With the development of smart cities, the demand for artificial intelligence (AI) based services grows exponentially. The existing works just focus on cloud- edge or edge-device cooperative AI which suffers low learning efficiency of AI, while edge-to-edge cooperative AI is still an unresolved issue. Moreover, the existing researches concentrate on the computation offloading of the AI-based task, ignoring that it is a brain-like task performing sophisticated processing to raw data, which leads to the high latency and low quality of the learning services. To address these challenges, this paper proposes an on-demand learning offloading mechanism for edge-to-edge cooperative AI. Firstly, the principle of the learning capability and its offloading are proposed for the formal description of the learning resources migration. Secondly, the proposed mechanism realizes the bilateral learning offloading utilizing edge-to-edge and cloud-edge collaborations to handle AI-based tasks with high learning efficiency and resource utilization rate. Moreover, we model the edge-to-edge learning offloading allocation based on the concatenation of deep neural network (DNN) subtasks and their heterogeneous requirement of learning resources. Simulation results indicate the rationality and efficiency of the proposed mechanism.

Anaphylaxis is an unanticipated systemic hypersensitivity reaction which can produce deleterious effects, even death, if not treated promptly. Preventive approach implies taking a thorough anamnesis with the emphasis on previously diagnosed allergies. If an allergic reaction occurred during previous surgery, a detailed documentation of administered anaesthetic agents and drugs would be crucial for the following anaesthesiologic management. Preoperative planning and avoiding cross-reactivity with drugs commonly used during anaesthesia are the key points to prevent an anaphylaxis. In case of emergency surgery when the exact identification of allergens is not possible, premedication prophylaxis should be considered. General measures for prevention of anaphylaxis could be undertaken as well, such as the choice of anaesthesiologic drugs and techniques in the operating theatre adequately equipped for the management of predictable anaphylaxis.

Abstract We develop a critique of government interventionism based on the Misesian calculation argument against socialism. If private property rights and relative prices based on supply and demand are necessary for successful economic coordination, then conventional market failure theories cannot be sustained. Government interventionism based on the idea of correcting “market failures” is analytically just a milder form of socialist central planning. Between the two, there are only differences in degree, not in kind. We criticize several public choice and law and economics scholars for disregarding this Misesian angle in their market failure theories. In our view they are reducible to arguments based on a fallacious political economy while perpetuating false neoclassical economic analysis of market failure theorists. We claim that government interventionism is just a milder form of socialist central planning. Therefore, the traditional arguments against the efficiency of central planning also apply to government interventions aiming at fixing market failures. In particular, we maintain that governments face the “knowledge problem”, which means that they cannot determine the optimal allocation of resources. In section two of this paper we discuss market failure and economic calculation. Section three is given over to our claim that the “Nirvana fallacy” is itself fallacious. The burden of section four is to address Coase and consequences. We conclude in section five.

BackgroundPhysical and biological properties of dust particles might affect the availability and distribution of chemicals associated to indoor dust; however it has not been adequately examined. In this study, household dust from Shanghai was fractionated into five particle sizes and size distribution, morphology, surface area, organic matter, microorganisms, elemental composition, metals and organophosphorus flame retardants (OPFRs) compositions were characterized. Also, household dust samples from Stockholm that has previously been characterized were included in the analysis of OPFRs for comparison.ResultsThe respirable fraction had a yield of 3.3% in mass percentage, with a particle size of 2.22 ± 2.04 µm. As expected, both metals and OPFRs concentrations increased with decreased particle size. Al and Fe dominated (66–87%) followed by the concentrations of Zn (5–14%) and Ga (1.8–5%) of the sum of 16 metals in the dust. The concentrations of OPFRs in Shanghai dust ranged from 5.34 to 13.7 µg/g (median: 7.21 µg/g), compared to household dust from Stockholm that ranged from 16.0 to 28.3 µg/g (median: 26.6 µg/g). Tris(2-chloroisopropyl) phosphate (TCIPP) and tris(2-chloroethyl) phosphate (TCEP) dominated in Shanghai dust samples while tris(2-butoxyethyl) phosphate (TBOEP) dominated in dust from Stockholm homes.ConclusionThe results showed that mass percentage for each particle size fraction was not evenly distributed. Furthermore, the particle-bound microorganisms and OPFRs increased with decreased particle size, whereas metals had the highest concentrations at specific dust sizes. Therefore, it is essential to select the proper particle size in order to assess any specific human exposure study to indoor pollutants.