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Mindful attention is characterized by acknowledging the present experience as a transientmental event. Early stages of mindfulness practicemay require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs—operationalizing effort—distribute changes in neural activity evoked during mindful attention across the white matter network.We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present.We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one’s own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.

This book presents a historicised account of the Feminist International Network of Resistance to Reproductive and Genetic Engineering (FINRRAGE). A coordinated effort during the 1980s and 1990s by an international group of women to create and disseminate feminist knowledge about the then-new field of reproductive technologies. Bringing insights from science and technology studies together with social movements and feminist theory, it seeks to examine larger questions about knowledge and expertise in activist engagements with rapidly-developing technologies, as well as explore an important and neglected episode of feminist history. Its findings will be relevant to scholars in science studies, gender and women's studies and social movements, as well as to anyone with an interest in reproductive technologies and the history of feminist activism.

In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control.

This paper investigates an adaptive neural network control strategy for a two-degree-of-freedom helicopter system with input saturation and unknown external disturbances. Firstly, the radial basis function neural network is used to compensate the uncertainty and input saturation error of the system. Furthermore, a disturbance observer is designed to deal with complex disturbances composed of unknown disturbances and neural network errors. By constructing and analyzing the Lyapunov function, the stability of the helicopter system is strictly guaranteed. Finally, the numerical simulations and experiments conducted on the Quanser laboratory platform reveal that the proposed control strategy is suitable and effective.

Access authentication scheme plays a foundational role in ensuring the security of communication networks. However, an access authentication scheme with high security and efficiency is still lacking in quantum communication networks. In this paper, we propose a lattice-based access authentication scheme for quantum communication networks in the manner of real-time interaction with the network control center, which could achieve properties of mutual authentication, conditional anonymity, data confidentiality, unforgeability, undeniability, and data integrity. We utilize the digital signature algorithm CRYSTALS-Dilithium and the key-establishment algorithm CRYSTALS-KYBER, both of which have been selected for standardization by the National Institute of Standards and Technology, to realize secure access authentication for users of the quantum communication networks. Specifically, in the quantum secure direct communication network, key-establishment is replaced by the verification of signatures encoded in quantum states. Our results demonstrate the feasibility of establishing a quantum-secure communication network.

Attentional control (AC) plays a causal role in various mental disorders and, within the metacognitive model, contributes to maladaptive repetitive cognitive processes such as rumination and worry. The Attention Training Technique (ATT), an auditory psychotherapeutic intervention, improves AC and is associated with the efficiency of large‐scale fronto‐parietal control networks (FPN). This study investigates the neural correlates of ATT by applying a newly tailored fMRI paradigm, focusing on FPN engagement and its relationship with AC. We adapted ATT to examine neural responses during ATT compared to passive listening in ATT‐naïve participants (N = 43) and ensured the robustness of results by validating the findings in a second independent sample (N = 28). To optimize the paradigm, we compared two ATT conditions, rapidly switching (ATTswitch) and selectively focusing (ATTfocus) attention, against multiple passive‐listening control conditions, to probe ATT‐related FPN activation. We also tested whether trial‐wise subjective effort and self/external focus ratings differentiated ATT from control trials, parametrically modulated FPN activation, and whether ATT‐related FPN activation correlated with trait AC. ATT versus control conditions activated the FPN (pFWE < 0.05). This effect was present in both ATT conditions, with stronger activation in the ATTswitch versus ATTfocus condition, and independent of the specific control condition. Ratings of self/external focus and effort significantly differentiated ATT from control conditions (all p < 0.001) and parametrically modulated FPN activation (pFWE < 0.05). All effects were replicated in the second sample. Across both samples, FPN activation in ATT versus control conditions and trial‐wise ratings related to trait AC. Using a novel fMRI paradigm in two independent samples, we demonstrate that the ATT is associated with activation of the FPN, a key network for AC and mental health. The relationship between FPN activation and self‐report measures supports the relevance of the data for understanding ATT and its links to clinical phenotypes. We adapted a newly tailored fMRI paradigm to assess the neural processes during the Attention Training Technique (ATT), which aims to increase the ability to disengage from perseverative thinking. Our results highlight that ATT engages the fronto‐parietal control network, with activation linked to task performance and self‐reported AC.

This paper presents an adaptive dynamic surface neural network control for a class of nonstrict-feedback uncertain nonlinear systems subjected to input saturation, dead zone and output constraint. The problem of input saturation is solved by designing an anti-windup compensator, and the issue of output constraint is addressed by introducing tan-type Barrier Lyapunov function. Furthermore, based on adaptive backstepping technique, a series of novel stabilizing functions are derived. First-order sliding mode differentiator is introduced into backstepping design to obtain the first-order derivative of virtual control. The real control input is obtained using dead-zone inverse method. It is proved that the proposed control scheme can achieve finite time convergence of the output tracking error into a small neighbor of the origin and guarantee all the closed-loop signals are bounded. Simulation results demonstrate the effectiveness of the proposed control scheme.

The energetic and entropic organization of the brain's functional activity in mild cognitive impairment (MCI) has yet to be fully characterized. Network Control Theory (NCT) is a multi‐modal approach that captures alterations in the brain's energetic landscape by combining the brain's functional activity and the structural connectome. Entropy is another complementary metric that can quantify the complexity and predictability in a neural time series, offering insights into the brain's dynamic functional activity. Our study aims to explore the differences in the brain's energetic and entropic landscape between people with MCI and healthy controls (HC). Four hundred ninety‐nine HC and 55 MCI patients were included. First, k‐means clustering was applied to functional MRI (fMRI) time series to identify commonly recurring brain activity states. Second, NCT was used to calculate the minimum energy required to transition between these brain activity states, otherwise known as transition energy (TE). The entropy of the fMRI time series as well as PET‐derived amyloid beta (Aβ) and tau deposition were measured for each brain region. The TE and entropy were compared between MCI and HC at the network, regional, and global levels using linear models where age, sex, and intracranial volume were added as covariates. The association of TE and entropy with Aβ and tau deposition was investigated in MCI patients using linear models where age, sex, and intracranial volume were controlled. Commonly recurring brain activity states included those with high (+) and low (‐) amplitude activity in visual (+/‐), default mode (+/‐), and dorsal attention (+/‐) networks. Compared to HC, MCI patients required lower transition energy in the limbic network (adjusted p = 0.028). Decreased global entropy was observed in MCI patients compared to HC (p = 7.29e‐7). There was a positive association between TE and entropy in the frontoparietal network (p = 7.03e‐3). Increased global Aβ was associated with higher global entropy in MCI patients (ρ = 0.632, p = 0.041). Lower TE in the limbic network in MCI patients may indicate either neurodegeneration‐related neural loss and atrophy or a potential functional upregulation mechanism in this early stage of cognitive impairment. Future studies that include people with Alzheimer's Disease (AD) are needed to better characterize the changes in the energetic landscape in the later stages of cognitive impairment. We examined the shifts in the brain energetics computed with network control theory and entropy in people with mild cognitive impairment (MCI) compared to healthy controls (HC). MCI patients exhibit lower transition energy in the limbic network, reduced global entropy, and a positive Aβ‐entropy association compared to HC, suggesting a disrupted energetic and entropic landscape as potential neuroimaging biomarkers of MCI.

This study considers an adaptive neural control for a two degrees of freedom helicopter nonlinear system preceded by system uncertainties, input backlash, and output constraints. First, a neural network is adopted to handle the hybrid effects of input backlash nonlinearities and system uncertainties. Subsequently, a barrier Lyapunov function is introduced to limit the output signals for further ensuring the safe operation of the system. The bounded stability of the closed-loop system is analyzed employing the direct Lyapunov approach. In the end, the simulation and experiment results are provided to demonstrate the validity and efficacy of the derived control.