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Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.

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Purpose Internet of things (IoT) is an interaction between more than one network to facilitate communication. These networks by themselves are complex networks. Therefore, IoT network is expected to grow at unprecedented scale involving other networks such as Mobile, VANET, and Wireless Sensor Networks (WSNs). In fact, modeling each network by itself is a complicated process. In addition, on a large scale, the communication among these networks increases the modeling complexity in which energy consumption could be critical due to large number of dropped messages. Therefore, this paper is a step forward towards modeling IoT complex network through gateway deployment. The paper answers the question of how to deploy these gateways in a way that guarantees an efficient and adaptive communication. Methods Two models/methods are proposed and examined which are geographical based and mobile ferry based models. Due to the complexity of the deployment problem in reality, the deployment problem is treated as a complex adaptive problem and simulated through different sets of experiments and settings. Results The two methods have been compared through set of experiments using ONE simulator with the same number of employed gateways in the two methods. The experiments shows that ferry based model outperforms geographical based model with 29% improvement in messages delivery probability. Additionally, when the number of mobile ferries are reduced by 34% compared to gateways that are distributed based on geographical area, the mobile ferries approach still outperform geographical area based approach when it comes to messages delivery probability. Conclusions The paper presents the two methods to model the complex internet of things environment and its sub networks interaction. The paper concludes that employing mobile ferries as gateways is better than deploying gateways based on geographical area when the sub networks interaction is facilitated in IoT network.

Many industrial wireless sensor network (IWSN) applications require real-time communications in which bounded delay requirements need to be satisfied. IWSN lossy links and limited resources of sensor nodes pose significant challenges for supporting real-time applications. Many IWSN routing algorithms focus on being energy efficient to extend the network lifetime, but the delay wasn't the main concern. However, these algorithms are unable to deal with real-time applications in which data packets need to be delivered to the sink node within a predefined real-time information. On the other hand, the most existing real-time routing schemes are often based on the desired deadline time (required delivery time) and end-to-end distance in the selection of forwarding node while the reliability of on-time data delivery, the effects of a collision, energy balance, and a number of a hop count to the sink node have largely been ignored. These issues can dramatically impact real-time performance. Therefore, the paper proposes a routing algorithm that achieves a balance between energy efficiency and reliability while being suitable for real-time applications as well. In addition, it reduces the effects of congestion by sufficiently utilizing the underloaded nodes to improve network throughput. Finally, the hop count to the sink is considered. This paper formulates the real-time routing problem into 0/1 Integer Linear Programming (ILP) problem and then proposes a Realtime Energy-Efficient Traffic-Aware approach (RTERTA) to solve the optimization problem for a large-scale IWSN. From the obtained simulation results, the proposed solution has proved to be able to enhance the network performance in terms of packets miss ratio, average end-to-end delay, packets delivery rate, as well as network lifetime.