Search Results (367)

The invasive Silver Carp (Hypophthalmichthys molitrix) in North America represents a promising resource for surimi production; however, its gel formability deteriorates significantly during frozen storage. This study investigated the deterioration of gel properties in Silver Carp surimi over six months of frozen storage, and showed that short-term frozen storage (<2 months) was beneficial for surimi gel-forming ability, while extended frozen storage (>2 months) tended to have detrimental effects. The adverse effect of long-term frozen storage could be mitigated via using food additives (e.g., manufactured microfiber, transglutaminase, and chicken skin collagen), among which transglutaminase was the most effective. Transglutaminase at a relatively low level (0.1 wt%) could effectively negate frozen storage’s effects, and produced surimi gel with quality attributes (e.g., gel strength, hardness, and chewiness) at levels comparable to those from fresh fish samples. To assess the effects of the addition of various food additives for quality improvement, a synthetic data-driven machine learning (SDDML) approach was developed. After testing multiple algorithms, the random forest model was shown to yield synthetic data points that represented experimental data characteristics the best (R² values of 0.871–0.889). It also produced improved predictions for gel quality attributes from control variables (i.e., additive levels) compared to using experimental data alone, showing the potential to overcome data scarcity issues when only limited experimental data are available for ML models. A synthetic dataset of 240 data points was shown to supplement the experimental dataset (60 points) well for assessment of the Frozen Silver Carp (FSC) surimi gel quality attributes. The SDDML method could be used to find optimal recipes for generating additive profiles to counteract the adverse effects of frozen storage and to improve surimi gel quality to upgrade underutilized invasive species to value-added food products. Full article

The garment manufacturing industry, being labor-intensive, has long faced challenges in automating the sewing process due to the flexibility and deformability of fabrics. This study proposes a novel strategy for automated sewing by integrating soft fingers and machine vision technology. Firstly, leveraging the flexibility and adjustability of soft fingers, combined with the motion characteristics of the sewing machine, a sewing model was established to achieve coordinated operation between the soft fingers and the sewing machine. Experimental results indicate that the fabric feeding speed and waiting time of the soft fingers are significantly correlated with the sewing speed and stitch density of the sewing machine, but not with the fabric properties. Secondly, machine vision technology was employed to inspect the quality of the sewn fabrics, achieving a classification accuracy of 97.84%. This study not only provides theoretical and technical support for the intelligent upgrading of the garment manufacturing industry but also lays the foundation for the automation of complex sewing processes such as quilting. Future research will further optimize the system’s performance and expand its applications in more complex sewing tasks. Full article

This study examines the establishment of China’s national supercomputing centers as an exogenous policy shock. Utilizing data from Chinese manufacturing enterprises listed between 2003 and 2023, it applies a multi-period difference-in-differences (DID) model to assess the impact of computing infrastructure on innovation within Chinese manufacturing enterprises. Results indicate that computing infrastructure significantly enhances manufacturing innovation, a finding that is robust across various tests. This effect is positively moderated by the internal R&D investment of enterprises and the external market share. Heterogeneity analysis reveals that the enhancement effect of computing infrastructure on innovation is more pronounced in non-state-owned enterprises, those located in the eastern region, and those with low ownership concentration. Furthermore, computing infrastructure not only boosts the quantity of innovation but also enhances its quality. This paper offers micro-level evidence for emerging countries to advance sustainable development, transformation, and upgrading of the manufacturing sector through computing infrastructure. Full article

High-quality development highlights the importance of environmental protection and green low-carbon development. The high-quality development of the manufacturing industry is not only the key content for achieving green transformation, but also an important cornerstone for building a modern national industrial system. Current research focuses on companies and governments, ignoring the important value of suppliers and consumers. As a result, existing mechanisms have failed to deliver the desired results. This paper constructs an evolutionary game model involving manufacturing enterprises, local governments, suppliers, and consumers, and systematically analyzes the strategy selection process of the four participating populations. On this basis, the impact of exogenous and endogenous factors on the evolutionarily stable strategy is studied at the microscopic level using numerical simulation methods. The results show that (1) increasing any of the endogenous factors, such as innovative capability, organization building, and industrial resources, can accelerate the evolution of manufacturing enterprises evolve to smart upgrade strategy. (2) Increasing any one of the exogenous factors, such as policy environment, industrial cooperation, and market demand, can accelerate the rate at which manufacturing enterprises choose to adopt the strategy of smart upgrade. The purpose of this paper is to provide a theoretical reference for the behavioral strategies of manufacturing enterprises, and to provide a realistic reference for local governments to build a mechanism to promote the high-quality development of the manufacturing industry. Full article

Modern manufacturing is increasingly shaped by the paradigm of Industry 4.0 (Smart Manufacturing). As one of its nine pillars, additive manufacturing plays a crucial role, enabling high-quality final products with improved profitability in minimal time. Advances in this field have facilitated the emergence of diverse technologies—such as Fused Deposition Modelling (FDM), Stereolithography (SLA), and Selective Laser Sintering (SLS)—allowing the use of metallic, polymeric, and composite materials. Within this context, Klipper v.0.12, an open-source firmware for 3D printers, addresses the performance limitations of conventional consumer-grade systems. By offloading computationally intensive tasks to an external single-board computer (e.g., Raspberry Pi), Klipper enhances speed, precision, and flexibility while reducing prototyping time. The purpose of this study is twofold: first, to identify and analyze bottlenecks in low-cost 3D printers and second, to evaluate how these shortcomings can be mitigated through the integration of supplementary hardware and software (Klipper firmware, Raspberry Pi, additional sensors, and the Mainsail interface). The scientific contribution of this study lies in demonstrating that a consumer-grade FDM 3D printer can be significantly upgraded through this integration and systematic calibration, achieving up to a 50% reduction in printing time while maintaining dimensional accuracy and improving surface quality. Full article

The conversion of legacy missile engines into space propulsion systems represents a strategic opportunity to accelerate Europe’s access to orbit while advancing sustainability and circular-economy goals. Rather than discarding decommissioned hardware, repurposing missile propulsion can reduce development timelines, retain valuable materials, and leverage proven architectures for new applications. This perspective outlines the potential of the Soviet-era Isayev S2.720 engine as a representative case, drawing on historical precedents of missile-to-launcher conversions worldwide. A three-pillar methodology is proposed to frame such efforts: (i) the adoption of cleaner propellants such as LOX–LCH₄ in place of toxic hypergolics; (ii) remanufacturing and upgrading of key subsystems through additive manufacturing, AI-assisted inspection, and digital twin modelling; and (iii) validation supported by dedicated testing, life-cycle assessment (LCA), and life-cycle costing (LCC). Beyond the technical aspects, the paper discusses retrofit applicability, cost considerations, and the role of standardization in enabling future certification. By positioning the S2.720 as a model, this study highlights the broader strategic value of adapting decommissioned propulsion systems for modern orbital use, providing insight into how Europe might integrate legacy assets into a more sustainable and resilient space transportation framework. Full article

Background/Objectives: The Wavelia Microwave Breast Imaging (MWBI) technology aims to increase sensitivity in dense breasts, where X-ray mammography is of limited value. Its potential contribution to the reduction in the false positives in breast cancer diagnosis, by developing MWBI image descriptors supporting malignant-to-benign lesion discrimination, is also being investigated. After a First-In-Human (FiH) study with interesting findings on a small dataset of 24 symptomatic breast lesions, an upgraded 2nd prototype of Wavelia was manufactured and tested on a larger and more diverse dataset, including 62 patients and a balanced distribution of malignant and benign symptomatic breast lesions. Methods: A set of technological and methodological evolutions, outlined in this article, was implemented in Wavelia#2 to handle the diversity in larger patient datasets. Multi-modal MWBI imaging is employed to parameterize the interaction mechanisms between the microwaves and the imaged breast at varying geometrical and tissue consistency conditions. MWBI Region-Of-Interest (ROI) extraction and characterization based on multidimensional radiomic feature vectors is implemented to expand the malignant-to-benign lesion diagnostics potential of MWBI compared to the limited scope of the FiH study with Wavelia#1, which employed three specific preselected features. Results: This study demonstrates significant diagnostic accuracy of multiple texture-based and intensity-based features to discriminate between malignant and benign breast lesions with Wavelia#2 MWBI. A phenomenological qualitative assessment of the false positive rate on healthy breasts is also presented for the MWBI technology for the first time. Conclusions: The analysis contributes to the rationalization of the MWBI imaging and image analysis outputs towards standardization, objective interpretability, and ultimate clinical acceptance. Full article

The integration of the industrial Internet and big data analytics is reshaping enterprise decision-making models and providing new momentum for the transformation and upgrading of traditional manufacturing industries. In this study, a decision support system based on multi-source heterogeneous data fusion was established. The system carries out data collection, storage, and processing, as well as visualization analysis. The system also performs time-series data feature extraction and unstructured data processing in a three-layer architecture model to train models and generate decision-making. In case studies, the effectiveness of the system in predictive maintenance of equipment, dynamic optimization of supply chains, and product quality traceability was verified. A fault prediction model was developed based on an improved random forest algorithm, and it showed a high level of accuracy. Optimization strategies, such as modular system design, dynamic knowledge base updating, and human–machine collaborative decision-making, can be formulated using the system. To evaluate the system, a three-dimensional evaluation index system was built, including technology maturity, application adaptability, and benefit–output ratio. The developed system effectively improved the efficiency of enterprise resource allocation, shortened abnormality response times, and enhanced market adaptability. By using edge computing and digital twin technologies, a more flexible distributed decision-making architecture can be created in the system, promoting data-driven and intelligent decision-making in manufacturing industry. Full article

Extreme ultraviolet (EUV) photomask inspection is a critical step in semiconductor manufacturing, requiring high-resolution, high-throughput solutions to detect nanometer-scale defects. Traditional actinic imaging systems relying on complex optics have a high cost of ownership and require frequent upgrades. An alternative is lensless imaging techniques based on ptychography, which offer high-fidelity reconstruction but suffer from slow throughput and high data demands. In particular, the ptychographic standard solver—the iterative Difference Map (DifMap) algorithm—requires many measurements and iterations to converge. We propose Ptycho-LDM, a hybrid framework integrating DifMap with a conditional Latent Diffusion Model for rapid and accurate phase retrieval. Ptycho-LDM alleviates high data acquisition demand by leveraging data-driven priors while offering improved computational efficiency. Our method performs coarse object retrieval using a resource-constrained reconstruction from DifMap and refines the result using a learned prior over photomask patterns. This prior enables high-fidelity reconstructions even in measurement-limited regimes where DifMap alone fails to converge. Experiments on actinic patterned mask inspection (APMI) show that Ptycho-LDM recovers fine structure and defect details with far fewer probe positions, surpassing the DifMap in accuracy and speed. Furthermore, evaluations on both noisy synthetic data and real APMI measurements confirm the robustness and effectiveness of Ptycho-LDM across practical scenarios. By combining generative modeling with physics-based constraints, Ptycho-LDM offers a promising scalable, high-throughput solution for next-generation photomask inspection. Full article

The digital economy is a key driver of industrial upgrading and regional growth. Focusing on Gansu Province—an under-represented, less-developed region in northwest China—this study constructs a multidimensional digital economy index (DEI) for 2009–2023 under a unified normalisation and weighting scheme. Two complementary MCDA approaches—entropy-weighted TOPSIS and SESP-SPOTIS—are implemented on the same 0–1 normalised indicators. Robustness is assessed using COMSAM sensitivity analysis and is benchmarked against a PCA reference. The empirical analysis then estimates log-elasticity models linking modern logistics production (MLP) and the DEI to the provincial GDP and sectoral value added, with inferences based on White heteroskedasticity–robust standard errors and bootstrap confidence intervals. Results show a steady rise in the DEI with a temporary dip in 2021 and recovery thereafter. MLP is positively and significantly associated with GDP and value added in the primary, secondary, and tertiary sectors. The DEI is positively and significantly associated with GDP, the primary sector, and the tertiary sector, but its effect is not statistically significant for the secondary sector, indicating a manufacturing digitalisation gap relative to services. Cross-method agreement and narrow sensitivity bands support the stability of these findings. Policy implications include continued investment in digital infrastructure and accessibility, targeted acceleration of manufacturing digitalisation, and the development of a “digital agriculture–smart logistics–green development” pathway to foster high-quality, sustainable regional growth. Full article

Metal additive manufacturing (AM) has become a crucial technology for rapid prototyping and enhancing the efficiency of producing lighter components. Despite these advantages, many challenges remain. We examine how mineral-rich developing countries can upgrade in the metal AM global value chain (GVC). We do so by applying the theory of GVCs and the multi-level perspective (MLP) framework to the metal powder segment. We investigate how Kazakhstan can link itself to the metal AM GVC by cooperating with China. Our case studies are based on 20 interviews with metal AM industry experts and scholars from Kazakhstan, China, and Europe. Using the MLP framework, we identify eight drivers that have enabled China to become prominent in the global metal AM industry. In addition, we identify eight barriers restricting Kazakhstan’s upgrading. For Kazakhstan to begin producing metal powders for AM, we suggest that its government start by implementing three policies, based on China’s experience: improve education and training systems, with a focus on advanced metallurgy; target AM industry segments in which cost, not quality, is a primary focus; and adopt international standards for metal AM-related activities. Our findings offer important lessons for other mineral-rich developing countries that may be more relevant than experiences from developed nations. Full article

Although logistics underpins the spatial architecture of supply chains, the causal contribution of logistics industry clustering to green total factor productivity (GTFP) remains under-identified relative to aggregate or manufacturing clustering. This study investigates both the local and spatial spillover effects of logistics industry clustering on green total factor productivity, utilizing panel data from 30 Chinese provinces spanning 2010 to 2023. The empirical results demonstrate that logistics industry clustering significantly enhances green total factor productivity within the local province and generates robust positive spillover effects in adjacent regions. Regional heterogeneity analysis reveals that in the eastern provinces, clustering of the logistics industry bolsters green total factor productivity both locally and regionally. In contrast, in the central region, such clustering only benefits neighboring provinces, while in the western region, its impact is not statistically significant for either local or neighboring green total factor productivity. Temporal heterogeneity analysis further indicates that the positive influence of logistics industry clustering on green total factor productivity has become more pronounced since 2018.Additionally, spatial mediation effect analysis uncovers that improvements in local green total factor productivity stem from logistics industry clustering’s capacity to enhance resource allocation efficiency and foster industrial upgrading. Notably, the spatial spillover effect dissipates entirely beyond a distance of 350 km. These findings establish logistics industry clustering as a high-leverage, cross-boundary tool for aligning regional logistics planning with green objectives, delineating the effective radius of collaboration to internalize externalities and providing practical guidance for developing economies. Full article

Over the past several decades, old industrialized regions worldwide have faced immense pressure to adapt to global economic shifts. Using one of China’s major industrial provinces, Hebei, as a representative case study, this study examines how the evolution of one of China’s old industrial provinces, Hebei’s industrial structure has influenced its economic growth from 1990 to 2023. Drawing on theories of structural transformation and endogenous growth, we argue that the reallocation of resources from lower-productivity sectors (e.g., agriculture) to higher-productivity sectors (manufacturing and services) can act as an engine of growth. We employ a shift-share analysis (SSA) to decompose Hebei’s economic growth into components attributable to national trends, industrial structure, and regional competitive performance. The results reveal a globally relevant pattern of stagnation: while Hebei’s growth largely benefited from nationwide economic expansion (national effect), its heavy industrial structure initially posed a drag on growth (negative structural effect) and its regional competitive advantage in steel and energy sectors has eroded over time (weakening competitive effect). Our regression analysis further shows that growth was overwhelmingly dependent on capital accumulation while the contribution of labor was statistically insignificant, pointing to a low-productivity trap common in such regions. By integrating these methods, this study provides a robust diagnostic framework for identifying the root causes of economic distress in legacy industrial regions both within and outside China. These findings underscore the importance of structural upgrading for sustainable growth and offer critical lessons for policymakers globally, highlighting the necessity of moving beyond extensive, capital-driven growth toward an intensive model focused on industrial diversification, innovation, and human capital to ensure the sustainable revitalization of post-industrial economies. Full article

As the world’s largest manufacturing country, China’s industrial carbon emission reduction is crucial to achieving its “dual carbon” goals. This paper takes Wuxi, a national low-carbon pilot city in Jiangsu Province, as a case, using a bottom-up factor decomposition model to study industrial carbon peak prediction and sector-specific emission reduction strategies. Results show that under the usual-growth scenario (UG), Wuxi’s industrial emissions keep growing and will not peak before 2030, reaching 122.18 million tCO₂ that year. Under the emission-controlled scenario (EC), with industrial structure optimization and energy intensity control, emissions peak in 2026 at 100.55 million tCO₂, 17.7% lower than the baseline. The reinforced-mitigation scenario (RM), combining in-depth structural adjustment and technological upgrade, sees the peak in 2025 at 94.22 million tCO₂, a 22.9% reduction. It is necessary to implement differentiated emission reduction strategies, focusing on high-emission and low-carbon productivity industries such as electricity and heat production, and ferrous metal smelting and rolling. Through precise management and control, the overall emission reduction efficiency can be improved, providing a reference paradigm for the low-carbon transformation of similar industrial cities. Full article

With the upgrading of consumption driving the transformation of the home furnishing industry towards personalized customization, panel furniture enterprises are confronted with a core contradiction between large-scale production and individualized demands: The traditional production management model is unable to cope with the chaos in production scheduling, resource waste, and low collaborative efficiency caused by small-batch and multi-variety orders. This paper proposes an intelligent production scheduling system that integrates Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Advanced Planning and Scheduling (APS), and Warehouse Management System (WMS), and elaborates on its data processing methods and specific application processes in each production stage. Compared with the traditional model, it effectively overcomes limitations such as coarse-grained planning, delayed execution, and information islands in middle-level systems, achieving deep collaboration between planning, workshop execution, and warehouse logistics. Empirical studies show that this system not only can effectively reduce the production costs of customized panel furniture manufacturers, enhance their market competitiveness, but also provides a digital transformation framework for the entire customized panel furniture manufacturing industry, with significant theoretical and practical value. Full article