Dynamic physical activity recommendation on personalised mobile health information service: A deep reinforcement learning approach

Mobile health (mHealth) information service makes healthcare management easier for users, who want to increase physical activity and improve health. However, the differences in activity preference among the individual, adherence problems, and uncertainty of future health outcomes may reduce the effect of the mHealth information service. The current health service system usually provides recommendations based on fixed exercise plans that do not satisfy the user specific needs. This paper seeks an efficient way to make physical activity recommendation decisions on physical activity promotion in personalised mHealth information service by establishing data-driven model. In this study, we propose a real-time interaction model to select the optimal exercise plan for the individual considering the time-varying characteristics in maximising the long-term health utility of the user. We construct a framework for mHealth information service system comprising a personalised AI module, which is based on the scientific knowledge about physical activity to evaluate the individual exercise performance, which may increase the awareness of the mHealth artificial intelligence system. The proposed deep reinforcement learning (DRL) methodology combining two classes of approaches to improve the learning capability for the mHealth information service system. A deep learning method is introduced to construct the hybrid neural network combing long-short term memory (LSTM) network and deep neural network (DNN) techniques to infer the individual exercise behavior from the time series data. A reinforcement learning method is applied based on the asynchronous advantage actor-critic algorithm to find the optimal policy through exploration and exploitation

Finite element analysis of laser shock peening of 2050-T8 aluminum alloy

AbstractLaser shock processing is a recently developed surface treatment designed to improve the mechanical properties and fatigue performance of materials, by inducing a deep compressive residual stress field. The purpose of this work is to investigate the residual stress distribution induced by laser shock processing in a 2050-T8 aeronautical aluminium alloy with both X-ray diffraction measurements and 3D finite element simulation. The method of X-ray diffraction is extensively used to characterize the crystallographic texture and the residual stress crystalline materials at different scales (macroscopic, mesoscopic and microscopic).Shock loading and materials’ dynamic response are experimentally analysed using Doppler velocimetry in order to use adequate data for the simulation. Then systematic experience versus simulation comparisons are addressed, considering first a single impact loading, and in a second step the laser shock processing treatment of an extended area, with a specific focus on impact overlap. Experimental and numerical results indicate a residual stress anisotropy, and a better surface stress homogeneity with an increase of impact overlap.A correct agreement is globally shown between experimental and simulated residual stress values, even if simulations provide us with local stress values whereas X-ray diffraction determinations give averaged residual stresses

GOVERNANCE MECHNISMS IN IS OUTSOURCING PROJECTS IN TRANSITION ECONMIES

Pevious IS outsourcing research studies failed to provide evidences on how IT client-vendor relationships should be governed to ensure project success and relational continuity. More importantly, it is even challenging for companies to achieve outsourcing success in transition economies facing an environment characterized by institutional instability. This article draws from theories of institutions and organizations to develop a model examining outsourcing relationship governance mechanisms which would affect outsourcing success in state-owned and non-state-owned Chinese companies. Results of 72 state-owned and 54 non-state-owned outsourcing projects show that the positive relationship between contractual governance and outsourcing success is stronger in state-owned firms than in non-state-owned firms. On the other hand, non-state-owned firms have stronger effects on the relationships between relational governance and outsourcing success, and between outsourcing success and relational continuity

IRS-HD: an intelligent personalized recommender system for heart disease patients in a tele-health environment

The use of intelligent technologies in clinical decision making support may play a promising role in improving the quality of heart disease patients’ life and helping to reduce cost and workload involved in their daily health care in a tele-health environment. The objective of this demo proposal is to demonstrate an intelligent prediction system we developed, called IRS-HD, that accurately advises patients with heart diseases concerning whether they need to take the body test today or not based on the analysis of their medical data during the past a few days. Easy-to-use user friendly interfaces are developed for users to supply necessary inputs to the system and receive recommendations from the system. IRS-HD yields satisfactory recommendation accuracy, offers a promising way for reducing the risk of incorrect recommendations, as well saves the workload for patients to conduct body tests every day

Residual stresses in surface induction hardening of steels: Comparison between experiment and simulation

Deep induction hardening has been performed on two batches of smooth cylindrical specimens with a hardening depth respectively around 2 mm and 3 mm. The distributions of axial and circumferential residual stresses are analysed for the two specimen batches by X-ray diffraction technique. The radial normal stress field is estimated through the use of the well known Moore and Evans correction. Finally, the experimental residual stresses are compared with those obtained from a multiphysic finite element modelling of the whole induction treatment process, including electromagnetic, thermal, metallurgical and mechanical phenomena. The simulated residual stress field is in good agreement with X-ray analysis especially at depths lower than one-tenth the specimen diameter. At deeper depths, a correction of the experimental X-ray analysis has been done to obtain realistic values

Optimization of microstructural evolution during laser cladding of Ni based powder on GCI glass molds

International audienceIn the glass industry, laser cladding is an innovative surfacing technique of depositing a layer of nickel to protect glass mold against corrosion, abrasion and thermal fatigue. This method (powder fusion by projection), well known in additive manufacturing represents a real technological leap for the glass industry. However, during laser cladding of Ni-based powder on gray cast iron, cracks can be observed for some process conditions. These cracks are often due to the Heat Affected Zone which creates structural stresses linked to the development of a martensitic structure in the ferritic matrix of the lamellar graphite cast iron. The aim of this work is to observe the impact of laser cladding (without substrate pre-heating usually used to limit cracking) on coating behavior but also on flake-graphite cast iron substrates. The microstructure and the mechanical properties were studied around the interface cladding/substrate. The impact of the processing parameters (power P, scanning speed v and powder feeding rate PFR was studied by using the ANOVA (ANalysis Of VAriance) technique. It has been observed that laser cladding on graphite cast iron without cracking is possible by limiting the linear energy induced by the process. Optimization of the processing parameters in order to obtain the industrial expected geometry of the coating has also been proposed