We present an improved kernel recursive least squares (KRLS) algorithm for the online prediction of nonstationary time series. In order to adaptively sparsify a selected kernel dictionary for the KRLS algorithm, the approximate linear dependency (ALD) criterion based KRLS algorithm is combined with the quantized kernel recursive least squares algorithm to provide an initial framework. In order to sufficiently track the strongly changeable dynamic characteristics due to nonstationarity, a forgetting factor is further inserted into the proposed combined algorithm. It is shown that our proposed algorithm, referred to as the FFIKRLS algorithm, provides a clearly compatible algorithm structure and can be improved by the existing modeling techniques from both mapping and weights updating perspectives. Numerical simulations using benchmark Lorenz time series in comparison with existing methods have demonstrated that the proposed algorithm has superior performance in terms of both predictive accuracy and kernel dictionary size.

The importance of object detection in intelligent logistics applications is increasingly recognized. However, current detector models suffer from challenges such as high computational cost and low detection accuracy, which limit their deployment on edge devices with limited computational power in logistics scenarios. To address these issues, this paper proposes a novel lightweight detector model (GBForkDet) based on YOLOv8 for forklift safety driving. Firstly, the Ghost module is integrated into YOLOv8 to optimize the Backbone feature extraction process, reducing the computational cost of the model. Then, a Bi-directional Omni-Dimensional Dynamic Neck (BiODNeck) is designed to fuse feature information in complex logistics scenarios. GBForkDet significantly improves the capture of contextual logistics background information by reconstructing the Neck of YOLOv8 with BiODNeck. This is attributed to cross-layer weighted feature fusion and a complementary focus on learning convolutional kernels in any convolutional layer along all four dimensions of the kernel space. Furthermore, the introduction of the Normalized Wasserstein distance (NWD) as an enhanced loss function improves the detection of small distant objects in logistics scenarios. Experimental results show that GBForkDet achieves a mAP of 92.7% and 95.3% on the established Forklift-3k and KITTI datasets while reducing the model parameters by 17.9% and the computational cost by 22.5% compared to the baseline YOLOv8s model. Under the Jetson Nano edge platform and 640×640 input size, the GBForkDet model achieves a remarkable inference time of 108.2 ms using TensorRT acceleration.

The transportation control infrastructure serves as the foundation for regional traffic signal control. However, in practice, this infrastructure is often imperfect and complex, characterized by factors such as heterogeneity and uncertainty, which pose significant challenges to existing methods and systems. Therefore, this paper proposes a novel approach to coordinated traffic signal control that emphasizes flexibility. To achieve this flexibility, we combine the flexible model of complex networks with robust fuzzy control methods. This approach enables us to overcome the complexity of the transportation control infrastructure and ensure efficient management of traffic signals. Additionally, to ensure long-term operational ease, we develop a regional traffic signal control system using steam computing technology, which provides high scalability and compatibility. Finally, computational experiments are performed to validate adaptability and performance of our proposed approach.

Traditional product demand forecasting has typically been modeled as a single time series problem relying exclusively on temporal information. However, temporal features alone are insufficient to capture complex demand dynamics in modern, interconnected markets. To address this limitation, we propose a product demand forecasting method based on a Spatiotemporal Hypergraph Attention Network (STHA), which jointly models temporal dependencies and higher-order spatial interactions among multiple market entities to enhance forecasting accuracy and robustness. STHA integrates a Long Short-Term Memory (LSTM) network with a Hawkes attention mechanism to capture temporal patterns and constructs a hypergraph to represent multi-entity relationships. It further incorporates hypergraph convolution and a hypergraph attention mechanism to dynamically aggregate higher-order spatial information and weight relational importance. Experiments on the Corporación Favorita sales dataset demonstrate that STHA substantially outperforms single time series benchmarks (ARIMA, LSTM, TCN, Transformer, and PatchTST), achieving notable reductions in MAE, RMSE, and MAPE—with improvements in MAPE exceeding 15 percentage points for certain stores. Compared with the graph-based STGCN model, STHA also exhibits superior robustness. These results demonstrate the effectiveness of STHA for complex multi-market-entity demand forecasting and highlight its potential as a reliable framework for improving inventory management and supply chain decision-making.

Regional Traffic Signal Control (RTSC) is believed to be a promising approach to alleviate urban traffic congestion. However, the current ecology of RTSC platforms is too closed to meet the needs of urban development, which has also seriously affected their own development. Therefore, the paper proposes virtualizing the traffic signal control devices to create software-defined RTSC systems, which can provide a better innovation platform for coordinated control of urban transportation. The novel architecture for RTSC is presented in detail, and microscopic traffic simulation experiments are designed and conducted to verify the feasibility.