Modern traffic management must balance the ability to predict complex, non-stationary flow patterns with the strict enforcement of safety and legal constraints (e.g., minimum pedestrian intervals, maximum cycle lengths). While generative models such as Variational Autoencoders (VAEs) can effectively capture traffic dynamics, purely data-driven approaches often fail to respect domain-specific rules. We propose a Neuro-VAE-Symbolic framework that combines the flexibility of VAE-based generation with the rule-enforcing capability of symbolic reasoning. By projecting raw neural outputs onto a feasible action space defined by traffic regulations, our method ensures both adaptability to dynamic demand and strict constraint compliance. Experiments on benchmark datasets show that our method reduces average waiting time by up to 15%, cuts queue lengths by 10–20%, and increases throughput by 5–8%, all while maintaining near-zero rule violations. These results demonstrate that neuro-symbolic integration enables high-fidelity traffic scenario generation alongside reliable, regulation-abiding decision-making, offering a robust solution for dynamic urban traffic control.

Pine Wilt Disease (PWD) poses a severe threat to the health of pine trees and has resulted in substantial losses to global pine forest resources. Due to the minute size of the pathogens and the concealed symptoms of PWD, early detection through remote sensing image technology is essential. However, in practical applications, remote sensing images are easily affected by factors such as cloud cover and changes in illumination, resulting in significant noise and blurriness in the images. These interference factors significantly reduce the accuracy of existing object detection models. Therefore, this paper presents a novel and highly robust methodology for detecting PWD, termed YOLOv8-RD. We synthesized the benefits of residual learning and Fuzzy Deep Neural Networks (FDNN) to develop a Residual Fuzzy module (ResFuzzy), which adeptly filters image noise and refines background features with enhanced smoothness. Simultaneously, we integrated a Detail Processing Module (DPM) into the ResFuzzy module to enhance the low-frequency detail features transmitted in residual learning. Furthermore, by incorporating the Dynamic upSampling operator (DySample), our model can dynamically adjust the sampling step size based on the variations in the input feature map during the upsampling process, thereby effectively recovering detail from the feature map. Our model exhibited exceptional robustness to severe noise. When evaluated on a PWD dataset with 100% interference samples at an intensity of 0.07, our model achieved an average precision improvement of 4.9%, 6.3%, 7.3%, and 3.0% compared to four most representative models, making it well-suited for PWD detection in interfering environments.

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In experimental scenarios, the hybrid multi-population genetic algorithm significantly improves CMGA and MMA, reducing UAVs’ total operation time by 4.8% and 13.8%, respectively. This demonstrates its efficiency in meeting the growing demand for low-cost delivery of consumer electronics. This method ensures that logistics operations remain agile and approachable to growing market needs, reinforcing the consumer technology industry’s capability to meet customer expectations in a viable landscape.

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network’s edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance Systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility.

In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and ldiversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%.

With the rapid evolution toward data-intensive applications and sustainable urban mobility, upcoming sixth-generation (6G) wireless networks must deliver enhanced coverage, high spectral efficiency, and energy optimization across densely populated areas. However, achieving these requirements poses significant challenges due to the dynamic nature of urban environments, high interference in multi-cell systems, and limitations in conventional passive beamforming technologies. To address these challenges, reconfigurable intelligent surface (RIS) is considered a highly promising approach for enabling and improving 6G wireless communications. This is because it has the ability to efficiently manipulate wireless channels at a lower cost. Considerable study has focused on the utilization of conventional diagonal RIS, in which each individual RIS component is linked to its own ground load but not interconnected with other elements. Nevertheless, the uncomplicated structure of classical RIS imposes restrictions on its ability to manipulate passive beamforming. In this study, we consider beyond diagonal RIS (BD-RIS) in the multi-cell transportation system, which goes beyond using diagonal phase shift matrices. In particular, we provide a new optimization framework to maximize the sum rate of BD-RIS assisted multi-cell transportation system by optimizing the power allocation of the base station and phase shift design of BD-RIS in each cell. We employ the block coordinate descent method to transform the original optimization problem and achieve a local optimal based on standard convex approaches. Numerical results demonstrate the benefits of adopting BD-RIS in multi-cell transportation systems compared to the classical RIS architecture.