High Precision Research Articles

Big data analytics for quality variation over work shifts in manufacturing systems

ABSTRACT In the manufacturing industry, mass production enables manufacturers to produce parts with high precision and lower costs. Multiple shifts operate production processes to maximize efficiency. Several researches have been conducted on the impact of shift work on labor’s health and habits. However, there have been few studies on the influence of shift work on the consistency of product quality. This paper provides a methodology to analyze the impact of different work shifts in a real electronic board manufacturing industry. The study uses big data and analytics to assess product quality from data. Non-parametric kernel density estimation is used to approximate the distribution of good products in each work slot. Then several metrics are used to measure the dissimilarity between the estimated densities. The approach can be leveraged in various problems related to process performance and quality. The obtained results show that there is no significant difference in terms of product quality between work shifts. These prove the consistency of the manufacturing processes and the homogeneity of performance across work shifts in the studied factory. Compared to the literature, the paper presents the first quantitative analysis to compare production process performance and product quality over shift works.

Effect of varied beam diameter of picosecond laser on Foturan glass volume microprocessing

Foturan glass is a photosensitive transparent material which has attracted much interest for microfluidic applications due to possibility of volume processing by ultrafast lasers. In this work, we have investigated the effect of picosecond laser on volume processing in Foturan glass when varying the beam diameter incident on a lens. To this end, specific laser focusing configurations have been designed using raytracing models and an analysis protocol has been developed in the lens focusing region in order to describe the focal point displacement occurring at the variation of the incident laser beam diameter. The numerically simulated results were explained in association with Rayleigh length and found to be in good agreement with the experimental data obtained at well-defined conditions. Specifically, it was found that the hollow microstructures developed by thermal treatment and chemical etching after laser irradiation were significantly displaced along the propagation direction when the incident beam diameter varied in the range of 1-3.5 times. This approach aims to bring an essential contribution to the field of ultrashort pulse lasers micro- and nanoprocessing in transparent materials proving that the laser beam focus position and its size can be precisely controlled with high precision by automated optics for the variation of incident laser beam diameter in predefined conditions. This approach has the potential for laser multi-beam processing at various volume depths using the same optics setup and may even be applicable to two-photon excitation microscopy. On the other hand, the processing protocol in Foturan glass may allow understanding transparent material modification by tailoring laser beam characteristics.

TerGEC: A graph enhanced contrastive approach for program termination analysis

ContextPrograms with non-termination behavior induce various bugs, such as denial-of-service vulnerability and memory exhaustion. Hence the ability to detect non-termination programs before software deployment is crucial. Existing detection methods are either execution-based or deep learning-based. Despite great advances, their limitations are evident. The former requires complex sandbox environments for execution, while the latter lacks fine-grained analysis. ObjectiveTo overcome the above limitations, this paper proposes a graph-enhanced contrastive approach, namely TerGEC, which combines both inter-class and intra-class semantics to carry out a more fine-grained analysis and exempt execution during the detection process. MethodsIn detail, TerGEC analyzes behaviors of programs from Abstract Syntax Trees (ASTs), thereby capturing intra-class semantics both syntactically and lexically. Besides, it incorporates contrastive learning to learn the discrepancy between program behaviors of termination and non-termination, thereby acquiring inter-class semantics. In addition, graph augmentation is designed to improve the robustness. Weighted contrastive loss and focal loss are also equipped in TerGEC to alleviate the classes-imbalance problem during the non-termination detection. Consequently, the whole detection process can be handled more fine-grained, and the execution can also be exempted due to the nature of deep learning. ResultsWe evaluate TerGEC on five datasets of both Python and C languages. Extensive experiments demonstrate TerGEC achieves the best performance overall. Among all experimented datasets, TerGEC outperforms state-of-the-art baselines by 8.20% in terms of mAP and by 17.07% in terms of AUC on average. ConclusionTerGEC is capable of detecting non-terminating programs with high precision, showing that the combination of inter-class and intra-class learning, along with our proposed classes-imbalance solutions, is significantly effective in practice.

Reinforcement learning for path planning of free-floating space robotic manipulator with collision avoidance and observation noise

This study introduces a novel approach for the path planning of a 6-degree-of-freedom free-floating space robotic manipulator, focusing on collision and obstacle avoidance through reinforcement learning. It addresses the challenges of dynamic coupling between the spacecraft and the robotic manipulator, which significantly affects control and precision in the space environment. An innovative reward function is introduced in the reinforcement learning framework to ensure accurate alignment of the manipulator’s end effector with its target, despite disturbances from the spacecraft and the need for obstacle and collision avoidance. A key feature of this study is the use of quaternions for orientation representation to avoid the singularities associated with conventional Euler angles and enhance the training process’ efficiency. Furthermore, the reward function incorporates joint velocity constraints to refine the path planning for the manipulator joints, enabling efficient obstacle and collision avoidance. Another key feature of this study is the inclusion of observation noise in the training process to enhance the robustness of the agent. Results demonstrate that the proposed reward function enables effective exploration of the action space, leading to high precision in achieving the desired objectives. The study provides a solid theoretical foundation for the application of reinforcement learning in complex free-floating space robotic operations and offers insights for future space missions.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model’s performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model’s efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

An Improved Fire Detection Approach Based On Yolo-v8 for Smart Cities

Systems for detecting fires are essential for preventing property damage and saving lives. defending people and property. Conventional techniques frequently depend on sensor-based strategies, which have limitations in intricate settings. In order to improve accuracy and efficiency, this study suggests an intelligent fire detection system that makes use of machine learning and computer vision techniques. The technology analyzes video streams in real time using deep learning algorithms to identify fire incidents based on visual patterns and attributes. Future research on fire detection systems will benefit from the information this study will provide for smoker and fire detection issues in both indoor and outdoor situations. The improved fire detection technique for smart cities that is based on the YOLOv8 algorithm is the smart fire detection system (SFDS), which uses deep learning to identify fire-specific properties in real-time. The SFDS strategy may be more cost-effective, reduce false alarms, and improve fire detection accuracy when compared to traditional methods. It can also be extended to find other intriguing aspects of smart cities, such as gas leakage or flooding. The proposed smart city framework consists of four primary levels: the application layer (i), cloud layer (iii), fog layer (ii), and internet of things layer (iv). The recommended technique uses fog, cloud computing, and the Internet of Things layer to collect and understand data in real time. This reduces the chance of damage to persons or property and enables faster reaction times. The SFDS demonstrated state- of-the-art performance in terms of precision and recall, with a high precision rate of 97.1% across all classes. Among the potential applications are intelligent security systems, forest fire monitoring, and public space fire safety management.

High Precision Raman Distributed Fiber Sensing Using Residual Composite Dual-Convolutional Neural Network

High Precision Raman Distributed Fiber Sensing Using Residual Composite Dual-Convolutional Neural Network

Multi-Bottleneck progressive propulsion network for medical image semantic segmentation with integrated macro-micro dual-stage feature enhancement and refinement

Semantic segmentation can assist doctors to locate key lesion areas intelligently, rapidly and with high precision in medical imaging, which is of great clinical significance for the diagnosis and rehabilitation of patients in various medical fields such as oncology and neurology. Existing segmentation networks face accuracy bottlenecks because enriched information at the decoding end can’t disperse over multiple layers of semantic depth. This is compounded by challenges like complex anatomical structures and varying imaging modalities. In this study, we propose a Multi-Bottleneck Progressive Semantic Segmentation Network (MBP-SSNet), which is essentially a new Baseline based on our proposal: Multi-Bottleneck based Progressive Propulsion Feature Guidance Network (MB-PPFG Net). Its Multi-Bottleneck solves the problem of coding and decoding multiplexed transmission of enriched information, and the propulsive transmission alleviates the problem of insufficient semantic fusion. In addition, under the macro–micro processing idea, MacroFocus Pre-Bottleneck Feature Enhancer (MPBFE) is proposed at the codec intersection in order to better enhance the image contextual semantic dependency association; while MicroDetail Post- Decoding Feature Refiner (MPDFR) is proposed at the tail of the network for fine-grained polishing. In the experiment, the MBP-SSNet was compared with 17 other models across 7 datasets, including diverse medical imaging scenarios, involving metric exploration, image comparison, process validation, and statistical analysis. Concurrently, 22 types of ablation analyses were conducted on it, along with detailed discussions on two critical issues. The experimental results demonstrate that MBP-SSNet achieves an average performance improvement of 0.6809 % in medical image segmentation compared to the optimal benchmark model. The official web address of MBP-SSNet is at: https://github.com/YF-W/MBP-SSNet.

Oxygen Isotopologues Resolved from Water Oxidation Electrocatalysis by Electron Paramagnetic Resonance Spectroscopy.

Electrocatalytic water oxidation is a key transformation in many strategies designed to harness solar energy and store it as chemical fuels. Understanding the mechanism(s) of the best electrocatalysts for water oxidation has been a fundamental chemical challenge for decades. Here, we quantitate evolved dioxygen isotopologue composition via gas-phase EPR spectroscopy to elucidate the mechanisms of water oxidation on metal oxide electrocatalysts with high precision. Isotope fractionation is paired with computational and kinetic modeling, showing that this technique is sensitive enough to differentiate O-O bond-forming steps. Strong agreement between experiment and theory indicates that for the nickel-iron layered double hydroxide─one of the best earth-abundant electrocatalysts to be studied─water oxidation proceeds via a dioxo coupling mechanism to form a side-bound peroxide rather than a hydroxide attack to form an end-bound peroxide.

Equipment design and parameters recommendation of rubber modified 3D printed asphalt in pavement maintenance

As an advanced technology for pavement crack maintenance with high automation level, high precision and high efficiency, 3D asphalt printing technology can effectively improve worker safety and ensure the stability of maintenance quality. In order to optimize the printing parameters of rubber modified 3D printed asphalt (R-3DPA), a three-axis asphalt 3D printer was developed in this study through the design and selection of each component, and it was used for the 3D printing of R-3DPA strips, layers, and structures. Based on the comparison of continuity, uniformity, and width of R-3DPA strips, the recommended extrusion nozzle diameter, printing temperature, and printing speed parameter combinations were superiorly selected. According to the analysis of filling degree, width error and thickness of R-3DPA layers, the optimal printing strip width was recommended. Through the evaluation of integrity, surface characteristics and accuracy of R-3DPA structures, the suitable printing layer thickness was determined. The test results showed that the three-axis asphalt 3D printer can be used to measure the parameters of R-3DPA, and the printing accuracy and scale meet the requirements. The recommended parameters for R-3DPA are an extrusion nozzle diameter of 2 mm, a printing temperature of 180 °C, a printing speed of 20 mm/s, a printing strip width of 2.25 mm and a printing layer thickness of 0.4 mm, resulting in structures with good integrity and precision.

Drop‐on‐Demand Electrohydrodynamic Printing of Nematic Liquid Crystals

In this paper, electrohydrodynamic (EHD) printing of nematic liquid crystals (LCs) is demonstrated. Miniscule LC droplets, as small as 1 micron, are generated with the EHD printing system and deposited with high precision onto a glass substrate. Herein, we investigate how the voltage waveform and pulse frequency applied to the print nozzle influences the dynamics of the deposition process and the final landed footprint diameter of the printed spherical‐cap‐shaped LC droplets at the glass substrate. To complement results from high‐speed shadowgraphy imaging, simulations were employed to model the jetting process and the formation of the Taylor Cone. Using EHD printing, we show results for two different printing modes: cone‐jetting and micro‐dripping. We highlight the benefits and drawbacks of each mode and conclude with the demonstration of a printed alphanumeric pattern that showcases the capability of EHD printing to deposit very small volumes of nematic LC in order to form well‐defined spatial patterns.This article is protected by copyright. All rights reserved.

Quality Control of mRNA Vaccines by Synthetic Ribonucleases: Analysis of the Poly-A-Tail.

The effectivity and safety of mRNA vaccines critically depends on the presence of correct 5' caps and poly-A tails. Due to the high molecular mass of full-size mRNAs, however, the direct analysis by mass spectrometry is hardly possible. Here we describe the use of synthetic ribonucleases to cleave off 5' and 3' terminal fragments which can be further analyzed by HPLC or by LC-MS. Compared to existing methods (e.g. RNase H), the new approach uses robust catalysts, is free of sequence limitations, avoids metal ions and combines fast sample preparation with high precision of the cut.

Microscopic modeling and experimental investigation of inner surface magnetorheological polishing based on particle micromechanics

Traditional surface polishing methods are no longer able to meet the ultra-precision requirements of the high-tech industry for the inner surface of the pipe, but magnetorheological polishing technology is very suitable due to its advantages of high precision, fast controllability and good deposition stability. However, there is even less investigation on the microforce analysis, chaining mechanism and micromodeling methods of magnetorheological polishing fluid (MRPF), and the polishing mechanism of MRPF has not been explored yet. As a step to completely develop the magnetorheological polishing (MRP) technique, this paper proposed the simulation method of MRPF based on particle dynamics, and the shear stress model of magnetorheological fluid (MRF) is optimized under the action of the magnetic field after performing the chain simulation. On the basis of the optimized shear stress model and the hexagonal close-packed structure of MRF, the holding mechanism of polishing abrasive particles is explored for the MRPF and the corresponding holding models are proposed. Then, the shear yield stress models and material removal model are also established for the inner surface polishing, respectively. Eventually, the above theoretical analysis and related models have been verified though the polishing experiment of the titanium alloy pipe.

Numerical Study of Drilling Parameters with Al 356 Alloy Using Bacterial Foraging Optimization

Numerical Study of Drilling Parameters with Al 356 Alloy Using Bacterial Foraging Optimization

Highly precisive arrangement of continuous carbon fiber and its reinforcing effect on hydroxyapatite

In order to efficiently prepare continuous fiber reinforced composites with high mechanical property, a macro-micro operation device was developed to arrange fiber with high precision. Continuous carbon fiber reinforced hydroxyapatite (CF/HA) composites with highly precisive fiber arrangement was subsequently prepared according to a demanded performance. The arranged fiber spacing and fiber angle of the CF preforms by this device was 101.15 ± 11.72 μm and 0.14 ± 0.38°, respectively. Compared to the traditional arrangement with the arranged fiber spacing of about 300–500 μm, the arrangement accuracy by this device enhanced about 66 %–80 %. The continuous CF/HA composites with designed fiber spacing prepared by this device possessed synchronously enhanced toughness and strength. Compressive strength of the CF/HA composites with designed fiber spacing of 300 μm (CF03p/HA) increased by 311.54 % compared with pure HA. Fracture toughness of the CF03p/HA composites was 1.68 MPa m1/2. Even though the fiber content in CF03p/HA composites was only one-sixth to one-tenth of that in the composites prepared by the traditional method, the compressive strength was 4.12 times that of pure HA. The study provides an effective method for preparing micro fiber reinforced composites featuring excellent mechanical performance even by adding low fiber content.

Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network

Speckle-shearing technology is widely used in defect detection due to its high precision and non-contact characteristics. However, the wrapped-phase recording defect information is often accompanied by a lot of speckle noise, which affects the evaluation of defect information. To solve the problems of traditional denoising algorithms in suppressing speckle noise and preserving the texture features of wrapped phases, this study proposes a speckle denoising algorithm called a speckle denoising convolutional neural network (SDCNN). The proposed method reduces the loss of texture information and the blurring of details in the denoising process by optimizing the loss function. Different from the previous simple assumption that the speckle noise is multiplicative, this study proposes a more realistic wrapped image-simulation method, which has better training results. Compared with representative algorithms such as BM3D, SDCNN can handle a wider range of speckle noise and has a better denoising effect. Simulated and real speckle-noise images are used to evaluate the denoising effect of SDCNN. The results show that SDCNN can effectively reduce the speckle noise of the speckle-shear wrapping phase and retain better texture details.

Miniaturized matrix solid-phase dispersion assisted by deep eutectic solvent for acrylamide determination in bread samples.

Acrylamide determination is important to state its quantity in baked food preventing any potential carcinogenic effects. Matrix solid-phase dispersion (MSPD) extraction is an extraction procedure based on a homogenization phase between a solid sample and a solid dispersing material to break sample increasing analyte extraction yield, often used for acrylamide determination. The addition of a green deep eutectic solvent (DES) during the MSPD homogenization phase improves the analyte extraction, giving the possibility to reduce the amount of organic solvent used. In this work, a miniaturized MSPD extraction assisted by a DES was developed to determine acrylamide in bread, using high-performance liquid chromatography coupled with mass spectrometry detection. The optimized procedure provides 1:1 (w/w) matrix-to-dispersing material ratio, 2mL of methanol as extraction solvent, and 50 μL of choline chloride-glycerol DES added during the homogenization phase. Method validation ensured good results with minimum recoveries of 90%, high precision with a maximum intra-day error of 4%, and inter-day error of 6%. Limit of detection and limit of quantification resulted to be 16μg/kg and 35μg/kg, respectively. This miniaturized extraction procedure represents a good alternative to those reported in the literature, guaranteeing great performance and respecting green chemistry principles.

Predictive mapping of organic carbon stocks in surficial sediments of the Canadian continental margin

Abstract. Quantification and mapping of surficial seabed sediment organic carbon have wide-scale relevance for marine ecology, geology and environmental resource management, with carbon densities and accumulation rates being a major indicator of geological history, ecological function and ecosystem service provisioning, including the potential to contribute to nature-based climate change mitigation. While global analyses can appear to provide a definitive understanding of the spatial distribution of sediment carbon, regional maps may be constructed at finer resolutions and can utilise targeted data syntheses and refined spatial data products and therefore have the potential to improve these estimates. Here, we report a national systematic review of data on organic carbon content in seabed sediments across Canada and combine this with a synthesis and unification of the best available data on sediment composition, seafloor morphology, hydrology, chemistry and geographic settings within a machine learning mapping framework. Predictive quantitative maps of mud content, dry bulk density, organic carbon content and organic carbon density were produced along with cell-specific estimates of their uncertainty at 200 m resolution across 4 489 235 km2 of the Canadian continental margin (92.6 % of the seafloor area above 2500 m) (https://doi.org/10.5683/SP3/ICHVVA, Epstein et al., 2024). Fine-scale variation in carbon stocks was identified across the Canadian continental margin, particularly in the Pacific Ocean and Atlantic Ocean regions. Overall, we estimate the standing stock of organic carbon in the top 30 cm of surficial seabed sediments across the Canadian shelf and slope to be 10.9 Gt (7.0–16.0 Gt). Increased empirical sediment data collection and higher precision in spatial environmental data layers could significantly reduce uncertainty and increase accuracy in these products over time.

Feasibility and safety report on robotic assistance for cervical pedicle screw fixation: a cadaveric study

This cadaveric study aimed to evaluate the safety and usability of a novel robotic system for posterior cervical pedicle screw fixation. Three human cadaveric specimens and C2-T3 were included. Freshly frozen human cadaver specimens were prepared and subjected to robot-assisted posterior cervical pedicle screw fixation using the robotic system. The accuracy of screw placement, breach rate, and critical structure violations were evaluated. The results were statistically compared with those of previous studies that used different robotic systems for cervical pedicle screw fixation. The robotic system demonstrated a high accuracy rate in screw placement. A significant number of screws were placed within predetermined safe zones. The total entry offset was 1.08 ± 0.83 mm, the target offset was 1.86 ± 0.50 mm, and the angle offset was 2.14 ± 0.77°. Accuracy rates comparable with those of previous studies using different robotic systems were achieved. The system was also feasible, allowing precise navigation and real-time feedback during the procedure. This cadaveric study validated the safety and usability of the novel robotic system for posterior cervical pedicle screw fixation. The system exhibited high precision in screw placement, and the results support the extension of the indications for robot-assisted pedicle screw fixation from the lumbar spine to the cervical spine.

Q-switched temperature fiber sensing device based on graded index and D-shaped multimode fiber

In the paper, a Q-switched temperature sensing device is proposed and verified, which consists of a modulator based on a large core multimode fiber and a graded index multimode optical fiber (LMF-GIMF), and a sensing unit formed by sandwiching a D-shaped multimode fiber (MMF) between two single mode fibers (SMF). Utilizing the modulator to transform continuous light into pulsed laser, a stable Er3+-doped Q-switched laser with a repetition frequency of 23.04 kHz and a modulation depth of 7.47 % has been achieved. Under its pumping, the SMF-MMF-SMF sensing structure performed thermal detection with a sensitivity of −81.5 pm/℃ as temperature varies in the range of 25 °C to 65 °C. The temperature sensing device based on Q-switching can be used as an independent pulse laser to realize the switch between the light source and the sensing device. At the same time, the sensing device can provide real-time feedback according to the change of the central wavelength, which allows for immediate adjustments in the operational state, enhancing the flexibility and responsiveness. In addition, the characteristics of the sensor based on Q-switched laser can reduce thermal damage and extends the service life of the sensor. As far as we know, this is the first report on Q-switched temperature sensing based on the LMF-GIMF modulator. The proposed sensing device has the advantages of high signal-to-noise ratio, low operating threshold, minimal thermal damage, and serves as an independent Q-switched laser source, which can be applied to pulse laser measurement with high precision and time resolution in the future.