Fire-detection technology is of great importance for successful fire-prevention measures. Image-based fire detection is one effective method. At present, object-detection algorithms are deficient in performing detection speed and accuracy tasks when they are applied in complex fire scenarios. In this study, a lightweight fire-detection algorithm, Light-YOLOv5 (You Only Look Once version five), is presented. First, a separable vision transformer (SepViT) block is used to replace several Cross Stage Partial Bottleneck with 3 convolutions (C3) modules in the final layer of a backbone network to enhance both the contact of the backbone network to global information and the extraction of flame and smoke features; second, a light bidirectional feature pyramid network (Light-BiFPN) is designed to lighten the model while improving the feature extraction and balancing speed and accuracy features during a fire-detection procedure; third, a global attention mechanism (GAM) is fused into the network to cause the model to focus more on the global dimensional features and further improve the detection accuracy of the model; and finally, the Mish activation function and SIoU loss are utilized to simultaneously increase the convergence speed and enhance the accuracy. The experimental results show that compared to the original algorithm, the mean average accuracy (mAP) of Light-YOLOv5 increases by 3.3%, the number of parameters decreases by 27.1%, and the floating point operations (FLOPs) decrease by 19.1%. The detection speed reaches 91.1 FPS, which can detect targets in complex fire scenarios in real time.

The recent advances in wearable sensors and intelligent human–machine interfaces have sparked a great many interests in conductive fibers owing to their high conductivity, light weight, good flexibility, and durability. As one of the most impressive materials for wearable sensors, conductive fibers can be made from a variety of raw sources via diverse preparation strategies. Herein, to offer a comprehensive understanding of conductive fibers, we present an overview of the recent progress in the materials, the preparation strategies, and the wearable sensor applications related. Firstly, the three types of conductive fibers, including metal-based, carbon-based, and polymer-based, are summarized in terms of their principal material composition. Then, various preparation strategies of conductive fibers are established. Next, the primary wearable sensors made of conductive fibers are illustrated in detail. Finally, a robust outlook on conductive fibers and their wearable sensor applications are addressed.

With the development of high-power fuel cell vehicles, heat dissipation requirements have become increasingly stringent. Although conventional cooling techniques improve the heat dissipation capacity by increasing the fan rotating speed or radiator dimensions, high energy consumption and limited engine compartment space prevent their implementation. Moreover, the insufficient heat transfer capacity of existing coolants limits the enhancement of heat dissipation performance. Therefore, exploring novel coolants to replace traditional coolants is important. Nanofluids composed of nanoparticles and base liquids are promising alternatives, effectively improving the heat transfer capacity of the base liquid. However, challenges remain that prevent their use in fuel cell vehicles. These include issues regarding the nanofluid stability and cleaning, erosion and abrasion, thermal conductivity, and electrical conductivity. In this review, we summarize the nanofluid applications in oil-fueled, electric, and fuel cell vehicles. Subsequently, we provide a comprehensive literature review of the challenges and future research directions of nanofluids as coolants in fuel cell vehicles. This review demonstrates the potential of nanofluids as an alternative thermal management system that can facilitate transition toward a low-carbon, energy-secure economy. It will serve as a reference for researchers to focus on new areas that could drive the field forward.

Additive manufacturing, as an innovative manufacturing technology compared to traditional subtractive manufacturing, offers greater design freedom and rapid prototyping capabilities. Material Extrusion (MEX), the most widely applied branch within additive manufacturing (AM), operates on the core principle of heating thermoplastic polymers or composite materials to a molten state, then depositing them layer by layer through a nozzle to form the final shape. However, the inherent contradiction between printing speed and build quality remains the key bottleneck limiting its widespread adoption. Desktop Material Extrusion techniques like Fused Filament Fabrication (FFF) offer high precision but require extended printing times. Meanwhile, industrial-scale Big Area Additive Manufacturing (BAAM) processes achieve high deposition rates yet suffer from insufficient accuracy. This paper systematically reviews the primary application domains of additive manufacturing technologies, elucidating their process flows and classification systems. Building upon this foundation, it systematically analyzes the contradiction and coupling relationship between high precision and high deposition speed in Material Extrusion technologies from aspects including hot-end flow, system thermal management, vibration, and printing parameters. It provides a reference for the subsequent design and optimization of high-precision, high-speed Material Extrusion (MEX) printers.

The fuel cell vehicle is one of the essential directions for developing new energy vehicles. But heat dissipation is a critical technical difficulty that needs to be solved urgently. Nano-coolant is a promising coolant that can potentially replace the existing coolant of a fuel cell. However, its thermal conductivity has a significant impact on heat dissipation performance, which is closely related to nanoparticles’ thermal conductivity, nanoparticles’ volume fraction, and the nano-coolant temperature. Many scholars have created the thermal conductivity models for nano-coolants to explore the mechanism of nano-coolants’ thermal conductivity. At present, there is no unified opinion on the mechanism of the micro thermal conductivity of the nano-coolant. Hence, this paper proposed a novel model to predict the thermal conductivity of ethylene glycol/deionized water-based nano-coolants. A corrected model was designed based on the Hamilton & Crosser model and nanolayer theory. Finally, a new theoretical model of nano-coolant thermal conductivity suitable for fuel cell vehicles was constructed based on the base fluid’s experimental data.

Bearing outside-diameter detection is a very important part of bearing detection. In this paper, bearing outside-diameter is detected by a method named Hough transform. For the curve known analytic function, Hough transform is effectual. However, the detection method based on traditional Hough transform has some disadvantages such as large amount of data, needing a very huge memory, slow speed and so on. Therefore, a more efficient method is introduced in the paper. In this way, the parameters of a circle are extracted, and this method can avoid introducing the points which are not a part of the circle into computation. The result which is relatively exact can be gotten with much small amount of data. This method improves the speed of bearing outside-diameter detection.