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Optimal Designs of the Group Runs Exponentially Weighted Moving Average X̄ and <i>t </i>Schemes
No full textThe analysis of an X̄ scheme often assumes that the process standard deviation is accurately assessed and remains constant. However, in practice, this is rarely true. Noting that the group runs (GR) scheme performs better than the synthetic scheme, in this research, we proposed the GR exponentially weighted moving average GR EWMA X̄ and t schemes and determined their true optimal parameters using the optimisation programmes. Our findings indicate that similar to the synthetic EWMA X̄ scheme, the proposed GR EWMA X̄ scheme is not resilient to errors in the estimation of the standard deviation of the process or when the standard deviation changes. Therefore, we also proposed the GR EWMA t scheme for surveilling the mean of a process. We demonstrate that this t scheme possesses the required robust characteristic. We showcase our developed schemes’ superiority over existing schemes in a detailed performance comparison. An illustrative example related to the hard-baking process is utilised to demonstrate the applicability of the suggested schemes
Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications
No full textThe role of additive manufacturing (AM) for healthcare applications is growing, particularly in the aspiration to meet subject-specific requirements. This article reviews the application of artificial intelligence (AI) to enhance pre-, during-, and post-AM processes to meet a wider range of subject-specific requirements of healthcare interventions. This article introduces common AM processes and AI tools, such as supervised learning, unsupervised learning, deep learning, and reinforcement learning. The role of AI in pre-processing is described in the core dimensions like structural design and image reconstruction, material design and formulations, and processing parameters. The role of AI in a printing process is described based on hardware specifications, printing configurations, and core operational parameters such as temperature. Likewise, the post-processing describes the role of AI for surface finishing, dimensional accuracy, curing processes, and a relationship between AM processes and bioactivity. The later sections provide detailed scientometric studies, thematic evaluation of the subject topic, and also reflect on AI ethics in AM for biomedical applications. This review article perceives AI as a robust and powerful tool for AM of biomedical products. From tissue engineering (TE) to prosthesis, lab-on-chip to organs-on-a-chip, and additive biofabrication for range of products; AI holds a high potential to screen desired process-property-performance relationships for resource-efficient pre- to post-AM cycle to develop high-quality healthcare products with enhanced subject-specific compliance specification
Experimental study on the bond-slip behavior between the shape steel and PVA fiber concrete
No full textThis study aims to investigate the bond-slip behaviour between shaped steel and polyvinyl alcohol (PVA) fibre-reinforced concrete, a critical aspect affecting the structural performance of steel-concrete composite structures. The experiment on 14 specimens, considering various parameters such as concrete strength, cover thickness, anchorage length, PVA fibre volume, and the presence of shear studs were conducted. A novel experimental method was devised to analyse the bond-slip characteristics between shaped steel and PVA fibre concrete. All specimens exhibited failure due to the bond between shaped steel and concrete. The load-slip curve exhibited four distinct stages: the initial stage, slip stage, descending stage, and horizontal residual stage. Notably, the addition of PVA fibre significantly enhanced the load-bearing capacity, with optimal performance observed at a fibre volume of 8 kg/m3, surpassing conventional concrete bond strength. Moreover, increasing PVA concrete strength, anchorage length, and the use of shear studs were found to augment the bond strength. To further understand this bond-slip behaviour, a constitutive model correlating bond strength with characteristic slip values was developed, and it aligns well with experimental results, validating its accuracy and applicability.</p
Cyclic behavior of a replaceable LYP steel link with corrugated web: Parametrical analyses and design recommendations
No full textIn order to establish a rapid recoverable structural system in earthquake prone area, a novel replaceable low yield point steel link with corrugated web (LCSW link), consisting of the low yield point steel corrugated web with the top flange, the bottom flange, and the endplates, was proposed and tested in previous research. In this paper, a series of finite element (FE) models was established and validated to further study and understand the influence of different design parameters on the cyclic behaviors in terms of the hysteretic curves, initial stiffness, over-strength factor and cumulative energy dissipation. The analytical results indicate that the hysteretic behaviors of the specimens were obviously affected by the span-to-height ratio, the ductility and energy dissipation capacity were significantly improved by using low yield point steel (LYP steel). Furthermore, some recommendations have been introduced for the design of LCSW links in economic and safety side: the smallest ratios for flange slenderness and CSW height-to-thickness were recommended as 8.33 and 95 respectively; Flange-to-web thickness ratio was recommended to be greater than 2.0. In addition, the corrugation angle of CSW was recommended to be more than 45°, and the horizontal panel-to-wavelength ratio can be initially taken as 0.34. Finally, simple design equations for the skeleton curves were proposed and validated for LCSW links with recommended geometric dimensions
Atmospheric carbon dioxide removal using layers of lime
No full textMetal oxides such as lime (CaO and Ca(OH)2) or magnesium oxide (MgO) react spontaneously with CO2 in the air, under ambient conditions, to form stable carbonate minerals. They are therefore, being used as reactive materials to remove carbon dioxide from the atmosphere to help prevent climate change. In these technologies ‘thin’ layers of calcium or magnesium oxides/hydroxides are spread over an area of land or inside tiered structures to contact the material with CO2 in the air. The proposed thickness of these layers varies by orders of magnitude between theoretical studies, from 3 to 100 mm, however, there is no published data describing the rates of carbonation as a function of layer thickness for lime. This study monitored the carbonation reaction of 2.5, 5, 10, 25 and 50 mm layers of CaO and Ca(OH)2 in ambient temperatures and concentrations of CO2. The results show that repeated spreading of thin layers (<10 mm every 5–10 days) resulted in the largest removal rate per spatial area (>2 t CO2 ha−1 day−1). However, given that the production costs of zero carbon lime may be substantially greater than the cost of land, it may be more economical to maximise conversion through extended periods between applications
TS-Net: An Emotion Recognition Network Based on Temporal-Spatial Features of EEG Signals
No full textEEG-based emotion recognition can effectively monitor the users’ real-time emotional states, provide more objective physiological data support for mental health assessment, and thus detect the users’ potential psychological problems in a timely manner. Although existing research has made notable advancements, The biological and topological information among brain areas has not been sufficiently utilized. To the end, the paper proposes an emotion recognition network, dubbed TS-Net, based on the temporal-spatial features of EEG signals. TS-Net contains a special-purpose temporal feature extraction component (1DCNN) and a special-purpose spatial feature extraction component (gMLP), which enable it to fully analyze users’ emotional states based on the neural activity intensity in different brain functional areas. Experiment results show that TS-Net reached an overall accuracy of 97.87%, 96.79%, and 97.99% for arousal, valence, and dominance evaluated with the dataset DREAMER, respectively, which demonstrates that TS-Net has outperformed the existing advanced methods for emotion recognition. Finally, we conducted tests on two self-collected emotion classification datasets, and our model also achieved satisfactory results
Characterizing few-cycle UV resonant dispersive waves through direct field sampling
No full textWe demonstrate compression of few-cycle ultraviolet (UV) resonant dispersive waves (RDWs) generated in a cascaded hollow capillary fiber setup using a Yb laser system. Temporal characterization is performed using both tunneling ionization with a perturbation for the time-domain observation of an electric field (TIPTOE) and self-diffraction frequency-resolved optical gating (SD-FROG), which show good agreement. Through careful dispersion management, we compress the RDW pulse to 6.9 fs at a ∼390-nm central wavelength. This is the first, to our knowledge, measurement of an RDW using the TIPTOE method and demonstrates the viability of this technique to reliably characterize few-cycle UV pulses with μJ pulse energies
Semi-supervised point cloud semantic segmentation via cross-learning for sewer inspection
No full textIn recent years, notable progress has been made in sewer inspection using point cloud semantic segmentation. While deep learning-based methods have shown considerable promise for fully supervised point cloud semantic segmentation, the labor-intensive and costly process of point labeling remains a challenge. This study proposes a semi-supervised point cloud semantic segmentation (SPCSS) method based on cross-learning principles. Our SPCSS method integrates a Local Feature Extraction Network (LFEN) and a Global Feature Extraction Network (GFEN) to address the unique challenges of sonar point clouds, which exhibit sparse axial sampling (due to slow sonar traversal) and dense radial noise (from acoustic scattering). Both labeled and unlabeled point clouds are processed by LFEN and GFEN, generating predictions for each individual point. Unlabeled points are then assigned pseudo-labels derived from the outputs of LFEN and GFEN, enabling the mutual updating of network parameters and fostering cross-learning between the two networks. This cross-learning mechanism captures both local and global features, thereby addressing the non-uniform spatial distribution of point clouds. Furthermore, our SPCSS method incorporates adaptive equalization sampling and reweighting strategies to mitigate performance degradation for rare but critical categories (e.g., external outliers, sedimention) caused by class imbalance and sparse labeled data. Experimental results demonstrate that our SPCSS method outperforms other semi-supervised approaches and achieves performance on par with state-of-the-art supervised learning methods
Geometric construction and reconfiguration analysis of multi-mode two-loop spatial mechanisms and their multi-loop extensions
No full textMulti-mode multi-loop spatial mechanisms (MMSMs) are an important class of reconfigurable mechanisms, yet their diversity remains highly limited. This paper focuses on the geometric construction and reconfiguration analysis of multi-mode two-loop spatial mechanisms (MTSMs) and their extensions to MMSMs. Using the construction method, three types of MTSMs with two motion modes are synthesized by combining two classical Bricard mechanisms while constraining their undesired motion modes. Reconfiguration analysis of the proposed MTSMs is conducted using dual quaternions and the natural exponential function substitution to prove their motion characteristics. Subsequently, the construction method is extended to synthesize novel MMSMs with two motion modes. Various MMSMs are formed and further adopted to construct double-layer MMSMs for multi-mode morphing wings. Finally, the mobility properties of the double-layer MMSMs in both the contraction-expansion and parallelogram modes are substantiated through dual quaternions. This work provides a novel idea for constructing MMSMs from MTSMs without altering their motion characteristics
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