We prove a sharp inequality between the Alexander-Taylor capacity and the functional capacity in a complex Sobolev space on a compact Kähler manifold. The latter space and capacity were introduced by Dinh, Sibony and Vigny.
Salim Davlatov, Alisher Zayniyev, Javohir Zokirov
et al.
Within Industry 4.0 manufacturing environments, Structural Health Monitoring (SHM) is recognized as mission-critical; nevertheless, extant Digital Twin (DT) implementations seldom achieve deep fusion with the production layer and consequently struggle to co-optimize structural integrity alongside operational efficiency. This paper therefore introduces, and subsequently validates, an integrated DT framework expressly conceived to close that lacuna. Four objectives guided the inquiry: first, to architect a distributed digital-twin topology underpinned by edge–cloud analytics capable of real-time SHM; second, to operationalize a machine-learning-driven predictive-maintenance regime that causally couples structural response data with both manufacturing process signatures and ambient environmental variables; third, to embed the resultant framework within incumbent MES/ERP ecosystems spanning multiple production facilities; and fourth, to quantify the concomitant reductions in maintenance expenditure, production downtime, and energy utilization. A longitudinal, 24-month, multi-site investigation furnished empirical corroboration. The framework couples a high-fidelity DT to legacy MES/ERP strata through a distributed edge-cloud fabric; an ensemble of machine-learning algorithms—Long Short-Term Memory networks prominent among them—was deployed for predictive anomaly detection. The system attained 96 % anomaly-detection accuracy (F1-score: 0.95) and translated this diagnostic precision into demonstrable operational gains: maintenance costs fell by 42.1 %, downtime by 31.1 %, and energy intensity by 23.2 % (p < 0.001). The edge-centric architecture reduced processing latency by 67 %, thereby enabling sub-50 ms integration with MES/ERP layers, while inter-site model transfer achieved 94.0 % adaptation efficacy. These findings substantiate the contention that principled integration of DTs with Industry 4.0 paradigms furnishes a transformative yet pragmatic pathway for manufacturing-oriented SHM. The framework’s verified capacity to enhance prognostic fidelity while simultaneously yielding sizeable operational dividends delineates a clear trajectory toward more resilient and resource-efficient industrial assets.
Additive manufacturing (AM) is revolutionizing the aerospace, transportation, energy, and biomedical fields due to its capacity for rapid production of geometrically complex components. The advancement of alloys for AM is critical to further boost those applications. The first-generation alloys for AM largely rely on conventional commercial alloys that were originally developed under the assumption of near-equilibrium solidification. However, some of these materials are incompatible with the non-equilibrium metallurgical behavior of AM, which may face issues like high crack susceptibility. Thus, the second-generation alloy design for AM builds upon conventional alloy systems by inoculation treatments with alloying elements (e.g., zirconium) or ceramic reinforcements (e.g., titanium carbide) to achieve better material printability and properties. Meanwhile, the limitations of commercial materials underscore the need for developing novel alloys specifically tailored for AM. The third-generation empirical approach for material design adopts a knowledge-driven strategy by leveraging established metallurgical principles and empirical correlations to guide targeted composition optimization. Such trial-and-error strategies for discovering new materials face substantial bottlenecks like long development cycles and high costs. Hence, it has propelled research toward the fourth-generation paradigm—data-driven artificial intelligence (AI) assisted materials design for AM, which is an effective and innovative material solution guided by AM-specific metallurgical features. Advances in AI and robotics will shift the future paradigm of AM-specific alloy design toward an autonomous AI-Lab, which leverages an intelligent AM agent and automated high-throughput AM printing and testing for materials discovery.
Materials of engineering and construction. Mechanics of materials, Industrial engineering. Management engineering
For a graph $G$, its $k$-th graph power $G^k$ is constructed by placing an edge between two vertices if they are within distance $k$. We consider the problem of deriving upper bounds on the Shannon capacity of graph powers by using spectral graph theory and linear optimization methods. First, we use the so-called ratio-type bound to provide an alternative and spectral proof of a result by Lovász [IEEE Trans. Inform. Theory 1979], which states that, for a regular graph, the Hoffman ratio bound on the independence number is also an upper bound on the Lovász theta number and, hence, also on the Shannon capacity. In fact, we show that Lovász' result holds in the more general context of graph powers. Secondly, we derive another bound on the Shannon capacity of graph powers, the so-called rank-type bound, which depends on a new family of polynomials that can be computed by running a simple algorithm. Lastly, we provide several computational experiments that demonstrate the sharpness of the two proposed algebraic bounds. As a byproduct, when these two new algebraic bounds are tight, they can be used to easily derive the exact values of the Lovász theta number (which relies on solving an SDP) and the Shannon capacity (which is not known to be computable) of the corresponding graph power.
İbrahim Turan, Barış Özlü, Hasan Basri Ulaş
et al.
In this study, the drilling of an Al 6082-T6 alloy and the effects of cutting tool coating and cutting parameters on surface roughness, cutting temperature, hole diameter, circularity, and cylindrical variations was investigated. In addition, the prediction accuracy of Taguchi, artificial neural networks (ANNs), and adaptive neuro-fuzzy inference system (ANFIS) methods was compared using both experimental results and Signal/Noise (S/N) ratios derived from the experimental results. The experimental design was prepared according to Taguchi L27 orthogonal indexing. As a result, it was observed that increasing the cutting speed and feed rate increases the cutting temperature hole error, circularity error and cylindricity error. Increasing the cutting speed positively affected the surface roughness, while increasing the feed rate led to an increase in the surface roughness. The lowest surface roughness, cutting temperature, hole diameter error and hole circularity error values were measured for the uncoated cutting tool. The minimum cylindricity variation was measured for drilling with TiAlN-coated cutting tools. The optimum cutting parameters were A1B1C3 (Uncoated, 0.11 mm/rev, 200 m/min) for surface roughness, A1B1C1 (Uncoated, 0.11 mm/rev, 120 m/min) for cutting temperature, hole error, circularity error and cylindricity error. In the estimation of the output parameters with Taguchi, ANNs and ANFIS, it was observed that the estimates made by converting the experimental values into S/N ratios were more accurate than the estimates made with the experimental results. The reliability coefficient and prediction ability of the ANN model were found to be higher than Taguchi and ANFIS models in estimating the output parameters.
Silver/gold (Ag/Au) core–shell nanostructures exhibit tunable plasmonic properties and enhanced catalytic performance, enabling applications across sensing, biomedicine, and environmental remediation. This review presents representative synthetic strategies for fabricating Ag/Au bimetallic core–shell nanostructures with three distinct morphologies: nanospheres, nanocubes, and nanowires. For each architecture, we cover the representative synthetic approaches, such as seed-mediated growth, one-pot synthesis, and evaporation deposition methods, along with their corresponding applications. This review provides discussions on the synthesis methods and applications through specific examples, offering researchers guidance for fabricating Ag/Au core–shell nanostructures with tailored morphologies while addressing major challenges in controlling bimetallic formation.
Thomas Jäkel, Sebastian Unsin, Benedikt Müller
et al.
This paper presents a cost-effective approach for automated surface quality measurement in reamed bores. The study involved drilling 4000 holes into 42CrMo S4V steel, of which 3600 underwent subsequent reaming. Utilizing a CNC-controlled gantry coupled with a mobile roughness measurement device through a compliant mechanism, surface data of every bore were efficiently gathered and processed. Additionally, analytical methods are presented that extend beyond standardized, aggregated metrics. We propose the evaluation of retraction grooves by using autocovariance. In addition, the correlation between the phase position of the waviness profile and the positional deviation of the bore is analyzed. The position deviation is also associated with bending moments that occur during reaming using a sensory tool holder. Furthermore, a 360-degree surface scan is presented to visually inspect the retraction groove. This approach aims to enhance understanding of the reaming process, ultimately improving bore quality, reducing component rejects, and extending tool lifespan.
Many manufacturing firms face considerable difficulties in building export capacity and selling their products in international markets. These firms often struggle with unpredictable cost changes, logistical problems along the supply chain, and rising labor expenses that could threaten the competitive edge of manufacturing operations. As there is also a clear absence of practical export models tailored to the unique needs of industrial firms, our study aims to offer a more holistic approach to assessing the impact of cost components on enhancing export-oriented production capacity (EOPC), a perspective not comprehensively provided by the comparative advantage theory, the Heckscher–Ohlin model, or the resource-based theory. While offering a comprehensive analysis of cost components in production, we argue that adjusting the resources, managing the costs, and enhancing production efficiency can significantly improve the EOPC of the manufacturing firms. Using primary data collected from 200 manufacturing firms in Oman during the period 2012–2016, multiple regression analysis followed by descriptive statistical analysis together with a correlation matrix indicates strong positive relationships between the EOPC and factors such as the raw material cost (RMC), labor wages (LW), labor force (LF), and R&D costs (RND). Multicollinearity assessment shows VIF values below the threshold, suggesting reliable estimates. Interaction terms and market conditions were integrated into the model, enhancing its predictive accuracy. Preliminary multiple regression analysis confirms the significant impact of the RMC, LW, LF, and R&D on the EOPC, while highlighting the importance of market conditions in moderating these effects. The model’s adjusted R2 value indicates a strong fit, showing that the independent variables account for a substantial proportion of the variance in the EOPC. Each variable’s importance is reflected in its coefficient, while p-values assess the statistical significance, highlighting which factors are crucial for enhancing export capabilities. Specifically, low p-values for cost components, labor force size, and wages confirm their significant influence, and varying market conditions further modulate these effects, demonstrating the accurate interplay between internal and external factors. Adjustments in cost components under varying market scenarios were analyzed, indicating optimal strategies for increasing the EOPC. Of the five scenarios proposed to distribute the cost either among some variables while keeping others constant or among all the factors, the best-case scenario adjusted all variables together, resulting in a 20% increment in exports. We conclude with some practical and policy implications for governments to support industries in accessing cheap resources through tax reductions on imported raw materials and efficient supply chains, while promoting innovation, technology adoption, and R&D investment at the firm level.
Properties of Riesz capacity are developed with respect to the kernel exponent $p \in (-\infty,n)$, namely that capacity is monotonic as a function of $p$, that its endpoint limits recover the diameter and volume of the set, and that capacity is left-continuous with respect to $p$ and is right-continuous provided (when $p \geq 0$) that an additional hypothesis holds. Left and right continuity properties of the equilibrium measure are obtained too.
Distribution of entanglement is an essential task in quantum information processing and the realization of quantum networks. In our work, we theoretically investigate the scenario where a central source prepares an N-partite entangled state and transmits each entangled subsystem to one of N receivers through noisy quantum channels. The receivers are then able to perform local operations assisted by unlimited classical communication to distill target entangled states from the noisy channel output. In this operational context, we define the EPR distribution capacity and the GHZ distribution capacity of a quantum channel as the largest rates at which Einstein-Podolsky-Rosen (EPR) states and Greenberger-Horne-Zeilinger (GHZ) states can be faithfully distributed through the channel, respectively. We establish lower and upper bounds on the EPR distribution capacity by connecting it with the task of assisted entanglement distillation. We also construct an explicit protocol consisting of a combination of a quantum communication code and a classical-post-processing-assisted entanglement generation code, which yields a simple achievable lower bound for generic channels. As applications of these results, we give an exact expression for the EPR distribution capacity over two erasure channels and bounds on the EPR distribution capacity over two generalized amplitude damping channels. We also bound the GHZ distribution capacity, which results in an exact characterization of the GHZ distribution capacity when the most noisy channel is a dephasing channel.
Accurately predicting the time-varying dynamic parameters of a workpiece during the milling of thin-walled parts is the foundation of adaptively selecting chatter-free machining parameters. Hence, a method for accurately and quickly predicting the time-varying dynamic parameters for milling thin-walled parts is proposed, which is based on the shell FEM and a three-layer neural network. The time-dependent dynamics of the workpiece can be calculated using the FEM by obtaining the geometrical parameters of the arc-faced junctions within the discrete cells of the initial and machined workpiece. It is unnecessary to re-divide the mesh cells of the thin-walled parts at each cutting position, which enhances the computational efficiency of the workpiece dynamics. Meanwhile, in comparison with the three-dimensional cube elements, the shell elements can reduce the number of degrees of freedom of the FEM model by 74%, which leads to the computation of the characteristic equation that is about nine times faster. The results of the modal test show that the maximum error of the shell FEM in predicting the natural frequency of the workpiece is about 4%. Furthermore, a three-layer neural network is constructed, and the results of the shell FEM are used as samples to train the model. The neural network model has a maximum prediction error of 0.409% when benchmarked against the results of the FEM. Furthermore, the three-layer neural network effectively enhances computational efficiency while guaranteeing accuracy.
Deepa Kareepadath Santhosh, Philipp Hoier, Franci Pušavec
et al.
This paper investigates the potential of utilizing lubricated liquid carbon dioxide (LCO<sub>2</sub> + MQL) as an alternative to conventional flood cooling in grinding operations. This approach could facilitate a transition towards fossil-free production, which is a significant challenge in industry. The alternative cooling–lubrication method relies on pre-mixed LCO<sub>2</sub> and oil and a single-channel minimum quantity lubrication (MQL) delivery method, which has already demonstrated potential in machining with geometrically defined cutting edges. However, this method has been less explored in grinding. This study primarily evaluates the grindability of AISI 4140 steel, examining surface roughness, residual stresses, microhardness, grinding forces, and specific energy for different cooling–lubrication methods. The results indicate that LCO<sub>2</sub> + MQL is capable of attaining surface roughness and microhardness that is comparable to that of conventional flood cooling, especially in the case of less aggressive, finish grinding. Nevertheless, the presence of higher tensile residual stresses in rough grinding suggests that the cooling capability may be insufficient. While the primary objective was to evaluate the technological viability of LCO<sub>2</sub> + MQL in terms of grindability, a supplementary cost-effectiveness analysis (CEA) was also conducted to assess the economic feasibility of LCO<sub>2</sub> + MQL in comparison to conventional flood cooling. The CEA showed that the costs of both the cooling–lubrication methods are very similar. In conclusion, this study offers insights into the technological and economic viability of LCO<sub>2</sub> + MQL as a sustainable cooling–lubrication method for industrial grinding processes.
Training models with varying capacities can be advantageous for deploying them in different scenarios. While high-capacity models offer better performance, low-capacity models require fewer computing resources for training and inference. In this work, we propose a novel one-stop training framework to jointly train high-capacity and low-capactiy models. This framework consists of two composite model architectures and a joint training algorithm called Two-Stage Joint-Training (TSJT). Unlike knowledge distillation, where multiple capacity models are trained from scratch separately, our approach integrates supervisions from different capacity models simultaneously, leading to faster and more efficient convergence. Extensive experiments on the multilingual machine translation benchmark WMT10 show that our method outperforms low-capacity baseline models and achieves comparable or better performance on high-capacity models. Notably, the analysis demonstrates that our method significantly influences the initial training process, leading to more efficient convergence and superior solutions.
Esta pesquisa tem como objetivo, a identificação dos principais elementos de modelos de maturidade e prontidão da Indústria 4.0, no tocante a priorização de projetos. Por meio de uma revisão bibliométrica e sistemática da literatura, com a finalidade de contribuir teoricamente no aprofundamento do tema e, assim, s analisar e reunir as pesquisas já disponíveis. Foi realizada a análise de redes e conteúdo para a identificação de lacunas na literatura. Os principais achados bibliométricos ressaltaram a concentração de artigos na Alemanha e na Itália, porém pode ser observado que nos últimos anos tem ocorrido uma descentralização dessa pesquisa para outros países. Após analisar os clusters, o autor Basl J. obteve destaque, por apresentar o maior número de publicações. Com relação a análise de redes, os Modelos de Maturidade e de prontidão, e o de Processo de Implementação e Avaliação da I4.0 mostraram maior relevância. Observou-se que propostas teóricas, além de integrações de metodologias para incorporar os projetos da I. 4.0. foram muito relevantes para fomentar esta pesquisa. Apesar da relevância do tema, ainda existem poucos trabalhos referentes a priorização de projetos. Que as escolhas, dificuldades e os fatores de sucesso precisam estar claramente alinhados com as estratégias adotadas, e que mesmo havendo a intenção ou um nível de prontidão, o caminho para um nível elevado de maturidade requer mudanças significativas nas empresas e pode levar algum tempo, para existir uma sinergia entre produtos, processos, e as estratégias da organização e sua cadeia de suprimentos.
Production management. Operations management, Production capacity. Manufacturing capacity
The 2022′s Youth Forum on Resources Chemicals and Materials was held on November 12–13, 2022, in Shenyang, Liaoning Province. Panel discussions focus on the cutting-edge researches on “Fine chemicals and advanced alloy materials” and “Utilization of fossil and renewable carbon resources”. This perspective summarizes the major directions of scientific research and technical developments aligned in the discussions. Fine chemical industry tends to pursue green and low-carbon products, intelligent product design, and start manufacturing. In recent years, great efforts have been made for transformation of cellulose into advanced electronic as well as life-service bio-materials and to the high-selectivity extraction of bio-base aromatic chemicals from lignin. Concerning high-end alloy materials, regulating deformation mechanism of crystal to construct bimodal microstructure seems highly prospective in harmonizing precipitate hardening effect and plastic deformation capacity. As we know, utilization of fossil carbon resources constitutes the major anthropogenic carbon emissions, and the related innovations thus should be, for possibly a long period, on increasing energy production efficiency and low-carbon cascaded conversion of fossil fuels, especially of coal.
Fabiane Fidelis Querino, Antônio Cleber da Silva, Mozar José de Brito
et al.
Different countries have different institutional levels due to the evolution and complexity of institutions. Although there is a lot of research available, the analysis of how institutional factors influence the internationalization process is disconnected, so this article has three main objectives: (1) to map the field and identify the main research streams and their conclusions; (2) propose an integrative framework to analyze the institutional factors driving the internationalization process of companies; and (3) develop a future research agenda to identify opportunities and trends for future studies. For this, an integrative literature review was developed, with 33 articles in the sample. These articles were divided into four lines of research: developed countries, developing countries, regional level, and small and medium enterprises. Government support was the only common factor among these four categories identified as motivators for internationalization.
Production management. Operations management, Production capacity. Manufacturing capacity
Sanjana Mukherjee, Kanika Kalra, Alexandra L Phelan
The COVID-19 pandemic highlighted significant gaps in equitable access to essential medical countermeasures such as vaccines. Manufacturing capacity for pandemic vaccines, therapeutics, and diagnostics is concentrated in too few countries. One of the major hurdles to equitable vaccine distribution was "vaccine nationalism", countries hoarded vaccines to vaccinate their own populations first which significantly reduced global vaccine supply, leaving significant parts of the world vulnerable to the virus. As part of equitably building global capacity, one proposal to potentially counter vaccine nationalism is to identify small population countries with vaccine manufacturing capacity, as these countries could fulfill their domestic obligations quickly, and then contribute to global vaccine supplies. This cross-sectional study is the first to assesses global vaccine manufacturing capacity and identifies countries with small populations, in each WHO region, with the capacity and capability to manufacture vaccines using various manufacturing platforms. Twelve countries were identified to have both small populations and vaccine manufacturing capacity. 75% of these countries were in the European region; none were identified in the African Region and South-East Asia Region. Six countries have facilities producing subunit vaccines, a platform where existing facilities can be repurposed for COVID-19 vaccine production, while three countries have facilities to produce COVID-19 mRNA vaccines. Although this study identified candidate countries to serve as key vaccine manufacturing hubs for future health emergencies, regional representation is severely limited. Current negotiations to draft a Pandemic Treaty present a unique opportunity to address vaccine nationalism by building regional capacities in small population countries for vaccine research, development, and manufacturing.
Danang Kumara Hadi, Andika Putra Setiawan, Oppy Valencia Indrian
et al.
Evaluating sustainability in the food supply chain has multiple benefits, including reducing environmental impact, meeting regulations, satisfying consumer demands, improving efficiency, and enhancing business reputation. This paper outlines a Sustainability Supply Chain Management (SSCM) framework using the Analytic Hierarchy Process (AHP) to weigh Key Performance Indicators (KPIs). With 12 variables and 39 KPIs, the method involves data collection, performance measure selection, and analysis. KPIs with lower weight values highlight areas needing improvement, such as recycling costs, personnel expenses, sales responsiveness, eco-friendly labelling, distribution efficiency, green product development, recycled material usage, employee capabilities, product reputation, worker satisfaction, and stakeholder trust. Based on KPI weights, recommendations include prioritizing energy use in production and marketing, ensuring timely product delivery, maintaining product quality, streamlining production processes, focusing on energy efficiency, promoting eco-labelling, fostering eco-awareness, and advancing environmental technology.
Industrial engineering. Management engineering, Production capacity. Manufacturing capacity