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With the continuous development of 5G, the era of the internet of everything is coming.Problems such as massive device connections, massive application requests, ultra-high network load and complex dynamic network environment pose great challenges to the optimization of 5G systems in the context of the internet of everything.Facing these challenges, artificial intelligence (AI) shows its unique advantages.Firstly, the advantages of deep learning driven AI algorithms in 5G system compared with conventional algorithms were briefly introduced.Then, the application of AI algorithms in multi-access edge computing (MEC) and mmWave massive multiple-input multiple-output (MIMO) system were described in detail, with advantages and disadvantages of each method being compared and analyzed.Finally, according to the existing research, the shortcomings of AI algorithms in 5G application scenarios were summarized and the future research directions were forecasted.

Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).

Alzheimer’s disease (AD) is a progressive disorder that affects cognitive brain functions and starts many years before its clinical manifestations. A biomarker that provides a quantitative measure of changes in the brain due to AD in the early stages would be useful for early diagnosis of AD, but this would involve dealing with large numbers of people because up to 50% of dementia sufferers do not receive formal diagnosis. Thus, there is a need for accurate, low-cost, and easy to use biomarkers that could be used to detect AD in its early stages. Potentially, electroencephalogram (EEG) based biomarkers can play a vital role in early diagnosis of AD as they can fulfill these needs. This is a cross-sectional study that aims to demonstrate the usefulness of EEG complexity measures in early AD diagnosis. We have focused on the three complexity methods which have shown the greatest promise in the detection of AD, Tsallis entropy (TsEn), Higuchi Fractal Dimension (HFD), and Lempel-Ziv complexity (LZC) methods. Unlike previous approaches, in this study, the complexity measures are derived from EEG frequency bands (instead of the entire EEG) as EEG activities have significant association with AD and this has led to enhanced performance. The results show that AD patients have significantly lower TsEn, HFD, and LZC values for specific EEG frequency bands and for specific EEG channels and that this information can be used to detect AD with a sensitivity and specificity of more than 90%.

Outbreaks of the Chikungunya virus (CHIKV) infection has been documented in over 40 countries, resulting in clinical symptoms characterized by fever and joint pain. Diagnosing CHIKV in a clinical lab setting is often omitted because of the high lab safety requirement. An infection system that mimics CHIKV infection will permit clinical evaluation of the production of neutralizing antibody for both disease diagnostics and treatment.We generated a CHIKV construct expressing CHIKV structural proteins. This construct permits the production of CHIKV pseudo-viral particles with a luciferase reporter. The pseudo-virus was able to infect a wide range of cell lines. The pseudovirus could be neutralized by the addition of neutralizing antibodies from patients.Taken together, we have developed a powerful system that can be handled at biosafety level 2 laboratories for evaluation of existence of CHIKV neutralizing antibodies.

The aim of this paper is to present video quality prediction models for objective non-intrusive, prediction of H.264 encoded video for all content types combining parameters both in the physical and application layer over Universal Mobile Telecommunication Systems (UMTS) networks. In order to characterize the Quality of Service (QoS) level, a learning model based on Adaptive Neural Fuzzy Inference System (ANFIS) and a second model based on non-linear regression analysis is proposed to predict the video quality in terms of the Mean Opinion Score (MOS). The objective of the paper is two-fold. First, to find the impact of QoS parameters on end-to-end video quality for H.264 encoded video. Second, to develop learning models based on ANFIS and non-linear regression analysis to predict video quality over UMTS networks by considering the impact of radio link loss models. The loss models considered are 2-state Markov models. Both the models are trained with a combination of physical and application layer parameters and validated with unseen dataset. Preliminary results show that good prediction accuracy was obtained from both the models. The work should help in the development of a reference-free video prediction model and QoS control methods for video over UMTS networks.