Role Playing Learning for Socially Concomitant Mobile Robot Navigation

In this paper, we present the Role Playing Learning (RPL) scheme for a mobile robot to navigate socially with its human companion in populated environments. Neural networks (NN) are constructed to parameterize a stochastic policy that directly maps sensory data collected by the robot to its velocity outputs, while respecting a set of social norms. An efficient simulative learning environment is built with maps and pedestrians trajectories collected from a number of real-world crowd data sets. In each learning iteration, a robot equipped with the NN policy is created virtually in the learning environment to play itself as a companied pedestrian and navigate towards a goal in a socially concomitant manner. Thus, we call this process Role Playing Learning, which is formulated under a reinforcement learning (RL) framework. The NN policy is optimized end-to-end using Trust Region Policy Optimization (TRPO), with consideration of the imperfectness of robot's sensor measurements. Simulative and experimental results are provided to demonstrate the efficacy and superiority of our method

Adaptive control of robotic manipulators with unified motion constraints

In this paper, we present an adaptive control of robotic manipulators with parametric uncertainties and motion constraints. Position and velocity constraints are considered and they are unified and converted into the constraint of the nominal input. An adaptive neural network control is developed to achieve trajectory tracking, while the problems of motion constraints are addressed by considering the saturation effect of the nominal input. The uniform boundedness of all closed-loop signals is verified through Lyapunov analysis. Simulation and experiment results on a 2 DOF robotic manipulator demonstrate the effectiveness of the proposed method

Efficient derivation of dopaminergic neurons from SOX1(-) floor plate cells under defined culture conditions.

BACKGROUND: Parkinson's disease (PD) is a severe neurodegenerative disease associated with loss of dopaminergic neurons. Derivation of dopaminergic neurons from human embryonic stem cells (hESCs) could provide new therapeutic options for PD therapy. Dopaminergic neurons are derived from SOX(-) floor plate (FP) cells during embryonic development in many species and in human cell culture in vitro. Early treatment with sonic hedgehog (Shh) has been reported to efficiently convert hESCs into FP lineages. METHODS: In this study, we attempted to utilize a Shh-free approach in deriving SOX1(-) FP cells from hESCs in vitro. Neuroectoderm conversion from hESCs was achieved with dual inhibition of the BMP4 (LDN193189) and TGF-β signaling pathways (SB431542) for 24 h under defined culture conditions. RESULTS: Following a further 5 days of treatment with LDN193189 or LDN193189 + SB431542, SOX1(-) FP cells constituted 70-80 % of the entire cell population. Upon treatment with Shh and FGF8, the SOX1(-) FP cells were efficiently converted to functional Nurr1(+) and TH(+) dopaminergic cells (patterning), which constituted more than 98 % of the entire cell population. However, when the same growth factors were applied to SOX1(+) cells, only less than 4 % of the cells became Nurr1(+), indicating that patterning was effective only if SOX1 expression was down-regulated. After transplanting the Nurr1(+) and TH(+) cells into a hemiparkinsonian rat model, significant improvements were observed in amphetamine induced ipslateral rotations, apomorphine induced contra-lateral rotations and Rota rod motor tests over a duration of 8 weeks. CONCLUSIONS: Our findings thus provide a convenient approach to FP development and functional dopaminergic neuron derivation.published_or_final_versio

Exploring the Opportunity of Augmented Reality (AR) in Supporting Older Adults Explore and Learn Smartphone Applications

The global aging trend compels older adults to navigate the evolving digital landscape, presenting a substantial challenge in mastering smartphone applications. While Augmented Reality (AR) holds promise for enhancing learning and user experience, its role in aiding older adults' smartphone app exploration remains insufficiently explored. Therefore, we conducted a two-phase study: (1) a workshop with 18 older adults to identify app exploration challenges and potential AR interventions, and (2) tech-probe participatory design sessions with 15 participants to co-create AR support tools. Our research highlights AR's effectiveness in reducing physical and cognitive strain among older adults during app exploration, especially during multi-app usage and the trial-and-error learning process. We also examined their interactional experiences with AR, yielding design considerations on tailoring AR tools for smartphone app exploration. Ultimately, our study unveils the prospective landscape of AR in supporting the older demographic, both presently and in future scenarios

Prevalence and risk factors of sarcopenia in idiopathic pulmonary fibrosis: a systematic review and meta-analysis

BackgroundSarcopenia often occurs as a comorbidity in many diseases which ultimately affects patient prognosis. However, it has received little attention in patients with idiopathic pulmonary fibrosis (IPF). This systematic review and meta-analysis aimed at determining the prevalence and risk factors of sarcopenia in patients with IPF.MethodsEmbase, MEDLINE, Web of Science, and Cochrane databases were searched using relevant MeSH terms until December 31, 2022. The Newcastle-Ottawa Scale (NOS) was used for quality assessment and data analysis were performed using Stata MP 17.0 (Texas, USA). A random effects model was adopted to account for differences between articles, and the I2 statistic was used to describe statistical heterogeneities. Overall pooled estimates obtained from a random effects model were estimated using the metan command. Forest plots were generated to graphically represent the data of the meta-analysis. Meta-regression analysis was used for count or continuous variables. Egger test was used to evaluate publication bias and, if publication bias was observed, the trim and fill method was used.Main resultsThe search results showed 154 studies, and five studies (three cross-section and two cohort studies) with 477 participants were finally included. No significant heterogeneity was observed among studies included in the meta-analysis (I2 = 16.00%) and our study's publication bias is low (Egger test, p = 0.266). The prevalence of sarcopenia in patients with IPF was 26% (95% CI, 0.22–0.31). The risk factors for sarcopenia in patients with IPF were age (p = 0.0131), BMI (p = 0.001), FVC% (p < 0.001), FEV1% (p = 0.006), DLco% (p ≤ 0.001), and GAP score (p = 0.003).ConclusionsThe pooled prevalence of sarcopenia in patients with IPF was 26%. The risk factors for sarcopenia in IPF patients were age, BMI, FVC%, FEV1%, DLco%, and GAP score. It is important to identify these risk factors as early as possible to improve the life quality of patients with IPF

Revealing internal flow behaviour in arc welding and additive manufacturing of metals

Internal flow behaviour during melt-pool-based metal manufacturing remains unclear and hinders progression to process optimisation. In this contribution, we present direct time-resolved imaging of melt pool flow dynamics from a high-energy synchrotron radiation experiment. We track internal flow streams during arc welding of steel and measure instantaneous flow velocities ranging from 0.1 m s−1 to 0.5 m s−1. When the temperature-dependent surface tension coefficient is negative, bulk turbulence is the main flow mechanism and the critical velocity for surface turbulence is below the limits identified in previous theoretical studies. When the alloy exhibits a positive temperature-dependent surface tension coefficient, surface turbulence occurs and derisory oxides can be entrapped within the subsequent solid as result of higher flow velocities. The widely used arc welding and the emerging arc additive manufacturing routes can be optimised by controlling internal melt flow through adjusting surface active elements

Shufeng Jiedu Capsules Alleviate Lipopolysaccharide-Induced Acute Lung Inflammatory Injury via Activation of GPR18 by Verbenalin

Background/Aims: Acute respiratory tract infection (ARTI) is the most common reason for outpatient physician office visits. Although powerful and significant in the treatment of infections, antibiotics used for ARTI inappropriately have been an important contributor to antibiotic resistance. We previously reported that Shufeng Jiedu Capsule (SJC) can effectively amplify anti-inflammatory signaling during infection. In this study, we aimed to systematically explore its composition and the mechanism of its effects in ARTI. Methods: Pseudomonas aeruginosa (PAK) strain was used to generate a mouse model of ARTI, which were then treated with different drugs or compounds to determine the corresponding anti-inflammatory roles. High-performance liquid chromatography-quadrupole time of flight-tandem mass spectrometry. was conducted to detect the chemical compounds in SJC. RNAs from the lung tissues of mice were prepared for microarray analysis to reveal globally altered genes and the pathways involved after SJC treatment. Results: SJC significantly inhibited the expression and secretion of inflammatory factors from PAK-induced mouse lung tissues or lipopolysaccharide-induced peritoneal macrophages. Verbenalin, one of the bioactive compounds identified in SJC, also showed notable anti-inflammatory effects. Microarray data revealed numerous differentially expressed genes among the different treatment groups; here, we focused on studying the role of GPR18. We found that the anti-inflammatory role of verbenalin was attenuated in GPR18 knockout mice compared with wild-type mice, although no statistically significant difference was observed in the untreated PAK-induced mice types. Conclusion: Our data not only showed the chemical composition of SJC, but also demonstrated that verbenalin was a significant anti-inflammatory compound, which may function through GPR18

Cardioprotection and lifespan extension by the natural polyamine spermidine

Aging is associated with an increased risk of cardiovascular disease and death. Here we show that oral supplementation of the natural polyamine spermidine extends the lifespan of mice and exerts cardioprotective effects, reducing cardiac hypertrophy and preserving diastolic function in old mice. Spermidine feeding enhanced cardiac autophagy, mitophagy and mitochondrial respiration, and it also improved the mechano-elastical properties of cardiomyocytes in vivo, coinciding with increased titin phosphorylation and suppressed subclinical inflammation. Spermidine feeding failed to provide cardioprotection in mice that lack the autophagy-related protein Atg5 in cardiomyocytes. In Dahl salt-sensitive rats that were fed a high-salt diet, a model for hypertension-induced congestive heart failure, spermidine feeding reduced systemic blood pressure, increased titin phosphorylation and prevented cardiac hypertrophy and a decline in diastolic function, thus delaying the progression to heart failure. In humans, high levels of dietary spermidine, as assessed from food questionnaires, correlated with reduced blood pressure and a lower incidence of cardiovascular disease. Our results suggest a new and feasible strategy for protection against cardiovascular disease

Review of Particle-Based Computational Methods and Their Application in the Computational Modelling of Welding, Casting and Additive Manufacturing

A variety of particle-based methods have been developed for the purpose of computationally modelling processes that involve, for example, complex topological changes of interfaces, significant plastic deformation of materials, fluid flow in conjunction with heat transfer and phase transformation, flow in porous media, granular flow, etc. Being different from the conventional methods that directly solve related governing equations using a computational grid, the particle-based methods firstly discretize the continuous medium into discrete pseudo-particles in mathematics. The methods then mathematically solve the governing equations by considering the local interaction between neighbouring pseudo-particles. Such solutions can reflect the overall flow, deformation, heat transfer and phase transformation processes of the target materials at the mesoscale and macroscale. This paper reviews the fundamental concepts of four different particle-based methods (lattice Boltzmann method—LBM, smoothed particle hydrodynamics—SPH, discrete element method—DEM and particle finite element method—PFEM) and their application in computational modelling research on welding, casting and additive manufacturing