Background: Internet gaming disorder (IGD) is receiving increasing attention owing to its effects on daily living and psychological function.Methods: In this study, electroencephalography was used to compare neural activity triggered by repeated presentation of a stimulus in healthy controls (HCs) and those with IGD. A total of 42 adult men were categorized into two groups (IGD,n = 21) based on Y-IAT-K scores. Participants were required to watch repeated presentations of video games while wearing a head-mounted display, and the delta (D), theta (T), alpha (A), beta (B), and gamma (G) activities in the prefrontal (PF), central (C), and parieto-occipital (PO) regions were analyzed.Results: The IGD group exhibited higher absolute powers of D_C, D_PO, T_C, T_PO, B_C, and B_POthan HCs. Among the IGD classification models, a neural network achieves the highest average accuracy of 93% (5-fold cross validation) and 84% (test).Conclusions: These findings may significantly contribute to a more comprehensive understanding of the neurological features associated with IGD and provide potential neurological markers that can be used to distinguish between individuals with IGD and HCs.

Sepsis is a life-threatening condition with a high mortality rate. Early prediction and treatment are the most effective strategies for increasing survival rates. This paper proposes a neural architecture search (NAS) model to predict the onset of sepsis with a low computational cost and high search performance by applying a genetic algorithm (GA). The proposed model shares the weights of all possible connection nodes internally within the neural network. Externally, the search cost is reduced through the weight-sharing effect between the genotypes of the GA. A predictive analysis was performed using the Medical Information Mart for Intensive Care III (MIMIC-III), a medical time-series dataset, with the primary objective of predicting sepsis onset 3 h before occurrence. In addition, experiments were conducted under various prediction times (0–12 h) for comparison. The proposed model exhibited an area under the receiver operating characteristic curve (AUROC) score of 0.94 (95% CI: 0.92–0.96) for 3 h, which is 0.31–0.26 higher than the scores obtained using the Sequential Organ Failure Assessment (SOFA), quick SOFA (qSOFA), and Simplified Acute Physiology Score (SAPS) II scoring systems. Furthermore, the proposed model exhibited a 12% improvement in the AUROC value over a simple model based on the long short-term memory neural network. Additionally, it is not only optimally searchable for sepsis onset prediction, but also outperforms conventional models that use similar predictive purposes and datasets. Notably, it is sufficiently robust to shape changes in the input data and has less structural dependence.

There has been a growing demand for portfolio management using robo-advisors, and hence, research on the automation of portfolio composition has been increasing. In this study, we propose a model that automates the portfolio structure by using the instability index of the financial time series and genetic algorithms (GAs). We use the instability index to filter the investment assets and optimize the threshold value used as a filtering criterion by applying a GA. For an empirical analysis, we use stocks, bonds, commodities exchange traded funds (ETFs), and exchange rate. We compare the performance of our model with that of risk parity and mean-variance models and find our model has better performance. Several additional experiments with our model using various internal parameters are conducted, and the proposed model with a one-month test period after one year of learning is found to provide the highest Sharpe ratio.

Time-series representation learning decomposes signals into interpretable factors such as trend and seasonality, and disentangled representation learning assigns these factors to distinct latent dimensions, enhancing interpretability and forecasting accuracy. However, existing methods achieve only statistical disentanglement: an intervention on one generative factor can still influence the latent assigned to the other, since unobserved common causes induce dependence between them. In time-series data, this entanglement is amplified by time-varying confounders, distorting causal effect estimates and degrading counterfactual prediction accuracy. We therefore propose CDTS²—Causal Downstreamer with Causally Disentangled Trend and Seasonality Time-Series Representations. CDTS² combines two complementary mechanisms: (i) dedicated subnetworks separate trend and seasonality, regularized by the Hilbert–Schmidt Independence Criterion (HSIC) to suppress residual dependence between the two latents; and (ii) a causal discovery objective infers a summary causal graph over the input variables and regularizes the shared representation underlying both latents to incorporate causal dependency structure during training. To assess causal disentanglement, we evaluate CDTS² on counterfactual prediction in medical and energy domains. Under severe confounding, CDTS² reduces RMSE by 11.6% and 17.1% over the second-best baselines on the tumor-growth and CityLearn datasets, while preserving the best factual-forecast accuracy. CDTS² also attains the highest Interventional Robustness Score (IRS) and Disentanglement–Completeness–Informativeness (DCI) metrics, indicating that it learns causally disentangled representations that remain robust under interventions.