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.

Industry 5.0 promotes the transformation of manufacturing toward flexibility, personalization, and sustainability. As a critical component of closed-loop manufacturing systems, disassembly operations urgently require more flexible and efficient human–robot collaboration models. To this end, this work, for the first time, proposes a multihuman–robot collaborative circular disassembly line balancing problem. By allowing workers to move between robotic workstations, the proposed system enhances operational flexibility. Furthermore, a multiworker mechanism is introduced to improve fault tolerance and system stability, overcoming the limitations of fixed worker positions in existing collaborative disassembly research. To solve this problem, we formulate a discrete-time mixed-integer programming model based on product AND/OR graphs, aiming to maximize disassembly profit. The model’s correctness is verified using CPLEX. Additionally, we develop a heterogeneous graph neural network-enhanced proximal policy optimization (PPO) algorithm. By integrating product and workstation information into a heterogeneous graph, the algorithm performs two-stage feature extraction and node embedding via graph neural networks. Based on these embeddings, the agent dynamically selects multiple actions per decision step to simulate the behavior of multiple workers moving simultaneously. Experimental results show that the proposed method outperforms traditional reinforcement learning algorithms such as PPO and deep Q-network algorithm in terms of disassembly profit. Moreover, it demonstrates strong generalization capability in cross-task transfer and scalability experiments involving different task graph sizes. The improved performance is achieved with acceptable computational time.

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.

Disassembly is a critical step in the recycling and reusing of end-of-life products. As Industry 5.0 emerges, manufacturing is shifting from a system-oriented approach to a human-centered paradigm, advancing human–robot collaboration to a new stage. However, existing studies on human–robot collaboration in disassembly lines generally overlook the mobility of workers. To fill the research gap, this work proposes a novel human–robot collaboration mode that considers both the mobility of workers during disassembly and the flexibility of collaboration time in human–robot interaction. Based on this model, this work proposes the human-assisted robotic circular disassembly line balancing problem and establishes a profit-oriented spatiotemporal decomposition mixed-integer programming model. A two-stage greedy proximal policy optimization algorithm is designed to solve it. To validate the effectiveness of the proposed model and algorithm, ten sets of benchmark instances are generated with different scales based on real product structure data. Comparative experiments with reinforcement learning algorithms and classical heuristic methods demonstrate the feasibility and significant superiority of the proposed algorithm in solving this type of problem.

Electric load inherently reflects the collective patterns of social activities and user behaviors, making their accurate prediction a challenging task. Accurate electric load forecasting is crucial for the planning, operation, scheduling, and market management of modern power systems, especially under the increasing complexity of residential energy consumption behaviors. From a data-driven modeling perspective, traditional load forecasting based solely on time-series data often fails to capture the social and behavioral dimensions underlying demand fluctuations. To address these challenges, this work presents an innovative electric load forecasting approach by using multifactor and time-series forecasting concepts. A comprehensive feature pool is first constructed by combining social and environmental factors, feature decomposition, and basis function transformation. Then, a metaheuristic-enhanced feature selection and modeling framework is proposed, which leverages a simulated annealing (SA) algorithm in conjunction with a hybrid deep learning architecture. Specifically, it encodes selected features as a solution of SA and evaluates it by a hybrid deep learning model that incorporates an attention mechanism, convolutional neural networks, and long short-term memory networks. In this way, it can effectively capture both temporal dependencies and social-behavioral influences on load patterns. The proposed approach is validated on 26 real-world datasets of residential electric load, which reveals that forecasting performance directly reflects aggregated social behavior in energy usage. Their synergistic effect achieves a maximum$\boldsymbol{R^{2}}$of 0.97 with a prediction error margin of less than 5% and enables the proposed approach to outperform several state-of-the-art peers. These results highlight the value of integrating social system factors with computational intelligence, showcasing the potential of the proposed method for practical applications in electric load forecasting.

Nowadays, short-term time-series prediction is increasingly vital across domains such as transportation, energy, and weather forecasting. Its high accuracy and rapid response provide crucial support for dynamic changes in these applications. Transformer-based methods, with their powerful temporal feature extraction, have emerged as a leading paradigm for this task. However, many Transformer-based methods exhibit excessive complexity for short-term horizons and rely too heavily on long-term dependencies. In this article, we propose the specialized sparse Transformer without shortcuts (SSTwS) to approach short-term multivariate time-series prediction (SMTP). We specialize in the Transformer-based prediction methods from two perspectives: simplifying the model structure and improving the self-attention mechanism. The SSTwS aims to balance performance and cost. Specifically, we removed residual connections designed for deep networks and enhanced convergence speed through the prepositioned layer normalization (LN). We developed a specialized sparse-centering self-attention mechanism, removed half of the linear projections typical in vanilla self-attention mechanisms. In addition, we leveraged the sparsity of the self-attention mechanism, employing five probabilistic methods combined with centering to select the dominant features. Extensive experiments on seven widely used datasets demonstrate the superiority of our SSTwS in terms of training speed and performance relative to existing state-of-the-art methods.

Bolt defect detection is critical to ensure the safety of transmission lines. However, the scarcity of defect images and imbalanced data distributions significantly limit detection performance. To address this problem, we propose a segmentationdriven bolt defect editing method (SBDE) to augment the dataset. First, a bolt attribute segmentation model (Bolt-SAM) is proposed, which enhances the segmentation of complex bolt attributes through the CLAHE-FFT Adapter (CFA) and Multipart- Aware Mask Decoder (MAMD), generating high-quality masks for subsequent editing tasks. Second, a mask optimization module (MOD) is designed and integrated with the image inpainting model (LaMa) to construct the bolt defect attribute editing model (MOD-LaMa), which converts normal bolts into defective ones through attribute editing. Finally, an editing recovery augmentation (ERA) strategy is proposed to recover and put the edited defect bolts back into the original inspection scenes and expand the defect detection dataset. We constructed multiple bolt datasets and conducted extensive experiments. Experimental results demonstrate that the bolt defect images generated by SBDE significantly outperform state-of-the-art image editing models, and effectively improve the performance of bolt defect detection, which fully verifies the effectiveness and application potential of the proposed method. The code of the project is available at https://github.com/Jay-xyj/SBDE.

Artificial intelligence-generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC’s evolution through four developmental milestones, ranging from early rule-based systems to modern transfer learning (TL) models, within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, this article employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this article addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.

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.

In recent years, with the rapid development of artificial intelligence and social media, the influence of word-of-mouth (WOM) on the diffusion of online content has become increasingly evident. Video platforms can use artificial intelligence to collect WOM data of programs and formulate corresponding pricing strategies. Based on this background, considering the impact of online WOM effects on the diffusion of on-demand content exclusive to members, this study constructs a two-stage product provision model for online video platforms, consisting of the premiere and follow-up broadcast stage. Based on expected utility theory, this research explores the pricing strategies for member-exclusive on-demand content under two profit models and analyzes the influence of program WOM attributes and program quality on optimal decision-making. The findings reveal that: When the premiere stage WOM for a program is either highly positive or negative, video platform should adopt an "advertising-dominant strategy". When the premiere stage WOM is moderate, a " fee-dominant strategy" is preferable. Higher program quality increases the platform's inclination toward the "fee-dominant strategy". The better the premiere stage WOM and program quality, the more users tend to watch during the premiere stage. Accordingly, both the program price and the platform's expected profit will vary to different degrees depending on these conditions.

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.

The applications of unmanned swarms have become increasingly widespread, gradually transforming production processes and daily life. Task allocation, the top-level design for unmanned swarm missions, is pivotal to maximizing the efficiency of the entire swarm. However, traditional optimization methods and intelligent algorithms, including Reinforcement Learning (RL), often struggle to adapt to the complex and unpredictable situations in these tasks. To address this challenge, we propose a novel Multi-Agent Proximal Policy Optimization (MAPPO) algorithm combined with the population-based learning and Gaussian Mixture Model (GMM)-based adjustment mechanisms (PG-MAPPO). In PG-MAPPO, the population-based learning mechanism is integrated to enable agents with diverse exploration preferences to uncover optimal collaboration patterns among Unmanned Aerial Vehicles (UAVs), thereby enhancing cooperative efficiency. The GMM-based adjustment mechanism dynamically adjusts UAV formations for each agent, significantly improving the swarm’s flexibility and adaptability in rapidly changing environments. To demonstrate the effectiveness of PG-MAPPO, a maritime rescue simulation containing multiple complex and dynamic scenarios is conducted. Experimental results show that our algorithm achieves higher rescue success rate with faster convergence and greater stability than state-of-the-art Multi-Agent Reinforcement Learning (MARL) methods in all scenarios. Notably, the PG-MAPPO algorithm improves the rescue success rate by 31.6% compared to the best-performing baseline under challenging conditions.

The advances of manufacturing technology accelerates the replacement of consumer products. The recycling of these out-of-date products not only has economic benefits but also contributes to environmental protection. Therefore, the disassembly and reuse of products have attracted great attention all over the world. The traditional human worker disassembly is characterized by high cost and low efficiency. Robots can work more efficiently, but they are not flexible enough to perform different tasks. On the other hand, the combination of a U-shaped disassembly line and a single-row linear disassembly line would offer unique advantages for various applications. This work studies a hybrid disassembly line balancing problem (HDLBP) based on human-robot collaboration. The special challenge with HDLBP is that we need to consider the work load balancing among different lines, in addition to workstations, to achieve optimal results. A combination of linear programming and integer one is proposed to solve the optimization model of HDLBP that is composed of linear and U-shaped disassembly lines, with the objective of maximal disassembly profit. The feasibility of the model is verified by commercial solver CPLEX in solving different size problem instances. Note to Practitioners—This work deals with issue of using human workers only or using robots alone in disassembly lines and the limitation of each type of disassembly line layout. Most of the existing disassembly operation assignment methods are based on the correlation between humans and robots and the factors that affect disassembly. This paper suggests that the selection of humans and robots based on an optimization model that can be solved CPLEX. To leverage the unique advantages offered by each type of disassembly layout, this paper suggests the use of hybrid disassembly lines. Based on the idea of mixed integer programming, a hybrid disassembly line model of human-robot collaboration is designed and solved by CPLEX. The experimental results show that the hybrid disassembly line of human-robot collaboration has obvious advantages over the disassembly line composed of worker-only or robot-only when disassembling products. In the future research, we will use reinforcement learning algorithm to solve the hybrid disassembly line balancing problem, and consider more details of the human-robot cooperative hybrid disassembly lines.

The escalating consumption and disposal of electronic products have spurred a pressing demand for environmental conservation. Traditional disassembly factories encounter challenges when handling discarded products from various locations, including high costs and limited flexibility. This study addresses a multifactory disassembly process optimization problem, taking into account worker posture and the selection of disassembly line types. Subsequently, a mathematical model to maximize profit is built. The reinforcement learning algorithm, Categorical deep Q network (DQN), is utilized to find optimal solutions. Experimental results are compared with those from CPLEX to validate the precision and viability of the proposed model. Furthermore, we compare the proposed solution with various reinforcement learning algorithms, including DQN, proximal policy optimization, and Advantage Actor–Critic. The effectiveness of the proposed model and algorithm is verified by experiments on several cases with different complexity scales.

Efficient disassembly is essential for the reintegration of end-of-life products into the remanufacturing process. Previous studies utilize human–robot collaboration and parallel workstations to enhance disassembly efficiency. However, the disassembly lines in these studies are typically independent of each other. As the number of disassembly lines in a plant increases, labor resources such as workers and robots become redundant, leading to low resource utilization and decreased disassembly revenue. This study proposes a novel disassembly scheme aimed at achieving high efficiency by leveraging parallelization and human–robot collaboration to share labor resources on a hybrid disassembly line. Specifically, this work develops a mixed-integer programming model to maximize disassembly profit. A discrete aquila optimizer algorithm, incorporating uniform variation and two-point crossover methods, provides the solution for the problem. Furthermore, the correctness of the proposed model and algorithm is verified within the solvable range of the commercial solver CPLEX. Finally, a comparative analysis of the proposed algorithm with the salp swarm algorithm, the fireworks algorithm, and the whale optimization algorithm demonstrates its superiority in solving the problem.

To reduce resource consumption and environmental impact, the manufacturing industry increasingly leans towards repurposing, repairing, or updating products. In a multifactory environment, considering the disassembly line balancing problem helps enterprises improve production efficiency and reduce costs. Thus, this work proposes a heterogeneous multifactory remanufacturing optimization problem, considering the disassembly techniques and U-shaped disassembly lines that are used in heterogeneous disassembly factories. A mixed integer programming model for profit maximization is established. Reinforcement learning methods open new avenues for addressing complex scheduling issues in actual production. This article utilizes the twin delayed deterministic policy gradient algorithm to solve the proposed problem. It validates the effectiveness of the algorithm by comparing it with CPLEX. Through various experimental cases, it demonstrates that this method achieves better convergence and higher profits compared to deep deterministic policy gradient, soft actor-critic, and advantage actor-critic algorithms.

The advancement of human–robot collaboration technology has positioned remanufacturing as a crucial part of the circular economy, driving both economic growth and environmental sustainability. In the era of Industry 5.0, these technologies enhance the efficiency and flexibility of disassembly tasks. However, most research on human–robot collaborative disassembly (HRCD) line balancing overlooks the mobility of workers. This study introduces a profit-oriented HRCD model incorporating mobile workers. To address large-scale HRCD challenges, it proposes a dynamic attraction rate mechanism that improves the traditional carnivorous plant algorithm (CPA), tackling issues of slow convergence and local optimization. The experimental framework includes three validation phases: 1) comparison with the exact solver IBM ILOG CPLEX Optimization Studio (CPLEX); 2) parameter sensitivity analysis; and 3) benchmarking against seven state-of-the-art algorithms. Results demonstrate that HRCD with mobile workers significantly boosts disassembly efficiency and reduces disassembly time compared to traditional methods. Additionally, it increases profits through flexible task allocation. In cases of incomplete disassembly, HRCD with mobile workers yields an average benefit increase of 87.64% over conventional disassembly modes. A comparative evaluation with other swarm intelligence algorithms further highlights the superior solution quality and time efficiency of the improved CPA.

Multifactory remanufacturing is a widely adopted sustainable manufacturing approach nowadays. Its complexity lies in coordinating the dismantling, remanufacturing, and resource circulation among factories to maximize resource reuse and minimize environmental impact. Proper worker scheduling is crucial in this process to ensure efficient workflow and maximal resource utilization. This study proposes and addresses an optimization problem for multifactory remanufacturing considering worker scheduling, which is mainly divided into two parts: worker scheduling and remanufacturing process optimization (MRPO). A mixed-integer programming (MIP) mathematical model is established with the objective of profit maximization. A discrete battle royale optimization (BRO) algorithm is proposed to solve this problem, with a novel encoding structure and three soldier search strategies devised to better search for the optimal solution. The correctness of the model is validated through experiments on cases of different scales and comparisons with the IBM CPLEX optimizer. Furthermore, comparisons with the carnivorous plant algorithm (CPA), whale optimization algorithm (WAO), dingo optimization algorithm, and migrating bird optimization algorithm demonstrate the superiority and effectiveness of the proposed algorithm.

Ecological environment and natural resource issues are becoming more and more prominent, which promotes the recycling of waste products for green economy. Disassembly plays a key role in the remanufacturing and reuse of waste products. However, with the rapid development of production automation, designers tend to ignore the fact that manual operation is more flexible. It is of great importance to consider human factors in a disassembly process. This work considers two human disassembly postures, namely standing and sitting. The multiobjective disassembly line balancing problem considering human posture changes is studied. A mathematical model with the objective functions of maximizing profit, minimizing the number of posture changes at a workstation, and minimizing the difference of maximum posture changes between any two workstations is established. The model is solved through a newly proposed Pareto-based discrete harmony search algorithm. Three neighborhood structures are designed to enlarge the search space for better solutions. Furthermore, an elite reserve strategy is used to improve the global optimization ability of the proposed algorithm. Finally, the proposed model and algorithm are applied to cases of different scales of complexities, and the effectiveness of the proposed model and algorithm is verified in comparison with four competitive algorithms.

Industry 5.0 enhances manufacturing ability through efficient human-machine interaction, combining human resources and robots to complete tasks more accurately and effectively. Artificial intelligence (AI) plays an essential role in Industry 5.0. As a branch in AI, multiagent deep reinforcement learning (MADRL) attracts vast attention in both academia and industry. However, there is a gap between virtual and physical environments in terms of howcleanan observed state is. In addition, state adversarial attacks can seriously impact the performance of MADRL. Hence, how to improve the robustness of MADRL algorithms is an important research topic. In this article, we propose an optimal policy-based state adversary attack method that would make the MADRL algorithm more robust when it is applied in the training process of agents. Two case studies related to Industry 5.0 and a general case study are presented in which robustness training against the optimal adversarial attack is tested. The MADRL algorithms involved in the experiments include centralized training and decentralized execution (CTDE) framework and shared experience actor-critic (SEAC) to demonstrate the universality of our method.

In the ever-evolving landscape of media and entertainment, where trends like short-form video content and new media platforms reign supreme, the need for innovative approaches across various industries becomes increasingly apparent. One such industry deeply impacted by these shifts is the film industry, where the creation and dissemination of film trailers stand as pivotal promotional strategies. Making a film trailer by hand is time consuming. However, the emerging artificial intelligence generated content (AIGC) technique has shown significant potential to enhance the efficiency. This article presents a novel model named TVMTrailer, which consists of a text-video generation network (TVGNet) and a video-music generation network (VMGNet). TVGNet employs an encoder–decoder framework, utilizing movie footage and synopses to generate movie trailers. Besides, VMGNet is proposed to generate sound track of our trailer. It combines video and audio features, and uses a transformer model for associative learning to adaptively generate audio clips with features, such as emotion, rhythm and beat. The effectiveness of TVMTrailer is demonstrated through experiment conducted on the proposed dataset and a comprehensive collection of over two thousand video-audio pairs from classic movies.

With the rapid development of information technology and logistics technology, traditional centralized factories are transforming into distributed production systems, forming a multi-factory manufacturing model. This study uses Petri nets to model the disassembly processes of end-of-life (EOL) products, integrates the disassembly line balancing issue with the resource sharing over multiple factories, propose a hybrid layout for multi-factory remanufacturing, and establishes a linear programming mathematical model that optimizes the disassembly profit. Deep deterministic policy gradient(DDPG), a deep reinforcement learning algorithm, is employed to solve the model. Experimental results demonstrate the feasibility of the proposed approach.

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.

This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning experience.We especially focus on Chat Generative Pre-trained Transformer (ChatGPT) owing to its considerable potential to supplement regular class learning.We address the strengths and weaknesses of learning with ChatGPT.Finally, we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

Product disassembly is critically important in recycling end-of-life products, reducing their negative impact on environmental pollution and minimizing resource waste. Disassembly line balancing problems have attracted much attention from researchers and industrial practitioners. Most of the existing studies, however, consider only human disassembly or robot disassembly alone. This work considers human-robot collaboration. It proposes an human-robot collaborative disassembly line balancing model considering stochastic task time, where an AND/OR graph is adopted to describe a product’s disassembly process. The objectives are to maximize the total profit and minimize energy consumption. A Pareto improved multi-objective shuffled frog leaping algorithm with a stochastic simulation strategy is proposed to solve the model. In addition, an elite strategy is introduced in global search to enhance the algorithm’s optimization capability. Through experiments on disassembling products of different sizes, the feasibility and effectiveness of this algorithm are demonstrated. Its comparison with some most popular state-of-the-art methods is performed.Note to Practitioners—This paper is motivated by the benefits of human-robot collaboration in the disassembly systems. The presented approach is suitable for disassembly lines with multiple objectives, and the weight of each objective cannot be accurately grasped. Most of the existing operation allocation methods are based on the correlation between humans and robots and the factors affecting disassembly. This paper suggests the selection of humans and robots is completely random and decided by an optimization algorithm. This paper designs an improved multi-objective shuffled frog leaping algorithm based on Pareto’s rule. Experimental results show that this algorithm can be applied to solve practical disassembly line balancing problems.

Proper disassembly operations organization and workstation assignment can help increase the efficiency of disassembly systems that are critical for recycling and remanufacturing of end-of-life (EOL) products. A parallel disassembly system layout allows diversification of disassembly tasks and increases flexibility. In this work, a parallel disassembly balancing model considering hiring workers with government benefits (WGB) is established. To quickly find an optimal solution to the model, a salp swarm algorithm (SSA) with a new encoding and decoding process is developed. Moreover, we use the well-known mathematical optimization technique CPLEX to verify the correctness of the proposed model and use a genetic algorithm (GA), a constrained decomposition approach with grids’ optimization (CDG), and a random search (RS) algorithm to show the effectiveness of the proposed algorithm. Experimental results show that the proposed algorithm can perform well on the proposed problem, which is conducive to the society accepting more WGB into the workplace.

Waste recycling is an important part of resource reuse and environmental protection. The study of disassembly lines deals with the process of recycling and remanufacturing end-of-life products. The performance of a disassembly line is affected by many factors, especially the operation cost of workstations, the precedence relationships among disassembly tasks, the skill level of workers, and their learning speed. This study considers the learning effect of disassembly workers, establish a mixed integer programming model of the disassembly balancing problem, and explores the search for optimal solution. It allocates tasks and multiskilled workers on workstations to maximize disassembly profits in the disassembly process. To solve it, an improved fruit fly optimization algorithm is proposed, and three methods are designed for the smell search. At the same time, the visual search is also designed to avoid the problem of falling into local optimum. The validity and effectiveness of the proposed algorithm are verified with experiments that compare the results with CPLEX, a well-known IBM optimizer, and some popular peer algorithms.

Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security of individuals and banks. However, most existing methods either leverage original features only or require manual feature engineering so that they show a weak ability to learn discriminative representations from transaction data. Moreover, criminals often commit fraud by imitating cardholders’; behaviors, which causes the poor performance of existing detection models. In this article, we propose an adaptive sampling and aggregation-based graph neural network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Specifically, we use cosine similarity and edge weights to adaptively select neighbors with similar behavior patterns for target nodes and then find multihop neighbors for fraudulent nodes. A neighbor diversity metric is designed by calculating the entropy of neighbors to tackle the camouflage issue of fraudsters and explicitly alleviate the oversmoothing phenomena. Extensive experiments on three real financial datasets demonstrate that ASA-GNN outperforms state-of-the-art ones.

To investigate the impacts of monetary policies on the jump risk of Chinese stock market, we introduce them into an exponential generalized autoregressive conditional heteroskedasticity with autoregressive jump intensity (EGARCH-ARJI) model. A new jump model, i.e., the EGARCH-ARJI model with monetary policy (EGARCH-AM), is constructed. Moreover, investor sentiment is considered to investigate the interaction effect of a monetary policy and investor sentiment on the jump intensity. Results show that the announcement of an interest rate policy has significantly positive effect on it, while the effects of the announcement and implementation of a required reserve ratio policy are not significant. In addition, the interaction effect of an interest rate policy and investor sentiment on the jump intensity is positive. The interaction effect of the announcement of a required reserve ratio policy and investor sentiment is negative. The interaction effect of the implementation of a required reserve ratio policy and investor sentiment does not exist. The research results are of guiding significance for policy makers and investors to fully learn the time-varying volatility and jump risk of stock markets.

The unmanned aerial vehicle (UAV) patrol inspection has become an efficient method to ensure the operation condition of transmission lines. The detection of key components with defects in transmission lines is a critical task in maintaining a power system’s stability. However, the complex inspection environment and the imbalance between the number of normal component samples and that of defect samples significantly affect the detection accuracy. In this article, we present a novel method for defect detection in UAV patrol images, based on a hierarchical convolutional vision transformer (HC-ViT) and a simple contrastive masked autoencoder (SC-MAE). The HC-ViT backbone integrates the advantages of vision transformer and convolution, while the SC-MAE is a self-supervised learning method that extracts useful features from normal samples. By introducing the normal features into the backbone, we enhance the performance of the defect detection task. We demonstrate the effectiveness of our method through experiments, and show that it can leverage a large amount of unlabeled normal images, reducing the need for manual annotation. Our method offers a new way to exploit the potential features of patrol inspection images.

The process of recycling and remanufacturing begins with disassembly. Through disassembly, the components with recycling value are decomposed. However, with the rapid development of production automation, designers often ignore the fact that manual operation is flexible but fails to achieve maximum production efficiency and profit. Therefore, the consideration of human factors in disassembly lines holds significant importance. This study delves into the multi-objective optimization of a U-shaped disassembly line balancing problem involving multiple products. A comprehensive objective function is developed, taking into account various factors including employee fatigue and other factors. To address the aforementioned problem, this study uses a collaborative resource allocation strategy within a multi-objective evolutionary algorithm based on decomposition. By comparing the results of different experimental cases, this paper shows that the proposed algorithm is more competitive than the carnivorous plant algorithm, fruit fly optimization algorithm, and Pareto archiving evolutionary strategy.

The adjustment effect of the anchoring effect and online reviews on consumer cognition has grown to be a significant element influencing business pricing. This study explores the effects of online reviews and price anchoring on company pricing and profits by building an online product pricing model based on expected utility theory from the perspective of consumer purchasing psychology. The findings show that firms must take consumer anchoring psychology into account when making decisions if they want to increase revenue. Different pricing strategies are used depending on the variables associated with the quality of online reviews. The higher the sensitivity coefficient of reviews, in particular when the quality of the reviews is higher than a specified value, the bigger the profit. In the anchor point, the optimal price is rising. When a company chooses a higher price policy, the optimal price steadily decreases with the degree of anchoring.

Price is an important index of consumers' purchase choice, and the price comparison behavior of consumers in the decision-making process also affects the profit and loss of their own purchase utility to varying degrees. Based on multinomial logit(MNL) model, the reference price is incorporated into product line development and design, and the pricing decision of product line and value-added services bundle is studied. The influence mechanism of reference price effect on optimal product pricing and maximum profit is analyzed, and the deviation of strategic decision-making caused by not considering reference price effect is discussed. The results show that the reference price effect has a positive impact on the lowest price products in the product line, but has a negative impact on the high price products in the product line, the total market share of the firm and the total profit. When the reference price effect is ignored, the pricing of different quality products and services in the product line will be higher or lower, and the total market share and total profit will be higher. The results can provide theoretical support for product line design and pricing decision.

This paper classifies and summarises the historical literature on carousel systems in automated storage and retrieval systems in recent years. As an automated storage and retrieval system for distribution centers and production facilities, carousels facilitate the storage and dispatching of goods, significantly improving warehouse turnover efficiency. Their performance have been investigated by many scholars and experts. As carousels evolve and upgrade, more and more innovative algorithms have been used to improve the efficiency of outbound carousel storage. In this paper, we collate articles investigating how the carousel system is stored inbound versus retrieved outbound. We then discuss articles on the dual-command model of automatic storage retrieval systems as a whole. By reviewing over 50 papers, we summarise research on how to store and unload goods, focusing on the performance of automatic storage retrieval systems under dual-command conditions. On this basis, we review the current research's limitations and suggest future research directions.

The progress of science and technology speeds up the replacement of products and produces a large number of end-of-life products. Traditional incineration causes a waste of resources and pollution to the environment. Disassembling and recycling end-of-life products are the recommended way to maximize the utilization of resources and reduce environmental pollution. Disassembly performance is affected by many factors, such as the disassembly posture of the human body, the fatigue of workers on a workstation, disassembly profit, and task precedence relationship. In this article, a mixed integer linear programming mathematical model for U-shaped layout disassembly line balancing problems is developed, in which the balance of workers’ fatigue indices is an optimization objective in addition to disassembly profits. An efficient solution to the problem that uses a collaborative resource allocation strategy of the multiobjective evolutionary algorithm is proposed. The linear programming solver CPLEX is used to verify the accuracy of the model and compared with the proposed algorithm. Experiments demonstrate that the algorithm is significantly superior to the CPLEX solver in handling large-scale cases. The proposed algorithm is also compared with two well-known algorithms, which further verifies its superiority.

The integration of artificial intelligence (AI) with cyber–physical–social systems (CPSS) creates new research opportunities and challenges with major societal implications. The behavioral and cognitive enhancement of intelligent systems promotes a productive and creative partnership and collaboration between humans and machines. Advancements in these areas enable adaptability, scalability, resiliency, safety, security, and usability that expand the horizons of CPSS.

Patient flow is the movement of patients through a healthcare facility. Over the past decades, healthcare service providers have spent tremendous effort in optimizing patient flow and reducing patient waiting time (WT), aiming to provide better healthcare service and higher patient satisfaction. There are many factors causing patient congestion; understaffing is undoubtedly a critical one. However, as staffing accounts for about 75% of the cost of all emergency medicine groups, emergency department (ED) staffing must match the demand for services. In this article, we attempt to employ a systematic and formal approach to find out optimal staffing levels for EDs. For this purpose, a hierarchal modeling process of patient flow in typical EDs with stochastic timed Petri nets (STPNs) is developed. Special attention is paid to resource requirements, resource sharing, and service duration. The evaluation of average patient WT is discussed based on simulation. Two staffing options are considered, and the decision on the optimal staffing level with each option is addressed. A software tool has been developed to aid the ED performance evaluation and staffing.

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.

With the applications of deep learning in safety-critical domains such as autonomous driving systems gaining ground, it demands rigorous verification to guarantee the safety and reliability of corresponding systems. As the intelligent component in such systems, neural networks (NNs) must be robust in that their outputs are not affected by minor perturbation to inputs. Many research studies have shown that formal methods are effective ways to the robustness verification of NNs. However, most of the existing approaches are focused on NNs that contain monotonic activation functions, such as ReLU, Tanh, and Sigmoid. In this work, we propose an approach to verify the robustness of NNs with the nonmonotonic activation function called Swish. Such networks have been proved to have a better performance on image classification than other NNs. In our approach, we turn the robustness verification problem into a constraint-solving problem using the linear approximation technique. We first model the affine function of an NN into a linear constraint model. Then, for nonlinear activation functions, we leverage an efficient approximation strategy to linearly approximate them. Finally, we utilize the constraint solver gurobi to solve the model, which reveals that the model satisfies the robustness property. We develop a prototype tool and evaluate it with open-sourced NNs. Experimental results showed the effectiveness and efficiency of our approach.

With the development of IoT, virtual reality, cloud computing, and digital twin technologies, the advent of metaverse has attracted increasing world attention. Metaverse integrates and applies multiple emerging technologies to cloud education, smart health, digital government, and emergency evacuation. Evacuation systems are of great importance to ensure life safety. Due to panic, people in a building may not be able to make the right judgment to choose an optimal path to leave the building in case of an emergency event such as a fire. As a branch of machine learning, deep reinforcement learning (DRL) can model an evacuation scene, collect real-time information, such as crowd distribution and disaster location, find the optimal escape path with a path-planning algorithm, induce the movement state of the crowd through dynamic guidance signs, and improve the evacuation efficiency. In this article, we apply DRL technology to solve the efficient emergency evacuation problem with the help of metaverse and show a training system built upon metaverse that would enable evacuees to choose the most efficient route and leave the building in the least amount of time. The information collected by various sensors, such as video cameras and smoke detectors, can give a whole picture of the status of the building in a real-time manner. The collected data are processed by cloud servers in which a DRL model is trained to dynamically guide evacuees. Experiments in different simulation scenes demonstrate that the proposed method is superior to the traditional static guidance method in saving evacuation time. It can effectively avoid major crowding along the evacuation route and improve evacuation efficiency.

Metaverse is an artificial virtual world mapped from and interacting with the real world. In metaverse, digital entities coexist with their physical counterparts. Powered by deep learning, metaverse is inevitably becoming more intelligent in the interactions between reality and virtuality. However, it is confronted with a nontrivial problem known as sim2real transfer when deep learning techniques try to bridge the reality gap between the physical world and simulations. In this article, we use multiagent deep reinforcement learning (MARL) to implement collective intelligence for digital entities as well as their physical counterparts. To model the immersive environments in metaverse, we define a nonstationary variant of Markov games and propose a recurrent MARL solution to it. Based on the solution, MARL sim2real transfer that bridges real and virtual multiple unmanned aerial vehicle (multi-UAV) systems is successfully conducted by employing recurrent multiagent deep deterministic policy gradient (R-MADDPG) with the domain randomization technique. Additionally, we use perception-control modularization to improve the generalization performance of MARL policies and make training more efficient.

Disassembly planning and sequencing play an important role in recycling a fast-growing number of end-of-life products. Optimal sequences can effectively reduce carbon emissions and save natural resources in the remanufacturing industry. Considering the development of intelligent manufacturing technology, this work deals with the optimization problem of selective disassembly sequences with an objective of maximizing disassembly profit. Disassembly sequences are generated based on AND/OR graphs. After setting up an environment matrix based on such graphs, this proposes a Q-learning technique to find an selective optimal disassembly sequence. The algorithm is applied to real-life disassembly cases. Experimental results show that the algorithm is superior a popularly-used genetic algorithm (GA) in both computing speed and solution quality through their various comparisons.

With more and more waste products are discarded, how to recycle them has become an urgent issue. Disassembling these discarded products is a critical step to take. With disassembly, we can maximize resource utilization and greatly save manufacturing costs. There are many influencing factors in a disassembly process. In this paper we consider the impact of disassembly tools deterioration rate on disassembly time and establish a mathematical model to minimize the disassembly time. We use the advantage actor-critic algorithm in reinforcement learning to solve this model. The correctness and superiority of the algorithm are verified by comparing with the actor-critic algorithm.

With the rapid development of recycling and remanufacturing technologies, disassembly line balancing problems (DLBP) have drawn great attention. Considering the limitation of disassembly by humans or robots alone, this paper focuses on human-robot collaborative disassembly lines. Specifically, this work proposes a multi-product human-robot collaborative disassembly line balancing model to tackle the inflexibility of single product disassembly and inconsistency in recovery values of different product components. Its objective is to maximize disassembly profit. As a commercially available solver, IBM’s CPLEX is used to obtain the exact solution of DLBP and verify the correctness of the proposed mathematical model. A discrete shuffled frog leading algorithm is newly designed to solve the sizable problems. Experimental results show that the proposed algorithm has a fast convergence rate and can find solutions consistent with those with CPLEX but requires much less time than the latter, thus advancing the field of disassembly automation.

Designing a disassembly line layout is an important part of the recycling process of end-of-life products. Parallel disassembly lines have the characteristics of high disassembly efficiency and can disassemble multiple different products simultaneously. This work formulates a mathematical model for optimizing such lines in terms of disassembly profit and the number of skills. It also proposes an improved bat algorithm based on the Pareto principle to solve the model. In order to verify the effectiveness and feasibility of the proposed algorithm, it is compared with the non-dominated sorting genetic algorithm and a decomposition-based multi-objective evolutionary algorithm. Experimental results indicate that this algorithm has outstanding solution capability and is thus suitable for solving parallel disassembly line balance problems.

Proper disassembly operation can help increase the recovery of industrial valuable supplies and end-of-life products. To solve a disassembly line balancing problem, this work focuses on a parallel layout and proposes an intelligent optimization method to maximize disassembly profits. It first formulates a parallel multi-product disassembly line balancing problem model by taking disabled workers into account. It then designs a salp swarm algorithm with innovative encoding and decoding processes. This work finally compares the proposed algorithm with a generic algorithm. Experimental results show that the newly proposed model and algorithm can well deal with the presented problem.

Under platform economy, prices, reviews, and sales are the three most concerned purchase decision factors for consumers. However, different customers have different sensitivity to the same decision factors. Therefore, it would be an important to study the sensitivity of the consumers to reviews, price, and sales. Based on the Multinominal Logit Model (MNL Model), this paper constructs a two-stage pricing model for new products of platform enterprises, and studies the influence of price, review and sales on enterprise profit. Then it analyzes the influence mechanism of consumers’ sensitivity to price change, product cost and consumers’ valuation of product quality on product pricing and enterprise profit. After that, it further formulates the two-stage optimal pricing strategy for product sales. Research shows that enterprises should not only consider consumers’ sensitivity to comments, price and sales volume, but also learn from the previous sales experience when making pricing strategy. The research results can provide theoretical basis and decision support for product pricing and operation management of platform enterprises.

In the freemium business model, how to price value-added services and design effective strategy to achieve the sustainability of value-added services promotion is of great significance to enterprises. By constructing a monopolistic freemium enterprise, this paper uses a Multinational Logit model (MNL model) to analyze value-added services pricing and two kinds of value-added services promotion strategies (the quality reduction strategy of basic product and the price discount strategy of value-added services) with heterogeneous sticky-users demand, and then discusses the optimal promotion strategy. The results show that: Both the quality reduction strategy of basic product and the price discount strategy of value-added services can have positive impacts on the profit of enterprise. The sticky users demand valuation plays a positive role in promoting the optimal profit of enterprise. The optimal promotion strategy is the price discount strategy of value-added services. This study can provide a theoretical basis and decision support for the operation and management of the freemium enterprises.

With the rapid development and upgrade of electronics and related technologies, more and more discarded and end-of-life products are generated and must be properly handled and recycled. Disassembly lines are a key to their efficient recycling process. A parallel disassembly line offers high profit, low energy consumption, and high efficiency. In this paper, a linear programming model for optimal human-robot collaborative disassembly is established. The goal is to maximize disassembly profit. An improved Moth-Flame optimizer (MFO) is proposed and the crossover part of the algorithm is improved based on this problem’s characteristics. Experiments with practical cases involving multiple products of disassembly are used to test the model and algorithm. The result shows that MFO has obvious advantages over a commonly-used algorithm in solving parallel disassembly line balancing problems.

With the advancement of society and technology, consumers are becoming more personalized and more willing to buy new products. To meet the diverse needs of consumers, the design and development of product lines have become an important strategic issue of enterprises. Based on the consumer choice model, this paper aims at the design and development of product lines and the purchase behavior of consumers. A two-stage pricing model is constructed under the condition of bundled sales of products and services. This paper analyzes the impact that enterprises should consider products with value-added services and consumers’ purchasing behavior on product line two-stage pricing. Research shows that the level of product value-added services and the degree of enterprise strategy will have an impact on the price of the product line and the enterprise’s profit. When the service level is higher, the enterprise’s product line price and profit will increase, and when the enterprise discount higher, the enterprise’s total profit and product line price will decrease.

The quality of durable consumer goods is more and more concerned by consumers, and the development of value-added services to improve product quality has become an important way for enterprises to obtain profits. Based on the multinomial logit (MNL) model, this paper establishes a product line optimization model considering value-added services, which helps find the optimal pricing, market share and maximum profit. Through numerical experiments, the effects of the ratio of service price to product price, product quality, service quality, utility loss caused by product failure on the optimal solutions are studied. The study finds that when developing a product line, increasing the relative price of services while reducing product pricing is the optimal strategy. The research results can provide theoretical basis and decision support for the pricing of durable consumer goods and value-added services.

With the development of science and technology, a large number of electronic products have been discarded and become waste products. To obtain economic benefits and protect the environment, disassembly lines are designed to disassemble valuable parts from waste products. This paper proposes a mathematical model for the human-robot collaborative multiproduct hybrid disassembly line balancing problem with the disassembly revenue being the objective. A hybrid line combines a single-row line and a U-shaped line. We use the multi-neighborhood parallel greedy search algorithm to solve the model. Based on the algorithm, an alternate neighborhood search scheme consisting of different actions is designed. Some real-world cases are used to examine the feasibility of the proposed algorithm. The experimental results show that the multi-neighborhood parallel greedy search algorithm can solve the multi-product hybrid disassembly line balancing problem effectively.

In the Internet era, the continuous innovation and progress of science and technology promote the renewal of electronic and electrical products and tend to shorten their life cycle. As the recycling rate of these waste products is very low, this causes a great waste of resources. How to disassemble and recycle valuable parts is a common problem faced by the world. In essence, the recycling of waste products by enterprises is to obtain most valuable parts and components from obsolete products to gain profits. This paper considers the traditional linear disassembly line, which is widely used in factories at present. By combining the Brainstorming optimization (BO) algorithm with the K-means clustering algorithm, this work proposes a novel Improved Brainstorming optimization algorithm to obtain the near optimal solution quickly. It is compared with an Artificial Bee Colony algorithm and Gray Wolf optimization algorithm to verify its superiority in solving disassembly line balancing problems.

With the rapid development of science and technology, a large amount of electronic waste is inevitably generated from various discarded and End-Of-Life electronic products. If these products are not handled properly, they can cause environmental pollution as well as loss of resources. As an important part of remanufacturing, disassembly is usually done manually with low efficiency and high labor cost. In this paper, parallel disassembly lines with multiple robots are proposed. These robots can run automatically and be used to perform disassembly in an optimal disassembly mode. A multitype robot can be flexibly set with multiple functions. A mathematical model is established to assign disassembly tasks to the robots such that a line can achieve the maximum profit and minimum carbon emissions. An improved multi-objective multi-verse optimizer is proposed and applied to a set of instances. Experimental results show that the algorithm has an overwhelming performance advantage over the other three commonly-used algorithms in solving this problem. It has better performance than the other peer algorithms in solving parallel disassembly line balancing problems.

The remanufacturing, recycling, and reusing of waste products are particularly important to solve the problem of the resource shortage. Disassembly is a key step in the recycling process. How to minimize the negative impact of greenhouse gases on the environment has attracted extensive attention. This paper studies the disassembly line balancing problem to minimize the carbon emissions generated in the disassembly process. A Q-learning algorithm in reinforcement learning is applied to solve the disassembly line balancing problem. Through the analysis and comparison with the state-action-reward-state’-action algorithm to address the same real-life cases, it is proved that the Q-learning algorithm has good performance in most cases. In terms of solution speed, the proposed method is faster in both small-scale and large-scale cases.

Nowadays, the recycling of waste products has attracted extensive attention in academia and industry. In the layout design of disassembly lines, single-row and U-shaped hybrid disassembly lines have different application scenarios. Considering workstation resource configuration, disassembly line cycle time, and disassembly task precedence relationship, we address a Multi-product Hybrid-disassembly-line-balancing Problem (MHP), and establish its mathematical model with the objective of disassembly profit maximization. In addition, the union variable neighborhood descent (U-VND) algorithm is used to solve the problem, in which two kinds of neighborhood structures composed of different actions is designed. Experimental results and comparative analysis show that the proposed algorithm can quickly obtain stable and high-quality solutions, which verifies the validity of the neighborhood structure and the correctness of the model.

This special section of IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS is a selection of nine articles presented in the Special Issue on “Advanced Machine Learning on Cognitive Computing for Human Behavior Analysis.” This special issue aims to provide a forum for researchers from the perspective of cognitive computing to present recent progress on state-of-the-art methods and applications to human behavior analysis. Our purpose is to review the new progress and achievements on deep learning, transfer learning, and their applications on cognitive computing for human behavior analysis in recent years.

Business process modeling can take place at three abstraction levels, namely the conceptual level for system requirements, the logical level for system specification, and the physical level for software development. The consistency between these process models is crucial in process mapping, process integration, and difference detection. Existing work either only provides a simple “yes” or “no” answer as the consistency result, or simply checks the consistency from the control flow perspective. This paper presents a systematic approach to the quantitative measurement of the consistency between business processes across different abstraction levels. We use the essential event constraints to quantify the consistency from the perspectives of control flow and data flow, where the importance of different essential event constraints can also be distinguished. Our approach is implemented in a prototype tool. We evaluate our approach using synthetic datasets, the results of which demonstrate the effectiveness and efficiency of our approach.

A health emergency is a situation that poses an immediate risk to health and life and requires urgent intervention to prevent its worsening. Emergency healthcare service is a real-time service, where timeliness is critical to mission success. Workflow management technology has received considerable attention in the healthcare field in recent years for the automation of both intra- and interorganizational healthcare processes. However, no work on timeliness analysis has been reported. In our previous work, we proposed Workflows Intuitive and Formal Approach (WIFA) formalism for emergency response workflow modeling. In this paper, we extend our WIFA formalism to take task execution times into account to support emergency response timeliness analysis. An example of emergency healthcare shows how the timed WIFA workflow model works.

This paper presents an online Earthquake Disaster Simulator (EDS) designed and developed based on active database technology. This web-based tool allows users to inject the epicenter data of an earthquake with parameters such as magnitude and quake duration in order to get a list of the areas affected based on the distance from the epicenter, and then estimate damages caused by the quake event, including both structural destruction and human death and injuries. An earthquake damage evaluation model is established that considers major factors of both the quake itself and the impacted geographical area. The tool also supports the damage assessment caused by aftershocks in which the degradation of structure's resistance to earthquake is factored in.

A workflow management system determines the flow of work according to predefined business process definitions. It manages the resources required to meet goals and provides monitoring facilities and control capabilities. Resources can become important decision factors when combined with control flow information. This is particularly true in an emergency response system where large quantities of resources, including emergency responders, ambulances, fire trucks, medications, food, clothing, etc., are required. In this paper, we introduce a resource-constrained and decision support workflow model. This model enables users to specify resource consumption and production when executing a task, and decision policies to choose a path at a given situation where multiple execution branches are available. The paper also presents an efficient resource requirement analysis algorithm that can be used to decide the minimum resource set that, if satisfied, the workflow can be executed along any possible path till finish without the occurrence of resource shortage. A case study is presented to show the use of the proposed approach.

The workflow management of incident command systems (ICSs) has been challenged by the systems' special requirements on flexibility, intuitiveness, and capacity of correctness verification. The significance of applying formal approaches to the modeling and analysis of workflows has been well recognized, and many such approaches have been proposed. However, these approaches require users to master considerable knowledge of the particular formalisms, which impacts the application of these approaches on a larger scale. This paper presents a new formal, yet intuitive, approach for the modeling and analysis of workflows, which attempts to overcome the aforementioned problem. In addition to the abilities of supporting workflow validation and enactment, this new approach possesses the distinguishing feature of allowing users who are not proficient in formal methods to build up and dynamically modify the workflow models that address the flexibility needs of ICSs.

Charging is one of the most important functionalities in a telecommunication service system. In a general packet radio service (GPRS) wireless network, the load of charging information flow depends on the intensity of call traffic and the size of charging records. During busy hours, the GPRS network might not be able to transfer charging records on a timely basis if new charging records are generated too fast. On the other hand, when a call happens, the related charging information must be collected and transferred to the billing system. If a failure of the data link occurs, a secondary data link must be employed to transfer the charging information. However, this redundant operation might result in charging information duplication. This paper formally addresses these two issues. A timed Petri net model is built to support the analysis of the charging system performance versus various factors when the system works in the normal status, which, in particular, gives the maximum supportable busy hour call attempts of the GPRS network. The Petri net approach is also used to model and verify the correctness of the redundancy operation in case a connection failure occurs.

Mobile agents provide a powerful and flexible paradigm for the development of autonomic computing systems. However, due to the security concern, mobile agents are not popularly used for real-world systems. In this paper, we define a security framework that can effectively protect mobile agents and agent systems from intruder attacking. In the framework, a mobile agent finder, which is extended with a registration protocol, is used to authenticate and authorize agent systems, incoming messages, and agents. We formally model the secure mobile agent finder using predicate transition nets, and analyze the models using model checking tool Spin. The results help us to develop high confidence applications using mobile agents. In addition, the modeling and analysis approach can be easily extended to develop other complex software systems.

System assembly is one of the major issues in engineering complex component-based systems. This is especially true when heterogeneous, COTS and GOTS distributed systems, typical in industrial applications, are involved. The goal of system assembly is not only to make constituent components work together, but also to ensure that the components as a whole behave consistently and guarantee certain end-to-end properties. Despite recent advances, there is a lack of understanding about software composability, as well as theory and techniques for checking and verifying component-based systems. A theory of software system constraints about components, their environment and about system as a whole is the necessary foundation toward solid understanding of the composability of component-based systems. In this paper, we present a systematic approach for constraint specification and constraint propagation in concert with design refinement with a novel technique to ensure consistency between system-wide and component constraints in a design composition process of component-based systems. The consistent constraint propagation is used in our approach to drive progressive verification of the design. It allows us to verify overall design composition without interference of internal details of component designs. Verification is done separately at architectural and component levels without having to compose results of component analyses. A component can be safely replaced with alternative design without re-verifying the overall system composition so long as the replacement conforms to the corresponding interface and component constraint(s).

To real-time system designers, end-to-end time delay between external inputs and outputs is among the most important constraints. To ensure these system-wide constraints are satisfied, each of the constituent components is subject to a set of derived intermediate constraints. Since the system-wide constraints allow many possibilities for the intermediate constraints based on design tradeoffs, an important issue is how to guarantee the consistency between system-wide constraints and intermediate component constraints. In this paper, we present a systematic method for the verification of consistency between a system's global timing constraints and intermediate component constraints. The essence of this technique is to construct a timing model for each component, based on component constraints. This model treats a component as a black box. When replacing each component with its timing model, we obtain a complete time Petri net model for system architecture, which allows us to verify the consistency between global and component constraints. The key contribution is twofold. First, our technique of verification is efficient by supporting incremental analysis and suppressing internal state space of components. Second, much of the verification process presented in this paper can be automated. We illustrate the consistency verification process through a flexible manufacturing system example.

Security system architecture governs the composition of components in security systems and interactions between them. It plays a central role in the design of software security systems that ensure secure access to distributed resources in networked environment. In particular, the composition of the systems must consistently assure security policies that it is supposed to enforce. However, there is currently no rigorous and systematic way to predict and assure such critical properties in security system design. A systematic approach is introduced to address the problem. We present a methodology for modeling security system architecture and for verifying whether required security constraints are assured by the composition of the components. We introduce the concept of security constraint patterns, which formally specify the generic form of security policies that all implementations of the system architecture must enforce. The analysis of the architecture is driven by the propagation of the global security constraints onto the components in an incremental process. We show that our methodology is both flexible and scalable. It is argued that such a methodology not only ensures the integrity of critical early design decisions, but also provides a framework to guide correct implementations of the design. We demonstrate the methodology through a case study in which we model and analyze the architecture of the Resource Access Decision (RAD) Facility, an OMG standard for application-level authorization service.

We present an approach for analysis of security system architecture. Constraint patterns are introduced to formally specify the generic form of security policies that all implementations of the system architecture must enforce. The analysis is driven by incrementally decomposing a system-wide constraint pattern into a set of constraint patterns of constituent components. Since there are potentially many ways to partition a security system, a key element of the analysis is to verify that the component constraint patterns are collectively consistent with the global constraint pattern under the given architecture. A "consistent" component constraint is then used as the basis for analyzing possible designs of the component. We show that our approach is both flexible and scalable, which not only ensures the consistency of critical early design decisions, but also provides a framework to guide correct implementations of the design.

A compositional modeling and performance evaluation technique for traffic control systems based on Stochastic Timed Petri Nets (STPN's) is presented. We use STPN's to specify traffic and traffic control at an intersection and use a random distribution model to model the motion of vehicles in a road segment between any two consecutive intersections. A traffic control system is thus modeled as a composition of individual intersection models and segment random distribution models. A technique is presented to incrementally evaluate the system's performance by analyzing intersections separately according to a carefully selected order. The analysis technique conforms to the accepted practice of transportation research. Compared to existing Petri net models of traffic control systems, our technique dramatically reduces the complexity of analysis.

Time Petri nets (TPNs) are a popular Petri net model for specification and verification of real-time systems. A fundamental and most widely applied method for analyzing Petri nets is reachability analysis. The existing technique for reachability analysis of TPNs, however, is not suitable for timing property verification because one cannot derive end-to-end delay in task execution, an important issue for time-critical systems, from the reachability tree constructed using the technique. In this paper, we present a new reachability based analysis technique for TPNs for timing property analysis and verification that effectively addresses the problem. Our technique is based on a concept called clock-stamped state class (CS-class). With the reachability tree generated based on CS-classes, we can directly compute the end-to-end time delay in task execution. Moreover, a CS-class can be uniquely mapped to a traditional state class based on which the conventional reachability tree is constructed. Therefore, our CS-class-based analysis technique is more general than the existing technique. We show how to apply this technique to timing property verification of the TPN model of a command and control (C2) system.

This paper introduces compositional time Petri net (CTPN) models. A CTPN is a modularized time Petri net (TPN), which is composed of components and connectors. The paper also proposes a set of component-level reduction rules for TPNs. Each of these reduction rules transforms a TPN component to a very simple one while maintaining the net's external observable timing properties. Consequently, the proposed method works at a coarse level rather than at an individual transition level. Therefore, one requires significantly fewer applications to reduce the size of the TPN under analysis than those existing ones for TPNs. The use and benefits of CTPNs and reduction rules are illustrated by modeling and analyzing the response time of a command and control system to its external arriving messages.

We present a new modeling method to evaluate the performance indices of traffic control systems where each intersection has two or four phases. The method is based on the stochastic timed Petri net models of isolated intersections. It considers one direction of traffic along a street each time, and the interactions between traffic of different directions are partially approximated by statistical models. By so doing, it dramatically reduces the computing complexity that other Petri net-based methods suffer due to the consideration of detailed interactions among all directions of traffic. An example is presented to show the potential application of the new method.

A compositional method for modeling and evaluation of performance indices of traffic control systems based on Stochastic Timed Petri Nets (STPN) is presented. We use STPN to specify traffic and traffic control at an intersection and use a random distribution model to model the motion of vehicles in a road segment between any two consecutive intersections. The traffic control system is thus modeled as a composition of individual intersection models and segment distribution models. A technique is presented to incrementally evaluate the system's performance by analyzing intersections separately, according to a carefully selected order. The analysis technique conforms to the accepted practice of transportation research. Compared to existing Petri net models of traffic control systems, our technique dramatically reduces the complexity of analysis.

An incremental approach for architectural modeling and analysis of real-time concurrent systems is presented. The approach integrates existing formal methods, more specifically time Petri nets and real-time computational tree logic, and leverages their complementary strengths in a way that allows us to systematically enforce that architectural design meets the system's timing requirements, and to incrementally verify the conformance. Consequently, our approach is able to provide better assurance to system design and yet reduce the complexity of analysis. The approach is based on a Real-time Architectural Specification (RAS) model, which provides a formal basis to systematically maintain a correlation between the (timing) requirements of a system and its architectural design. Based on RAS, we further present a method to verify timing properties of a system design. This method helps conquer the complexity of analysis in two dimensions. Horizontally at each design level, incremental verification is achieved by introducing TPN reduction rules that allow us to compose analysis results on individual system components. Vertically across design levels, incremental verification is achieved by propagating higher-level constraints to lower-level designs so that we can safely plug a component design into a high-level architecture without having to re-verify the entire model. A naval command and control (C2) system is used throughout the paper to demonstrate the concept and usability of our approach.

In this paper, we propose a set of component-level reduction rules for TPN. Each of these reduction rules transforms a TPN component to a constant size of simple one while maintains the net's external observable timing properties. Consequently, our method works at a coarser level than that works in individual transition level, and fewer applications of our rules are needed to reduce the size of the TPN under analysis. We illustrate the use and benefits of our reduction rules by modeling and analyzing the response time of a command and control system to its external arriving messages.