Electronic healthcare (e-health) has gained more and more research attention in recent years, due to its flexibility and convenience. E-health is efficiently enabled by telecare medicine information system (TMIS). TMIS provides seamless transfer and timely sharing of medical information for specific healthcare services. Since communications in TMIS are carried out through unreliable channels, data security and user privacy concerns become prominent. With traditional single-server architecture, users must store massive credentials, which causes inefficient communication and significant overhead. Moreover, user credentials in previously proposed schemes are stored at server side, suffering potential risks. Our work proposes a three-factor user-controlled single sign-on (UCSSO) with fast authentication and privacy protection for TMIS. The contributions of this paper are as follows. Our work integrates three factors including password, smart card and biometrics in authentication procedure, for providing a high-security and privacy-preserved communication. We introduce single sign-on solution that allows users to log in to multiple servers using a single password. User-controlled mechanism is proposed to address insider attacks and the risk that registration center may be compromised. The proposed scheme is designed with fast authentication mechanism that helps to efficiently establishes new session key. Our work is proved secure using BAN logic, ROR model, and AVISPA toolset. The results of performance comparison show that our scheme provides more security properties and bears the least overhead, compared with competitive schemes.

In nuclear power plant construction scheduling, a project is generally defined by its dependent preparation time, the time required for construction, and its reactor installation time. The issues of multiple construction teams and multiple reactor installation teams are considered. In this paper, a hierarchical particle swarm optimization algorithm is proposed to solve the nuclear power plant construction scheduling problem and minimize the occurrence of projects failing to achieve deliverables within applicable due times and deadlines.

[Purpose] Stroke patients are unable to move on their own and must be rehabilitated to allow the nervous system to trigger and restore its function. Traditional practice is to use electrode caps to extract brain wave features and combine them with assistive devices. However, there are problems that the electrode cap is not easy to wear, and the potential recognition is not good, and different extraction methods will affect the accuracy of the Brain-Computer Interfaces (BCI), which still has room for improvement. [Participants and Methods] The brainwave headphones used in this experiment do not must a conductive gel to get a good EEG for neural induction and drive the upper limb rehabilitation robot. Next, 8 stroke patients and 200 normal participants were invited for a 4-week rehabilitation training. The effectiveness of the training was determined using Fast Fourier Transform (FFT), Magnitude squared coherence (MSC) feature extraction methods, and five machine learning techniques that induced flicker frequencies. [Results] The results show that the optimal steady-state visual evoked flicker frequency is 6 Hz, and the identification rate of FFT is about 5.2% higher than that of the MSC method. Using an optimized model for different feature extraction methods can improve the recognition rate by 1.3%–9.1%. [Conclusion] The images based on Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS) index improvement, and functional Magnetic Resonance Imaging (fMRI) show that the sensory region of brain movement has become a concentrated activation phenomenon. Besides strengthening the feature extraction method also lets the elbow has an obvious recovery effect.