LGC-Net: A Lightweight Gyroscope Calibration Network for Efficient Attitude Estimation

This paper presents a lightweight, efficient calibration neural network model for denoising low-cost microelectromechanical system (MEMS) gyroscope and estimating the attitude of a robot in real-time. The key idea is extracting local and global features from the time window of inertial measurement units (IMU) measurements to regress the output compensation components for the gyroscope dynamically. Following a carefully deduced mathematical calibration model, LGC-Net leverages the depthwise separable convolution to capture the sectional features and reduce the network model parameters. The Large kernel attention is designed to learn the long-range dependencies and feature representation better. The proposed algorithm is evaluated in the EuRoC and TUM-VI datasets and achieves state-of-the-art on the (unseen) test sequences with a more lightweight model structure. The estimated orientation with our LGC-Net is comparable with the top-ranked visual-inertial odometry systems, although it does not adopt vision sensors. We make our method open-source at: https://github.com/huazai665/LGC-Ne

Reassessing two contrasting Late Miocene-Holocene stratigraphic frameworks for the Pearl River Mouth Basin, northern South China Sea

High-resolution 2D/3D seismic and biostratigraphic data are used to reassess two contrasting Late Miocene-Holocene stratigraphic frameworks (SFI and SFII) for the northern slope of the Pearl River Mouth Basin (PRMB), northern South China Sea. The major stratigraphic boundaries (T1-base Quaternary and T2-base Pliocene) derived from SFI and SFII are different in depth. The trends of average sedimentation rates from Late Miocene to Quaternary were estimated according to the major boundaries in order to highlight the differences between these two frameworks. The seismic reflection terminations as erosional and truncation features were recognized through the seismic data implying that the submarine channels and mass-transport deposits are valid stratigraphic markers of Late Miocene-Holocene basin development on the northern South China Sea margin and other continental margins. The sediments from hydrate drilling sites in the Shenhu Area consist of two allochthonous sedimentary units with similar lithology and grain sizes: a) fine-grained turbidites at the bottom, and b) fine-grained submarine landslides at the top. Nannofossil assemblages with relatively minute sizes were likely to be reworked due to the setting of widespread mass wasting on the middle to lower continental slope. A key result of this study is that the stratigraphic framework based on a combination of seismic and sequence stratigraphic data is more suitable for stratigraphic correlations across the northern slope of the PRMB. Our findings provide a robust foundation to rebuild the tectono-sedimentary evolution of the PRMB and also resolve the uncertainties in the Late Miocene-Holocene stratigraphic frameworks of the study area

Yeast Probiotics Shape the Gut Microbiome and Improve the Health of Early-Weaned Piglets

Weaning is one of the most stressful challenges in the pig’s life, which contributes to dysfunctions of intestinal and immune system, disrupts the gut microbial ecosystem, and therefore compromises the growth performance and health of piglets. To mitigate the negative impact of the stress on early-weaned piglets, effective measures are needed to promote gut health. Toward this end, we tamed a Saccharomyces cerevisiae strain and developed a probiotic Duan-Nai-An, which is a yeast culture of the tamed S. cerevisiae on egg white. In this study, we tested the performance of Duan-Nai-An on growth and health of early-weaned piglets and analyzed its impact on fecal microbiota. The results showed that Duan-Nai-An significantly improved weight gain and feed intake, and reduced diarrhea and death of early-weaned piglets. Analysis of the gut microbiota showed that the bacterial community was shaped by Duan-Nai-An and maintained as a relatively stable structure, represented by a higher core OTU number and lower unweighted UniFrac distances across the early weaned period. However, fungal community was not significantly shaped by the yeast probiotics. Notably, 13 bacterial genera were found to be associated with Duan-Nai-An feeding, including Enterococcus, Succinivibrio, Ruminococcus, Sharpea, Desulfovibrio, RFN20, Sphaerochaeta, Peptococcus, Anaeroplasma, and four other undefined genera. These findings suggest that Duan-Nai-An has the potential to be used as a feed supplement in swine production

Marine fungus Aspergillus c1. sp metabolite activates the HSF1/PGC-1α axis, inducing a thermogenic program for treating obesity

Background and aims: Obesity is one of the most prevalent diseases worldwide with less ideal approved agents in clinic. Activating the HSF1/PGC-1α axis in adipose tissues has been reported to induce thermogenesis in mice, which presents a promising therapeutic avenue for obesity treatment. The present study aimed to identified novel natural HSF1 activator and evaluated the therapeutic effects of the newly discovered compound on obesity-associated metabolic disorders and the molecular mechanisms of these effects.Methods: Our previous reported HSF1/PGC-1α activator screening system was used to identify novel natural HSF1 activator. The PGC-1α luciferase activity, immunoblot, protein nuclear-translocation, immunofluorescence, chromatin immunoprecipitation assays were used to evaluate the activity of compound HN-001 in activating HSF1. The experiments of mitochondrial number measurement, TG assay and imaging, cellular metabolic assay, gene assays, and CRISPR/Cas 9 were applied for investigating the metabolic effect of HN-001 in C3H10-T1/2 adipocytes. The in vivo anti-obesity efficacies and beneficial metabolic effects of HN-001 were evaluated by performing body and fat mass quantification, plasma chemical analysis, GTT, ITT, cold tolerance test, thermogenesis analysis.Results: HN-001 dose- and time-dependently activated HSF1 and induced HSF1 nuclear translocation, resulting in an enhancement in binding with the gene Pgc-1α. This improvement induced activation of adipose thermogenesis and enhancement of mitochondrial oxidation capacity, thus inhibiting adipocyte maturation. Deletion of HSF1 in adipocytes impaired mitochondrial oxidation and abolished the above beneficial metabolic effects of HN-001, including adipocyte browning induction, improvements in mitogenesis and oxidation capacity, and lipid-lowering ability. In mice, HN-001 treatment efficiently alleviated diet-induced obesity and metabolic disorders. These changes were associated with increased body temperature in mice and activation of the HSF1/PGC-1α axis in adipose tissues. UCP1 expression and mitochondrial biogenesis were increased in both white and brown adipose tissues of HN-001-treated mice.Conclusion: These data indicate that HN-001 may have therapeutic potential for obesity-related metabolic diseases by increasing the capacity of energy expenditure in adipose tissues through a mechanism involving the HSF1/PGC-1α axis, which shed new light on the development of novel anti-obesity agents derived from marine sources

Predictive Modeling of Machining Temperatures with Force–Temperature Correlation Using Cutting Mechanics and Constitutive Relation

Elevated temperature in the machining process is detrimental to cutting tools—a result of the effect of thermal softening and material diffusion. Material diffusion also deteriorates the quality of the machined part. Measuring or predicting machining temperatures is important for the optimization of the machining process, but experimental temperature measurement is difficult and inconvenient because of the complex contact phenomena between tools and workpieces, and because of restricted accessibility during the machining process. This paper presents an original analytical model for fast prediction of machining temperatures at two deformation zones in orthogonal cutting, namely the primary shear zone and the tool–chip interface. Temperatures were predicted based on a correlation between force and temperature using the mechanics of the cutting process and material constitutive relation. Minimization of the differences between calculated material flow stresses using a mechanics model and a constitutive model yielded an estimate of machining temperatures. Experimental forces, cutting condition parameters, and constitutive model constants were inputs, while machining forces were easily measurable by a piezoelectric dynamometer. Machining temperatures of AISI 1045 steel were predicted under various cutting conditions to demonstrate the predictive capability of each presented model. Close agreements were observed by verifying them against documented values in the literature. The influence of model inputs and computational efficiency were further investigated. The presented model has high computational efficiency that allows real-time prediction and low experimental complexity, considering the easily measurable input variables

Prediction of Temperature Distribution in Orthogonal Machining Based on the Mechanics of the Cutting Process Using a Constitutive Model

This paper presents an original method of predicting temperature distribution in orthogonal machining based on a constitutive model of various materials and the mechanics of their cutting process. Currently, temperature distribution is commonly investigated using arduous experiments, computationally inefficient numerical analyses, and complex analytical models. In the method proposed herein, the average temperatures at the primary shear zone (PSZ) and the secondary shear zone (SSZ) were determined for various materials, based on a constitutive model and a chip-formation model using measurements of cutting force and chip thickness. The temperatures were determined when differences between predicted shear stresses using the Johnson–Cook constitutive model (J–C model) and those using a chip-formation model were minimal. J–C model constants from split Hopkinson pressure bar (SHPB) tests were adopted from the literature. Cutting conditions, experimental cutting force, and chip thickness were used to predict the shear stresses. The temperature predictions were compared to documented results in the literature for AISI 1045 steel and Al 6082-T6 aluminum in multiple tests in an effort to validate this methodology. Good agreement was observed for the tests with each material. Thanks to the reliable and easily measurable cutting forces and chip thicknesses, and the simple forms of the employed models, the presented methodology has less experimental complexity, less mathematical complexity, and high computational efficiency

Evaluation of an Analytical Model in the Prediction of Machining Temperature of AISI 1045 Steel and AISI 4340 Steel

This paper evaluates a physics-based analytical model in the prediction of machining temperature of AISI 1045 steel and AISI 4340 steel. The prediction model was developed based on the Johnson-Cook constitutive model (J-C model) and mechanics of the orthogonal cutting process. The average temperatures at two shear zones were predicted by minimizing the difference between calculated stresses using the J-C model and calculated stresses using the mechanics model. In this work, (1) the influence of input Johnson-Cook model constants, cutting force, and chip thickness on the accuracy of predictions are investigated with sensitivity analyses, in which multiple sets of available J-C constants and varying cutting force and chip thickness are used for the temperature prediction in machining AISI 1045 steel. The larger the input deviation, the larger prediction deviation. The temperature at the primary shear zone is more susceptible to the deviation of inputs than the temperature at the secondary shear zone. (2) The machining temperatures are also predicted in machining AISI 4340 steel using cutting tools with various specifications to demonstrate its predictive capability. Good agreements are observed upon validation to available experimental data in the literature. (3) Lastly, the advantage and limitation of the temperature model are discussed with comparison other analytical temperature models. Considering the reliable and easily measurable input requirements and sufficient predictive capability, this temperature model can be employed for effective and efficient machining temperature prediction

Analytical Prediction of Balling, Lack-of-Fusion and Keyholing Thresholds in Powder Bed Fusion

This paper proposes analytical modeling methods for the prediction of balling, lack-of-fusion and keyholing thresholds in the laser powder bed fusion (LPBF) additive manufacturing. The molten pool dimensions were first predicted by a closed-form analytical thermal model. The effects of laser power input, boundary heat loss, powder size distribution and powder packing pattern were considered in the calculation process. The predicted molten pool dimensions were then employed in the calculation of analytical thresholds for these defects. Reported experimental data with different materials were compared to predictions to validate the presented analytical models. The predicted thresholds of these defects under various process conditions have good agreement with the experimental results. The computation time for the presented models is less than 5 min on a personal computer. The optimized process window for Ti6Al4V was obtained based on the analytical predictions of these defects. The sensitivity analyses of the value of threshold to the laser power and scanning speed were also conducted. The proposed analytical methods show higher computational efficiency than finite element methods, without including any iteration-based computations. The acceptable predictive accuracy and low computational time will make the proposed analytical strategy be a good tool for the optimization of process conditions for the fabrication of defects-free complex products in laser powder bed fusion

Analytical Prediction of Balling, Lack-of-Fusion and Keyholing Thresholds in Powder Bed Fusion

This paper proposes analytical modeling methods for the prediction of balling, lack-of-fusion and keyholing thresholds in the laser powder bed fusion (LPBF) additive manufacturing. The molten pool dimensions were first predicted by a closed-form analytical thermal model. The effects of laser power input, boundary heat loss, powder size distribution and powder packing pattern were considered in the calculation process. The predicted molten pool dimensions were then employed in the calculation of analytical thresholds for these defects. Reported experimental data with different materials were compared to predictions to validate the presented analytical models. The predicted thresholds of these defects under various process conditions have good agreement with the experimental results. The computation time for the presented models is less than 5 min on a personal computer. The optimized process window for Ti6Al4V was obtained based on the analytical predictions of these defects. The sensitivity analyses of the value of threshold to the laser power and scanning speed were also conducted. The proposed analytical methods show higher computational efficiency than finite element methods, without including any iteration-based computations. The acceptable predictive accuracy and low computational time will make the proposed analytical strategy be a good tool for the optimization of process conditions for the fabrication of defects-free complex products in laser powder bed fusion

Analytical Modeling of the Temperature Using Uniform Moving Heat Source in Planar Induction Heating Process

The planar induction heating possesses more difficulties in industry application compared with traditional spiral induction coils in mostly heat treatment processes. Numerical approaches are adopted in the power distribution and temperature prediction during the induction heating process, which has a relatively low computational efficiency. In this work, an analytical calculation model of the planar induction heating with magnetic flux concentrator is investigated based on the uniform moving heating source. In this model, the power density in the surface of the workpiece induced by coils is calculated and applied into the analytical model of the temperature calculation using a uniform moving heat source. Planar induction heating tests are conducted under various induction coil parameters and the corresponding temperature evolution is obtained by the infrared imaging device NEC R300W2-NNU and the thermocouples. The final surface temperature prediction is compared to the finite element simulation results and experimental data. The analytical results show a good match with the finite element simulation and the experimental results, and the errors are in reasonable range and acceptable. The analytical model can compute the temperature distribution directly and the computational time is much less than the finite element method. Therefore, the temperature prediction method in this work has the advantage of less experimental and computational complexity, which can extend the analytical modeling methodology in induction heating to a broader application