Search Results (28)

Additive manufacturing (AM) is increasingly used in aerospace applications; however, most machine-learning (ML) models remain alloy-specific and require retraining when applied to new materials. To address this limitation, this study proposes a Physics-Regularized Transfer Learning (Physics-TL) framework for cross-alloy prediction in additive manufacturing. A neural network was first trained using Ti-6Al-4V as a source alloy and subsequently adapted to AlSi10Mg and 316L stainless steel using approximately 40 samples per alloy. Physics-based descriptors, including volumetric energy density and thermal diffusivity, were incorporated through a regularized loss function to improve physical consistency and data efficiency. The proposed framework was compared with a baseline neural network trained without transfer learning or physics-based constraints. Across repeated randomized train–test evaluations, the Physics-TL model achieved lower prediction errors, improved training stability, and better agreement with physically meaningful process–property relationships. Additional dataset sensitivity, ablation, and training–validation analyses provided supporting evidence of stable learning behaviour under limited-data conditions. Although the study is limited by dataset size and should be regarded as a proof-of-concept investigation, the results demonstrate the feasibility of combining transfer learning with physics-guided learning to support cross-alloy knowledge transfer in additive manufacturing. The proposed framework offers a promising pathway toward more data-efficient and physically informed predictive modelling for aerospace material qualification, process optimization, and future multi-material manufacturing systems. Full article

High-pressure hydrogen exposure may induce transport and diffusion–relaxation–controlled changes in elastomeric sealing materials that differ from conventional fluid aging. Hydrogen uptake through solution–diffusion processes can lead to swelling, redistribution of molecular mobility, viscoelastic evolution, and, under certain conditions, cavitation or microvoid formation during decompression, which may affect long-term sealing performance. This review compiles experimental results for commonly used elastomers, including Nitrile Butadiene Rubber (NBR), hydrogenated nitrile butadiene rubber (HNBR), Fluoroelastomer (FKM), Ethylene Propylene Diene Monomer (EPDM), and silicone, and summarizes reported ranges of hydrogen diffusivity, solubility, and permeability under high-pressure conditions. These transport characteristics are compared with mechanical and microstructural observations obtained from Dynamic Mechanical Analysis (DMA), Nuclear Magnetic Resonance (NMR), decompression testing, and micro-computed tomography (µXCT) imaging. Available evidence suggests that hydrogen-induced changes are predominantly governed by physical processes, including swelling, plasticization-like mobility changes, and constraint redistribution, while extensive chemical degradation of the polymer backbone is generally limited under clean hydrogen conditions. Materials with similar conventional mechanical properties may, therefore, exhibit different hydrogen uptake, viscoelastic response, and resistance to decompression damage. Conventional single-point mechanical tests, such as tensile measurements, may not fully capture the time-dependent viscoelastic evolution relevant to sealing performance. This work proposes a multiscale characterization framework integrating transport, viscoelastic, molecular, and microstructural analysis for more reliable evaluation of elastomers in hydrogen service, supporting improved qualification strategies for high-pressure hydrogen systems. Full article

Raman Distributed Temperature Sensing (DTS) provides spatially distributed temperature measurements along optical fibers and is increasingly used for monitoring large-scale infrastructures and experimental facilities, enabling three-dimensional reconstruction of temperature fields. However, such measurements involve specific implementation constraints and may be affected by significant errors, with uncertainties influenced by factors such as calibration, environmental conditions, spatial resolution effects, and fiber positioning. Ensuring the metrological validity of Raman-based DTS measurements therefore requires a rigorous quantification of the associated measurement uncertainties. In this work, a complete uncertainty analysis of Raman-based DTS measurements is performed following the principles of the Guide to the Expression of Uncertainty in Measurement (GUM). A measurement model describing the relationship between Raman backscattered signals and temperature is established, and all relevant uncertainty sources are identified and quantified. The methodology is applied to a full-scale experimental facility equipped with a DTS interrogator and a dedicated calibration setup. Uncertainty propagation is performed using both first-order Taylor series expansion and Monte Carlo simulation, providing consistent results. The analysis shows that calibration uncertainty, spatial dispersion of the temperature field and fiber positioning within the reconstructed temperature field represent the dominant contributions to the combined uncertainty. The proposed approach provides a rigorous framework for the metrological qualification of Raman DTS systems and offers practical guidance for improving measurement reliability in distributed temperature monitoring applications. Full article

We study Maximum Entropy density estimation on continuous domains under finitely many moment constraints, formulated as the minimization of the Kullback–Leibler divergence with respect to a reference measure. To model uncertainty in empirical moments, constraints are relaxed through convex penalty functions, leading to an infinite-dimensional convex optimization problem over probability densities. The main contribution of this work is a rigorous convex-analytic treatment of such relaxed Maximum Entropy problems in a functional setting, without discretization or smoothness assumptions on the density. Using convex integral functionals and an extension of Fenchel duality, we show that, under mild and explicit qualification conditions, the infinite-dimensional primal problem admits a dual formulation involving only finitely many variables. This reduction can be interpreted as a continuous-domain instance of partially finite convex programming. The resulting dual problem yields explicit primal–dual optimality conditions and characterizes Maximum Entropy solutions in exponential form. The proposed framework unifies exact and relaxed moment constraints, including box and quadratic relaxations, within a single variational formulation, and provides a mathematically sound foundation for relaxed Maximum Entropy methods previously studied mainly in finite or discrete settings. A brief numerical illustration demonstrates the practical tractability of the approach. Full article

Protective cultures are an increasingly industrially relevant biopreservation tool for meat and meat products, responding to simultaneous demands for microbiological safety, extended shelf life, and reduced reliance on synthetic preservatives within clean-label frameworks. This review summarizes current advances in protective cultures applied to meat systems, with emphasis on technological functions, efficacy boundaries, and safety-related due diligence. We discuss the dominant inhibitory mechanisms of lactic acid bacteria and related protective taxa—acidification, competitive exclusion, and antimicrobial metabolites (including bacteriocins)—and highlight why performance is strongly strain- and matrix-dependent under realistic storage conditions. Practical applications are reviewed across raw meats (spoilage delay under refrigeration and vacuum/MAP) and processed or ready-to-eat products, where post-processing surface application emerges as a critical control point for limiting Listeria monocytogenes outgrowth during chilled storage. Key implementation constraints include technological compatibility and sensory neutrality, which are influenced by product buffering capacity, salt content, available fermentable substrates, packaging atmosphere, and temperature. From a safety perspective, we synthesize evidence on antimicrobial resistance in food-associated cultures and outline contemporary qualification strategies combining phenotypic susceptibility testing with genome-based screening to exclude acquired and potentially transferable resistance determinants. Overall, protective cultures should be viewed as a targeted hurdle integrated into holistic preservation systems rather than a standalone substitute for hygiene and process control. Full article

Nonsmooth multiobjective mathematical programming problems with equilibrium constraints (NMMPEC) are studied in this article in the Hadamard manifold setting. In the context of $\left(\mathrm{NMMPEC}\right)$, the generalized Guignard constraint qualification (GGCQ) is formulated within the framework of Hadamard manifolds. Moreover, Karush–Kuhn–Tucker (KKT)-type necessary optimality conditions are derived for $\left(\mathrm{NMMPEC}\right)$. Thereafter, we explore constraint qualifications (CQ) tailored to $\left(\mathrm{NMMPEC}\right)$ in the Hadamard manifold setting. Interrelations between these constraint qualifications are subsequently derived. It is further demonstrated that the proposed constraint qualifications, when satisfied, ensure that GGCQ holds. It is noteworthy that constraint qualifications and optimality conditions for $\left(\mathrm{NMMPEC}\right)$ have not been investigated in the Hadamard manifold setting. Full article

The purpose of this research is to develop approximate weak and strong stationary conditions for interval-valued multiobjective optimization problems with vanishing constraints (IVMOPVC) involving nonsmooth functions. In many real-world situations, the exact values of objectives are uncertain or imprecise; hence, interval-valued formulations are used to model such uncertainty more effectively. The proposed approximate weak and strong stationarity conditions provide a robust framework for deriving meaningful optimality results even when the usual constraint and data qualifications fail. We first introduce approximate variants of these qualifications and establish their relationships. Secondly, we establish some approximate KKT type necessary optimality conditions in terms of approximate weak strongly stationary points and approximate strong strongly stationary points to identify type-2 $\mathcal{E}$-quasi weakly Pareto and type-1 $\mathcal{E}$-quasi Pareto solutions of the IVMOPVC. Lastly, we show that the approximate weak and strong strongly stationary conditions are sufficient for optimality under some approximate convexity assumptions. All the outcomes are well illustrated by examples. Full article

The rolling system for stainless steel, particularly in the production of diamond plates, represents a complex industrial control scenario. The process requires precise load distribution to effectively manage pattern height, due to the high strength, hardness, and required dimensional accuracy of the material. This paper addresses the limitations of offline methods, which include heavy reliance on initial conditions, intricate parameter settings, susceptibility to local optima, and suboptimal performance under stringent constraints. A Multi-Objective Adaptive Rolling Iteration method that incorporates local constraints (MOARI-LC) is proposed. The MOARI-LC method simplifies the complex multi-dimensional nonlinear constrained optimization problem of load distribution, into a one-dimensional multi-stage optimization problem without explicit constraints. This simplification is achieved through a single variable cycle iteration involving reduction rate and rolling equipment selection. The rolling results of HBD-SUS304 show that the pattern height to thickness ratio obtained by MOARI-LC is 0.20–0.22, which is within a specific range of dimensional accuracy. It outperforms the other two existing methods, FCRA-NC and DCRA-GC, with results of 0.19~0.24 and 0.15~0.25, respectively. MOARI-LC has increased the qualification rate of test products by more than 25%, and it has also been applied to the other six industrial production experiments. The results show that MOARI-LC can control the absolute value of the rolling force prediction error of the downstream stands of the hot strip finishing rolls within 5%, and the absolute value of the finished stand within 3%. These results validate the scalability and accuracy of MOARI-LC. Full article

This article investigates robust optimality conditions and duality results for a class of nonsmooth multiobjective programming problems with vanishing constraints under data uncertainty (UNMPVC). Mathematical programming problems with vanishing constraints constitute a distinctive class of constrained optimization problems because of the presence of complementarity constraints. Moreover, uncertainties are inherent in various real-life problems. The aim of this article is to identify an optimal solution to an uncertain optimization problem with vanishing constraints that remains feasible in every possible future scenario. Stationary conditions are necessary conditions for optimality in mathematical programming problems with vanishing constraints. These conditions can be derived under various constraint qualifications. Employing the properties of convexificators, we introduce generalized standard Abadie constraint qualification (GS-ACQ) for the considered problem, UNMPVC. We introduce a generalized robust version of nonsmooth stationary conditions, namely a weakly stationary point, a Mordukhovich stationary point, and a strong stationary point (RS-stationary) for UNMPVC. By employing GS-ACQ, we establish the necessary conditions for a local weak Pareto solution of UNMPVC. Moreover, under generalized convexity assumptions, we derive sufficient optimality criteria for UNMPVC. Furthermore, we formulate the Wolfe-type and Mond–Weir-type robust dual models corresponding to the primal problem, UNMPVC. Full article

In this article, we investigate a class of non-smooth semidefinite multiobjective programming problems with inequality and equality constraints (in short, NSMPP). We establish the convex separation theorem for the space of symmetric matrices. Employing the properties of the convexificators, we establish Fritz John (in short, FJ)-type necessary optimality conditions for NSMPP. Subsequently, we introduce a generalized version of Abadie constraint qualification (in short, NSMPP-ACQ) for the considered problem, NSMPP. Employing NSMPP-ACQ, we establish strong Karush-Kuhn-Tucker (in short, KKT)-type necessary optimality conditions for NSMPP. Moreover, we establish sufficient optimality conditions for NSMPP under generalized convexity assumptions. In addition to this, we introduce the generalized versions of various other constraint qualifications, namely Kuhn-Tucker constraint qualification (in short, NSMPP-KTCQ), Zangwill constraint qualification (in short, NSMPP-ZCQ), basic constraint qualification (in short, NSMPP-BCQ), and Mangasarian-Fromovitz constraint qualification (in short, NSMPP-MFCQ), for the considered problem NSMPP and derive the interrelationships among them. Several illustrative examples are furnished to demonstrate the significance of the established results. Full article

In this paper, we investigate constraint qualifications and optimality conditions for multiobjective mathematical programming problems with vanishing constraints (MOMPVC) on Hadamard manifolds. The MOMPVC-tailored generalized Guignard constraint qualification (MOMPVC-GGCQ) for MOMPVC is introduced in the setting of Hadamard manifolds. By employing MOMPVC-GGCQ and the intrinsic properties of Hadamard manifolds, we establish Karush–Kuhn–Tucker (KKT)-type necessary Pareto efficiency criteria for MOMPVC. Moreover, we introduce several MOMPVC-tailored constraint qualifications and develop interrelations among them. In particular, we establish that the MOMPVC-tailored constraint qualifications introduced in this paper turn out to be sufficient conditions for MOMPVC-GGCQ. Suitable illustrative examples are furnished in the framework of well-known Hadamard manifolds to validate and demonstrate the importance and significance of the derived results. To the best of our knowledge, this is the first time that constraint qualifications, their interrelations, and optimality criteria for MOMPVC have been explored in the framework of Hadamard manifolds. Full article

This article deals with a class of nonsmooth interval-valued multiobjective semi-infinite programming problems with vanishing constraints (NIMSIPVC). We introduce the VC-Abadie constraint qualification (VC-ACQ) for NIMSIPVC and employ it to establish Karush–Kuhn–Tucker (KKT)-type necessary optimality conditions for the considered problem. Regarding NIMSIPVC, we formulate interval-valued weak vector, as well as interval-valued vector Lagrange-type dual and scalarized Lagrange-type dual problems. Subsequently, we establish the weak, strong, and converse duality results relating the primal problem NIMSIPVC and the corresponding dual problems. Moreover, we introduce the notion of saddle points for the interval-valued vector Lagrangian and scalarized Lagrangian of NIMSIPVC. Furthermore, we derive the saddle-point optimality criteria for NIMSIPVC by establishing the relationships between the solutions of NIMSIPVC and the saddle points of the corresponding Lagrangians of NIMSIPVC, under convexity assumptions. Non-trivial illustrative examples are provided to demonstrate the validity of the established results. The results presented in this paper extend the corresponding results derived in the existing literature from smooth to nonsmooth optimization problems, and we further generalize them for interval-valued multiobjective semi-infinite programming problems with vanishing constraints. Full article

This article deals with mathematical programs with vanishing constraints (MPVCs) involving lower semi-continuous functions. We introduce generalized Abadie constraint qualification (ACQ) and MPVC-ACQ in terms of directional convexificators and derive necessary KKT-type optimality conditions. We also derive sufficient conditions for global optimality for the MPVC under convexity utilizing directional convexificators. Further, we introduce a Wolfe-type dual model in terms of directional convexificators and derive duality results. The results are well illustrated by examples. Full article

This study focuses on migrant women and their civic participation in civil society organizations and/or immigrant associations. Despite women’s migration having a long global history and being of academic interest, extensive knowledge of this situation has increased substantially in recent decades; research on the civic participation of immigrant women in Portugal is still incipient. The structural conditions affecting these women’s mobility processes remain overlooked, concealing their vulnerabilities. Additionally, success stories of migrant women, which could serve as inspirations for others, are often invisible. This exploratory research examines the role of female immigrant leaders and the demands they face in facilitating immigrants’ integration into Portuguese society. Eight qualitative interviews were conducted with diverse immigrant organizations in Portugal, advocating for immigrant rights and promoting integration through various strategies. The results reveal that migrant women’s experiences and participation in leadership roles are shaped not only by their migrant background and their qualifications but also by the difficulties they encountered upon arrival in Portugal. These leaders tend to focus on constraints, particularly regarding the organization’s sustainability, rather than emphasizing opportunities for civic participation. Nevertheless, this study also reveals that participation in IOs leads to increased autonomy and a heightened sense of empowerment for these women. It grants them a voice, visibility, and recognition both in the host society and their own communities. Overall, the study sheds light on the significance of recognizing immigrant women’s contributions and challenges, as well as the crucial role played by immigrant organizations in promoting integration and advocating for immigrants’ rights in Portugal. It also emphasizes the need for the government to financially support these organizations. Full article

This article is focused on the investigation of Mond–Weir-type robust duality for a class of semi-infinite multi-objective fractional optimization with uncertainty in the constraint functions. We first establish a Mond–Weir-type robust dual problem for this fractional optimization problem. Then, by combining a new robust-type subdifferential constraint qualification condition and a generalized convex-inclusion assumption, we present robust $\epsilon$-quasi-weak and strong duality properties between this uncertain fractional optimization and its uncertain Mond–Weir-type robust dual problem. Moreover, we also investigate robust $\epsilon$-quasi converse-like duality properties between them. Full article