Dynamic Task Offloading for Digital Twin-Empowered Mobile Edge Computing via Deep Reinforcement Learning

Limited by battery and computing re-sources,the computing-intensive tasks generated by Internet of Things(IoT)devices cannot be processed all by themselves.Mobile edge computing(MEC)is a suitable solution for this problem,and the gener-ated tasks can be offloaded from IoT devices to MEC.In this paper,we study the problem of dynamic task offloading for digital twin-empowered MEC.Digital twin techniques are applied to provide information of environment and share the training data of agent de-ployed on IoT devices.We formulate the task offload-ing problem with the goal of maximizing the energy efficiency and the workload balance among the ESs.Then,we reformulate the problem as an MDP problem and design DRL-based energy efficient task offloading(DEETO)algorithm to solve it.Comparative experi-ments are carried out which show the superiority of our DEETO algorithm in improving energy efficiency and balancing the workload.

The Analysis of the Effect of China＇s Monetary Policy in Open Economy

Firstly, this paper theoretically proves that with the development of open economy the effect of monetary policy will become poor. Secondly, the paper empirically analyzes the impact of open economy on our monetary policy. We conclude that there no longer exists long stable relationship between money supply and economic growth. Finally, in four aspects the paper concretely points out that with the open of economy great changes will take place in the implementation of our monetary policy.

Lactate-mediated medium-chain fatty acid production from expired dairy and beverage waste

Fruits,vegetables,and dairy products are typically the primary sources of household food waste.Currently,anaerobic digestion is the most used bioprocess for the treatment of food waste with concomitant generation of biogas.However,to achieve a circular carbon economy,the organics in food waste should be converted to new chemicals with higher value than energy.Here we demonstrate the feasibility of medium-chain carboxylic acid(MCCA)production from expired dairy and beverage waste via a chain elongation platform mediated by lactate.In a two-stage fermentation process,the first stage with optimized operational conditions,including varying temperatures and organic loading rates,transformed expired dairy and beverage waste into lactate at a concentration higher than 900 mM C at 43℃.This lactate was then used to produce>500 mM C caproate and>300 mM C butyrate via microbial chain elongation.Predominantly,lactate-producing microbes such as Lactobacillus and Lacticaseibacillus were regulated by temperature and could be highly enriched under mesophilic conditions in the firststage reactor.In the second-stage chain elongation reactor,the dominating microbes were primarily from the genera Megasphaera and Caproiciproducens,shaped by varying feed and inoculum sources.Cooccurrence network analysis revealed positive correlations among species from the genera Caproiciproducens,Ruminococcus,and CAG-352,as well as Megasphaera,Bacteroides,and Solobacterium,indicating strong microbial interactions that enhance caproate production.These findings suggest that producing MCCAs from expired dairy and beverage waste via lactate-mediated chain elongation is a viable method for sustainable waste management and could serve as a chemical production platform in the context of building a circular bioeconomy.

Efficient sampling methods for characterizing POIs on maps based on road networks

With the rapid development of location-based services, a particularly important aspect of start-up marketing research is to explore and characterize points of interest （PoIs） such as restaurants and hotels on maps. However, due to the lack of direct access to PoI databases, it is necessary to rely on existing APIs to query Pols within a region and calculate PoI statistics. Unfortunately, public APIs generally im- pose a limit on the maximum number of queries. Therefore, we propose effective and efficient sampling methods based on road networks to sample PoIs on maps and provide unbiased estimators for calculating PoI statistics. In general, the more intense the roads, the denser the distribution of PoIs is within a region. Experimental results show that compared with state-of-the-art methods, our sampling methods improve the efficiency of aggregate statistical estimations.

医源性脑淀粉样血管病的研究进展

医源性脑淀粉样血管病(iCAA)是由既往医疗操作引发的脑小血管病,常表现为青年人群的自发性脑叶出血。目前临床医师对这一新兴概念尚缺乏认识,同时该病的医源性暴露途径及背后机制尚不明确。明确医源性暴露途径不仅有助于诊断,而且对散发性淀粉样血管病的发病机制将有所启迪,也可能影响脑出血患者的手术决策。因此,笔者查阅文献,追溯最早的相关病例,对不同医源性暴露途径、临床影像表现及预后进行综述,并探讨未来可能的研究方向。