## 2 Overview of Research Directions Based on the Special Issue Papers
The defined results provide an efficient way to understand the metrics of AoI, throughput, and success probability in hybrid spectrum access-based CRN. [PERSON] et al. [36] present a cognitive approach to pervasive service composition, where the implementation of InfoCom tools and cognitive functions interact to create comprehensive composite services. The central hypothesis is that cognitive theory can help solve real problems requiring a comprehensive service composition. To test the approach, in this paper the case of urban uncertainty is presented. In various countries, street robbery with firearms is one of the problems that has a great impact due to its frequency. This article proposes a comprehensive service to deter criminals from committing their crimes. The results obtained by simulating our proposal in our case study are promising. [PERSON] et al. [37] used logistic regression models to examine the incremental predictive power of positive psychological factors relative to pre-enrolment measures of academic achievement. The study is based on the data of 302 business and economics undergraduate students of the Budapest University of Technology and Economics. Coping was found to be the most important factor highlighting the importance of stress management for students. We also found that the well-chosen psychological factors that measure coping, personality traits, the psychological immune system, emotional intelligence and PERMA (P-positive emotion, E-commitment, R-relationships, M-meaning, A-performances) factors, as well as university admission score and academic performance are significantly better predicted than when relying solely on pre-enrolment performance measures. [PERSON] et al. [38] developed an application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology. Using deep learning object detection technology based on the You Only Look (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in visible light mode. Using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy reaches 80% and the recall rate is close to 83%. [PERSON] et al. [39] examine the deployment of a cost-effective biofeed-back system that provides anxiety awareness during online examination activities. Human anxiety is classified based on the evaluation of biosignals related to skin conductance, skin temperature, and heart rate. The first goal of the study is to verify the performance of the presented system. Thus, the convergent validity of the system was tested in terms of self-report measures of anxiety. The second aim of this study is to identify the participants' personality dimensions based on the Technology Readiness Index (TRI) that influence their academic performance and real-time anxiety as provided by a biofeedback tool during study trials. [PERSON] et al. [40] focused on the study of problematic speech activity detection using EEG data. The novel approach used in the research involved the use of visual stimuli, such as reading and colour naming, as well as speech activity signals detectable with EEG technology. The proposed solution is based on a shallow feedforward artificial neural network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, and curvature were calculated from the original signal of the 16 EEG electrodes. In the field of brain and computer interface applications, the new approach was used to calculate additional features from the minimum phase signal. The experimental results demonstrated a speech detection accuracy of 86.80% and 83.69%, respectively, for the F1 value based on the analysis of EEG signals from one subject and several subjects. [PERSON] et al. [41] examined human-computer interactions for vertical, horizonal, and mixed layouts of recommending interfaces of four major e-commerce stores, and was based on our document object model events-based behavior analysis tool.