Search Results (20,792)

In the operational management of e-commerce platforms, online reviews and user feedback render the issue of anticipated product failure more transparent. The anticipated product failures are often negatively correlated with product quality, while related service guarantees can help customers avoid utility losses caused by such failures. Additionally, the network effect characteristics of products significantly influence customer purchasing behavior and firms’ pricing strategies. This paper employs the multinomial logit (MNL) model to establish an optimization framework for product line and value-added services pricing that accounts for the anticipated failure and associated services. It analyses three scenarios: developing a single product, homogeneous products, and heterogeneous products, deriving optimal price, market share, and maximum profit. Theoretical analysis focuses on how the optimal solutions for single and homogeneous products vary with changes in anticipated failure-induced utility losses, negative network effects, product quality, and service quality. In the numerical experiment, the study explores the effects of variations in utility losses from anticipated failure, network effects, and product and service quality on optimal solutions for heterogeneous products. Finally, the importance of incorporating anticipated failure-induced utility losses into product line and service pricing decisions is emphasized. Full article

Background: Artificial Intelligence (AI) is increasingly applied in endurance sports to optimize performance, enhance recovery, and personalize nutrition and supplementation. This review synthesizes current knowledge on AI applications in endurance sports, emphasizing implications for metabolic health, nutritional strategies, and recovery optimization, while also addressing ethical considerations and future directions. Methods: A narrative review was conducted using targeted searches of PubMed, Scopus, and Web of Science with cross-referencing. Extracted items included sport/context, data sources, AI methods including machine learning (ML), validation type (internal vs. external/field), performance metrics, comparators, and key limitations to support a structured synthesis; no formal risk-of-bias assessment or meta-analysis was undertaken due to heterogeneity. Results: AI systems effectively integrate multimodal physiological, environmental, and behavioral data to enhance metabolic health monitoring, predict recovery states, and personalize nutrition. Continuous glucose monitoring combined with AI algorithms allows precise carbohydrate management during prolonged events, improving performance outcomes. AI-driven supplementation strategies, informed by genetic polymorphisms and individual metabolic responses, have demonstrated enhanced ergogenic effectiveness. However, significant challenges persist, including measurement validity and reliability of sensor-derived signals and overall dataset quality (e.g., noise, missingness, labeling error), model performance and generalizability, algorithmic transparency, and equitable access. Furthermore, limited generalizability due to homogenous training datasets restricts widespread applicability across diverse athletic populations. Conclusions: The integration of AI in endurance sports offers substantial promise for improving performance, recovery, and nutritional strategies through personalized approaches. Realizing this potential requires addressing existing limitations in model performance and generalizability, ethical transparency, and equitable accessibility. Future research should prioritize diverse, representative, multi-site data collection across sex/gender, age, and race/ethnicity. Coverage should include performance level (elite to recreational), sport discipline, environmental conditions (e.g., heat, altitude), and device platforms (multi-vendor/multi-sensor). Equally important are rigorous external and field validation, transparent and explainable deployment with appropriate governance, and equitable access to ensure scientifically robust, ethically sound, and practically relevant AI solutions. Full article

Ceramic matrix composites (CMCs) are extensively utilized in aero engines due to their high-temperature stability; however, they are prone to environmental corrosion at high temperatures, and environmental barrier coatings (EBCs) are necessary to resist oxidation and corrosion. Among various EBC materials, AlTaO₄ offers high cost-effectiveness and low thermal expansion coefficients (TECs), but its resistance to SiO₂ erosion and high-temperature stability remain unclear. We investigated the influences of SiO₂ additions on the structures and thermal properties of AlTaO₄; and AlTaO₄ mixtures containing 10 wt.% SiO₂ were kept at 1400 °C for 30–120 h. AlTaO₄ exhibited excellent high-temperature phase stability, and SiO₂ dissolved into AlTaO₄ to generate a solid solution. XRD Rietveld refinement was employed to confirm the position of Si in the lattices, while SEM and EDS characterizations demonstrated the homogeneous distribution of Si, Al, and Ta elements. At 1200 °C, the TECs of SiO₂-AlTaO₄ (4.65 × 10⁻⁶ K⁻¹) were close to those of SiC (4.5–5.5 × 10⁻⁶ K⁻¹). Additionally, the addition of SiO₂ could reduce TECs of AlTaO₄, a feature that helped alleviate the interface thermal stress between AlTaO₄ and the Si bond coat in the EBC systems. At 900 °C, the thermal conductivity was reduced by 26.9% compared to that of AlTaO₄, and the lowest value was 1.65 W·m⁻¹·K⁻¹. Accordingly, SiO₂ will enter the lattices of AlTaO₄ after heat treatments at 1400 °C, and SiO₂ additions will reduce the thermal conductivity and TECs of AlTaO₄, which is beneficial for its EBC applications. Full article

In this article, a Nd₂Fe₁₄B_p/2024Al composite was prepared using high-energy ball milling, magnetic field cold isostatic pressing, and microwave sintering. The influence of powder particle size on microstructure and mechanical properties was discussed. The experimental results demonstrated that a ball milling duration of 10 h yielded powders with an average particle size of 5 μm, resulting in a refined and homogeneous microstructure, with a hardness value of 115 HV. Additionally, the densification process of the microwave-sintered sample was analyzed. When the sintering temperature was 490 °C, in-depth analysis was conducted on the effect of Nd₂Fe₁₄B addition on the microstructure and properties of the composite. The results showed that when the addition of Nd₂Fe₁₄B was 15 wt.%, the microstructure of the composite was uniform with fewer pores, and the Nd₂Fe₁₄B phase was evenly distributed on the matrix. At the same time, the compactness, microhardness, yield strength, and compressive strength of the composite also reached their optimal values, which were 94.3%, 136 HV, 190.5 MPa, and 248.9 MPa, respectively. When the addition of Nd₂Fe₁₄B reached 20 wt.%, the magnetic properties of the composite were slightly better than those of 15 wt.% Nd₂Fe₁₄B addition. However, based on the goal of preparing a high-magnetic and high-performance aluminum-based composite, considering the microstructure, mechanical properties, and magnetic properties comprehensively, it is believed that 15 wt.% is the optimal addition amount of Nd₂Fe₁₄B. Full article

Achieving a best-fit prosthetic socket is essential to comfort, functional performance, and long-term residual limb health in lower-limb amputees. To our knowledge, no previous study has quantitatively compared in vivo residual limb geometry under vacuum versus non-vacuum conditions using high-resolution computed tomography (CT). In this patient-specific case study of a bilateral knee-disarticulation (KD) amputee, both residual limbs were scanned under standardized conditions: one enclosed in a vacuum-compressed sleeve and the contralateral limb untreated as a natural control, thereby minimizing inter-subject variability. CT-based 3D reconstructions enabled volumetric and cross-sectional quantification, including symmetry/asymmetry analysis of paired limbs, while finite element analysis (FEA) assessed the biomechanical consequences for socket performance. Vacuum application resulted in a 4.1% reduction in total limb volume and a 5.3% reduction in mid-thigh cross-sectional area, with regionally asymmetric displacement of soft tissues. FEA demonstrated that vacuum-induced geometry reduced peak Von Mises stresses (27.43 MPa to 15.83 MPa), minimized maximum displacement (1.72 mm to 0.88 mm), and improved minimum factor of safety (~2.0 to ~3.0), while homogenizing contact pressure distribution (peak fell from 2.42 to 1.28 N/mm²). These findings provide preliminary CT-based evidence that vacuum application induces measurable morphological adaptations with implications for socket conformity, comfort, and load transfer. While limited to a single patient, this study highlights the potential of vacuum-induced modeling to inform personalized prosthetic socket design. Full article

Large multiple datasets were simulated through sampling, and regression modeling results were compared with known parameters—an analysis undertaken here for the first time on such a scale. The study demonstrates that the impact of multicollinearity on the quality of parameter estimates is far stronger than commonly assumed, even at low or moderate correlations between predictors. The standard practice of assessing the significance of regression coefficients using t-statistics is compared with the actual precision of estimates relative to their true values, and the results are critically examined. It is shown that t-statistics for regression parameters can often be misleading. Two novel approaches for selecting the most effective variables are proposed: one based on the so-called reference matrix and the other on efficiency indicators. A combined use of these methods, together with the analysis of each variable’s contribution to determination, is recommended. The practical value of these approaches is confirmed through extensive testing on both simulated homogeneous and heterogeneous datasets, as well as on a real-world example. The results contribute to a more accurate understanding of regression properties, model quality characteristics, and effective strategies for identifying the most reliable predictors. They provide practitioners with better analytical tools. Full article

We present a hybrid partial differential equation-agent-based model (PDE-ABM). In our framework, tumor cells secrete tumor angiogenic factor (TAF), while endothelial cells chemotactically migrate and branch in response. Reaction–diffusion PDEs for TAF, oxygen, and cytotoxic drug are coupled to discrete stochastic dynamics of tumor cells and endothelial tip cells, ensuring multiscale integration. Motivated by observed perfusion heterogeneity in tumors and its pharmacokinetic consequences, we conduct a linear stability analysis for a reduced endothelial–TAF reaction–diffusion subsystem and derive an explicit finite-domain threshold for Turing instability. We demonstrate that bidirectional coupling, where endothelial cells both chemotactically migrate along TAF gradients and secrete TAF, is necessary and sufficient to generate spatially periodic vascular clusters and inter-cluster hypoxic regions. These emergent patterns produce heterogeneous drug penetration and resistant niches. Our results identify TAF clearance, chemotactic sensitivity, and endothelial motility as effective levers to homogenize perfusion. The model is two-dimensional and employs simplified kinetics, and we outline necessary extensions to three dimensions and saturable kinetics required for quantitative calibration. The study links reaction–diffusion mechanisms with clinical principles and suggests actionable strategies to mitigate resistance by targeting endothelial–TAF feedback. Full article

This study leverages nanoemulsion technology to engineer a novel liquid formulation combining Eicosapentaenoic acid (EPA) and Nattokinase (NK), aiming to enhance their application potential in functional foods. Both EPA and NK are well recognized for their pronounced anti-thrombotic, anti-inflammatory, and lipid-lowering properties, which are critical for the prevention and management of cardiovascular diseases. However, their practical application in functional foods is hampered by inadequate gastrointestinal stability and suboptimal bioavailability. Here, an EPA-NK nanoemulsion was fabricated using high-pressure homogenization technology. We systematically evaluated its environmental stability, anti-thrombotic activity, and intervention efficacy against carrageenan-induced black-tail thrombosis. The results demonstrated that the nanoemulsion not only enhanced the potential for oral bioavailability based on in vitro stability and preliminary in vivo efficacy trends of EPA and NK but also notably potentiated their synergistic anti-thrombotic efficacy, thereby providing robust theoretical and technical support for the development of next-generation health-promoting functional foods targeting thrombotic disorders. Full article

The European mink Mustela lutreola is one of the most threatened carnivores in Europe, having suffered dramatic range contractions and severe population fragmentation. Accurate knowledge of its genetic diversity is crucial for conservation planning, yet earlier studies based on partial mitochondrial markers offered limited resolution and often underestimated haplotype richness. In this study, complete mitochondrial genomes from four extant populations (Russia, n = 11; Romania, n = 16; Germany, n = 24; France–Spain, n = 15) were analysed using a suite of non-parametric and asymptotic estimators (Fisher’s α, ACE, Jackknife1, Bootstrap, Chao1-based iNEXT) together with negative binomial modelling. A total of 41 haplotypes were detected, but extrapolated estimates indicated substantially higher richness, particularly in populations dominated by singletons. Rarefaction and extrapolation analyses revealed that sample sizes of 70–130 individuals per population are needed to approach complete haplotype detection. The France–Spain and Romania populations harboured the highest predicted richness, whereas Germany and Russia, both represented by ex situ stocks, showed lower diversity. These results refine earlier assumptions of extreme homogeneity in the Western population and demonstrate that significant mitochondrial variation persists at the continental scale. The study provides quantitative benchmarks for sampling design and genetic management, supporting preservation of evolutionary potential in this critically endangered species. Full article

Cold-climate cities remain poorly studied, yet their spontaneous flora is strongly shaped by severe winters and short growing seasons. Heilongjiang Province, the northernmost region of China, provides a valuable case study given its rapid urbanization. As an important component of urban biodiversity, the diversity distribution patterns of spontaneous plants and their underlying causes remain underexplored from multi-scale and multi-dimensional perspectives. Therefore, this study aimed to test how climatic subzones and habitat types jointly influence spontaneous plant diversity across urban landscapes in 14 cities of Heilongjiang Province. Based on vegetation surveys, we applied calculations of α- and β-diversity, along with hierarchical clustering, across climatic subzones, cities, and habitat types to elucidate the diversity patterns and their multi-scale driving mechanisms. The results showed the following: (1) A total of 778 spontaneous plant species were recorded, belonging to 98 families and 395 genera. Native plants accounted for 58.7%, and non-native plants accounted for 41.3% (including 77 invasive species). (2) Perennial herbs dominated overall (45.2%), consistent with winter filtering, whereas annual/biennial herbs were more common in warmer subzones such as II B2. (3) Forest gaps (FG) and shrub–grassland gaps (SG) supported the most diverse spontaneous plant communities, highlighting habitat heterogeneity. (4) Species richness peaked in subzone II B2 and was lowest in subzone I A1, while abandoned land (SA) and shrub–grassland gaps (SG) supported the richest communities. (5) β-diversity analyses indicated homogenization under extreme cold in subzone I A1 and greater turnover in warmer subzone II B2, reflecting contrasting climatic filters. The “light patches” in FG habitats and the “disturbance filtering” in LA habitats further shaped the differences in local communities. This study reveals the diversity distribution patterns and adaptation strategies of spontaneous plants in cold cities, emphasizing their integration into urban planning while addressing the dominance of invasive species. Full article

In Kähler geometry, Calabi extremal metrics serves as a class of more available special metrics than Kähler metrics with constant scalar curvatures, as a generalization of Kähler Einstein metrics. In recent years, Maxwell–Einstein metrics (or conformally Kähler Einstein–Maxwell metrics) appeared as another alternative choice for Calabi extremal metrics. It turns out that some similar metrics defined by Futaki and Ono have similar roles in the Kähler geometry. In this paper, we prove that for some completions of certain line bundles, there is at least one k-generalized Maxwell–Einstein metric defined by Futaki and Ono conformally related to a metric in any given Kähler class for any integer $3\le k\le 13$. Full article

Hematite nanoparticles ($\mathrm{\alpha }-{\mathrm{Fe}}_2{\mathrm{O}}_3 \mathrm{NPs}$) were synthesized through a thermal conversion of synthetic and biosynthesized nanomaghemite ($\mathrm{\gamma }-{\mathrm{Fe}}_2{\mathrm{O}}_3 \mathrm{NPs}$) precursors. X-ray diffraction data confirmed phase-pure hematite with crystallite sizes of 54 and 56 nm for the H1 and H2 samples, respectively. Transmission electron microscopy (TEM) revealed a bimodal-like distribution feature (peaks at 18.5 and 35.5 nm) for the H1 sample, while the histogram plot of the H2 sample displayed a homogeneous particle size distribution with a mean size of 28 nm. X-ray photoelectron spectroscopy confirmed Fe³⁺ ions as the dominant oxidation state in both samples. In addition, while ⁵⁷Mössbauer spectroscopy indicated relaxation effects and line broadening for the H1 sample at both 300 K and 16 K, consistent with incomplete $\mathrm{\gamma }\to \mathrm{\alpha }$ transformation, the H2 sample exhibited spectra at the same temperatures resembling a bulk-like hematite. Magnetometry supported these findings since the H1 sample showed enhanced coercivity (2.2 kOe) and remanence (0.23 emu/g), features attributed to a residual ferrimagnetic contribution of $\mathrm{\gamma }-{\mathrm{Fe}}_2{\mathrm{O}}_3 \mathrm{NPs}$, and the H2 sample exhibited weaker ferromagnetism, as typically found in nanoscale hematite. These results highlight the synergistic use of X-ray photoelectron and Mössbauer spectroscopies, and magnetic measurements to reveal subtle multiphase coexistence, demonstrating that precursor chemistry and biosynthetic functionalization decisively govern the structural and magnetic evolution of $\mathrm{\gamma }\to \mathrm{\alpha }-{\mathrm{Fe}}_2{\mathrm{O}}_3 \mathrm{NPs}$. Full article

Cross-linked polymers are indispensable in advanced applications, but suffer from poor recyclability due to permanent covalent networks. Herein, we report recyclable supramolecular polyurea elastomers that integrate ureidopyrimidinone-based quadruple hydrogen-bonding motifs directly into the polymer backbone. The dynamic and reversible nature of these motifs imparts the SPUEs with remarkable malleability and reprocessability while preserving the robustness of conventional polyureas. The SPUEs display remarkable mechanical robustness, solvent resistance, and facile reprocessability through hot-pressing, producing homogeneous films with minimal performance loss. Impressively, tensile strength, elongation at break, and toughness retained high recovery after reprocessing, demonstrating excellent closed-loop mechanical recyclability. This work showcases supramolecular engineering as a powerful strategy to reconcile mechanical robustness with recyclability in cross-linked polymers, offering new opportunities for sustainable thermosets and elastomers in circular materials design. Full article

The increasing complexity of urban traffic networks has highlighted the potential of Multi-Agent Reinforcement Learning (MARL) for Traffic Signal Control (TSC). However, most existing MARL methods assume homogeneous observation and action spaces among agents, ignoring the inherent heterogeneity of real-world intersections in topology and signal phasing, which limits their practical applicability. To address this gap, we propose HAPS-PPO (Heterogeneity-Aware Policy Sharing Proximal Policy Optimization), a novel MARL framework for coordinated signal control in heterogeneous road networks. HAPS-PPO integrates two key mechanisms: an Observation Padding Wrapper (OPW) that standardizes varying observation dimensions, and a Dynamic Multi-Strategy Grouping Learning (DMSGL) mechanism that trains dedicated policy heads for agent groups with distinct action spaces, enabling adequate knowledge sharing while maintaining structural correctness. Comprehensive experiments in a high-fidelity simulation environment based on a real-world road network demonstrate that HAPS-PPO significantly outperforms Fixed-time control and mainstream MARL baselines (e.g., MADQN, FMA2C), reducing average delay time by up to 44.74% and average waiting time by 59.60%. This work provides a scalable and plug-and-play solution for deploying MARL in realistic, heterogeneous traffic networks. Full article

Single-image dehazing of high-voltage power transmission lines (HPTLs) using deep learning methods confronts two critical challenges: the non-homogeneous haze distribution in HPTL images and the unavailability of paired clear images for supervised training. To overcome these issues, this paper proposes a novel dehaze neural network, named FIF-RSCT-Net, that employs a hybrid supervised-to-unsupervised iterative learning approach according to the characteristic of HPTL single images. The FIF-RSCT-Net incorporates the Spatial–Channel Feature Intersection modules and Residual Separable Convolution Transformers to enhance the feature representation capability. Crucially, this novel architecture could learn more generalized dehazing knowledge that can be transferred from the original image domain to HPTL scenarios. In the dehazing knowledge transformation, an unsupervised iterative learning mechanism based on the Line Segment Detector is designed to optimize the restoration of power transmission lines. The effectiveness of FIF-RSCT-Net on the original image domain is demonstrated in the comparative experiments of the I-Haze, O-Haze, NH-Haze, and SOTS datasets. Our methodology achieves the best average PSNR of 24.647 dB and SSIM of 0.8512. And the qualitative evaluation of unsupervised iterative learning results shows that the missed line segments are exhibited during progressive training iterations. Full article