Command and control in modern air combat is showing pressing demand for intelligent decision-making. Research on intelligent decision-making technique relies on data accumulation by combat simulations, which still remains underdeveloped at present. In this paper we introduce a tactical-level air combat simulation system for intelligent decision-making, which can simulate air combat between formations, and has multiple application modes, namely Man-Man, Man-Machine, and Machine-Machine. This system can collect diversified and fine-grid data, and provide data support for training of intelligent air-combat decision-making models, validation of intelligent decision-making algorithms, and deduction of tactical plans. A combat example simulated by the system is presented and analyzed in detail.

Objectives: This study aimed to investigate the pain situation, functional limitations, treatment used, care-seeking behaviors, and educational preferences of adults with pain in mainland China. Methods: An online questionnaire was developed through expert validation, and participants were recruited via social media platforms. Inclusion criteria required having access to the Internet and smartphones, while individuals with significant cognitive impairments or severe mental illness were excluded. Results: 1566 participants, predominantly male (951) with a mean age of 30.24, were included. A total of 80.1% of the respondents reported experiencing pain, with over half suffering from chronic pain. Pain primarily affects the neck, lower back, and upper back, especially chronic low back pain. Pain significantly impacted various aspects of life, including mood, physical activity, work performance, family dynamics, and social relationships, particularly among chronic pain sufferers (p-value: < 0.001). Analgesics (66.9%) and self-management (80–94.3%) were the most used pain management strategies, with respondents with chronic pain reporting higher usage and effectiveness of medication than those with acute pain (p-value: < 0.001). Participants also expressed a greater interest in online education and psychotherapy interventions, especially through mobile applications. Conclusions: Chronic pain is highly prevalent in mainland China, leading to emotional distress, decreased work competency, and social isolation, with a strong demand for pain education through smartphone applications.

To evaluate the effects of stepped care (SC) on perinatal depression and to provide the first systematic description of the core components and delivery mo

Background: The widespread prevalence of chronic pain (CP) significantly impacts daily functioning worldwide. In mainland China, maintaining engagement in biopsychosocial interventions remains challenging. Gamification, designed based on self-determination theory, can enhance motivation, while machine learning (ML) algorithms can assist clinicians in dynamically optimizing pain management. Objective: This study aimed to (1) evaluate the preliminary effectiveness of a gamified pain management (GPM) program on CP and psychological outcomes and (2) identify key factors of significant pain improvements through the application of ML to guide intervention adjustments. Methods: A single-arm, pre-post study was conducted with 16 participants with CP in mainland China, recruited via social media using convenience sampling. Participants engaged in a 10-week web-based GPM intervention consisting of education, physical activities, and gamified elements, including points, avatars, and feedback. Primary outcomes were pain intensity and interference measured by the Brief Pain Inventory. Secondary outcomes included anxiety, depression, and quality of life. Analysis included paired t tests, and ML models were trained to predict clinically meaningful pain reductions. Shapley additive explanations, least absolute shrinkage and selection operator regression, association rule mining, and Kaplan-Meier survival analysis were used to identify key predictors and optimal sessions and intervention durations across subgroups. Results: A total of 16 participants were engaged, with a mean age of 27.63 (SD 9.584) years. Results from paired t tests reported significant improvements in pain intensity (decreased by 27.3%, 95% CI 1.061 to 3.064; P =.001), pain interference (decreased by 27.3%, 95% CI 8.159-17.216; P <.001), and psychological distress, including anxiety ( t ₁₅ =3.538, 95% CI 0.969 to 3.906; P =.003) and depression ( t ₁₅ =4.559, 95% CI 2.230 to 6.145; P <.001). The gradient boosting model demonstrated the highest predictive accuracy (area under the curve=0.89 and accuracy=0.82). Least absolute shrinkage and selection operator regression identified session 3 (β=−0.45, 95% CI −0.68 to −0.22; P <.001) and session 5 (β=−0.32, 95% CI −0.59 to −0.05; P =.02) as most predictive of clinical success, while association rule mining revealed effective session combinations for different patient subgroups. Time-to-event analyses indicated that individuals with low back pain and higher baseline severity required longer intervention durations for improvement (5 wk; P =.03). Conclusions: This pilot study presents an innovative method that combines ML with dynamic engagement data from a GPM program during interventions, rather than relying on static baseline data in prior studies. The results show preliminary efficacy and identify specific optimal session combinations and personalized treatment durations for different pain subgroups. These exploratory findings contribute to the field by providing a data-driven method for adaptive, personalized digital health interventions that move beyond one-size-fits-all strategies, potentially enabling clinicians to modify content and dosage to improve engagement and outcomes if validated in larger sample trials.

We study the problem of online generalized linear regression in the stochastic setting, where the label is generated from a generalized linear model with possibly unbounded additive noise. We provide a sharp analysis of the classical follow-the-regularized-leader (FTRL) algorithm to cope with the label noise. More specifically, for $\sigma$-sub-Gaussian label noise, our analysis provides a regret upper bound of $O(\sigma^2 d \log T) + o(\log T)$, where $d$ is the dimension of the input vector, $T$ is the total number of rounds. We also prove a $\Omega(\sigma^2d\log(T/d))$ lower bound for stochastic online linear regression, which indicates that our upper bound is nearly optimal. In addition, we extend our analysis to a more refined Bernstein noise condition. As an application, we study generalized linear bandits with heteroscedastic noise and propose an algorithm based on FTRL to achieve the first variance-aware regret bound.