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Background: Neuroinflammation plays a crucial role in the repair of spinal cord injury (SCI), with microglia, pivotal in neuroinflammation, driving either degeneration or recovery in this pathological process. Recently, plasma-derived exosomes (denoted Exos) have presented a high capacity for promoting functional recovery of SCI through the anti-inflammatory effects, and pretreated exosomes are associated with better outcomes. Thus, we aimed to explore whether melatonin-pretreated plasma-derived exosomes (denoted MExo) could exert superior effects on SCI, and attempted to elucidate the potential mechanisms. Methods: Electron microscopy, nanoparticle tracking analysis, and western blot were applied to delineate the distinctions between Exos and MExos. To assess their therapeutic potentials, we established a contusion SCI rat model, complemented by a battery of in vitro experiments comparing both groups. Subsequently, a miRNA microarray analysis was conducted, followed by a series of rescue experiments to elucidate the specific role of miRNAs in MExos. To further delve into the molecular mechanisms involved, we employed western blot analysis and the luciferase reporter gene assay. Results: Melatonin promoted the release of exosome from plasma, concurrently amplifying their anti-inflammatory properties. Furthermore, it was discerned that MExos facilitated a transition in microglia polarization from M1 to M2 phenotype, a phenomenon more pronounced than that observed with Exos. In an endeavor to elucidate this variance, we scrutinized miRNAs exhibiting elevated expression levels in MExos, pinpointing miR-138-5p as a pivotal element in this dynamic. Following this, an in-depth investigation into the role of miR-138-5p was undertaken, which uncovered its efficacy in driving phenotypic alterations within microglia. The analysis of downstream genes targeted by miR-138-5p revealed that it exerted a negative regulatory influence on SOX4, which was found to obstruct the generation of M2-type microglia and the secretion of anti-inflammatory cytokines, thereby partially elucidating the mechanism behind miR-138-5p′s regulation of microglia polarization. Conclusions: We innovatively observed that melatonin enhanced the anti-inflammatory function of Exos, which further decreased the expression of SOX4 by delivering miR-138-5p. This inhibition promoted the conversion of M1 microglia to M2 microglia, thus offering a viable option for the treatment of SCI. The translational potential of this article: This study highlights that melatonin enhances the anti-inflammatory function of Exos through delivery of miR-138-5p. Activation of miR-138-5p/SOX4 axis by engineered melatonin-pretreated plasma exosomes may be a potential target for SCI treatment.

Abstract The thermal recovery is facing the challenge of improving quality and efficiency. Steam-assisted gravity drainage (SAGD) steam circulation has a profound implications for the formal production. There is a lack of research on the situation when the steam injection pressure of injection well is lower than that of production well during startup stage. In this paper, the effects of diverse injection pressure difference on SAGD steam circulation and production stage are investigated by analytical modeling and numerical simulation, especially when the steam injection pressure of producer well is larger than that of injection well, and reliable results are obtained by temperature falloff test and model comparison validation. Meanwhile, in order to minimize the factors affecting the simulation accuracy, a sensitivity analysis of the temperature prediction model for the startup stage is carried out using Monte Carlo method and the finest possible mesh is used in the numerical simulation. The results show that:①The preheating results are faster and more uniform than the conventional preheating method when steam injection pressure of producer is greater than that of injector, and the subsequent production indexes are also superior to those of the conventional preheating method. ②The injected steam temperature had the greatest effect on the prediction accuracy of the analytical model; The finer the numerical simulation grid division, the lower the midpoint temperature of the horizontal well pair; ③An optimal range of injection pressure differences that achieves the best balance between preheating efficiency and thermal recovery effectiveness is achieved with P prod-P inj in the range of 400–500 kPa. ④The preheating method investigated in this paper minimizes the effect by unfavorable factors such as reservoir non-homogeneity, which holds the potential for more uniform, time-saving preheating and without the addition of field equipment.

In this study, the optimization of processing parameters of Inconel 718 using electrical discharge machining (EDM) with three kinds of renewable dielectrics (soybean oil, sunflower seed oil, and rapeseed oil) was studied. As input process parameters, pulse current, pulse duration, duty ratio, and dielectric type were all utilized. The machining performance indices of EDM were material removal rate (MRR), surface roughness (Ra), energy pulse ratio per volume (EEV), and exhaust emission characteristics (EEC). The response surface method (RSM) was used to carry out the central composite design of the EDM experiment, and it looked into how process parameters affected machining performance. In addition, the extreme learning machine (ELM)-improved integrated beta-distribution cuckoo search (IBCS) algorithm was used to optimize the EDM process, and the optimization results were verified by experiments. The research results demonstrate that the predicted results are generally better than the experimental results, and the maximum and average deviations of the experimental results are within the acceptable range (maximum deviation

Heavy mental contamination in urban land is a serious environmental issue for large cities. Visible and near-infrared spectroscopy has been rapidly developed as a new method for estimating copper (Cu) levels, which is one of the heavy metals. Spectral pretreatment is essential for reducing noise and enhancing analysis. In the traditional method, the entire spectrum is uniformly pretreated. However, in reality, the influence of pretreatment on the spectrum may vary depending on the wavelengths. Limited research has been conducted on breaking down the entire spectrum into distinct parts for individualized pretreatment, an innovative method called piecewise pretreatment. This study gathered 250 topsoil samples (0–20 cm) in Shenzhen City, southwest China, and obtained their vis-NIR spectra (350–2500 nm) in the laboratory. This study divided the spectrum into three parts, each processed by six commonly used spectral pretreatments. The number of pretreated parts varied from 1 to 3, resulting in 342 PLSR models being built. Compared to the traditional method, piecewise pretreatment showed an increase in mean residual predictive deviation (RPD) from 1.55 to 1.71 and an increase in the percentage of positive outcomes in ∆RPD from 33.33% to 55.56%. Thus, we concluded that piecewise pretreatment generally outperforms the traditional method. Furthermore, piecewise pretreatment aims to choose the most effective pretreatment method for each part to optimize the Cu estimation model.

The high level of stress and dimension deviation induced by glass molding are the main causes of the low yield rate of large, irregular glass components on vehicles. To solve this issue, a numerical model of large glass component molding was established in this study, which aimed to analyze the dominant factors of molding quality and achieve a synergistic balance between quality characteristics and energy consumption. The results show that molding temperature is the dominant factor affecting the energy consumption and residual stress, and the molding pressure is the main factor affecting the dimension deviation. Furthermore, the NSGA-II optimization algorithm was used to optimize the maximum residual stress, dimension deviation, and energy consumption with the numerical results. The combination of a heating rate of 1.95 °C/s, holding time of 158 s, molding temperature of 570 °C, molding pressure of 34 MPa, and cooling rate of 1.15 °C/s was determined to be the optimized scheme. The predictive error of the numerical result, based on the optimized scheme, was experimentally verified to be less than 20%. It proved the accuracy of the model in this study. These results can provide guidance for the subsequent precision molding of large, irregular glass components.

In recent years, as an important functional material, glass has been widely used in architecture, electronics, optics, and other fields. As an emerging glass processing technology, the glass molding process (GMP) has received widespread attention and research in recent years. In this paper, we study the modeling and analysis of different heat transfer strategies for the energy efficiency of large-sized automotive instrument glass. The heat transfer model of the metal heating plate–conducting plate mold is established, the thermal energy efficiency in the forming process of large automobile glass is analyzed, and the energy efficiency of the mold in the heating stage is compared. The energy consumption per piece generated by the GMP heating device is reduced from 4865.2 to 4668.5 kJ, a reduction of 4.04%. By optimizing the heat flow density, the energy consumption per piece generated by the GMP heating device was reduced from 4865.2 to 4625.5 kJ, a reduction of 4.92%, meeting the sustainable manufacturing requirements.

Interbedded shale (IBS) impedes the development of a steam chamber and poses a serious threat to the success of any steam-assisted gravity drainage (SAGD) operation in a heavy-oil reservoir. Breaking up IBS has been a continual challenge for the industry. This study summarizes experimental studies on the geomechanical properties and failure behaviour of IBS during thermal stimulation. IBS cores were collected by means of fully sealed coring in northwestern China. Mineral composition analysis, porosity, and permeability measurement, as well as imbibition tests, were initiated to evaluate the potential of imbibition. First, shear failure behaviour was studied through triaxial compression tests. Second, pore structure and failure processes of IBS were investigated through thermal stimulation experiments. Mudstone IBS was considered as the focus. Its porosity was approximately 7% and permeability was approximately 100 nD while in-situ stress was applied. Although IBS contained considerable smectite components, it didn’t disintegrate under high-effective stress. Last, shear failure behaviour of IBS was observed by computer tomography (CT) scanning when IBS was heated up to 180 °C under constant radial confining pressure and axial constraint of strain. While imbibition causes disintegration of IBS, it is unlikely when under in-situ stress conditions. Fortunately, shear failure can be achieved under uniaxial boundary conditions with rapid heating.

The requirements of 5G/6G promote progress in the miniaturization of the antenna array, which promotes the development of a closely spaced decoupling technique. However, the present techniques face the common problem of beam tilt if the spacing is close. Thus, a pattern-corrected, closely-spaced technique is proposed in this paper for the two patch antennas with the λ0/20 edge-to-edge distance of the H-plane. The corresponding structure, which is inserted at the center of the spacing, consists of a vertical wall with a single substrate and two symmetrical T-type metals, and a slot at the center is reserved to adequately accommodate the vertical wall. The vertical strip at the other end of the T-type metal is connected to the ground of the patch antenna, while the parallel strip is placed exactly above the patch substrate. After an exact analysis, a prototype was fabricated and measured, and the results showed that the measurements agreed well with those of the simulations, the decoupling coefficients in the 5.8 GHz band were below −20 dB, and the measured radiation pattern at 5.81 GHz was corrected to the broadside from 28° and the maximum realized gain was 5.30 dB.

Previous studies have reported that T cell immunoglobulin domain and mucin domain-3 (TIM-3) 574T>G and 1516G>T are associated with the risk of asthma. However, the results are inconsistent due to the small sample size and varied age in studies. We performed this meta-analysis to systematically evaluated the effect of TIM-3 574T>G and 1516G>T genetic polymorphisms on asthma. Eligible articles that reported an association between TIM-3 574T>G and 1516G>T genetic polymorphisms and asthma were searched in PubMed, Medline, EMBASE, Google Scholar, and China National Knowledge Infrastructure up to April 2020. Random or fixed-effects models were used to calculate the summary of odds ratios (ORs) and 95% confidence intervals (CIs) to detect any potential associations between TIM-3 genetic polymorphisms and asthma. Subgroup and sensitivity analyses were performed to assess the potential sources of heterogeneity and the robustness of the pooled estimation. Publication bias was analyzed using the Egger test. A total of 11 case-control studies including 2077 asthma patients and 2122 control subjects were finally analyzed (published data form 2004-2018). The pooled results indicated that TIM-3 574T>G genetic polymorphisms were significantly associated with an increased risk of asthma under the dominant model (GG vs. GT +TT: ORs=2.26, 95% CI 1.09-4.69) and allele model (G vs T: ORs=2.60, 95% CI 1.20-5.64). However, no significant associations between TIM-3 1516G>T genetic polymorphisms with asthma in any model was found. No evidence of publication bias was observed. Our study indicates that TIM-3 574T>G genetic polymorphisms were associated with increased risk of asthma and the TIM-3 1516G>T genetic polymorphisms may not be correlated with asthma.

Physical modeling is critical to study the performance of certain operation in heavy oil reservoirs. A well-designed experiment should guarantee the information gathered from lab would be applied to predict the thermal process in the field. To meet this requirement, the initial and boundary condition similarity between lab and field should be satisfied. It is reasonable to follow certain scaling criteria to fabricate the physical model. In addition to these conventional guidelines, this paper makes following recommendations to ensure a successful thermal recovery experiment, • To control and mitigate the steam channeling between the sand-pack and apparatus wall, the back wall is designed as it can be pushed enough to increase contact pressure. • Heat loss should be handled carefully, which impacting steam chamber growing and causing heat accumulation around the model. • A data acquisition system, based on PXI platform and Labview software, for the thermal recovery experiments had been proved valuable in evaluating the spreading progress of steam chamber.

A dam is a super-structure widely used in water conservancy engineering fields, and its long-term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long-time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K-means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K-means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping-I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.

This paper proposed a hybrid intelligent process model, based on a hybrid model combining the two-temperature model (TTM) and molecular dynamics simulation (MDS) (TTM-MDS). Combined atomistic-continuum modeling of short-pulse laser melting and disintegration of metal films [Physical Review B, 68, (064114):1–22.], and Gaussian process regression (GPR), for micro-electrical discharge machining (micro-EDM) were also used. A model of single-spark micro-EDM process has been constructed based on TTM-MDS model to predict the removed depth (RD) and material removal rate (MRR). Then, a GPR model was proposed to establish the relationship between input process parameters (energy area density and pulse-on duration) and the process responses (RD and MRR) for micro-EDM machining. The GPR model was trained, tested, and tuned using the data generated from the numerical simulations. Through the GPR model, it was found that micro-EDM process responses can be accurately predicted for the chosen process conditions. Therefore, the hybrid intelligent model proposed in this paper can be used for a micro-EDM process to predict the performance.

Electrical discharge machining (EDM) is a non-traditional process, which can cut materials with a high melting point, high hardness, high strength, and low brittleness. However, the kerosene (dielectric of EDM) produces aerosols and toxic gases at high temperatures, which seriously affect the health of operators and air quality. This means that it is not conducive to the green manufacturing and sustainable development of EDM. In this study, thereafter, sunflower seed oil (SSO) and kerosene were used as dielectrics of EDM for machine SKD11, and the machining performance of the two dielectrics under different current, duty ratio, pulse duration and electrodes were comparatively analyzed, such as material remove rate (MRR), surface roughness (Ra), energy efficiency per volume (EEV) and exhaust emissions characteristics (EEC). This investigation found that the minimum value of EEV in SSO was 0.3879 kJ/mm3, which was about 25% lower than the minimum value of 0.4849 kJ/mm3 in kerosene. The emission rate of Cu electrode in SSO was 62.017 µg/min, which was lower than that in 78.857 µg/min, decreasing by about 21.36%, in kerosene. In addition, a super depth of field optical micro-scope was subsequently used in the experiments to observe the diameter of the debris. The results indicated that SSO has a larger proportion of debris of more than 35 µm in diameter. Therefore, SSO can be adopted as a substitute for kerosene dielectric to improve the sustainability of electrical discharge machining and realize green manufacturing.

Liquid crystal display (LCD) is a display device based on liquid crystal electro-optic effect, and LCDs have gradually appeared and have become an indispensable part of people’s lives. In the development of LCD technology, the detection of Mura defects is a key concern in the manufacturing process. The Mura defect is a kind of display defect with low contrast and an irregular shape. This study first explains the mechanism of Mura defects in the LCD manufacturing process and classifies typical Mura defects. Then, three main purposes for the defect detection of LCDs are compared, and the advantages and disadvantages are conducted. Following that, this research examines reviews the linked literature on image preprocessing, feature extraction, dimension reduction, and classifiers of Mura defects. Finally, the future development trend and research direction of Mura defect detection based on machine vision can be drawn by this study.

Editor's Note: this Article has been retracted; the Retraction Note is available at https://doi.org/10.1038/s41598-021-88654-1 .

Background: Percutaneous vertebroplasty (PVP) typically involves conventional lower-viscosity cement injection via bipedicular approach. Limited evidence is available comparing the clinical outcomes and complications in treating osteoporotic vertebral compression fractures (OVCFs) with PVP using high-viscosity cement through unipedicular or bipedicular approach. Methods and design: Fifty patients with OVCFs were randomly allocated into two groups adopting unipedicular or bipedicular PVP. The efficacy of unipedicular and bipedicular PVP was assessed by comparing operation time, X-ray exposure time, incidence of complications, vertebral height restoration, and improvement of the visual analogue scale (VAS), Oswestry disability index (ODI) and Short Form-36 (SF-36) General Health Survey scores. Results: The mean operative and exposure time to X-rays in the unipedicular PVP group was less than that of the bipedicular group (p < 0.05). No statistically significant differences were observed in the VAS score, ODI score, SF-36 score, cement leakage rate or vertebral height restoration between the two groups (p > 0.05). Conclusion: Unipedicular and bipedicular PVP are safe and effective treatments for OVCF. Compared with bipedicular PVP, unipedicular PVP entails a shorter surgical time and lower X-ray irradiation. [ABSTRACT FROM AUTHOR]

Dynamic displacement response is an essential indicator for assessing structural state and performance. Vision-based structural displacement monitoring is considered as a promising approach. However, the current vision-based methods usually only focus on certain application scenarios. This study introduces a Sparse Bayesian Learning-based (SBL) algorithm to enhance robustness, accuracy, and computational efficiency in target tracking. Furthermore, a robust and versatile Vision-based Dynamic Displacement Monitoring System (VDDMS) was developed, capable of monitoring displacements of varying application scenarios. The robustness of the proposed algorithm under changing illumination conditions is validated through a specially designed indoor experiment. The feasibility of field application of VDDMS is confirmed through an outdoor shear wall shaking table test. Furthermore, a large-scale bridge shaking table test is conducted to evaluate the reliability and versatility of VDDMS in monitoring natural feature targets on large structures subjected to different seismic excitations. The root mean square error, when compared to laser displacement sensors, ranges from 0.2% to 2.9% of the peak-to-peak displacement. Additionally, VDDMS accurately identifies multi-order frequencies in bridge structures. The study investigates the influence of initial template selection on accuracy, highlighting the significance of distinctive texture features. Moreover, two error evaluation schemes are proposed to quickly assess the reliability of vision-based displacement sensing technologies in various application scenarios. [ABSTRACT FROM AUTHOR]

The thermal recovery is facing the challenge of improving quality and efficiency. Steam-assisted gravity drainage (SAGD) steam circulation has a profound implications for the formal production. There is a lack of research on the situation when the steam injection pressure of injection well is lower than that of production well during startup stage. In this paper, the effects of diverse injection pressure difference on SAGD steam circulation and production stage are investigated by analytical modeling and numerical simulation, especially when the steam injection pressure of producer well is larger than that of injection well, and reliable results are obtained by temperature falloff test and model comparison validation. Meanwhile, in order to minimize the factors affecting the simulation accuracy, a sensitivity analysis of the temperature prediction model for the startup stage is carried out using Monte Carlo method and the finest possible mesh is used in the numerical simulation. The results show that:①The preheating results are faster and more uniform than the conventional preheating method when steam injection pressure of producer is greater than that of injector, and the subsequent production indexes are also superior to those of the conventional preheating method. ②The injected steam temperature had the greatest effect on the prediction accuracy of the analytical model; The finer the numerical simulation grid division, the lower the midpoint temperature of the horizontal well pair; ③An optimal range of injection pressure differences that achieves the best balance between preheating efficiency and thermal recovery effectiveness is achieved with Pprod-Pinj in the range of 400–500 kPa. ④The preheating method investigated in this paper minimizes the effect by unfavorable factors such as reservoir non-homogeneity, which holds the potential for more uniform, time-saving preheating and without the addition of field equipment. [ABSTRACT FROM AUTHOR]

Heavy mental contamination in urban land is a serious environmental issue for large cities. Visible and near-infrared spectroscopy has been rapidly developed as a new method for estimating copper (Cu) levels, which is one of the heavy metals. Spectral pretreatment is essential for reducing noise and enhancing analysis. In the traditional method, the entire spectrum is uniformly pretreated. However, in reality, the influence of pretreatment on the spectrum may vary depending on the wavelengths. Limited research has been conducted on breaking down the entire spectrum into distinct parts for individualized pretreatment, an innovative method called piecewise pretreatment. This study gathered 250 topsoil samples (0–20 cm) in Shenzhen City, southwest China, and obtained their vis-NIR spectra (350–2500 nm) in the laboratory. This study divided the spectrum into three parts, each processed by six commonly used spectral pretreatments. The number of pretreated parts varied from 1 to 3, resulting in 342 PLSR models being built. Compared to the traditional method, piecewise pretreatment showed an increase in mean residual predictive deviation (RPD) from 1.55 to 1.71 and an increase in the percentage of positive outcomes in ∆RPD from 33.33% to 55.56%. Thus, we concluded that piecewise pretreatment generally outperforms the traditional method. Furthermore, piecewise pretreatment aims to choose the most effective pretreatment method for each part to optimize the Cu estimation model. [ABSTRACT FROM AUTHOR]

The high level of stress and dimension deviation induced by glass molding are the main causes of the low yield rate of large, irregular glass components on vehicles. To solve this issue, a numerical model of large glass component molding was established in this study, which aimed to analyze the dominant factors of molding quality and achieve a synergistic balance between quality characteristics and energy consumption. The results show that molding temperature is the dominant factor affecting the energy consumption and residual stress, and the molding pressure is the main factor affecting the dimension deviation. Furthermore, the NSGA-II optimization algorithm was used to optimize the maximum residual stress, dimension deviation, and energy consumption with the numerical results. The combination of a heating rate of 1.95 °C/s, holding time of 158 s, molding temperature of 570 °C, molding pressure of 34 MPa, and cooling rate of 1.15 °C/s was determined to be the optimized scheme. The predictive error of the numerical result, based on the optimized scheme, was experimentally verified to be less than 20%. It proved the accuracy of the model in this study. These results can provide guidance for the subsequent precision molding of large, irregular glass components. [ABSTRACT FROM AUTHOR]

In recent years, as an important functional material, glass has been widely used in architecture, electronics, optics, and other fields. As an emerging glass processing technology, the glass molding process (GMP) has received widespread attention and research in recent years. In this paper, we study the modeling and analysis of different heat transfer strategies for the energy efficiency of large-sized automotive instrument glass. The heat transfer model of the metal heating plate–conducting plate mold is established, the thermal energy efficiency in the forming process of large automobile glass is analyzed, and the energy efficiency of the mold in the heating stage is compared. The energy consumption per piece generated by the GMP heating device is reduced from 4865.2 to 4668.5 kJ, a reduction of 4.04%. By optimizing the heat flow density, the energy consumption per piece generated by the GMP heating device was reduced from 4865.2 to 4625.5 kJ, a reduction of 4.92%, meeting the sustainable manufacturing requirements. [ABSTRACT FROM AUTHOR]

Gait authentication as a technique that can continuously provide identity recognition on mobile devices for security has been investigated by academics in the community for decades. However, most of the existing work achieves insufficient generalization to complex real-world environments due to the complexity of the noisy real-world gait data. To address this limitation, we propose an intelligent Implicit Real-time Gait Authentication (IRGA) system based on Deep Neural Networks (DNNs) for enhancing the adaptability of gait authentication in practice. In the proposed system, the gait data (whether with complex interference signals) will first be processed sequentially by the imperceptible collection module and data preprocessing module for improving data quality. In order to illustrate and verify the suitability of our proposal, we provide analysis of the impact of individual gait changes on data feature distribution. Finally, a fusion neural network composed of a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is designed to perform feature extraction and user authentication. We evaluate the proposed IRGA system in heterogeneous complex scenarios and present start-of-the-art comparisons on three datasets. Extensive experiments demonstrate that the IRGA system achieves improved performance simultaneously in several different metrics. [ABSTRACT FROM AUTHOR]

The requirements of 5G/6G promote progress in the miniaturization of the antenna array, which promotes the development of a closely spaced decoupling technique. However, the present techniques face the common problem of beam tilt if the spacing is close. Thus, a pattern-corrected, closely-spaced technique is proposed in this paper for the two patch antennas with the λ0/20 edge-to-edge distance of the H-plane. The corresponding structure, which is inserted at the center of the spacing, consists of a vertical wall with a single substrate and two symmetrical T-type metals, and a slot at the center is reserved to adequately accommodate the vertical wall. The vertical strip at the other end of the T-type metal is connected to the ground of the patch antenna, while the parallel strip is placed exactly above the patch substrate. After an exact analysis, a prototype was fabricated and measured, and the results showed that the measurements agreed well with those of the simulations, the decoupling coefficients in the 5.8 GHz band were below −20 dB, and the measured radiation pattern at 5.81 GHz was corrected to the broadside from 28° and the maximum realized gain was 5.30 dB. [ABSTRACT FROM AUTHOR]

This paper proposed a hybrid intelligent process model, based on a hybrid model combining the two-temperature model (TTM) and molecular dynamics simulation (MDS) (TTM-MDS). Combined atomistic-continuum modeling of short-pulse laser melting and disintegration of metal films [Physical Review B, 68, (064114):1–22.], and Gaussian process regression (GPR), for micro-electrical discharge machining (micro-EDM) were also used. A model of single-spark micro-EDM process has been constructed based on TTM-MDS model to predict the removed depth (RD) and material removal rate (MRR). Then, a GPR model was proposed to establish the relationship between input process parameters (energy area density and pulse-on duration) and the process responses (RD and MRR) for micro-EDM machining. The GPR model was trained, tested, and tuned using the data generated from the numerical simulations. Through the GPR model, it was found that micro-EDM process responses can be accurately predicted for the chosen process conditions. Therefore, the hybrid intelligent model proposed in this paper can be used for a micro-EDM process to predict the performance. [ABSTRACT FROM AUTHOR]

Development of a new machine vision‐based algorithm, to estimate potato's shape and size, will have great significance to agricultural industry in developing countries. Machine vision, one of hot technique, uses machines to execute measurement, calculating, and judgment instead of human eyes. In this study, a new machine vision algorithm, based on support vector machine, was proposed for estimating potato's shape and size. After captured the square RGB image with 512 × 512 pixels, the original image was processed by gray scale and linear transformation, filtering, entity, and boundary detecting. Then, geometric characteristics, image wavelet moment, and fractal dimension of boundary were extracted for marking the potato's characteristic. In addition, the effect of four kind of support vector machine (SVM) kernel function was comparing by several experiments with 64 potato samples. By experimental confirmation, SVM based on the polynomial kernel is more suitable for discriminating potato's shape with an accuracy of 88.89%; SVM based on the linear kernel is more suitable for estimating potato's size with an accuracy of 87.41%. Practical applications: Potato is a high‐yield, nutrient‐rich crop with high nutritional value. This study proposes a potato shape and size estimation algorithm based on machine vision. Through experimental verification, the detection accuracy of the algorithm initially meets the actual engineering application. The shape and size of potatoes can be detected to classify potatoes. In the process of food processing such as potato chips, the classification of potatoes is conducive to improving the quality of food processing. [ABSTRACT FROM AUTHOR]

Editor's Note: this Article has been retracted; the Retraction Note is available at https://doi.org/10.1038/s41598-021-88654-1. [ABSTRACT FROM AUTHOR]

Purpose: This is the first systematic review and meta-analysis to detect the incidence and risk factors for postoperative shoulder imbalance (PSI) in scoliosis.Methods: A systematic online search was conducted to identify eligible studies. ES, OR and WMD with 95% CI were used to assess the incidence and risk factors associated with PSI.Results: Twenty-six studies were recruited. The pooled incidence of PSI was 25% (95% CI 20-31%). The incidence in Lenke 1 AIS, Lenke 2 AIS, Lenke 5 AIS and mixed AIS was 20% (9-31%), 26% (15-37%), 31% (17-45%) and 27% (19-35%), respectively. Using RSH ≥10 mm as the criterion of PSI, we found that preoperative LC, postoperative RSH, correction rate of MTC at follow-up were primary risk factors for PSI at follow-up. In the analysis of using RSH ≥20 mm as the criterion of PSI, our results showed that Risser sign, postoperative RSH, correction rate of PTC at follow-up, and LC at follow-up were contributing to PSI. Besides, scoliosis patients with PSI were more likely to suffer from adding-on.Conclusions: The pooled incidence of PSI in scoliosis was 25%. Risser sign, preoperative LC, postoperative RSH, correction rate of PTC at follow-up, correction rate of MTC at follow-up, and LC at follow-up were risk factors for PSI in patients with scoliosis. Adding-on might be a compensatory mechanism for PSI. It is recommended that (1) sufficient correction of PTC and moderate correction of MTC and LC in the operation should be performed; (2) PSI should be prevented not only for the patients' postoperative appearance, but also for preventing the adding-on phenomenon. [ABSTRACT FROM AUTHOR]

Objective: To compare the clinical outcome and complications of high viscosity and low viscosity polymethyl methacrylate bone cement PVP for severe OVCFs. Methods: From December 2010 to December 2012,32 patients with severe OVCFs were randomly assigned to either group H using high viscosity cement (n = 14) or group L using low viscosity cement (n = 18). The clinical outcomes were assessed by the Visual Analog Scale (VAS), Oswestry Disability Index (ODI), Short Form-36 General Health Survey (SF-36), kyphosis Cobb's angle, vertebral height, and complications. Results: Significant improvement in the VAS, ODI, SF-36 scores, kyphosis Cobb's angle, and vertebral height were noted in both the groups, and there were no significant differences between the two groups. Cement leakage was seen less in group H. Postoperative assessment using computed tomography identified cement leakage in 5 of 17 (29.4%) vertebrae in group H and in 15 of 22 (68.2%) vertebrae in group L (P=0.025). Conclusions: The PVP using high viscosity bone cement can provide the same clinical outcome and fewer complications compared with PVP using low viscosity bone cement. [ABSTRACT FROM AUTHOR]

An increasing number of individuals are suffering from lower back and neck pain caused by intervertebral disc degeneration each year. Although the application of mesenchymal stem cells (MSCs) has provided desirable results in the treatment of intervertebral disc degeneration, there are multiple risks associated with the directed application of MSCs. An increasing number of studies have suggested that stem cells, through the release of extracellular nanovesicles, have vital functions in tissue regeneration and repair with low risk. The present study investigated the effect of extracellular nanovesicles derived from adipose-derived stem cells (ADSCs) on nucleus pulposus (NP) cells from patients with intervertebral disc degeneration. Human NP cells were obtained from patients with intervertebral disc degeneration undergoing surgical procedures in addition to ADSCs from liposuction patients. ADSC-derived extracellular nanovesicles were isolated and characterized. The differentiation and biological activity of NP cells cultured with or without ADSC-derived extracellular nanovesicles were assessed and inflammatory factors and intervertebral disc degeneration-associated markers were also measured. The results indicated that extracellular nanovesicles derived from ADSCs increased the migration and proliferation of NP cells and inhibited inflammatory activity, suggesting their utility for the treatment of intervertebral disc degeneration. [ABSTRACT FROM AUTHOR]

Liquid crystal display (LCD) is a display device based on liquid crystal electro-optic effect, and LCDs have gradually appeared and have become an indispensable part of people's lives. In the development of LCD technology, the detection of Mura defects is a key concern in the manufacturing process. The Mura defect is a kind of display defect with low contrast and an irregular shape. This study first explains the mechanism of Mura defects in the LCD manufacturing process and classifies typical Mura defects. Then, three main purposes for the defect detection of LCDs are compared, and the advantages and disadvantages are conducted. Following that, this research examines reviews the linked literature on image preprocessing, feature extraction, dimension reduction, and classifiers of Mura defects. Finally, the future development trend and research direction of Mura defect detection based on machine vision can be drawn by this study. [ABSTRACT FROM AUTHOR]