Infrared image generation is essential in scenarios with low illumination or complex environments, but the scarcity of aligned visible–infrared data and the lack of physical realism in generated results remain key challenges. However, existing generative models often overlook the thermodynamic principles underlying infrared imaging, resulting in synthetic images that are visually plausible yet physically inaccurate. In this paper, we propose Infrared Physics-guided Latent Diffusion (IPLD), a novel framework that integrates physics-guided modeling into a latent diffusion process for high-fidelity synthesis of infrared images. Central to IPLD is the Temperature–Emissivity–Environmental Radiance (TeR) decomposition, which decomposes thermal signals into temperature, emissivity, and environmental radiance components, governed by the laws of blackbody radiation. To enhance the environmental radiance modeling, we introduce Environmental Radiance Map Estimation (ERME), a hybrid local–global estimation mechanism that preserves both spatial detail and thermal consistency. Furthermore, a novel Skip Connection Diffusion Transformer (SCDT) is proposed to strengthen and balance semantic structure and fine-grained details during image reconstruction. Extensive experiments on public datasets demonstrate that IPLD outperforms state-of-the-art Generative Adversarial Network (GAN)-based and diffusion-based methods, achieving superior results in Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), Learned Perceptual Image Patch Similarity (LPIPS), and Fréchet Inception Distance (FID) metrics. Ablation studies validate the complementary value of TeR, ERME, and SCDT in improving radiative realism. Our approach establishes a new paradigm for physically grounded image translation, offering enhanced generalization and reliability for downstream perception tasks such as target detection.

Driven by accelerated product obsolescence and frequent consumer replacements, electronic waste is growing rapidly. Waste recycling, as a core component of resource reuse, has become an important means of alleviating resource scarcity and reducing environmental pollution. In the process of recycling discarded products, the efficiency of disassembly operations is crucial. To improve disassembly efficiency and maximize resource utilization, this work proposes a hybrid disassembly line structure that incorporates both linear and U-shaped workstations. Shared labor is introduced between adjacent disassembly lines, allowing workers to flexibly execute tasks across lines. This resource-sharing mechanism enhances task coordination and reduces idle time, contributing to improved system efficiency. Using a precedence relationship graph to model dependencies among tasks, we develop a mathematical model aimed at maximizing profit. We use an exact solver to verify the model and adopt a variant of dueling deep Q-network, called PER-Dueling DQN (PDDQN), which incorporates prioritized experience replay to enhance sampling efficiency and solve the model optimally. A simulation environment aligned with this problem is constructed for the reinforcement learning agent. We compare the proposed method with other reinforcement learning approaches, including advantage actor-critic, proximal policy optimization, and trust region policy optimization. Through experiments on disassembling products of different sizes, the feasibility and effectiveness of PDDQN are demonstrated, exhibiting significant advantages over other methods.

Digital screen exposure has become ubiquitous, yet its causal impact on self-regulation traits remains poorly understood due to methodological limitations in observational research. This study develops a methodological innovation by integrating Mendelian randomization (MR) with advanced multivariable modeling and machine-learning instrument selection. Unlike conventional MR applications primarily in biomedical research, our framework extends genetically informed causal inference into computational social systems. Specifically, we propose a multivariable MR approach enhanced by least absolute shrinkage and selection operator (LASSO)-based instrumental variable selection, which disentangles the independent causal effects of multiple, genetically correlated digital exposures. Using genome-wide association summary data from over 450 000 individuals from the UK Biobank, we investigate the behavioral effects of mobile phone use, computer use, computer gaming, and television viewing on self-regulation traits. The results show that mobile phone and computer use increase risk-taking tendencies, television viewing is associated with lower risk-taking scores and higher perseverance scores, possibly reflecting the passive and sequential nature of this activity, while computer use and gaming are linked to diminished endurance. These findings not only uncover causal pathways between digital activity patterns and psychological traits but also demonstrate a robust methodological advance for applying MR in complex behavioral systems. By demonstrating how screen exposure causally shapes core personality traits, this study provides actionable insights for technology designers to develop personality-aware digital interfaces, for policymakers to establish evidence-based screen time guidelines differentiated by media type, and for parents and educators to implement personalized digital engagement strategies that account for individual differences in self-regulation capacity.

As the demand for disassembling end-of-life products grows, limitations in traditional disassembly line design, low efficiency, and high resource consumption become increasingly evident. Particularly in large-scale disassembly tasks, where the cost of conventional remanufacturing rises and the technologies fail to meet high-efficiency requirements. The integration of robots into disassembly lines is a promising solution to alleviate these issues. This work presents a multi-product hybrid disassembly line balancing problem that considers machine wear rates and establishes a mixed-integer programming model guided by profit maximization to address it. An improved fireworks algorithm is used in the proposed approach. The developed solution is compared with genetic and ant colony algorithms. Evaluation results and analysis demonstrated the competitive efficiency and stability of our approach.

The disassembly and assembly line balancing problem (DALP) is a critical task in industrial production, involving the efficient organization of disassembly and assembly tasks to improve the productivity and flexibility of production lines. In practical applications, task allocation, robot movement, and workstation layout optimization are key factors affecting production efficiency. This study proposes an improved parallel advantage actor-critic algorithm to address DALP with space constraints due to robot movement. Considering the limitations of workstation space, this approach optimizes the robot's movement paths between workstations, reducing the cost of opening workstations, and optimizing task allocation strategies. To enhance the convergence speed and stability of the conventional Parallel A2C algorithm, action space optimization and a greedy strategy are incorporated into the algorithm. Experimental results demonstrate that the improved parallel advantage actor-critic outperforms the A2C and AC algorithms in terms of efficiency and performance, particularly in handling disassembly tasks with space constraints, significantly improving the operational efficiency and economic benefits of the production line.

Product disassembly is significant for recycling scrapped products and reducing environmental pollution and resource waste. The recovery, reuse, and recycling of industrial products is crucial in modern industry. Manual disassembly efficiency significantly impacts the disassembly line’s overall effectiveness, especially workers’ skill level and learning efficiency. This paper proposes a multi-period personnel scheduling problem that considers worker learning effects. A mixed integer programming model for the disassembly balance problem was established to maximize disassembly profit. This problem is solved using a new reinforcement learning algorithm, the importance-weighted actor-learner architecture (IMPALA). The correctness and effectiveness of the proposed algorithm are verified through comparative experiments with the famous IBM optimizer CPLEX and some popular peer algorithms.

In urban safety, intelligent transportation, and smart security applications, robust pedestrian detection is paramount. Methods that rely solely on visible light imaging struggle in low-light or adverse weather conditions. To address these challenges, we propose dual-modality pedestrian detection (DMPD)—a novel dual-modality pedestrian detection framework that fuses visible and infrared imaging through innovative fusion strategies. The method integrates a modal alignment module to reduce pixel-level misalignment, a differential modal fusion module to effectively combine complementary features while suppressing noise, and a mix module that enhances multiscale feature extraction via integrated convolution and self-attention mechanisms. Furthermore, the enhanced YOLOv7 is used to further boost feature representation and detection accuracy. Experimental results on the public dataset demonstrate that DMPD achieves a detection$mA{{P}_{50}}$of 97.1% and a real-time speed of 118 FPS, outperforming state-of-the-art methods under both normal and adverse conditions, including fog, rain, and snow. These results confirm the effectiveness of the proposed fusion strategy in harnessing the complementary strengths of visible and infrared modalities, thereby offering a highly robust and scalable solution for pedestrian detection in complex urban environments.

Infrared small target detection (ISTD) holds significant importance in domains such as maritime search and rescue, and autonomous driving. To enhance the detection capabilities of infrared small targets against complex backgrounds, a novel detection algorithm based on an improved Detection Transformer (DTER) is proposed. This algorithm leverages the DTER detection framework and the EDSR network, utilizing super-resolution reconstructed images as inputs. It incorporates the Enhanced Multi-Scale Attention (EMA) module and an improved backbone structure. Moreover, it employs a micro-target detection encoder head with a new feature layer S2 to elevate the quality of minute feature extraction. The proposed method achieved a mAP@50 of 96% and mAP@(50:95) of 54.6% on a public dataset. Compared to current state-of-the-art methods for infrared small target detection, it demonstrates superior capabilities in reducing false positives and misses while maintaining commendable real-time performance.

Overcrowding in emergency departments (EDs) is a common problem encountered by healthcare systems worldwide. Its essence is the imbalance between the need for emergency care and available resources, such as doctors, nurses, medical supplies, and treatment facilities and spaces. Such an imbalance increases with the volume of visiting patients. To solve the problem of ED overcrowding, service providers need to ensure rational allocation of resources in the emergency process to the greatest extent. This article uses stochastic timed Petri nets (STPNs) as a modeling and simulation tool to optimize the resource allocation in the emergency care workflow. On the basis of STPN simulation architecture, we propose a novel “observation-response” block (ORB) to adaptively supplement the corresponding resources according to the local crowding situation in the emergence workflow, so as to reduce the waiting time of patients in urgent need of treatment. In this article, models of patient arrival, triage, and examination process are constructed. Then, considering the waiting time of patients as the optimization objective, the statistical simulation based on STPN models is performed to verify the effectiveness of the proposed ORB block in the emergency workflow resource optimization process. The presented work provides a feasible way for the optimal ED resource allocation.