Hasil untuk "Production capacity. Manufacturing capacity"

Jingru Lu, Zhenbin Yang, Jianming Zheng

We formulate the Gao-Jafferis-Wall traversable wormhole protocol as a quantum channel and compute its quantum channel capacity. We show that this capacity is governed by the time derivative of an out-of-time-ordered correlator, hence by operator size growth in the holographic dual, and that its growth is bounded above by the Einstein gravity limit. The channel capacity therefore provides a natural benchmark for quantum simulations of traversable wormholes.

Carl H June, Regina M Young, Bruce L Levine et al.

Background Engineering chimeric antigen receptor (CAR) T cells with logic-gated synthetic Notch (synNotch) receptor circuits can enhance specificity and mitigate on-target/off-tumor toxicity. However, the conventional synNotch system uses two lentiviral vectors encoding the synNotch receptor and inducible CAR, requiring dual transduction and cell sorting, which limits clinical translation. Integrating the synNotch-CAR circuit into a single lentiviral vector could overcome this limitation, yet manufacturing CAR T cells with large transgenes remains challenging, as increasing transgene size drastically reduces lentiviral titers and T cell transduction efficiency. Current production workflows compensate for low transduction efficiency by sorting transduced cells, further impeding clinical translation. Consequently, these constraints have limited the broader development of synNotch-CAR T cell therapies.Methods We engineered a single-vector synNotch (svsNotch) system that integrates all components of the conventional dual-vector circuit into one lentiviral vector to facilitate clinical translation. To overcome the low lentiviral titers and T cell transduction efficiency caused by the large svsNotch transgene, we established an optimized CAR T cell production workflow for effector T cells with large lentiviral transgenes.Results Our optimized workflow increased T cell transduction rates by up to 14.8-fold and enabled the production of effector T cells with lentiviral transgenes exceeding the effective packaging capacity limit of 9.2 kb. As a proof of concept, we engineered human epidermal growth factor receptor 2 (HER2)-mesothelin (MSLN) svsNotch (9.2 kb), in which a synNotch receptor targeting HER2 regulates the expression of a second-generation 4-1BBζ CAR against MSLN to enable selective targeting of double-positive HER2+MSLN+ ovarian tumors. In vitro, HER2-MSLN svsNotch T cells demonstrated superior specificity to conventional dual-vector synNotch-CAR T cells, with selective cytotoxicity against HER2+MSLN+ but not HER2koMSLN+ tumor cells. To enable in vivo monitoring, we engineered HER2-MSLN-click beetle green (CBG) svsNotch (10.1 kb) incorporating CBG luciferase. In mouse models using constitutive CAR T cells as controls, HER2-MSLN-CBG svsNotch T cells exhibited minimal cytotoxicity in the absence of HER2 and superior efficacy against HER2lowMSLNhigh and HER2highMSLNhigh tumors.Conclusion These data establish a framework for engineering logic-gated single-vector immunotherapies and provide an optimized workflow for generating CAR T cells with transgenes that exceed current size limitations.

B. Adam Williams, P. Michelle Fitzsimmons, Lavinia M. Lewis et al.

Abstract The “100 Days Mission” aims to compress the timeline for delivering safe and effective vaccines in response to future pandemics. Here, we present insights from Pfizer leaders involved in the COVID-19 vaccine effort on key enablers for rapid large-scale manufacture of pandemic vaccines to achieve this ambitious goal. For pharmaceutical companies, these enablers include robust governance models, secure supply chains, innovative production strategies, and maintained “warm” manufacturing capacity. Additionally, we examine the crucial role of government support through regulatory harmonization, enhanced global surveillance, and improved logistics. By addressing these critical factors, the global community can better prepare for rapid vaccine manufacturing in response to future pandemic threats.

Stefan Bielmeier, Gerald Friedland

We investigate how feature correlations influence the capacity of Dense Associative Memory (DAM), a Transformer attention-like model. Practical machine learning scenarios involve feature-correlated data and learn representations in the input space, but current capacity analyses do not account for this. We develop an empirical framework to analyze the effects of data structure on capacity dynamics. Specifically, we systematically construct datasets that vary in feature correlation and pattern separation using Hamming distance from information theory, and compute the model's corresponding storage capacity using a simple binary search algorithm. Our experiments confirm that memory capacity scales exponentially with increasing separation in the input space. Feature correlations do not alter this relationship fundamentally, but reduce capacity slightly at constant separation. This effect is amplified at higher polynomial degrees in the energy function, suggesting that Associative Memory is more limited in depicting higher-order interactions between features than patterns. Our findings bridge theoretical work and practical settings for DAM, and might inspire more data-centric methods.

Eli Shemuel, Oron Sabag, Haim H. Permuter

The capacity of a channel with an energy-harvesting (EH) encoder and a finite battery remains an open problem, even in the noiseless case. A key instance of this scenario is the binary EH channel (BEHC), where the encoder has a unit-sized battery and binary inputs. Existing capacity expressions for the BEHC are not computable, motivating this work, which determines the capacity to any desired precision via convex optimization. By modeling the system as a finite-state channel with state information known causally at the encoder, we derive single-letter lower and upper bounds using auxiliary directed graphs, termed $Q$-graphs. These $Q$-graphs exhibit a special structure with a finite number of nodes, $N$, enabling the formulation of the bounds as convex optimization problems. As $N$ increases, the bounds tighten and converge to the capacity with a vanishing gap of $O(N)$. For any EH probability parameter $η\in \{0.1,0.2, \dots, 0.9\}$, we compute the capacity with a precision of ${1e-6}$, outperforming the best-known bounds in the literature. Finally, we extend this framework to noisy EH channels with feedback, and present numerical achievable rates for the binary symmetric channel using a Markov decision process.

Zhen Wu, Si-Qi Zhou

Quantum channel capacities play a central role in quantum Shannon theory, a formalism built upon rigorous coding theorems for noisy channels. Evaluating exact capacity values for general quantum channels remains intractable due to superadditivity. As a step toward understanding this phenomenon, we construct the generalized direct sum (GDS) channel, extending conventional direct sum channels through a direct sum structure in their Kraus operators. This construction forms the basis of the GDS framework, encompassing classes of channels with single-letter formula for quantum capacities and others exhibiting striking capacity features. The quantum capacity can approach zero yet display unbounded superadditivity combined with erasure channels. Private and classical capacities coincide and can become arbitrarily large, resulting in an unbounded gap with the quantum capacity. Providing a simpler and more intuitive approach, the framework deepens our understanding of quantum channel capacities.

Danial Rahnama, Graziano Chila, Sivakumar Narayanswamy

This study presents the optimization of a laser ablation process designed to achieve the precise removal of polyamide coatings from ultra-thin platinum wires. Removing polymer coatings is a critical challenge in high-reliability manufacturing processes such as aerospace thermocouple fabrication. The ablation process must not only ensure the complete removal of the polyamide insulation but also maintain the tensile strength of the wire to withstand mechanical handling in subsequent manufacturing stages. Additionally, the exposed platinum surface must exhibit low surface roughness to enable effective soldering and be free of thermal damage or residual debris to pass strict visual inspections. The wires have a total diameter of 65 µm, consisting of a 50 µm platinum core encased in a 15 µm polyamide coating. By utilizing a UV laser with a wavelength of 355 nm, average power of 3 W, a repetition rate range of 20 to 200 kHz, and a high-speed marking system, the process parameters were systematically refined. Initial attempts to perform the ablation in an air medium were unsuccessful due to inadequate thermal control and incomplete removal of the polyamide coating. Hence, a water-assisted ablation technique was explored to address these limitations. Experimental results demonstrated that a scanning speed of 1200 mm/s, coupled with a line spacing of 1 µm and a single ablation pass, resulted in complete coating removal while ensuring the integrity of the platinum substrate. The incorporation of a water layer above the ablation region was considered crucial for effective heat dissipation, preventing substrate overheating and ensuring uniform ablation. The laser’s spot diameter of 20 µm in air and a focal length of 130 mm introduced challenges related to overlap control between successive passes, requiring precise calibration to maintain consistency in coating removal. This research demonstrates the feasibility and reliability of water-assisted laser ablation as a method for a high-precision, non-contact coating material.

Kenzie A. Timmons, Ali Nasiri, Donald P. Bishop

This research focused on developing the processing parameters required to fabricate UNS C64200 aluminum–silicon–bronze (ASB) using laser powder bed fusion (LPBF) additive manufacturing. A full factorial design of experiments (DOE), followed by a central composite DOE, was employed to statistically optimize the as-built density while varying laser power, scan speed, and hatch spacing. Parameter sets that yielded high-density (>99.9%) products were then utilized to manufacture specimens to determine mechanical properties in both the as-built and heat-treated states. The as-built samples exhibited high tensile strength but relatively low ductility and absorbed impact energy, owing to the presence of a mixed α/β’ microstructure. Heat treatment at 620 °C eliminated the martensitic β’ phase, which manifested significant gains in ductility and absorbed energy. As such, the final tensile properties and impact toughness exceeded the Defence Standard minimum requirements for conventionally processed ASB.

Thomas Thrower, Susanna E. Riley, Seungmee Lee et al.

Abstract Cultivated meat promises to address some of the pressing challenges associated with large-scale production of animals for food. An important limitation to realising such promise is the lack of readily available cell lines that can be expanded robustly for scale-up culture while maintaining the capacity to differentiate into tissues of interest, namely fat and muscle. Here, we report a porcine mesenchymal stem cell line (FaTTy) which, uniquely, upon spontaneously immortalisation acquired enhanced adipogenic efficiency, close to 100%, that has now been maintained for over 200 population doublings. FaTTy is able to differentiate with high efficiency in both 2D and 3D contexts and produces mature adipocytes upon prolonged differentiation. Moreover, FaTTy adipocytes display fatty acid profiles largely similar to native pig fat but with higher monounsaturated-to-saturated ratios. FaTTy displays minor aneuploidy, characterised by lack of Y chromosome, and lacks typical genetic or functional properties of tumorigenic cells. These highly distinctive characteristics, together with its non-genetically modified nature, make FaTTy a very attractive, potentially game-changing resource for food manufacturing, and particularly cultivated meat.

Aleksandar Mitrašinović, Teodora Đurđević, Jasmina Nešković et al.

The field of metal additive manufacturing has witnessed significant growth in recent years, with technology offering the ability to produce complex geometries that are challenging to manufacture using the traditional methods. In situ monitoring and control of the manufacturing process are crucial for increasing the production capacity and improving the quality of manufactured parts. This article provides a comparative analysis of computational, indirect, and direct methods for in situ temperature monitoring during additive manufacturing of metal alloy components. Furthermore, it discusses the current status, recent improvements, and perspectives for in situ temperature measurements. The basic principles of thermal imaging, two-color pyrometry, and millimeter-wave radiometry are explored, highlighting their limitations for addressing challenges related to material emissivity and rapid changes in building material composition. Overcoming the challenges related to the inaccessibility of the chamber where the parts are formed, direct temperature measurements would allow for the integration of collected information into big data systems. Within the framework of Industry 4.0, this approach offers a viable alternative to the conventional metal shaping processes, improving the production capacity and part quality. This research aims to contribute to ongoing advancements in metal additive manufacturing and its potential to completely replace traditional metal casting practices in the Industry 4.0 era.

Md. Kutub Uddin, Md. Saiful Islam, Md Abrar Jahin et al.

This paper focuses on the generalized grouping problem in the context of cellular manufacturing systems (CMS), where parts may have more than one process route. A process route lists the machines corresponding to each part of the operation. Inspired by the extensive and widespread use of network flow algorithms, this research formulates the process route family formation for generalized grouping as a unit capacity minimum cost network flow model. The objective is to minimize dissimilarity (based on the machines required) among the process routes within a family. The proposed model optimally solves the process route family formation problem without pre-specifying the number of part families to be formed. The process route of family formation is the first stage in a hierarchical procedure. For the second stage (machine cell formation), two procedures, a quadratic assignment programming (QAP) formulation, and a heuristic procedure, are proposed. The QAP simultaneously assigns process route families and machines to a pre-specified number of cells in such a way that total machine utilization is maximized. The heuristic procedure for machine cell formation is hierarchical in nature. Computational results for some test problems show that the QAP and the heuristic procedure yield the same results.

Kaifeng Bu, Arthur Jaffe

We investigate the role of magic resource in the quantum capacity of channels. We consider the quantum channel of the recently proposed discrete beam splitter with the fixed environmental state. We find that if the fixed environmental state is a stabilizer state, then the quantum capacity is zero. Moreover, we find that the quantum capacity is nonzero for some magic states, and the quantum capacity increases linearly with respect to the number of single-qudit magic states in the environment. We also bound the maximal quantum capacity of the discrete beam splitter in terms of the amount of magic resource in the environmental states. These results suggest that magic resource can increase the quantum capacity of channels; it sheds new insight into the role of stabilizer and magic states in quantum communication.

Mojtaba A. Farahani, Fadi El Kalach, Austin Harper et al.

TSF is growing in various domains including manufacturing. Although numerous TSF algorithms have been developed recently, the validation and evaluation of algorithms hold substantial value for researchers and practitioners and are missing. This study aims to fill this gap by evaluating the SoTA TSF algorithms on thirteen manufacturing datasets, focusing on their applicability in manufacturing. Each algorithm was selected based on its TSF category to ensure a representative set of algorithms. The evaluation includes different scenarios to evaluate the models using two problem categories and two forecasting horizons. To evaluate the performance, the WAPE was calculated, and additional post hoc analyses were conducted to assess the significance of observed differences. Only algorithms with codes from open-source libraries were utilized, and no hyperparameter tuning was done. This allowed us to evaluate the algorithms as "out-of-the-box" solutions that can be easily implemented, ensuring their usability within the manufacturing by practitioners with limited technical knowledge. This aligns to facilitate the adoption of these techniques in smart manufacturing systems. Based on the results, transformer and MLP-based architectures demonstrated the best performance with MLP-based architecture winning the most scenarios. For univariate TSF, PatchTST emerged as the most robust, particularly for long-term horizons, while for multivariate problems, MLP-based architectures like N-HITS and TiDE showed superior results. The study revealed that simpler algorithms like XGBoost could outperform complex algorithms in certain tasks. These findings challenge the assumption that more sophisticated models produce better results. Additionally, the research highlighted the importance of computational resource considerations, showing variations in runtime and memory usage across different algorithms.

Paulo Henrique Grossi Dornelas, João da Cruz Payão Filho, Victor Hugo Pereira Moraes e Oliveira et al.

To reduce manufacturing costs, energy companies aim to maximize the deposition rate during welding operations by increasing the interpass temperature (IT), thereby minimizing the cooling time. However, IT can significantly affect weldment performance, particularly its Charpy V-notch (CVN) impact energy (toughness). The present study investigates the effect of increasing IT beyond the limit specified by the ASME B31.3 (315 °C) on the CVN impact energy (−30 °C) of the simulated coarse-grained heat-affected zone (CGHAZ) of a 2.25Cr-1Mo steel submerged arc welded (SAW). The CGHAZ thermal cycles were obtained through finite element method simulations and physically replicated using a Gleeble machine. The increase in IT beyond the ASME-specified limit significantly reduces the CVN impact energy of the CGHAZ. However, the values obtained remained above the minimum required threshold (NORSOK M630, 42 J). The main effect of increased IT was grain coarsening. Additionally, an inverse linear relationship was observed between effective grain size (EGS) and CVN impact energy. The steel’s microstructure showed non-significant sensitivity to variations in IT within the studied range. These findings suggest that, under the conditions studied, increasing IT could be a viable option for optimizing production by reducing welding time and potentially lowering costs.

OYAROMADE, Rasheed, ISAYOMI, Abiodun Samuel, IBRAHIM-AYEDE, Sulaiman

Until recent years, the performance of the Nigeria’s manufacturing sector has been characterized by downward pressures. Given the present shocks in the global energy markets and upward review of borrowing costs by the Central Bank of Nigeria, the historical developments around the manufacturing output appear to be subject to renewal. Consequently, this study investigates the impact of interest rate structure and energy pricing on the Nigerian manufacturing output from 1980 to 2021. Secondary data obtained from World Development Indicators and the CBN’s Statistical Bulletins were anchored by an autoregressive distributed lag (ARDL) model and error correction modelling. The findings revealed that manufacturing output is inelastic in its response to changes in interest rate while it is elastic in its response to energy price. This is less puzzling as the Nigerian manufacturers seem to favour availability of credit facilities over low interest rates. Also, productive activities respond immediately to a given shock in the energy price, especially the price of diesel. On this basis, this paper concludes that energy pricing is a strong predictor of Nigeria’s manufacturing output. Consequently, policy makers should institute a preferential treatment on energy distribution towards the manufacturers. This would make production more attractive, thereby boosting their capacity utilization.

Peng Gao, Zarek Nieduzak, Joshua Krantz et al.

This research investigates the correlation between polymer melt viscosity, tensile properties, and injection molding energy consumption for three grades of polypropylene: a virgin grade, a recycled grade, and a modified recycled grade. Cold runner and hot runner molds are considered. The experiments focus on characterizing the thermal and mechanical energy drawn by the injection molding machine during the cycle. The data collected from the experiments are used to calculate the embodied energy as a function of the polymer viscosity and processing conditions. The analysis of the relationship between polymer rheology and processing provided guidelines for the molded parts’ embodied energy and mechanical characteristics. These guidelines and estimation techniques will support sustainable design for manufacturing practices.

Adrian Marius Deaconu, Javad Tayyebi, Mihai-Lucian Rîtan

The maximum capacity path problem is to find a path from a source to a sink which has the maximum capacity among all paths. This paper addresses an extension of this problem which considers loss factors. It is called the generalized maximum capacity path problem. The problem is a network flow optimization problem whose network contains capacities as well as loss factors for arcs. The aim of the problem is to find a path from an origin to a destination so as to send a maximum flow along the path considering loss factors and respecting capacity constraints. The paper presents a zero-one formulation of the problem and moreover, it presents two efficient algorithms which solve the problem in polynomial time.

Jun Liu, Jie Xiao, Jihui Yang et al.

Energy storage is important for electrification of transportation and for high renewable energy utilization, but there is still considerable debate about how much storage capacity should be developed and on the roles and impact of a large amount of battery storage and a large number of electric vehicles. This paper aims to answer some critical questions for energy storage and electric vehicles, including how much capacity and what kind of technologies should be developed, what are the roles of short-term storage and long-duration storage, what is the relationship between energy storage and electrification of transportation, and what impact will energy storage have on materials manufacturing and supply chain. Accelerating the deployment of electric vehicles and battery production has the potential to provide terawatt-hour scale storage capability for renewable energy to meet the majority of the electricity need in the United States. However, it is critical to greatly increase the cycle life and reduce the cost of the materials and technologies. Long-lasting lithium-ion batteries, next generation high-energy and low-cost lithium batteries are discussed. Many other battery chemistries are also briefly compared, but 100 % renewable utilization requires breakthroughs in both grid operation and technologies for long-duration storage. New concepts like dual use technologies should be developed.

Benjamin Baumgärtner, Richard Rothfelder, Sandra Greiner et al.

X-ray-computed tomography (CT) is today’s gold standard for the non-destructive evaluation of internal component defects such as cracks and porosity. Using automated standardized evaluation algorithms, an analysis can be performed without knowledge of the shape, location, or size of the defects. Both the measurement and the evaluation are based on the fact that the component has no internal structures or cavities. However, additive manufacturing (AM) and hybrid subtractive procedures offer the possibility of integrating internal structures directly during the building process. The examination of powder bed fusion (PBF) samples made of Ti64 and PA12 showed that the standardized evaluation methods were not able to identify internal structures correctly. Different evaluation methods for the CT-measured values were analyzed and recommendations on a procedure for measuring internal structures are given.

Antonios Chrysargyris, Stavros Louka, Spyridon A. Petropoulos et al.

The industrial manufacturing of essential oils (EOs) generates a sizable volume of bulk solid waste (SW) that needs to be disposed of. The present study evaluated the potential of using <i>Origanum dubium</i> wastes (ODW) and <i>Sideritis cypria</i> waste (SCW) obtained after EO distillation for partial peat substitution (0–5–10–20–40% <i>v</i>/<i>v</i>) in <i>Portulaca oleracea</i> production. Both ODW and SCW increased pH, electrical conductivity, organic matter, and mineral content, but negatively affected the total porosity and aeration of the growing media. Plant growth was inhibited, especially when high ratios of residues were used, and this was reflected by leaf stomatal conductance and chlorophyll decrease, as well as by the activation of several nonenzymatic (phenols, flavonoids, and antioxidant capacity) and enzymatic (catalase, superoxide dismutase, and peroxidase) mechanisms and the increase in lipid peroxidation and hydrogen peroxide, indicating stress conditions. Despite that both ODW and SCW were rich in minerals, plants could not accumulate them. It can be concluded that both ODW and SCW have the potential to be used in the growing media at low ratios up to 10%, with increased antioxidant content in the final product. Nonetheless, the growing media properties, i.e., total pore space and aeration, still need to be improved to result in sufficient yields.