Joint Optimization for Mobile Edge Computing-Enabled Blockchain Systems: A Deep Reinforcement Learning Approach

A mobile edge computing (MEC)-enabled blockchain system is proposed in this study for secure data storage and sharing in internet of things (IoT) networks, with the MEC acting as an overlay system to provide dynamic computation offloading services. Considering latency-critical, resource-limited, and dynamic IoT scenarios, an adaptive system resource allocation and computation offloading scheme is designed to optimize the scalability performance for MEC-enabled blockchain systems, wherein the scalability is quantified as MEC computational efficiency and blockchain system throughput. Specifically, we jointly optimize the computation offloading policy and block generation strategy to maximize the scalability of MEC-enabled blockchain systems and meanwhile guarantee data security and system efficiency. In contrast to existing works that ignore frequent user movement and dynamic task requirements in IoT networks, the joint performance optimization scheme is formulated as a Markov decision process (MDP). Furthermore, we design a deep deterministic policy gradient (DDPG)-based algorithm to solve the MDP problem and define the multiple and variable number of consecutive time slots as a decision epoch to conduct model training. Specifically, DDPG can solve an MDP problem with a continuous action space and it only requires a straightforward actor–critic architecture, making it suitable for tackling the dynamics and complexity of the MEC-enabled blockchain system. As demonstrated by simulations, the proposed scheme can achieve performance improvements over the deep Q network (DQN)-based scheme and some other greedy schemes in terms of long-term transactional throughput

A Novel Classified Ledger Framework for Data Flow Protection in AIoT Networks

The edge computing node plays an important role in the evolution of the artificial intelligence-empowered Internet of things (AIoTs) that converge sensing, communication, and computing to enhance wireless ubiquitous connectivity, data acquisition, and analysis capabilities. With full connectivity, the issue of data security in the new cloud-edge-terminal network hierarchy of AIoTs comes to the fore, for which blockchain technology is considered as a potential solution. Nevertheless, existing schemes cannot be applied to the resource-constrained and heterogeneous IoTs. In this paper, we consider the blockchain design for the AIoTs and propose a novel classified ledger framework based on lightweight blockchain (CLF-LB) that separates and stores data rights at the source and enables a thorough data flow protection in the open and heterogeneous network environment of AIoT. In particular, CLF-LB divides the network into five functional layers for optimal adaptation to AIoTs applications, wherein an intelligent collaboration mechanism is also proposed to enhance the across-layer operation. Unlike traditional full-function blockchain models, our framework includes novel technical modules, such as block regenesis, iterative reinforcement of proof-of-work, and efficient chain uploading via the system-on-chip system, which are carefully designed to fit the cloud-edge-terminal hierarchy in AIoTs networks. Comprehensive experimental results are provided to validate the advantages of the proposed CLF-LB, showing its potentials to address the secrecy issues of data storage and sharing in AIoTs networks

Aerobic Exercise Decreases Negative Affect by Modulating Orbitofrontal-Amygdala Connectivity in Adolescents

Long-term negative affect in adolescence is associated with impairment in quality of life, interpersonal function, and social adaptation. Although physical exercise could decrease negative emotion, the underlying mechanism remains largely unknown. Acute exercise with controlled intensity might be a good experimental paradigm to unravel the potential neural mechanisms underlying the effects of physical exercise on negative affect. In this study, twenty-three males in late adolescence were randomly assigned to acute exercise group (AG) or control group. The experiment contained pre-test and post-test session interleaved with 30-min moderate-intensity exercise or seated rest. In each session, a resting-state fMRI scanning was conducted followed by completing Positive and Negative Affect Schedule and Profile of Mood State. Bilateral amygdala was used as seed region to calculate t voxel-wised functional connectivity (FC) of amygdala to whole brain. The results demonstrated, for the first time, that AG exhibited increased FC between right amygdala and right orbital frontal cortex. Significantly decreased negative affect was also observed in AG. Moreover, the increased rOFC-amygdala FC was also associated with the decreased depression score. Our findings suggest that exercise-induced decreased negative affect might be modulated by functional interactions of amygdala with both cognitive control and limbic networks, which offers a meaningful insight for clinical treatment and prevention of emotional disorders in late adolescence.</p

AlCl3·6H2O-Catalyzed Friedel-Crafts Alkylation of Indoles by the para-Quinone Methide Moiety of Celastrol

A classical Friedel-Crafts alkylation of different indoles catalyzed by AlCl3·6H2O has been developed for a well-known important natural product, celastrol, resulting in a series of derivatives for further biological evaluation. The catalyst loading was reduced to 5 mol %, the reaction proceeds at ambient temperature and reaction time is only 3 h. The product yields range from 20% to 99%. A reaction mechanism is also proposed, based on our experiment results

Integrating Chronic Obstructive Pulmonary Disease Treatment With 8-Week Tai Chi Chuan Practice: An Exploration of Mind-Body Intervention and Neural Mechanism

Objective:&amp; nbsp;This study aims to explore the effect of integrating routine treatment with Tai Chi Chuan (TCC) intervention on the clinical symptom of patients with Chronic Obstructive Pulmonary Disease (COPD) from clinical and neurological perspectives.&amp; nbsp;Methods:&amp; nbsp;Twenty patients with COPD were recruited for regular treatment combined with 8-week TCC rehabilitative practice. Clinical symptoms were evaluated by Chronic Obstructive Pulmonary Symptom Assessment Scale (CAT) and Modified Dyspnea Scale (mMRC) at baseline and after treatment. Resting-state MRI scan was also performed with multiline T2-weighted echo-planar imaging (EPI) to acquire their functional images before and after the treatment. TCC rehabilitation involved a total of 8 weeks of practice with 90 min per session, three times a week.&amp; nbsp;Results:&amp; nbsp;After an 8-week integration routine treatment with TCC practice, the patient&#39;s clinical symptoms improved significantly. Imaging analysis showed that COPD patients exhibited decreased Degree of Centrality (DC) in the right inferior frontal gyrus (IFG), right middle frontal gyrus, bilateral cingulate cortex, bilateral precuneus, and right precentral gyrus. Moreover, correlation analysis found that the decreased DC in the right IFG was positively correlated with the CAT improvements.&amp; nbsp;Conclusion:&amp; nbsp;The routine treatment involving TCC rehabilitation practice could improve the clinical symptoms of patients with COPD. The right IFG might be a key brain region to contribute to the neural mechanism underlying integrative intervention on the clinical symptoms in COPD. These findings provide neurological evidence for treating COPD rehabilitation practice with mind-body practice based on Chinese culture to some extent, which also advances the understanding of the efficacy of TCC as the adjuvant technology from a neuroscience perspective.</p

Effects of 8-Week Tai Chi Chuan Practice on Mindfulness Level

Objectives Tai Chi Chuan (TCC) is a common mindfulness-based aerobic exercise. However, the evidence on the effects of TCC practice on mindfulness has been controversial. The aim of this study was to explore whether TCC practice with an emphasis on interoceptive awareness could improve mindfulness levels in a healthy adult population. Methods Sixty-one healthy adults without mind-body practice experience were divided into the TCC and control groups. Participants in the TCC group received classic Yang-style supervised TCC practice for 8 weeks, whereas those in the control group did not receive any intervention. The Five Facet Mindfulness Questionnaire and Multidimensional Assessment of Interoceptive Awareness were administrated at baseline, the end of week 8, and the end of week 24. Results In terms of mindfulness, significant interaction between group and time was found in describing, acting with awareness, nonjudging, and nonreactivity. For interoceptive awareness, there were marginally significant interaction effects of time and group in attention regulation, self-regulation, and trust. TCC practice significantly improved the above-mentioned dimensions of mindfulness and interoceptive awareness. Moreover, we still observed increased describing, acting with awareness, and nonjudging in mindfulness, and trusting in interoceptive awareness for the TCC group at the end of week 24. Conclusions This study indicated that 8-week TCC practice with a component of interoceptive awareness could increase mindfulness levels, which could still be observed in the 24th week. These findings are of great significance for health practice and treating emotional disorders utilizing mind-body practice as an alternative and complementary medicine

RES: An Interpretable Replicability Estimation System for Research Publications

Reliable and faithful research is the cornerstone of breakthrough advancements and disruptive innovations. Assessing the credibility of scientific findings and claims in research publications has long been a time-consuming and challenging task for researchers and decision-makers. In this paper, we introduce RES - an intelligent system that assists humans in analyzing the credibility of scientific findings and claims in research publications in the field of social and behavioral sciences by estimating their replicability. The pipeline of RES consists of four major modules that perform feature extraction, replicability estimation, result explanation, and sentiment analysis respectively. Our evaluation based on human experts' assessments suggests that the RES has achieved adequate performance. The RES is also built with a Graphical User Interface (GUI) that is publicly accessible at https://tamu-infolab.github.io/RES/