Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. In this work, a sensor array consisting of three commercial MOS sensors was employed to discriminate between three target gases, CO, H₂ and a mixture of the two, which are characteristic gases released during the thermal runaway of lithium batteries. In this work, an integrated model that makes the classification stage results one of the feature inputs for the concentration regression stage was employed, successfully reducing the RMSE of the concentration regression results. In addition, we also explored the influence of the selection of the response time length on the classification and regression tasks, achieving the best results in a short time through the optimum algorithm. To assess the impact of time duration sensor data on the results, we selected four time windows of different length and extracted the corresponding sensor response data for subsequent processing. Initially, principal component analysis (PCA) was used to visualise the clustering of the three target gas samples at room temperature, providing a preliminary data analysis. For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. The results demonstrated that MLP achieved 99.23% classification accuracy during the 20 s response period. In the concentration regression phase, we combined the classification results with the raw features to create a new feature set, which was then input into a multi-output MLP regression model. The root mean square error (RMSE) employing the new feature set was used to measure the prediction error. Ultimately, the findings showed that the input of combined features significantly reduced the regression error for the mixed gas.

Edge computing perfectly integrates cloud computing centers and edge-end devices together, but there are not many related researches on how the edge-end node devices work to form an edge network and what the protocols used to implement the communication among nodes in the edge network. Aiming at the problem of coordinated communication among edge nodes in the current edge computing network architecture, this paper proposes an edge network routing and forwarding protocol based on target tracking scenarios. This protocol can meet the dynamic changes of node locations, and the elastic expansion of node scale. Individual node failures will not affect the overall network, and the network ensures efficient real-time with less communication overhead. The experimental results display that the protocol can effectively reduce the communications volume of the edge network, improve the overall efficiency of the network, and set the optimal sampling period, so as to ensure that the network delay is minimized.

Compared to a single horizontal or inclined tunnel, a U-shaped underwater tunnel combines both types. If such tunnels catch fire, the resulting scenarios will lead to varying intensities of induced airflow, which significantly impact the internal heat transfer mechanisms. This study numerically simulated the effects of varying induced airflow strengths on the heat transfer proportion and temperature distribution within the tunnel. Key variables including the inclination angle of tunnel sections, the heat release rate (HRR) of the fire source, and its distance to the tunnel opening were systematically investigated. The results indicate that when the fire is in the horizontal tunnel section, the primary factor affecting the temperature field distribution is the HRR of the fire. As the fire source moves toward the inclined tunnel section, a transition to strong induced airflow occurs above the corner at low angles of inclination. As the slope increases, the transition position shifts downstream, toward the lower side of the corner. At this point, the tunnel is affected by strong induced airflow. Using dimensionless analysis, models were developed for temperature field distribution under strong and weak induced airflow, guiding underwater tunnel spray activation temperature and firefighting and rescue efforts.

Real-time vehicle warning systems are critical for collision prevention, yet they face stringent sub-10 ms latency requirements under severe energy and computational constraints. This review systematically surveys low-latency edge computing architectures for such systems, explicitly comparing CPU-based, GPU-accelerated, FPGA-based, ASIC/NPU-embedded, fog, and cloud-only processing paradigms. We examine edge intelligence frameworks for intelligent transportation systems, the computational demands of collision avoidance algorithms, V2X communication protocols, and hardware accelerators. A key contribution is a comparative analysis of latency, power consumption, and area trade-offs, revealing that FPGA accelerators achieve deterministic sub-millisecond processing at 5–15 W, while emerging NPUs offer 1–5 W alternatives for fixed-function inference. A critical synthesis of the literature identifies major gaps: the absence of standardized benchmarks, insufficient field-testing of FPGA prototypes, and underutilized potential of approximate computing in safety loops. Furthermore, we introduce fog computing as a vital intermediary layer to bridge edge-cloud gaps. This review consolidates over 58 core studies and offers practical, actionable insights for designing next-generation vehicular safety systems.