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The objective of this article is real-time detection of a change-point in the baseline distribution of the frequency signal generated by Phasor Measurement Units (PMUs) that could indicate potential for voltage collapse, false data injection, or other security threats. The alarm flags an anomaly event when the cumulative sum (CUSUM) of the log-likelihood departures of the Hurst exponent of the frequency from its baseline statistic exceeds a threshold set up as a compromise between the conflicting objectives of minimum detection delay and acceptable False Alarm Rate. As main theoretical contribution, an extra protection layer is developed that provides an estimate of the time to the alarm event, giving the Transmission System Operator (TSO) or an autonomous agent more time to deploy proactive measures to avoid large catastrophic system states. The proposed method is illustrated by a retrospective analysis of the 2012 Indian blackout that reveals that 10-12 minutes before the voltage collapse, a significant increase in the Hurst exponent of the frequency could have been detected. Conceptually, this shows that PMUs provide significant value towards ensuring autonomous cognition in the power grid by enabling the abnormal events to be fault-detected in an unsupervised fashion.

Collective cell behavior contributes to all stages of cancer progression. Understanding how collective behavior emerges through cell-cell interactions and decision-making will advance our understanding of cancer biology and provide new therapeutic approaches. Here, we summarize an interdisciplinary discussion on multicellular behavior in cancer, draw lessons from other scientific disciplines, and identify future directions.

Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of machine learning and control theory problems. In this article, we provide for the first time a scalable distributed solution for these two problems by leveraging dynamical consensus-like protocols to find the SCCs. The proposed solution has a time complexity of $\mathcal {O}(NDd_{\text{in-degree}}^{\max })$, where $N$ is the number of vertices in the network, $D$ is the (finite) diameter of the network, and $d_{\text{in-degree}}^{\max }$ is the maximum in-degree of the network. Additionally, we prove that our algorithm terminates in $D+2$ iterations, which allows us to retrieve the finite diameter of the network. We perform exhaustive simulations that support the outperformance of our algorithm against the state of the art on several random networks, including Erdős–Rényi, Barabási–Albert, and Watts–Strogatz networks.

Adults aged 60 and over are most vulnerable to mild traumatic brain injury (mTBI). Nevertheless, the extent to which chronological age (CA) at injury affects TBI-related brain aging is unknown. This study applies Gaussian process regression to T1-weighted magnetic resonance images (MRIs) acquired within ∼7 days and again ∼6 months after a single mTBI sustained by 133 participants aged 20–83 (CA μ±σ= 42.6 ± 17 years; 51 females). Brain BAs are estimated, modeled, and compared as a function of sex and CA at injury using a statistical model selection procedure. On average, the brains of older adults age by 15.3 ± 6.9 years after mTBI, whereas those of younger adults age only by 1.8 ± 5.6 years, a significant difference (Welch’s t32=  − 9.17, p≃ 9.47 × 10−11). For an adult aged ∼30 to ∼60, the expected amount of TBI-related brain aging is ∼3 years greater than in an individual younger by a decade. For an individual over ∼60, the respective amount is ∼7 years. Despite no significant sex differences in brain aging (Welch’s t108= 0.78, p> 0.78), the statistical test is underpowered. BAs estimated at acute baseline versus chronic follow-up do not differ significantly (t264= 0.41, p> 0.66, power = 80%), suggesting negligible TBI-related brain aging during the chronicstage of TBI despite accelerated aging during the acutestage. Our results indicate that a single mTBI sustained after age ∼60 involves approximately ∼10 years of premature and lasting brain aging, which is MRI detectable as early as ∼7 days post-injury.

In this paper, we focus on sensor selection, that is, determine the minimum number of state variables that need to be measured, to monitor the evolution of the entire biological system, that is, all state variables, when modeled by discrete-time fractional-order systems (DTFOS) that are subject to modeling errors, process, and measurement noise. These systems are particularly relevant while modeling the spatiotemporal dynamics of processes in which the impact of long-range memory cannot be properly modeled by multivariate autoregressive integrative moving-average models. Therefore, the DTFOS enable a unified state-space framework to model the evolution of several biological (e.g., stem cell growth and bacteria evolution) and physiological signals (e.g., electroencephalogram and electromyogram). Therefore, in this paper, we focus on the solution to four different (yet related) problems of sensor selection for DTFOS, which are motivated by constraints on the data acquisition that are enforced by the detrimental impact of the sensing mechanisms to the biological system, the cost of performing the measurements with the current sensing technology, or spatial constraints that limit the number of sensors that can be deployed. Toward determining the solution to these problems that we show to be NP-hard, we leverage the representation of the DTFOS to derive new objectives and conditions that, ultimately, enable us to efficiently approximate a solution to the different problems by exploiting the submodularity structure, which enables us to establish suboptimality guarantees.

In this survey, we review several approaches for predicting performance of Network-on-Chip (NoC)-based multicore systems, starting from the traffic models to the complex NoC models for latency evaluation. We first review typical traffic models to represent the application workloads in NoC. Specifically, we review Markovian and non-Markovian (e.g., self-similar or long-range memory-dependent) traffic models and discuss their applications on multicore platform design. Then, we review the analytical techniques to predict NoC performance under given input traffic. We investigate analytical models for average as well as maximum delay evaluation. We also review the developments and design challenges of NoC simulators. One interesting research direction in NoC performance evaluation consists of combining simulation and analytical models in order to exploit their advantages together. Toward this end, we discuss several newly proposed approaches that use hardware-based or learning-based techniques. Finally, we summarize several open problems and our perspective to address these challenges.

Reducing energy consumption in multiprocessor systems-on-chip (MPSoCs) where communication happens via the network-on-chip (NoC) approach calls for multiple voltage/frequency island (VFI)-based designs. In turn, such multi-VFI architectures need efficient, robust, and accurate runtime control mechanisms that can exploit the workload characteristics in order to save power. Despite being tractable, the linear control models for power management cannot capture some important workload characteristics (e.g., fractality, nonstationarity) observed in heterogeneous NoCs; if ignored, such characteristics lead to inefficient communication and resources allocation, as well as high power dissipation in MPSoCs. To mitigate such limitations, we propose a new paradigm shift from power optimization based on linear models to control approaches based on fractal-state equations. As such, our approach is the first to propose a controller for fractal workloads with precise constraints on state and control variables and specific time bounds. Our results show that significant power savings can be achieved at runtime while running a variety of benchmark applications.