With the fast development of Mobile Internet, data traffic generated by end devices is anticipated to witness substantial growth in the future years. However, processing tasks locally will cause latency due to the limited resources of the end devices. Edge-cloud collaboration, an effective solution for latency-sensitive applications, is attracting greater attention from both industry and academia. It combines the advantages of the cloud center with abundant computing resources and edge nodes with low-latency capabilities. In this paper, we propose a two-stage task offloading framework with edge-cloud collaboration to assist end devices processing latency-sensitive tasks either on the edge servers or in the cloud center. As for homogeneous task offloading, in the first stage, the competitive end devices offload tasks to the edge gateways. We formulate the selfish task offloading problem among end devices as a potential game. In the second stage, the edge nodes request resources from the cloud center to process end devices tasks due to their limited resources. Then, we consider the heterogeneous task offloading problem and use intelligent optimization algorithm to obtain the optimal offloading strategy. Simulation results show that the service prices of edge nodes influence the decisions and task offloading costs of end devices. We also verify the intelligent optimization algorithm can achieve optimal performance with low complexity and fast convergence.

Considering the large number of returns in online sales and the network externalities of e-platforms, we develop a decentralized model and a centralized model to explore the impacts of returns and network externalities on e-commerce supply chain (ECSC) decisions. We show that in the decentralized model, the service level, price, market demand, and ECSC members’ profits increase with the network externality strength. However, the service level and price increase, while the market demand and ECSC members’ profits decrease with the product return rate. The centralized model is the optimal operating mode when it is properly coordinated. We design the “commission and return cost-sharing” contract to optimize ECSC, in which the proportion of the e-platform’s sharing of the return handling cost is exactly equal to the proportion of the system profit after coordination. Based on the decentralized model, we develop two extended models in which we endogenize the impacts of the service level and return rate on the network externality strength. Through comparisons between the extended and decentralized models, we show that high-quality service can improve ECSC’s profitability, while a high return rate hurts its economic performance.

Existing pre-trained models like Distil HuBERT excel at uncovering hidden patterns and facilitating accurate recognition across diverse data types, such as audio and visual information. We harnessed this capability to develop a deep learning model that utilizes Distil HuBERT for jointly learning these combined features in speech emotion recognition (SER). Our experiments highlight its distinct advantages: it significantly outperforms Wav2vec 2.0 in both offline and real-time accuracy on RAVDESS and BAVED datasets. Although slightly trailing HuBERT’s offline accuracy, Distil HuBERT shines with comparable performance at a fraction of the model size, making it an ideal choice for resource-constrained environments like mobile devices. This smaller size does come with a slight trade-off: Distil HuBERT achieved notable accuracy in offline evaluation, with 96.33% on the BAVED database and 87.01% on the RAVDESS database. In real-time evaluation, the accuracy decreased to 79.3% on the BAVED database and 77.87% on the RAVDESS database. This decrease is likely a result of the challenges associated with real-time processing, including latency and noise, but still demonstrates strong performance in practical scenarios. Therefore, Distil HuBERT emerges as a compelling choice for SER, especially when prioritizing accuracy over real-time processing. Its compact size further enhances its potential for resource-limited settings, making it a versatile tool for a wide range of applications.

Thanks to the emerging integration of algorithms and simulators, recent Driving Simulators (DS) find enormous potential in applications like advanced driver-assistance devices, analysis of driver’s behaviours, research and development of new vehicles and even for entertainment purposes. Driving simulators have been developed to reduce the cost of field studies, allow more flexible control over circumstances and measurements, and safely present hazardous conditions. The major challenge in a driving simulator is to reproduce realistic motions within hardware constraints. Motion Cueing Algorithm (MCA) guarantees a realistic motion perception in the simulator. However, the complex nature of the human perception system makes MCA implementation challenging. The present research aims to improve the performance of driving simulators by proposing and implementing the MCA algorithm as a control problem. The approach is realized using an actual vehicle model integrated with a detailed model of the human vestibular system, which accurately reproduces the driver’s perception. These perception motion signals are compared with simulated ones. A 2-DOF stabilized platform model is used to test the results from the two proposed control strategies, Proportional Integrator and Derivative (PID) and Model Predictive Control (MPC).

In the face of a significant public health event, consumers may either increase their panic buying or decrease their willingness to make purchases. This study focuses on the impact of a significant public health event on offline store sales and consumer consumption, utilizing data from chain convenience stores in Hefei and Wuhu during early 2019 and early 2020 in China. Employing a difference-in-differences model, the study investigates the effect of the significant public health event outbreak on weekly store sales, order numbers, and consumer consumption in terms of product quantities, transaction amount, average amount per order, and transaction frequency. Different from prior literature that finds hoarding behavior of consumers online, the findings of this paper indicate a significant reduction in stores’ offline weekly sales and order numbers, as well as consumers’ offline weekly consumption across the four dimensions, as a result of the significant public health event outbreak. Additionally, employing a mediation model, the study explores the pathway of population mobility through which the significant public health event adversely affects offline consumption. Furthermore, subset analysis is conducted for stores located in different areas and consumers with varying characteristics, revealing that the aforementioned conclusions predominantly apply to stores situated in office areas and residential areas, as well as consumers with either no apparent preference for different product categories or a noticeable preference for food.