Accurate Thickness Research Articles

Impact of plasma power on plasma enhanced atomic layer deposited TiO2 as a spacer

As the size of devices is scaled down, micro-patterning technology is increasing in importance. In micro-patterning, spacers require precise thickness and uniform deposition at low temperature. To meet these requirements, atomic layer deposition (ALD), which can be controlled to an accurate thickness, was introduced. Therefore, we investigated plasma-enhanced ALD using radicals for high reactivity. Optical emission spectroscopy and a Langmuir probe were used to evaluate plasma properties such as radical intensity and plasma density at different plasma powers. Regardless of plasma power, growth per cycle and compositions of Ti and O remained constant. X-ray photoelectron spectroscopy showed that application of 500 W decreased the area of Ti2O3 from 20.2 % to 4.5 % compared with 100 W due to the increased amount of oxygen radicals. X-ray reflectivity results showed a 4.09 g/cm3 density of TiO2 film at 100 W, which increased to 4.2 g/cm3 at 500 W, indicating a decrease of Ti2O3. Meanwhile, the wet etch rate of TiO2 film was 1.72 Å/min at 100 W and 0.8 Å/min at 500 W, and the denser film had superior etch resistance. Finally, transmission electron microscopy showed that step coverage of TiO2 films improved from 90.9 % to 99.9 % with increasing power. Thus, application of high power effectively improved the properties of TiO2 films.

Anatomical compensation of retinal nerve fiber layer improves the detection of glaucoma between ethnicities.

The study aimed to evaluate the impact of compensating retinal nerve fiber layer (RNFL) thickness for demographic and anatomical factors on glaucoma detection in Chinese and Indian adults. A population-based study included 1995 healthy participants (1076 Chinese and 919 Indians) to construct a multivariable linear regression compensation model. This model was applied to 357 Chinese glaucoma patients, 357 healthy Chinese, and 357 healthy Indians using Cirrus spectral-domain optical coherence tomography (OCT). The compensated RNFL thickness considered age, refractive error, optic disc parameters, and retinal vessel density. Results showed that although the average RNFL thickness was significantly higher in Chinese participants compared to Indians, the compensation model reduced this difference to nonsignificance. Moreover, the compensation model significantly improved the area under the receiver operating characteristic curve (0.90 vs. 0.78; p<0.001), sensitivity (75% vs. 51%), and specificity (67% vs. 32%) in distinguishing Chinese glaucoma patients from healthy Indian individuals. The compensation model significantly enhanced the diagnostic accuracy of RNFL thickness in distinguishing glaucoma in the Chinese ethnic group compared to the OCT instrument's default values. These results suggest that modifying RNFL measurements based on individual characteristics can yield substantial benefits for glaucoma detection across ethnicities.

Multiple dimensional rheology approach for oral texture prediction of yogurts

Rheological approach has been widely applied to investigate the flow behavior of fluid food for the understanding as well as prediction of oral textural sensation of such products. However, since oral texture sensation is highly dynamic and influenced by multiple contributing factors (i.e. oral temperature, oral processing time, saliva mixing, and varied shear rate), conventional rheological measurements can hardly be precise in predicting oral textural sensation and mouthfeel. Here, we propose a very different rheology approach, which synchronizes four major dimensions/variables in one set of rheological measurements: the oral residence time, the bolus temperature, the saliva ratio, and the oral shear rate. Results indicated that the multiple dimensional rheology approach improved the predictive accuracy of the in-mouth thickness of low-temperature yogurt and provided a new measure for quantifying the mouth-melting of yogurt. It appeared that apparent viscosity at a temperature of 13 °C, a saliva ratio of 20 %, and a shear rate of 1 s-1 gave the most reliable prediction of the in-mouth thickness of yogurt. This novel approach provides a much-improved accuracy of texture prediction which requires a minimal number of rheology tests. We believe that, even though this experimental approach was initially designed for low-temperature yogurt drinks, it is also applicable to the texture and mouthfeel prediction of many other food products.

An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits

Fusing three-dimensional (3D) and multispectral (MS) imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge. Acquiring high-quality 3D MS point clouds (3DMPCs) of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure. Here, we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field (NeREF) for radiometric calibration. This approach was used to acquire 3DMPCs of perilla, tomato, and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness (EWT) estimation. The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6% compared with the fixed viewpoints alone. The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error (RMSE) of 58.93% for extracted reflectance spectra. The RMSE for chlorophyll content and EWT predictions decreased by 21.25% and 14.13% using partial least squares regression with the generated 3DMPCs. Collectively, our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions, which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits, and thus will facilitate plant biology and genetic studies as well as crop breeding.

Accurate Ultrasonic Thickness Measurement for Arbitrary Time-Variant Thermal Profile.

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods.

Efficient and Accurate 3D Thickness Measurement in Vessel Wall Imaging: Overcoming Limitations of 2D Approaches Using the Laplacian Method.

The clinical significance of measuring vessel wall thickness is widely acknowledged. Recent advancements have enabled high-resolution 3D scans of arteries and precise segmentation of their lumens and outer walls; however, most existing methods for assessing vessel wall thickness are 2D. Despite being valuable, reproducibility and accuracy of 2D techniques depend on the extracted 2D slices. Additionally, these methods fail to fully account for variations in wall thickness in all dimensions. Furthermore, most existing approaches are difficult to be extended into 3D and their measurements lack spatial localization and are primarily confined to lumen boundaries. We advocate for a shift in perspective towards recognizing vessel wall thickness measurement as inherently a 3D challenge and propose adapting the Laplacian method as an outstanding alternative. The Laplacian method is implemented using convolutions, ensuring its efficient and rapid execution on deep learning platforms. Experiments using digital phantoms and vessel wall imaging data are conducted to showcase the accuracy, reproducibility, and localization capabilities of the proposed approach. The proposed method produce consistent outcomes that remain independent of centerlines and 2D slices. Notably, this approach is applicable in both 2D and 3D scenarios. It allows for voxel-wise quantification of wall thickness, enabling precise identification of wall volumes exhibiting abnormal wall thickness. Our research highlights the urgency of transitioning to 3D methodologies for vessel wall thickness measurement. Such a transition not only acknowledges the intricate spatial variations of vessel walls, but also opens doors to more accurate, localized, and insightful diagnostic insights.

Assessment of left ventricular wall thickness and dimension: accuracy of a deep learning model with prediction uncertainty.

Left ventricular (LV) geometric patterns aid clinicians in the diagnosis and prognostication of various cardiomyopathies. The aim of this study is to assess the accuracy and reproducibility of LV dimensions and wall thickness using deep learning (DL) models. A total of 30,080 unique studies were included; 24,013 studies were used to train a convolutional neural network model to automatically assess, at end-diastole, LV internal diameter (LVID), interventricular septal wall thickness (IVS), posterior wall thickness (PWT), and LV mass. The model was trained to select end-diastolic frames with the largest LVID and to identify four landmarks, marking the dimensions of LVID, IVS, and PWT using manually labeled landmarks as reference. The model was validated with 3,014 echocardiographic cines and the accuracy of the model was evaluated with a test set of 3,053 echocardiographic cines. The model accurately measured LVID, IVS, PWT, and LV mass compared to study report values with a mean relative error of 5.40%, 11.73%, 12.76%, and 13.93%, respectively. The 𝑅2 of the model for the LVID, IVS, PWT, and the LV mass was 0.88, 0.63, 0.50, and 0.87, respectively. The novel DL model developed in this study was accurate for LV dimension assessment without the need to select end-diastolic frames manually. DL automated measurements of IVS and PWT were less accurate with greater wall thickness. Validation studies in larger and more diverse populations are ongoing.

Enhancing Fixed Partial Denture Pontic Fabrication: An In Vitro Comparative Study of the Digital and Manual Techniques.

Background Advancements in computer-aided design (CAD) and computer-aided manufacturing (CAM) technology have significantly improved the accuracy and consistency of producing fixed partial dentures (FPDs) compared to traditional manual methods. However, the fully digital transfer of mock-up morphology to final FPDs is not yet fully explored. Proper pontic design, which avoids direct gingival contact, is essential for maintaining oral hygiene and preventing tissue irritation. Aim and objectives This study aims to compare the effectiveness of digital versus manual methods in FPD pontic fabrication, focusing on the trueness of digitally fabricated FPD patterns. Key objectives include assessing thickness, vertical gaps, and anatomical accuracy to determine the advantages of CAD-CAM technologies over traditional techniques. Materials and methods In this in vitro study, a total of 45 FPD pontics were fabricated and divided into three groups (15 each): digitally fabricated (using CAD software and CAM systems), manually fabricated (using traditional wax-up techniques), and a control group (typodont teeth). Tooth preparation was performed on a typodont, and impressions were taken to create casts. One cast was scanned and digitally designed, while the other was used for manual fabrication. Outcome assessments included vertical gap measurement using a stereo microscope, thickness evaluation with a digital caliper, and anatomical similarity assessment by independent evaluators. Statistical analysis involved one-way analysis of variance (ANOVA), post hoc Tukey's analysis, and unpaired t-tests using SPSS software version 26.0 (IBM Inc., Armonk, New York). Statistical significance was set at 0.05. Results The digital group exhibited lower mean thickness at the incisal (1.92±0.130 mm vs. 2.46±0.219 mm for manual, p=0.000), middle (7.00±0.223 mm vs. 8.88±0.983 mm for manual, p=0.001), and cervical sites (9.06±0.134 mm vs. 10.08±0.454 mm for manual, p=0.000). No significant differences were found between the digital and control groups. No significant differences were observed between digital, manual, and control groups at any site (p=0.688 to 0.997). The digital group demonstrated superior accuracy and consistency compared to the control group (mean value of 1.00±0.00 vs. 2.93±0.798, p=0.000). Conclusion CAD-CAM technology greatly improves the precision and consistency of FPD pontic fabrication compared to traditional manual techniques. Digital methods produce thinner pontics with superior anatomical accuracy, although vertical gap measurements are similar across methods. These findings emphasize the benefits of CAD-CAM in enhancing prosthetic outcomes and suggest potential improvements in clinical practices for prosthodontic rehabilitation.

Vertical Ex Vivo Dermoscopy in Assessment of Malignant Skin Lesions.

The role of vertical ex vivo dermoscopy relevant to clinical diagnosis has not been investigated yet. Study objectives were defining, describing, and determining the importance of the structures visible using vertical ex vivo dermoscopy in the diagnosis of malignant skin lesions, as well as determining their accuracy in the assessment of tumor margins. A prospective, descriptive study was conducted in two University centers. Digital images of completely excised skin lesions, fixed in formalin, before histopathological diagnosis were used for analysis. BCCs had the most diverse dermoscopic presentation on the vertical section, while SCCs showed a similar presentation in most cases. Vertical dermoscopy of thin melanomas was almost identical, unlike nodular melanomas. Thickness accuracy assessed by dermatologist was 0.753 for BCC, 0.810 for SCC, and 0.800 for melanomas, whereas assessment by pathologist was 0.654, 0.752, and 0.833, respectively. The accuracy of tumor width assessment was 0.819 for BCCs, 0.867 for SCCs and 1.000 for melanoma as estimated by a Dermatologist. Interobserver agreement was 0.71 for BCC, 0.799 for SCC and 0.832 for melanomas. Vertical ex vivo dermoscopy may contribute to the distinction between BCCs, SCCs, and melanomas. Moreover, regardless of the doctor's specialty, it enables a good assessment of the tumor's margins.

Synthesis of low-k SiONC thin films by plasma-assisted molecular layer deposition with tetraisocyanatesilane and phloroglucinol

Low-k SiONC thin films with excellent thermal stabilities were deposited using plasma-assisted molecular layer deposition (PA-MLD) with a tetraisocyanatesilane (Si(NCO)4) precursor, N2 plasma, and phloroglucinol (C6H3(OH)3). By adjusting the order of the N2 plasma exposure steps within the PA-MLD process, we successfully developed a deposition technique that allows accurate control of thickness at the Ångström level via self-limiting reactions. The thicknesses of the thin films were measured through spectroscopic ellipsometry (SE). By tuning the N2 plasma power, we facilitated the formation of –NH2 sites for phloroglucinol adsorption, achieving a growth per cycle of 0.18 Å cycle−1 with 300 W of N2 plasma power. Consequently, the thickness of the films increased linearly with each additional cycle. Moreover, the organic linkers within the film formed stable bonds through surface reactions, resulting in a negligible decrease in thickness of approximately −11% even upon exposure to a high annealing temperature of 600 °C. This observation was confirmed by SE, distinguishing the as-prepared film from previously reported low-k films that fail to maintain their thickness under similar conditions. X-ray photoelectron spectroscopy (XPS) and current–voltage (I–V) and capacitance–voltage (C–V) measurement were conducted to evaluate the composition, insulating properties, and dielectric constant according to the deposition and annealing conditions. XPS results revealed that as the plasma power increased from 200 to 300 W, the C/Si ratio increased from 0.37 to 0.67, decreasing the dielectric constant from 3.46 to 3.12. Furthermore, there was no significant difference in the composition before and after annealing, and the hysteresis decreased from 0.58 to 0.19 V owing to defect healing, while maintaining the leakage current density, breakdown field, and dielectric constant. The low dielectric constant, accurate thickness control, and excellent thermal stability of this MLD SiONC thin film enable its application as an interlayer dielectric in back-end-of-line process.

Accurate thickness evaluation of thermal barrier coatings using microwave resonator sensor

Thermal barrier coatings (TBC) are frequently employed in aircraft engines and turbine blades for protection against high temperatures. Constant exposure to in-service stresses, however, leads to a variety of anomalies in TBC that could prove to be potentially catastrophic for the assemblies they protect. This raises the need for inspection using non-destructive techniques (NDT) to ensure the reliability and structural integrity of the coated assemblies. Thickness evaluation of the topcoat of the TBC is paramount to determining structural integrity of the TBC. Microwaves are particularly suited for such applications since they readily penetrate low loss TBC. However, the performance of previous microwave-based TBC thickness evaluation techniques was limited in accuracy to 15 μm. In this paper, accurate thickness evaluation of thermal barrier coating (TBC) is performed using a microwave resonator sensor operating at a relatively low frequency (<1 GHz). TBC thicknesses as low as 100 μm are evaluated based on shift in resonant frequency of the probe. Overall, practically relevant thicknesses ranging from 100 μm to 600 μm are evaluated herein and multiple trials are performed to ascertain repeatability and assess uncertainty in measurements. For a 250 μm thick TBC sheet, a mean error of 0.9 μm was accomplished which translates to 0.36 % error with a standard deviation of 1.5 μm. Compared to other microwave sensors reported in the literature, a better estimation accuracy is demonstrated in this work, in particular for thicknesses less than <250 μm.

Terahertz nondestructive layer thickness measurement and delamination characterization of GFRP laminates

Three-dimensional nondestructive location of defects, such as delaminations, in glass fiber-reinforced polymer (GFRP) laminates remains a challenge. Terahertz techniques have shown promise, but their success relies on advanced signal-processing techniques applied to the raw data. The current work presents an advance in the quantitative three-dimensional nondestructive location of delaminations in GFRP laminates. Namely, terahertz time-of-flight tomography, together with adaptive sparse deconvolution based on a two-step iterative shrinkage-thresholding algorithm, as well as the Canny edge-detection operator, are employed in nondestructive measurement of layer thicknesses and to extract the edges of delaminations in GFRP laminates. Compared with the commonly used frequency wavelet-domain deconvolution method or previous implementations of sparse deconvolution, the adaptive sparse deconvolution approach provides a clearer and rapid stratigraphic reconstruction of GFRP laminates while yielding accurate thickness information for each resin layer and low sensitivity to noise. In addition, the proposed edge-detection algorithm presents better performance in estimating the transverse size of delaminations, compared to the common −6 dB drop approach. Finally, our experiments verify the effectiveness of the proposed signal and image processing approaches for three-dimensional localization of delamination defects in GFRP laminates and the quantitative characterization of layer thickness.

GMSA-Net: A Transmission Line Ice Thickness Identification Network Based on Global Micro Strip Awareness.

Ice-covered transmission lines seriously affect the normal operation of the power transmission system. Resonance deicing based on different ice thicknesses is an effective method to solve the issue of ice-covered transmission lines. In order to obtain accurate ice thickness of transmission lines, this paper designs an ice thickness of transmission line recognition model based on Global Micro Strip Awareness Net (GMSA-Net) and proposes a Mixed Strip Convolution Module (MSCM) and a global micro awareness module (GMAM). The MSCM adapts to the shape of ice-covered transmission lines by using strip convolutions with different receptive fields, improving the encoder's ability to extract ice-covered features; the GMAM perceives through both global and micro parts, mining the connections between semantic information. Finally, the ice thickness of the generated segmented image is calculated using the method of regional pixel statistics. Experiments are conducted on the dataset of ice-covered transmission lines. The mean Intersection over Union (mIoU) of image segmentation reaches 96.4%, the balanced F-Score (F1-Score) is 98.1%, and the identification error of ice thickness is within 3.8%. Experimental results prove that this method can accurately identify the ice thickness of transmission lines, providing a control basis for the application of resonant deicing engineering.

The 'Radiant Effect': Recent Sonographic Image-Enhancing Technique and Its Impact on Nuchal Translucency Measurements.

Background: This study assesses the effects of the 'Radiant' image enhancement technique on fetal nuchal translucency (NT) measurements during first-trimester sonographic exams. Methods: A retrospective analysis of 263 ultrasound images of first-trimester midsagittal sections was conducted. NT measurements were obtained using a semi-automatic tool. Statistical methods were applied to compare NT measurements with and without 'Radiant' enhancement. An in vitro setup with predefined line distances provided additional data. Results: Incremental increases in NT measurements were observed with varying levels of 'Radiant' application: an average increase of 0.19 mm with 'Radiant min', 0.24 mm with 'Radiant mid', and 0.30 mm with 'Radiant max.' The in vitro results supported these findings, showing consistent effects on line thickness and measurement accuracy, with the smallest mean deviation occurring at the 'Radiant mid' setting. Conclusions: 'Radiant' image enhancement leads to significant increases in NT measurements. To avoid systematic biases in clinical assessments, it is advisable to disable 'Radiant' during NT measurement procedures. Further studies are necessary to corroborate these findings and to consider updates to the NT reference tables based on this technology.

Electron Energy-Loss Spectroscopy Method for Thin-Film Thickness Calculations with a Low Incident Energy Electron Beam

In this study, the thickness of a thin film (tc) at a low primary electron energy of less than or equal to 10 keV was calculated using electron energy-loss spectroscopy. This method uses the ratio of the intensity of the transmitted background spectrum to the intensity of the transmission electrons with zero-loss energy (elastic) in the presence of an accurate average inelastic free path length (λ). The Monte Carlo model was used to simulate the interaction between the electron beam and the tested thin films. The total background of the transmitted electrons is considered to be the electron transmitting the film with an energy above 50 eV to eliminate the effect of the secondary electrons. The method was used at low primary electron energy to measure the thickness (t) of C, Si, Cr, Cu, Ag, and Au films below 12 nm. For the C and Si films, the accuracy of the thickness calculation increased as the energy of the primary electrons and thickness of the film increased. However, for heavy elements, the accuracy of the film thickness calculations increased as the primary electron energy increased and the film thickness decreased. High accuracy (with 2% uncertainty) in the measurement of C and Si thin films was observed at large thicknesses and 10 keV, where tλ≈1. However, in the case of heavy-element films, the highest accuracy (with an uncertainty below 8%) was found for thin thicknesses and 10 keV, where tλ≤0.29. The present results show that an accurate film thickness measurement can be obtained at primary electron energy equal to or less than 10 keV and a ratio of tλ≤2. This method demonstrates the potential of low-loss electron energy-loss spectroscopy in transmission electron microscopy as a fast and straightforward method for determining the thin-film thickness of the material under investigation at low primary electron energies.

Accuracy of sonographic lower segment thickness and prediction of vaginal birth after caesarean in a resourced-limited setting; Prospective study.

To assess the accuracy of ultrasound measurement of the lower uterine segment (LUS) thickness against findings at laparotomy, and to investigate its correlation with the success rate of vaginal birth after one previous caesarean delivery (CD) in a resource-limited setting. Prospective study. Obstetrics and Gynaecology department in a tertiary hospital in Ghana. Women with one previous CD undergoing either a trial of labour (TOLAC) or elective CD. Myometrial lower uterine segment thickness (mLUS) and full lower uterine segment thickness (fLUS) were measured with transvaginal ultrasound (TVUS). The women were managed according to local protocols with the clinicians blinded to the ultrasound measurements. The LUS was measured intraoperatively for comparison with ultrasound measurements. Lower uterine segment findings at laparotomy, successful vaginal birth. A total of 311 pregnant women with one previous CD were enrolled; 147 women underwent elective CD and 164 women underwent a TOLAC. Of the women that underwent TOLAC, 96 (58.5%) women had a successful vaginal birth. The mLUS was comparable to the intraoperative measurement in the elective CD group with LUS thickness <5 mm (bias of 0.01, 95% CI -0.10 to 0.12 mm) whereas fLUS overestimated LUS <5 mm (bias of 0.93, 95% CI 0.80-1.06 mm). Successful vaginal birth rate correlated with increasing mLUS values (odds ratio 1.30, 95% CI 1.03-1.64). Twelve cases of uterine defect were recorded. LUS measurement ≤2.0 mm was associated with an increased risk of uterine defects with a sensitivity of 91.7% (95% CI 61.5-99.8%) and specificity of 81.8% (95% CI 75.8-86.8%). Accurate TVUS measurement of the LUS is technically feasible in a resource-limited setting. This approach could help in making safer decisions on mode of birth in limited-resource settings.

Bias correction of Arctic sea ice thickness products based on factor selection and machine learning methods

With the accelerated melting of Arctic sea ice in the context of climate change, accurate sea ice thickness observations are needed for long-term climate simulation, short-term prediction, and shipping navigation. Limited by their special geographical location, in-situ data of Arctic sea ice thickness is scarce, and the quality of observation products from existing satellites and models is uneven. In this situation, we selected the key meteorological and marine environmental factors that affect the thickness of sea ice based on the thermodynamic law of sea ice, the traditional method of causal analysis, and the data mining ability of machine learning. Then, we constructed bias correction models combining these factors with geographic and temporal information, integrating random forest, extreme gradient boosting tree, and generalised regression neural network to correct the sea ice thickness data. The segmented regression models of thick and thin ice were constructed according to the characteristics of sea ice thickness products. The results show that the proposed bias correction model of sea ice thickness can effectively improve the quality of existing products and reduce the bias from the in-situ data.

An industrial IoT-based deformation resistance prediction and thickness control method of cold-rolled strip in steel production systems

Different from traditional analytical and numerical simulation methods, data-driven methods can more effectively describe the effects of nonlinear and coupling factors. Although the fluctuations of deformation resistance and thickness for hot-rolled strips significantly influence the thickness accuracy in strip cold rolling, the traditional deformation resistance prediction model and thickness control mode don't involve that. Thus, based on the Industrial Internet of Things (IIOT) platform and data-driven technology, this work proposed a new method for deformation resistance prediction and a new mode of feed-forward control for strip thickness. IIOT platform can collect production data from the various levels to provide a complete data set. The deformation resistance prediction model is established by adopting the differential evolutionary algorithm to optimize the bi-directional long and short-term memory network. In addition, the feed-forward control (DFFC) strategy for strip thickness is proposed based on deformation resistance prediction. An application results of a 1420 mm production line of CR indicated that the established prediction model could provide a high accuracy prediction for deformation resistance, and compared to traditional thickness gauges-based automatic gauge control methods, the DDFC can capture the effect of the fluctuations of deformation resistance and thickness for the hot-rolled strip to improve thickness accuracy effectively.

General strategy for developing thick-film micro-thermoelectric coolers from material fabrication to device integration

Micro-thermoelectric coolers are emerging as a promising solution for high-density cooling applications in confined spaces. Unlike thin-film micro-thermoelectric coolers with high cooling flux at the expense of cooling temperature difference due to very short thermoelectric legs, thick-film micro-thermoelectric coolers can achieve better comprehensive cooling performance. However, they still face significant challenges in both material preparation and device integration. Herein, we propose a design strategy which combines Bi2Te3-based thick film prepared by powder direct molding with micro-thermoelectric cooler integrated via phase-change batch transfer. Accurate thickness control and relatively high thermoelectric performance can be achieved for the thick film, and the high-density-integrated thick-film micro-thermoelectric cooler exhibits excellent performance with maximum cooling temperature difference of 40.6 K and maximum cooling flux of 56.5 W·cm−2 at room temperature. The micro-thermoelectric cooler also shows high temperature control accuracy (0.01 K) and reliability (over 30000 cooling cycles). Moreover, the device demonstrates remarkable capacity in power generation with normalized power density up to 214.0 μW · cm−2 · K−2. This study provides a general and scalable route for developing high-performance thick-film micro-thermoelectric cooler, benefiting widespread applications in thermal management of microsystems.

3D printing of an artificial intelligence-generated patient-specific coronary artery segmentation in a support bath

Accurate segmentation of coronary artery tree and personalized 3D printing from medical images is essential for CAD diagnosis and treatment. The current literature on 3D printing relies solely on generic models created with different software or 3D coronary artery models manually segmented from medical images. Moreover, there are not many studies examining the bioprintability of a 3D model generated by artificial intelligence (AI) segmentation for complex and branched structures. In this study, deep learning algorithms with transfer learning have been employed for accurate segmentation of the coronary artery tree from medical images to generate printable segmentations. We propose a combination of deep learning and 3D printing, which accurately segments and prints complex vascular patterns in coronary arteries. Then, we performed the 3D printing of the AI-generated coronary artery segmentation for the fabrication of bifurcated hollow vascular structure. Our results indicate improved performance of segmentation with the aid of transfer learning with a Dice overlap score of 0.86 on a test set of 10 coronary tomography angiography images. Then, bifurcated regions from 3D models were printed into the Pluronic F-127 support bath using alginate + glucomannan hydrogel. We successfully fabricated the bifurcated coronary artery structures with high length and wall thickness accuracy, however, the outer diameters of the vessels and length of the bifurcation point differ from the 3D models. The extrusion of unnecessary material, primarily observed when the nozzle moves from left to the right vessel during 3D printing, can be mitigated by adjusting the nozzle speed. Moreover, the shape accuracy can also be improved by designing a multi-axis printhead that can change the printing angle in three dimensions. Thus, this study demonstrates the potential of the use of AI-segmented 3D models in the 3D printing of coronary artery structures and, when further improved, can be used for the fabrication of patient-specific vascular implants.