Experiment, simulation and analysis on coupling hydrodynamic forces under key parameters for a spherical underwater exploration robot

As a novel underwater exploration robot, BYSQ-2 spherical robot uses the heavy pendulum to change the attitudes with the characteristics of small steering resistance and high compressive strength. However, the greater water resistance in the process of moving forward obstructs the rapid movement, because the robot has a spherical shell and only one propeller. The maximum speed was obtained only 0.6 m/s according to experimental tests and theoretical calculations. In order to improve the movement speed, the robot’s virtual assembly model was built to study the coupling hydrodynamic forces between the spherical shell and the propeller by CFD method. The coupling hydrodynamic forces were analyzed and summarized under different key structural parameters that include the pipe diameter and the shell diameter. Furthermore, in the conditions of different rotational speed, propeller thrust and water resistance of robot were simulated and calculated. According to the simulation results of the model with the appropriate structural parameters, it was demonstrated that the speed of the robot was improved obviously in the process of moving forward

Characteristic analysis and fluctuation control for a underactuated spherical underwater robot

With robots used widely in many fields in recent years, the underwater robot with various characteristics has been thoroughly researched. As a new type of underwater spherical robot, BYSQ-2 uses the heavy pendulum to adjust the attitude, which is flexible and novel. However, it has been not fully understood that how the heavy pendulum would affect the underactuated robot’s regular movement. In this paper a fluctuation characteristic for the robot is shown, and then an adaptive control method is proposed to suppress the fluctuation. Based on the simplified structure of the robot, a swing phenomenon of the heavy pendulum is found. Moreover, the reason for the fluctuation is analyzed in the processes of the accelerating and pitching. A dynamic equation for this model is established to accurately calculate the characteristic, and the virtual simulation proves the validity of the theoretical calculation. The characteristics of this coupling fluctuation are summarized by changing motion parameters and structure parameters. The results prove that the pendulum’s length and the controlling process are closely related with the velocity fluctuation of the robot. Moreover, in order to suppress the fluctuations, a pitching controller is designed to prevent the heavy pendulum from swinging based on the method of neural network sliding mode. The RBF neural network is used to compensate the nonlinearity and disturbance uncertainties, and two sliding mode structures make the swing rapidly inhibited. At the same time, the pitch angle's error also got convergence. The stability of the control system is proof by Lyapunov and Barbalat theories. Finally, the simulation and experiment show that the control method is feasible and excellent, which can fulfill the suppressed control for the fluctuation of the robot

MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds

3D semantic segmentation on multi-scan large-scale point clouds plays an important role in autonomous systems. Unlike the single-scan-based semantic segmentation task, this task requires distinguishing the motion states of points in addition to their semantic categories. However, methods designed for single-scan-based segmentation tasks perform poorly on the multi-scan task due to the lacking of an effective way to integrate temporal information. We propose MarS3D, a plug-and-play motion-aware module for semantic segmentation on multi-scan 3D point clouds. This module can be flexibly combined with single-scan models to allow them to have multi-scan perception abilities. The model encompasses two key designs: the Cross-Frame Feature Embedding module for enriching representation learning and the Motion-Aware Feature Learning module for enhancing motion awareness. Extensive experiments show that MarS3D can improve the performance of the baseline model by a large margin. The code is available at https://github.com/CVMI-Lab/MarS3D

Texture Generation on 3D Meshes with Point-UV Diffusion

In this work, we focus on synthesizing high-quality textures on 3D meshes. We present Point-UV diffusion, a coarse-to-fine pipeline that marries the denoising diffusion model with UV mapping to generate 3D consistent and high-quality texture images in UV space. We start with introducing a point diffusion model to synthesize low-frequency texture components with our tailored style guidance to tackle the biased color distribution. The derived coarse texture offers global consistency and serves as a condition for the subsequent UV diffusion stage, aiding in regularizing the model to generate a 3D consistent UV texture image. Then, a UV diffusion model with hybrid conditions is developed to enhance the texture fidelity in the 2D UV space. Our method can process meshes of any genus, generating diversified, geometry-compatible, and high-fidelity textures. Code is available at https://cvmi-lab.github.io/Point-UV-DiffusionComment: Accepted to ICCV 2023, Ora

Gene expression insights: Chronic stress and bipolar disorder: A bioinformatics investigation

Bipolar disorder (BD) is a psychiatric disorder that affects an increasing number of people worldwide. The mechanisms of BD are unclear, but some studies have suggested that it may be related to genetic factors with high heritability. Moreover, research has shown that chronic stress can contribute to the development of major illnesses. In this paper, we used bioinformatics methods to analyze the possible mechanisms of chronic stress affecting BD through various aspects. We obtained gene expression data from postmortem brains of BD patients and healthy controls in datasets GSE12649 and GSE53987, and we identified 11 chronic stress-related genes (CSRGs) that were differentially expressed in BD. Then, we screened five biomarkers (IGFBP6, ALOX5AP, MAOA, AIF1 and TRPM3) using machine learning models. We further validated the expression and diagnostic value of the biomarkers in other datasets (GSE5388 and GSE78936) and performed functional enrichment analysis, regulatory network analysis and drug prediction based on the biomarkers. Our bioinformatics analysis revealed that chronic stress can affect the occurrence and development of BD through many aspects, including monoamine oxidase production and decomposition, neuroinflammation, ion permeability, pain perception and others. In this paper, we confirm the importance of studying the genetic influences of chronic stress on BD and other psychiatric disorders and suggested that biomarkers related to chronic stress may be potential diagnostic tools and therapeutic targets for BD

Rapid inactivation of human respiratory RNA viruses by deep ultraviolet irradiation from light-emitting diodes on a high-temperature-annealed AlN/Sapphire template

Efficient and eco-friendly disinfection of air-borne human respiratory RNA viruses is pursued in both public environment and portable usage. The AlGaN-based deep ultraviolet (DUV) light-emission diode (LED) has high practical potentials because of its advantages of variable wavelength, rapid sterilization, environmental protection, and miniaturization. Therefore, whether the emission wavelength has effects on the disinfection as well as whether the device is feasible to sterilize various respiratory RNA viruses under portable conditions is crucial. Here, we fabricate AlGaN-based DUV LEDs with different wavelength on high-temperature-annealed (HTA) AlN/Sapphire templates and investigate the inactivation effects for several respiratory RNA viruses. The AlN/AlGaN superlattices are employed between the template and upper n-AlGaN to release the strong compressive stress (SCS), improving the crystal quality and interface roughness. DUV LEDs with the wavelength of 256, 265, and 278 nm, corresponding to the light output power of 6.8, 9.6, and 12.5 mW, are realized, among which the 256 nm-LED shows the most potent inactivation effect in human respiratory RNA viruses, including SARS-CoV-2, influenza A virus (IAV), and human parainfluenza virus (HPIV), at a similar light power density (LPD) of ~0.8 mW/cm2 for 10 s. These results will contribute to the advanced DUV LED application of disinfecting viruses with high potency and broad spectrum in a portable and eco-friendly use

A High Serum Level of Taurocholic Acid is Correlated with the Severity and Resolution of Drug-induced Liver Injury

Background & Aims Alterations in the serum levels of bile acids are associated with drug-induced liver injury (DILI). We investigated the association between serum levels of bile acids and the severity and outcome of DILI, along with the potential role of variants in the ATP binding cassette subfamily B member 11 ( ABCB11) gene and expression of its product, ABCB11 (also called BSEP). Methods We performed this prospective study of 95 patients (median age, 53 years; 73.7% female) with DILI from August 2018 through August 2019. Patients were matched for age, gender, and body mass index with healthy individuals (n=100; healthy controls) and patients with chronic hepatitis B (n=105; CHB controls). We collected demographic and biochemical data at baseline and 1 week, 1 month, 3 months, and 6 months after DILI onset and at the time of biochemical recovery, liver failure or liver transplantation. Serum levels of bile acids were measured using high-performance liquid-chromatography tandem mass-spectrometry. All 27 exons of ABCB11 were sequenced and expression of BSEP were analyzed by immunohistochemistry in liver biopsy specimens. Results Levels of 30 of the 37 bile acids analyzed differed significantly between patients with DILI and healthy controls. Changes in levels of taurocholic acid (TCA), glycocholic acid, taurochenodeoxycholate, and glycochenodeoxycholate associated with the increased levels of bilirubin and greater severity of DILI, and were also associated with CHB. Cox regression analysis showed that only change in the levels of TCA independently associated with biochemical resolution of DILI. Combination of TCA level (≥ 1955.41 nmol/L), patient age, and DILI severity was associated with abnormal blood biochemistry at 6 months after DILI onset (area under the curve, 0.81; 95% confidence interval, 0.71–0.88; sensitivity, 0.69; specificity, 0.81). ABCB11 missense variants were not associated with differences in the serum bile acid profiles, DILI severity, or clinical resolution. However, lower levels of BSEP in bile canaliculi in liver biopsies were associated with altered serum levels of bile acids. Conclusions In this prospective study performed in Chinese patients, we found that the serum levels of TCA were associated with the severity and clinical resolution of DILI. Reduced protein expression of BSEP in liver tissue, rather than variants of the ABCB11 gene were associated with altered serum levels of bile acids

Dynamical alterations of brain function and gut microbiome in weight loss

ObjectiveIntermittent energy restriction (IER) is an effective weight loss strategy. However, little is known about the dynamic effects of IER on the brain-gut-microbiome axis.MethodsIn this study, a total of 25 obese individuals successfully lost weight after a 2-month IER intervention. FMRI was used to determine the activity of brain regions. Metagenomic sequencing was performed to identify differentially abundant gut microbes and pathways in from fecal samples.ResultsOur results showed that IER longitudinally reduced the activity of obese-related brain regions at different timepoints, including the inferior frontal orbital gyrus in the cognitive control circuit, the putamen in the emotion and learning circuit, and the anterior cingulate cortex in the sensory circuit. IER longitudinally reduced E. coli abundance across multiple timepoints while elevating the abundance of obesity-related Faecalibacterium prausnitzii, Parabacteroides distasonis, and Bacterokles uniformis. Correlation analysis revealed longitudinally correlations between gut bacteria abundance alterations and brain activity changes.ConclusionsThere was dynamical alteration of BGM axis (the communication of E. coli with specific brain regions) during the weight loss under the IER

Global Carbon Budget 2023

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land-use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based f CO2 products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. Additional lines of evidence on land and ocean sinks are provided by atmospheric inversions, atmospheric oxygen measurements, and Earth system models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and incomplete understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2022, EFOS increased by 0.9 % relative to 2021, with fossil emissions at 9.9 ± 0.5 Gt C yr−1 (10.2 ± 0.5 Gt C yr−1 when the cement carbonation sink is not included), and ELUC was 1.2 ± 0.7 Gt C yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 11.1 ± 0.8 Gt C yr−1 (40.7±3.2 Gt CO2 yr−1). Also, for 2022, GATM was 4.6±0.2 Gt C yr−1 (2.18±0.1 ppm yr−1; ppm denotes parts per million), SOCEAN was 2.8 ± 0.4 Gt C yr−1, and SLAND was 3.8 ± 0.8 Gt C yr−1, with a BIM of −0.1 Gt C yr−1 (i.e. total estimated sources marginally too low or sinks marginally too high). The global atmospheric CO2 concentration averaged over 2022 reached 417.1 ± 0.1 ppm. Preliminary data for 2023 suggest an increase in EFOS relative to 2022 of +1.1 % (0.0 % to 2.1 %) globally and atmospheric CO2 concentration reaching 419.3 ppm, 51 % above the pre-industrial level (around 278 ppm in 1750). Overall, the mean of and trend in the components of the global carbon budget are consistently estimated over the period 1959–2022, with a near-zero overall budget imbalance, although discrepancies of up to around 1 Gt C yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows the following: (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living-data update documents changes in methods and data sets applied to this most recent global carbon budget as well as evolving community understanding of the global carbon cycle. The data presented in this work are available at https://doi.org/10.18160/GCP-2023 (Friedlingstein et al., 2023)