Hanoi University of Industry

Seafood spoilage produces volatile basic compounds that can be detected from package headspace, but practical colorimetric freshness labels must remain stable and quantitative under the high-humidity conditions typical of seafood packaging. The purpose of this study was to develop a humidity-tolerant headspace freshness indicator and to evaluate whether its color response could be quantitatively translated into seafood shelf-life decisions. Pitaya-peel betacyanin was immobilized in a pectin–chitosan polyelectrolyte complex film to improve pigment retention, reduce humidity-induced baseline drift, and maintain sensitivity to volatile bases. The resulting label showed a monotonic color response to NH3 over the tested concentration range and retained a strong signal under near-saturated relative humidity. Compared with single-polymer films, the pectin–chitosan matrix reduced pigment leaching and improved color stability in wet headspace conditions. A two-step calibration workflow was established by converting color difference, ΔE, first to predicted headspace NH3 and then to total volatile basic nitrogen, TVB-N. In packaged whiteleg shrimp stored under refrigerated and retail-mimic temperature-abuse conditions, the label response closely followed the increases in TVB-N, total viable count, and pH during spoilage. Across 45 package–time points, TVB-N prediction achieved RMSE values of 2.13–2.24 mg N/100 g and R2 values of 0.947–0.960. Freshness classification into Fresh, Warning, and Spoiled categories reached an overall accuracy of 0.867 with Cohen's κ = 0.792. These results indicate that the PEC–betacyanin label can provide a humidity-resilient and quantitative tool for non-destructive seafood freshness monitoring and shelf-life decision support.

Polymethyl methacrylate (PMMA) is widely used in optical systems due to its high transparency and stable refractive properties. However, achieving deterministic nanometer-scale finishing on complex three-dimensional geometries, particularly hemispherical components, remains a major challenge. Conventional magnetorheological finishing (MRF) systems are primarily designed for planar or simple curved surfaces and lack effective magnetic field concentration and uniformity within confined hemispherical cavities, resulting in limited polishing stability and controllability. To address this limitation, a mechanism-driven hemispherical Halbach-array-based MRF system is proposed, in which magnetic field topology is engineered to regulate the rheological behavior of the polishing fluid and the associated material removal mechanism. By optimizing magnet geometry and spatial arrangement while keeping magnet material and quantity constant, a spherical Halbach configuration was developed, achieving a peak magnetic flux density of 1.97 T with enhanced inward field concentration and gradient stability. This strengthened magnetic confinement improves polishing ribbon stiffness and removal uniformity. A coupled magnetic-rheological-mechanical framework was established to clarify the relationship between magnetic field intensity, shear stress evolution, and material removal modes under varying inlet pressure, fluid velocity, rotational speed, and cutting depth. Finite element simulations and experimental measurements show good agreement in magnetic field distribution and removal behavior. The optimized system achieved uniform material removal and a consistent nanometer-scale surface finish (Ra = 1 nm) across the hemispherical PMMA surface. This field-engineered Halbach-MRF strategy extends deterministic magnetorheological polishing to complex three-dimensional polymer optics and provides a new approach for precision manufacturing of advanced optical components.

The integration of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is a pivotal enabler for ubiquitous connectivity in 6G networks. Multiple UAVs have been proposed to serve multiple terrestrial users in a small-cell mobile system. However, optimizing multi-UAV 3D placement to maximize the multi-user system throughput remains a challenging non-convex problem. Existing meta-heuristic algorithms like particle swarm optimization (PSO) offer high precision but suffer from prohibitive computational latency. Conversely, deep learning approaches promise rapid inference but often fail to achieve precise coordinate localization. To bridge this gap, this paper proposes a novel hybrid AI-driven framework combining an adaptive multi-start PSO (AMS-PSO) for high-fidelity data generation and a hybrid U-Net with local refinement for real-time placement. Extensive simulations reveal that the performance of the proposed framework depends on the UAV-to-User density ratio. In scenarios with adequate resources, the hybrid U-Net achieves near-optimal performance, closely matching the AMS-PSO benchmark and significantly outperforming standard heuristics. Even in resource-constrained scenarios, it maintains competitive performance. Furthermore, at high UAV densities, where optimization gains saturate, our method retains a critical advantage in computational speed. Overall, the proposed framework reduces inference time by orders of magnitude compared to iterative heuristics, making it highly viable for dynamic, real-time network orchestration.

We present a self-consistent analysis of the fluctuation-induced shift of the superconducting critical temperature in layered superconductors within the time-dependent Ginzburg–Landau Lawrence–Doniach framework. Using the self-consistent Gaussian approximation, we derive explicit analytical expressions for the shift of the superconducting critical temperature that incorporate the contributions of order parameter fluctuations. Explicit results for two-dimensional and three-dimensional superconductor are also given. We reveal a fundamental dimensional crossover: while the Ginzburg–Levanyuk number Gi, which characterizes the width of the fluctuation-dominated critical region, alone governs the suppression of the critical temperature in three-dimensional (3D) superconductors, the suppression in two-dimensional (2D) and layered superconductors depends additionally on the material’s geometry, namely the layer thickness and interplane spacing. Physically, a reduction in interplane spacing or an increasing in layer thickness suppresses superconducting fluctuations, which in turn diminishes the suppression of the transition temperature. Our theoretical results are consistent with thermodynamic analysis and formulated using experimentally measurable parameters, offering a systematic approach for analyzing fluctuation phenomena in highly anisotropic superconductors and artificially layered materials.

Participation in global value chains (GVCs) is considered a vital pathway for developing countries to achieve sustainable development, yet the mechanisms ensuring this integration is both deep and sustainable remain underexplored. While many studies emphasize innovation’s role in firm performance, its specific impact on facilitating sustainable GVC participation lacks sufficient evidence, especially in contexts like Vietnam – a rapidly integrating economy. This study addresses this gap by investigating the impact of innovation on firms’ participation in GVCs. We analyze this proposition by using panel data from the World Bank Enterprise Surveys (2005–2023) and a probit model. Our key findings show that both product and process innovations strongly contribute to firm participation in GVCs. The results are robust when considering alternative measurements and different specifications. In addition, the results are heterogeneous across firm size and ownership structure. The value of this study for sustainable development is clear that innovation is the engine allowing firms from developing nations to not just join GVCs, but to potentially move up the value chain. Therefore, policies that foster innovation are fundamental development strategies, essential for transforming GVC integration into a true driver of inclusive growth and long-term sustainability.