Subclinical Psychiatric Symptomatology and a Proposed Diagnostic Criterion Separating Psychopathological Procrastinators From Trait Procrastinators.

Procrastination trait describes irrational delays of scheduled tasks despite clear awareness of the adverse consequences of doing so. Although procrastination is well-known to be linked to psychiatric or pathological processes, the criterion for "psychopathological procrastination" distinguishing from the procrastination trait is understudied. This is a 5-year longitudinal observational study. Participants (N = 464) completed measures of trait procrastination in 2018, with a follow-up conducted in 2023 (N = 267) collecting subclinical symptomatology. A constrained multivariate direct gradient model (cmDGM) was employed to prospectively predict subclinical psychiatric symptomatology formulated by the DSM-5 framework. The two-stage psychopathological connectome model was then constructed to constitute a "diagnostic criterion" reflecting "psychopathological procrastination." Procrastination prospectively predicted subclinical psychopathological symptoms and unhealthy lifestyles. Subclinical bridge hubs of "failure to self-regulate delays," "failure to control adverse consequences," "useless to self-change," "out-of-control irruptions," "poor sleep quality," and "negative emotional reactions" were identified in the two-stage psychopathological network. These hubs constituted the 9-item pathological procrastination diagnostic criterion (3PDC) with good diagnostic performance (AUC = 0.82, p < 0.01). The present study revealed the predictive role of procrastination for subclinical psychiatric symptomatology and further established the subclinical 3PDC to lay the foundation for the "diagnostics of psychopathological procrastinators." [Abstract copyright: © 2025 John Wiley & Sons Ltd.

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013–2022), the first ten-year stage of the lifespan CCNP (2013–2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” in the Chinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community (https://ccnp.scidb.cn) at the Science Data Bank

Stochastic programming based multi-arm bandit offloading strategy for internet of things

In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things (IoT) users, Multi-access Edge Computing (MEC) migrates computing and storage capabilities from the remote data center to the edge of network, providing users with computation services quickly and directly. In this paper, we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading, where the connection between the IoT user and the MEC servers is uncertain. This uncertainty would be the main obstacle to assign the task accurately. Consequently, if the assigned task cannot match well with the real connection time, a migration (connection time is not enough to process) would be caused. In order to address the impact of this uncertainty, we formulate the offloading decision as an optimization problem considering the transmission, computation and migration. With the help of Stochastic Programming(SP), we use the posteriori recourse to compensate for inaccurate predictions. Meanwhile, in heterogeneous networks, considering multiple candidate MEC servers could be selected simultaneously due to overlapping, we also introduce the Multi-Arm Bandit (MAB) theory for MEC selection. The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method (SMM) for offloading in terms of reward, cost, energy consumption and delay. The results show that SMM can achieve about 20% improvement compared with the traditional offloading method that does not consider the randomness, and it also outperforms the existing SP/MAB based method for offloading