Search Results (1,608)

In recent years, China has vigorously promoted the EPC mode in the construction industry. However, under the weak trust environment of China’s construction industry, both owners and general contractors are involved in the design stage of EPC projects. Owing to conflicting interests in the design stage, there is a two-stage design interest paradox between the owners and general contractors of Chinese EPC projects, and this causes significant difficulties and challenges for project implementation. To resolve this paradox, this study proposes the “DART-PDCA” design management model by integrating value co-creation theory with the PDCA cycle. Applied to the Yuzhou High-speed Rail Station Square and Related Infrastructure PPP Project and the extended case, the model demonstrates how it resolves the paradox by (1) establishing structured dialogue platforms for aligning evolving design intentions, (2) enhancing information access and transparency through agreed protocols, and (3) facilitating dynamic risk assessment and allocation mechanisms. The results confirm that (1) the two-stage design interest paradox negatively impacts design management quality in China’s low-trust environment; and (2) the “DART-PDCA” design management model effectively resolves this paradox, leading to demonstrable improvements in design management quality, efficiency, and stakeholder alignment. This research forges novel interdisciplinary linkages among owner–general contractor relationships, design management, and EPC projects, providing critical insights into managing multi-organizational dynamics in complex EPC project environments. Full article

Integrating optical metrology into steelmaking and metalworking processes faces challenges not only from harsh conditions but also from a limited understanding of metrology concepts. The literature often overlooks distinctions between different uncertainty sources. This paper proposes a model for the quantification of uncertainty in dimensional measurements of open-die forged components, addressing the different uncertainty sources related to the measurand variability, to the instrumental uncertainty and to the definitional uncertainty. Guidelines for their evaluation are provided, and two case-studies related to measurement of forged shafts are presented and discussed. Full article

The thermal deformation behaviour of a spray-formed 7055 as-forged aluminium alloy was studied using isothermal hot-press tests under different deformation conditions (strain rates of 0.01, 0.1, 1, and 10 s⁻¹, temperatures of 340, 370, 400, 430, and 460 °C). An Arrhenius constitutive model was developed using flow stress data corrected for friction and temperature, yielding a correlation coefficient (R) of 0.9877, an average absolute relative error (AARE) of 4.491%, and a deformation activation energy (Q) of 117.853 kJ/mol. Processing maps integrating instability criteria and power dissipation efficiency identified appropriate processing parameters at 400–460 °C/0.08–0.37 s⁻¹. Furthermore, this study investigated how strain rate and temperature influence microstructural evolution. Microstructural characterization revealed that both dynamic recovery (DRV) and dynamic recrystallization (DRX) occur simultaneously during thermal deformation. At low temperatures (≤400 °C), DRV and continuous dynamic recrystallization (CDRX) dominated; at 430 °C, deformation microstructures and recrystallized grains coexisted, whereas abnormal grain growth prevailed at 460 °C. The prevailing mechanism of dynamic softening was influenced by the applied strain rate. At lower strain rates (≤0.1 s⁻¹), discontinuous dynamic recrystallization (DDRX) was the primary mechanism, whereas CDRX became dominant at higher strain rates (≥1 s⁻¹), and dislocation density gradients developed within adiabatic shear bands at 10 s⁻¹. Full article

To address the coarse and mixed grain phenomena in ultra-large martensitic stainless steel forgings, this study investigated the hot deformation behavior of 04Cr13Ni5Mo martensitic stainless steel under deformation conditions of 950–1200 °C and strain rates of 0.001–0.1 s⁻¹ using Gleeble-1500D thermomechanical simulation tests. Based on the experimental data, the flow stress curves of the steel were obtained, and a dynamic recrystallization (DRX) kinetic model was established. The model was then integrated into finite element software for simulation to verify its reliability, providing theoretical guidance for optimizing high-temperature forging processes. The results demonstrate that dynamic recrystallization in 04Cr13Ni5Mo steel occurs more readily at temperatures above 1050 °C and strain rates below 0.1 s⁻¹. Under the selected hot compression test condition (1100 °C/0.01 s⁻¹), the simulated grain size in the central deformation zone was 48.98 μm, closely matching the experimentally measured value of 48.18 μm. This agreement confirms the reliability of finite element-based prediction and control of grain size in martensitic stainless steel forgings. Full article

Al-Cu3-Si-Mg alloy prepared by molten metal die forging (MMDF) under a pressure of 118 MPa was solution-treated at different temperatures and times, and the evolution behavior of the non-equilibrium eutectic in the microstructure was observed using an optical microscope and scanning electron microscope. The results show that the initial solidification structure of Al-Cu3-Si-Mg before solution treatment consists of irregular eutectic (α+Al₂Cu), strip compound Q (Al₅Cu₂Mg₈Si₆), polygonal phase φ(Al_xTi₉La₂Ce₆Cu), spherical particle θ(Al₂Cu) and cross-shaped β(Mg₂Si) near the grain boundary. After solution treatments, the irregular eutectic at grain boundaries is dissolved. In the solution temperature range of 480 °C~510 °C, the irregular eutectic fraction decreased with the increase in solution temperature, and the grain size of other compounds such as Q (Al₅Cu₂Mg₈Si₆) and the spherical particle phase θ(Al₂Cu) also showed a decreasing trend. However, all phases do not change significantly with the increase in solution temperature when the solution temperature is between 510 °C and 540 °C. It was determined experimentally that the holding time of 30 min at each temperature is the solution limit. Based on the experimental results, a dissolution model of intergranular irregular eutectic was established as ${\displaystyle \frac{dE}{dt}}=-{\displaystyle \frac{4P\sqrt{\pi tD}+2rk}{kPD}}$. Full article

In modern industrial manufacturing, the mechanical properties of large bearing housing castings are critical to equipment reliability and lifespan. Traditional prediction methods relying on experimental testing and empirical formulas face challenges such as high costs, limited samples, and inadequate generalization capabilities. This study presents a machine learning approach for predicting mechanical properties of ZG270-500 cast steel, integrating multivariate data (chemical composition, process parameters) to establish an efficient predictive model. Utilizing real-world production data from a certain foundry and forging plant, the research implemented preprocessing steps including outlier handling, data balancing, and normalization. A systematic comparison was conducted on the performance of four algorithms: Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results indicate that under small-sample conditions, the SVR model outperforms other models, achieving a coefficient of determination (R²) between 0.85 and 0.95 on the test set for mechanical properties. The root mean square errors (RMSE) for yield strength, tensile strength, elongation, reduction in area, and impact energy are 7.59 MPa, 7.52 MPa, 0.68%, 1.47%, and 5.51 J, respectively. Experimental validation confirmed relative errors between predicted and measured values below 4%. SHAP value analysis elucidated the influence mechanisms of key process parameters (e.g., pouring speed, normalization holding time) and elemental composition on mechanical properties. This research establishes an efficient data-driven approach for large casting performance prediction and provides a theoretical foundation for guiding process optimization, thereby addressing the research gap in performance prediction for large bearing housing castings. Full article

Despite being designed considering infinite fatigue-life, failures of motor crankshafts forged from DIN 34CrNiMo6 steels have been reported in Brazilian power plants. As such, the present work aims to discuss the failure of a crankshaft within this context, with the purpose of verifying whether the stresses developed in critical locations of the component were in accordance with the steel’s fatigue limits, as well as if the material exhibits an adequate resistance to crack propagation. Taking into consideration a set of critical-plane stress-based multiaxial fatigue criteria, namely Findley, Matake, McDiarmid and Susmel and Lazzarin, the fatigue behaviour of the material is analysed and discussed. Furthermore, $da/dN$ versus $\mathsf{\Delta }K$ experiments were carried out with the purpose of determining the DIN 34CrNiMo6 steel’s crack growth threshold $\mathsf{\Delta }{K}_{th}$ and comparing it to the $\mathsf{\Delta }{K}_{th}$ of three other commercially available steels (DIN 42CrMo4, SAE 4140 and SAE 4340). The selected multiaxial fatigue criteria indicated that the stresses developed throughout the component were not sufficient to drive the crankshaft to failure, thus indicating safety. On the other hand, the DIN 34CrNiMo6 steel presented the lowest $\mathsf{\Delta }{K}_{th}$ ($6.6\mathrm{M}\mathrm{P}\mathrm{a}{\mathrm{m}}^{1/2}$) among all the considered steels ($10.86$, $12.38$ and $7.22\mathrm{M}\mathrm{P}\mathrm{a}{\mathrm{m}}^{1/2}$ for the DIN 42CrMo4, SAE 4140 and SAE 4340, respectively), therefore being susceptible to shorter fatigue lives in comparison to the other materials. Full article

This study introduces a promising authentication framework utilizing brain–computer interface (BCI) technology to enhance both security protocols and user experience. A key strength of this approach lies in its reliance on objective, physiological signals—specifically, brainwave patterns—which are inherently difficult to replicate or forge, thereby providing a robust foundation for secure authentication. The authentication system was developed and implemented in four sequential stages: signal acquisition, preprocessing, feature extraction, and classification. Objective feature extraction methods, including Fisher’s Linear Discriminant (FLD) and Discrete Wavelet Transform (DWT), were employed to isolate meaningful brainwave features. These features were then classified using advanced machine learning techniques, with Quadratic Discriminant Analysis (QDA) and Convolutional Neural Networks (CNN) achieving accuracy rates exceeding 99%. These results highlight the effectiveness of the proposed BCI-based system and underscore the value of objective, data-driven methodologies in developing secure and user-friendly authentication solutions. To further address usability and efficiency, the number of BCI channels was systematically reduced from 64 to 32, and then to 16, resulting in accuracy rates of 92.64% and 80.18%, respectively. This reduction streamlined the authentication process, demonstrating that objective methods can maintain high performance even with simplified hardware and pointing to future directions for practical, real-world implementation. Additionally, we developed a real-time application using our custom dataset, reaching 99.75% accuracy with a CNN model. Full article

The pursuit of universal, symmetric semantic representations within large language models (LLMs) faces a fundamental challenge: the inherent asymmetry of natural languages. Different languages exhibit vast disparities in syntactic structures, lexical choices, and cultural nuances, making the creation of a truly shared, symmetric embedding space a non-trivial task. This paper aims to address this critical problem by introducing a novel framework to forge robust and symmetric multilingual sentence embeddings. Our approach, named DACL (Dynamic Asymmetric Contrastive Learning), is anchored in two powerful asymmetric learning paradigms: Contrastive Learning and Dynamic Curriculum Learning (DCL). We extend Contrastive Learning to the multilingual context, where it asymmetrically treats semantically equivalent sentences from different languages (positive pairs) and sentences with distinct meanings (negative pairs) to enforce semantic symmetry in the target embedding space. To further refine this process, we incorporate Dynamic Curriculum Learning, which introduces a second layer of asymmetry by dynamically scheduling training instances from easy to hard. This dual-asymmetric strategy enables the model to progressively master complex cross-lingual relationships, starting with more obvious semantic equivalences and advancing to subtler ones. Our comprehensive experiments on benchmark cross-lingual tasks, including sentence retrieval and cross-lingual classification (XNLI, PAWS-X, MLDoc, MARC), demonstrate that DACL significantly outperforms a wide range of established baselines. The results validate our dual-asymmetric framework as a highly effective approach for forging robust multilingual embeddings, particularly excelling in tasks involving complex linguistic asymmetries. Ultimately, this work contributes a novel dual-asymmetric learning framework that effectively leverages linguistic asymmetry to achieve robust semantic symmetry across languages. It offers valuable insights for developing more capable, fair, and interpretable multilingual LLMs, emphasizing that deliberately leveraging asymmetry in the learning process is a highly effective strategy. Full article

Pairing-free certificateless signature (PF-CLS) schemes are ideal authentication solutions for resource-constrained environments like the Internet of Things (IoT) due to their low computational, storage, and communication resource requirements. However, it has come to light that many PF-CLS schemes are vulnerable to forged signature attacks. In this paper, we use a novel attack method to prove that a class of PF-CLS schemes with the same security vulnerabilities cannot resist Type I adversary attacks, and we find that, even if some schemes are improved to invalidate existing attack methods, they still cannot defend against the new attack method proposed in this paper. Subsequently, we introduce an enhanced scheme proven to be resilient against both types of adversary attacks under the random oracle model (ROM). Performance analysis shows that, compared with several existing PF-CLS schemes, our scheme offers higher computational efficiency. Full article

With the rapid development of information technology and the deep integration of the Internet platform, the scale and form of financial transactions continue to grow and expand, significantly improving users’ payment experience and life efficiency. However, financial transactions bring us convenience but also expose many security risks, such as money laundering activities, forged checks, and other financial fraud that occurs frequently, seriously threatening the stability and security of the financial system. Due to the imbalance between the proportion of normal and abnormal transactions in the data, most of the existing deep learning-based methods still have obvious deficiencies in learning small numbers sample classes, context modeling, and computational complexity control. To address these deficiencies, this paper proposes a symmetrical structure-based GAN-CNN model for lightweight financial fraud detection. The symmetrical structure can improve the feature extraction and fusion ability and enhance the model’s recognition effect for complex fraud patterns. Synthetic fraud samples are generated based on a GAN to alleviate category imbalance. Multi-scale convolution and attention mechanisms are designed to extract local and global transaction features, and adaptive aggregation and context encoding modules are introduced to improve computational efficiency. We conducted numerous replicate experiments on two public datasets, YelpChi and Amazon. The results showed that on the Amazon dataset with a 50% training ratio, compared with the CNN-GAN model, the accuracy of our model was improved by 1.64%, and the number of parameters was reduced by approximately 88.4%. Compared with the hybrid CNN-LSTM–attention model under the same setting, the accuracy was improved by 0.70%, and the number of parameters was reduced by approximately 87.6%. The symmetry-based lightweight architecture proposed in this work is novel in terms of structural design, and the experimental results show that it is both efficient and accurate in detecting imbalanced transactions. Full article

Against the background of global climate change and increasing ecological vulnerability, enhancing ecosystem resilience has become a core task for coping with environmental shocks and achieving sustainable development. The urban energy structure plays a critical role in influencing the green development of the economy and the enhancement of environmental resilience. Existing studies have revealed the role of energy structure transformation in the identification of macroeconomic performance and environmental outcomes, but have neglected its impact on ecosystem resilience. This paper exploits the implementation of the New Energy Demonstration City pilot policy as a quasi-natural experiment. Using panel data of Chinese prefecture-level cities from 2010 to 2022, it constructs a multidimensional evaluation system of urban ecosystem resilience and employs a difference-in-differences (DID) model to empirically examine the impact of energy structure transformation on urban ecosystem resilience. It is found that energy structure transition significantly enhances urban ecosystem resilience, and this conclusion is verified through a series of robustness tests. Mechanism analysis shows that energy structure transformation comprehensively enhances urban ecosystem resilience through strengthening institutional regulation, optimizing resource allocation, promoting energy substitution, and enhancing public awareness. Heterogeneity analysis indicates that the strengthening effect of energy structure transition on urban ecosystem resilience is inclusive, and that this positive effect is greater in cities characterized by lower resource endowment and weaker governance capacity. This paper reveals the intrinsic mechanism of urban energy transition for ecological resilience enhancement, and provides an energy transition path for building more resilient urban ecosystems. Full article

To address incomplete die filling, high cracking tendency, and severe die wear in the conventional forging of AISI-410 martensitic stainless steel U-shaped forgings, an optimized billet volume pre-allocation strategy was proposed. Two improved forging schemes for the U-shaped forgings were designed: the Arc Concave Flattening Scheme (adding arc-shaped concave features to the flattening die for corner volume compensation) and Preformed Volume Allocation Scheme (incorporating a preforming step for strategic volume pre-allocation at ends and corners). Finite Element Analysis employing the Oyane damage model and Archard wear model was employed to simulate and optimize the forging process. The optimal scheme was applied to production trials. The results demonstrated that the Preformed Volume Allocation Scheme significantly improved the geometric compatibility between the billets and the final forging die cavity. As a result, the billet’s temperature, strain, and equivalent stress uniformity increased, reducing cracking tendency. Moreover, the rise in the mitigated temperature and stress concentration resulted in reduced final forging die wear. Production trials confirmed a qualified rate of ~96% (34% higher than the Original Scheme). The final forging die service life reached 300 pieces per refurbishment cycle, showing a 50% improvement. This work provides theoretical and practical guidance for optimizing the forging processes of complex martensitic stainless steel components. Full article

This study investigates the influence of temperature measurement accuracy on tool failure mechanisms in industrial hot forging processes. Challenges related to extreme operational conditions, including high temperatures, limited access to measurement surfaces, and optical interferences, significantly hinder reliable data acquisition. Thermal imaging, pyrometry, thermocouples, and finite element modeling were employed to characterize temperature distributions in forging tools and billets. Analysis of multi-stage forging of stainless steel valve forgings revealed significant discrepancies between induction heater settings and actual billet surface temperatures, measured by thermal imaging. This thermal non-uniformity led to localized underheating and insufficient dissolution of hard inclusions, confirmed by dilatometric tests, resulting in billet jamming and premature tool failure. In slender bolt-type forgings, excessive or improperly controlled billet temperatures increased adhesion between the forging and tool surface, causing process resistance, billet sticking, and accelerated tool degradation. Additional challenges were noted in tool preheating, where non-uniform heating and inaccurate temperature assessment compromised early tool performance. Measurement errors associated with thermal imaging, particularly due to thermal reflections in robotic gripper monitoring, led to overestimated temperatures and overheating of gripping elements, impairing forging manipulation accuracy. The results emphasize that effective temperature measurement management, including cross-validation of methods, is crucial for assessing tool condition, enhancing process reliability, and preventing premature failures in hot forging operations. Full article

The aim of this work was to assess the developments in the Greek collective bargaining system and the wage policy after the period of the Memoranda of Understanding with the lenders. Moreover, it discusses the critical role of collective bargaining (CB) in the Greek economy and society and its contributions to forging a new balance between capital and labor in the post-memoranda era. Finally, it provides a number of proposals that could improve the state of play in the field. Firstly, a comprehensive approach to the current debate on the key issues of collective bargaining was portrayed. Secondly, the main developments in the Greek case of collective bargaining and the wage policy were recorded. Thirdly, an effort to interpret the pertinent developments that could lead to the diffusion of a decent work status in the domestic labor market was made. Finally, this work examined whether the current situation of collective bargaining threatens Greek employees’ living and working conditions, which were regarded as being at stake during the memoranda period. Full article