a scalable and reliable solution for dynamic multi-agent coordination. Full article

In this article, we investigate the fractional soliton solutions as well as the chaotic analysis of the fourth-order nonlinear Ablowitz–Kaup–Newell–Segur wave equation. This model is considered an intriguing high-order nonlinear partial differential equation that integrates additional spatial and dispersive effects to extend the concepts to more intricate wave dynamics, relevant in engineering and science for understanding complex phenomena. To examine the solitary wave solutions of the proposed model, we employ sophisticated analytical techniques, including the generalized projective Riccati equation method, the new improved generalized exponential rational function method, and the modified F-expansion method, along with mathematical simulations, to obtain a deeper insight into wave propagation. To explore desirable soliton solutions, the nonlinear partial differential equation is converted into its respective ordinary differential equations by wave transforms utilizing $\beta$-fractional derivatives. Further, the solutions in the forms of bright, dark, singular, combined, and complex solitons are secured. Various physical parameter values and arrangements are employed to investigate the soliton solutions of the system. Variations in parameter values result in specific behaviors of the solutions, which we illustrate via various types of visualizations. Additionally, a key aspect of this research involves analyzing the chaotic behavior of the governing model. A perturbed version of the system is derived and then analyzed using chaos detection techniques such as power spectrum analysis, Poincaré return maps, and basin attractor visualization. The study of nonlinear dynamics reveals the system’s sensitivity to initial conditions and its dependence on time-decay effects. This indicates that the system exhibits chaotic behavior under perturbations, where even minor variations in the starting conditions can lead to drastically different outcomes as time progresses. Such behavior underscores the complexity and unpredictability inherent in the system, highlighting the importance of understanding its chaotic dynamics. This study evaluates the effectiveness of currently employed methodologies and elucidates the specific behaviors of the system’s nonlinear dynamics, thus providing new insights into the field of high-dimensional nonlinear scientific wave phenomena. The results demonstrate the effectiveness and versatility of the approach used to address complex nonlinear partial differential equations. Full article

Social marketing modifies individual behavior to achieve specific outcomes, mitigating environmental pressures. While proven effective in influencing consumer behavior, empirical studies on its impact on the tourism sector remain limited. This study examines how various social marketing channels influence tourists’ consumption decisions and contributes to achieving SDGs 11 and 12 by reviewing the existing methods of disseminating social marketing content. A conceptual model grounded in theory was developed and empirically tested. In particular, it focuses on the establishment of direct and indirect multi-route effects between social marketing and consumer behavior and introduces different influencing factors. Given the scarcity of research on collective culture, quantitative methods were employed, with data collected through questionnaires in mainland China. Results indicate that social marketing media significantly influence tourist behavior, with three mediators—subjective norms, personal values, and communication channels—playing varying roles across media types (events, public relations, and traditional media). Subjective norms, values, and communication channels act as mediators. This study bridges social marketing, tourist behavior, and SDG attainment, offering novel insights and practical implications for tourism practitioners. Full article

Background: Type 2 diabetes (T2D) is a major global health concern influenced by sociodemographic and lifestyle factors. This study compared T2D risk scores between commerce and industry sectors and assessed the associations of age, sex, education, physical activity, diet, and smoking with elevated risk. Methods: This cross-sectional study included 56,856 men and 12,872 women employed in the commerce (n = 27,448) and industry (n = 42,280) sectors across Spain. Anthropometric, clinical, and biochemical data were collected. Four validated T2D risk scores (QDscore, Finrisk, Canrisk, and TRAQ-D) were calculated. Multinomial logistic regression models estimated adjusted odds ratios (ORs) for high-risk categories by sociodemographic and lifestyle characteristics. Results: Women in the industrial sector had significantly higher age, BMI, waist circumference, and lipid levels than those in commerce; differences among men were less marked. Across all participants, higher T2D risk scores were independently associated with physical inactivity (OR up to 12.49), poor Mediterranean diet adherence (OR up to 6.62), industrial employment (OR up to 1.98), and older age. Male sex was strongly associated with high Canrisk scores (OR = 6.31; 95% CI: 5.12–7.51). Conclusions: Employment in the industrial sector, combined with sedentary behavior and poor dietary habits, is independently associated with higher predicted T2D risk. Workplace prevention strategies should prioritize multicomponent interventions targeting modifiable risk factors, especially in high-risk subgroups such as older, less-educated, and inactive workers. Full article

This study evaluates leadership uniformity—the degree to which the proposer role is evenly distributed among validator nodes over time—in Quorum-based Byzantine Fault Tolerance (QBFT), a Byzantine Fault-Tolerant (BFT) consensus algorithm used in permissioned blockchain networks. By introducing simulated follower timeouts derived from uniform, normal, lognormal, and Weibull distributions, it models a range of network conditions and latency patterns across nodes. This approach integrates Raft-inspired timeout mechanisms into the QBFT framework, enabling a more detailed analysis of leader selection under different network conditions. Three leader selection strategies are tested: Direct selection of the node with the shortest timeout, and two quorum-based approaches selecting from the top 20% and 30% of nodes with the shortest timeouts. Simulations were conducted over 200 rounds in a 10-node network. Results show that leader selection was most equitable under the Weibull distribution with shape $k=0.5$, which captures delay behavior observed in real-world networks. In contrast, the uniform distribution did not consistently yield the most balanced outcomes. The findings also highlight the effectiveness of quorum-based selection: While choosing the node with the lowest timeout ensures responsiveness in each round, it does not guarantee uniform leadership over time. In low-variability distributions, certain nodes may be repeatedly selected by chance, as similar timeout values increase the likelihood of the same nodes appearing among the fastest. Incorporating controlled randomness through quorum-based voting improves rotation consistency and promotes fairer leader distribution, especially under heavy-tailed latency conditions. However, expanding the candidate pool beyond 30% (e.g., to 40% or 50%) introduced vote fragmentation, which complicated quorum formation in small networks and led to consensus failure. Overall, the study demonstrates the potential of timeout-aware, quorum-based leader selection as a more adaptive and equitable alternative to round-robin approaches, and provides a foundation for developing more sophisticated QBFT variants tailored to latency-sensitive networks. Full article

Slender masonry structures, in the absence of disintegration phenomena, can be idealized as rigid bodies subjected to seismic excitation. In this study, a closed-form expression for the behavior factor (q-factor) associated with overturning collapse under out-of-plane seismic loading is derived. The analysis considers five-step pulse seismic inputs. In the proposed approach, valid for slender masonry structures, sliding failure is neglected, and collapse is assumed to occur when, at the end of the seismic excitation, the rotation of the structure reaches a value equal to its slenderness. Based on this criterion, it is possible to derive a formulation for the q-factor as a function of a dimensionless parameter that combines the geometric characteristics of the slender structure and the period of the applied accelerogram. To validate the proposed formulation, a comparative analysis is conducted against the results obtained from a numerical integration of the motion equation using a set of 20 natural accelerograms recorded in Italy. The characteristic period of each accelerogram is evaluated through different methodologies, with the aim of identifying the most suitable approach for application in simplified seismic assessment procedures. Full article

Background/Objectives: The increasing clinical integration of 3D-printed definitive resins requires a comprehensive understanding of their physicochemical properties and adhesive behavior. However, there is limited evidence regarding the optimal surface treatment and bonding strategies for clear aligner composite attachments on these materials. This study aimed to characterize a 3D-printed definitive resin, evaluate the effects of surface treatments on its surface topography, and compare the shear bond strength (SBS) of the bonded attachments using different adhesive systems, both before and after thermocycling. Methods: A total of 120 rectangular specimens were fabricated from a 3D printed dental resin (Crowntec^®, SAREMCO Dental AG—Mexico City, Mexico). For physicochemical characterization, six samples underwent scanning electron microscopy/energy-dispersive spectroscopy, X-ray diffraction, and thermogravimetric analysis. To evaluate surface topography, 42 polished specimens were assigned to three groups: untreated (control), etched with 4% hydrofluoric acid (HFA), or sandblasted with 50 µm Al₂O₃ (AA). Each group was subdivided for SEM observation and surface roughness (Ra) measurement. For SBS testing, 72 additional samples received the same surface treatments and were further subdivided according to the adhesive system: Transbond™ XT Primer (TXT) or Single Bond Universal (SBU). Results: The AA group showed the highest Ra (2.21^±0.30 µm), followed by HFA (0.81^±0.20 µm) and control (0.07^±0.30 µm) (p < 0.001). The highest SBS was observed in the AA + SBU group, followed by AA + TXT. Conclusions: Sandblasting with Al₂O₃ particles, combined with a universal adhesive, significantly improved bond strength, suggesting a viable protocol for 3D printed definitive composites in aligner attachment applications. Full article

This study investigates the impact of teacher decisions and other contextual factors on indoor air quality (IAQ) in mechanically ventilated elementary school classrooms using low-cost air quality sensors. Four classrooms at a K–8 school in San Jose, California, were monitored for airborne particulate matter (PM), carbon dioxide (CO₂), temperature, and humidity over seven weeks. Each classroom was equipped with an HVAC system and a portable air cleaner (PAC), with teachers having full autonomy over PAC usage and ventilation practices. Results revealed that teacher behaviors, such as the frequency of door/window opening and PAC operation, significantly influenced both PM and CO₂ levels. Classrooms with more active ventilation had lower CO₂ but occasionally higher PM_2.5 due to outdoor air exchange, while classrooms with minimal ventilation showed the opposite pattern. An analysis of PAC filter material and PM morphology indicated distinct differences between indoor and outdoor particle sources, with indoor air showing higher fiber content from clothing and carpets. This study highlights the critical role of teacher behavior in shaping IAQ, even in mechanically ventilated environments, and underscores the potential of low-cost sensors to support informed decision-making for healthier classroom environments. Full article