Fatigue driving behavior recognition in all-weather real driving environments is a challenging task. Accurate recognition of fatigue driving behavior is helpful to improve traffic safety. The facial landmark detector is crucial to fatigue driving recognition. However, existing facial landmark detectors are mainly aimed at stable front face color images instead of side face gray images, which is difficult to adapt to the fatigue driving behavior recognition in real dynamic scenes. To maximize the driver’s facial feature information and temporal characteristics, a fatigue driving behavior recognition method based on a multi-scale facial landmark detector (MSFLD) is proposed. First, a spatial pyramid pooling and multi-scale feature output (SPP-MSFO) detection model is built to obtain a face region image. The MSFLD is a lightweight facial landmark detector, which is composed of convolution layers, inverted bottleneck blocks, and multi-scale full connection layers to achieve accurate detection of 23 key points on the face. Second, the aspect ratios of the left eye, right eye and mouth are calculated in accordance with the coordinates of the key points to form a fatigue parameter matrix. Finally, the combination of adaptive threshold and statistical threshold is used to avoid misjudgment of fatigue driving recognition. The adaptive threshold is dynamic, which solves the problem of the difference in the aspect ratio of the eyes and mouths of different drivers. The statistical threshold is a supplement to solve the problem of driver’s low eye threshold and high mouth threshold. The proposed methods are evaluated on the Hunan University Fatigue Detection (HNUFDD) dataset. The proposed MSFLD achieves a normalized mean error value of 5.4518%, and the accuracy of the fatigue driving recognition method based on MSFLD achieves 99.1329%, which outperforms that of state-of-the-art methods.

Fatigue driving is a serious threat to road safety, which is why accurately identifying fatigue driving behavior and warning drivers in time are of great significance in improving traffic safety. However, accurately recognizing fatigue driving is still challenging due to large intra-class variations in facial expression, continuity of behaviors, and illumination conditions. A fatigue driving recognition method based on feature parameter images and a residual Swin Transformer is proposed in this paper. First, the face region is detected through spatial pyramid pooling and a multi-scale feature output module. Then, a multi-scale facial landmark detector is used to locate 23 key points on the face. The aspect ratios of the eyes and mouth are calculated based on the coordinates of these key points, and a feature parameter matrix for fatigue driving recognition is obtained. Finally, the feature parameter matrix is converted into an image, and the residual Swin Transformer network is presented to recognize fatigue driving. Experimental results on the HNUFD dataset show that the proposed method achieves an accuracy of 96.512%, thus outperforming state-of-the-art methods.

The wide application of gas-fired units has enhanced the interdependency between electrical power system and natural gas system and raised the necessity on coordinated analysis on integrated energy systems. This paper focuses on the optimal electricity-gas flow (OEGF) analysis of an integrated electricity and natural gas system (IEGS) and proposes a linearized OEGF model considering electricity transmission losses (E TL). The modified direct current (DC) power flow model embedded ETL and a steady-state gas flow formulation are adopted to describe the IEGS. The E TL is modeled by a quadratic function of electrical phase angles. To address the nonlinear ETL model and nonconvex gas flow equations, the Taylor expansion is employed for linearization to reduce the computation burden. To obtain the high-quality initial points for Taylor expansion and then guarantee the accuracy of linearization, a two-stage linearized method is further developed to solve the proposed OEGF model. Numerical results on an IEGS with the IEEE 14-bus system and a 12-nodel gas system demonstrate the effectiveness of proposed OEGF method.

Cancer is a serious and complex disease caused by uncontrolled cell growth and is becoming one of the leading causes of death worldwide. Anticancer peptides (ACPs), as a bioactive peptide with lower toxicity, emerge as a promising means of effectively treating cancer. Identifying ACPs is challenging due to the limitation of experimental conditions. To address this, we proposed a dual‐channel‐based deep learning method, termed ACP‐DPE, for ACP prediction. The ACP‐DPE consisted of two parallel channels: one was an embedding layer followed by the bi‐directional gated recurrent unit (Bi‐GRU) module, and the other was an adaptive embedding layer followed by the dilated convolution module. The Bi‐GRU module captured the peptide sequence dependencies, whereas the dilated convolution module characterised the local relationship of amino acids. Experimental results show that ACP‐DPE achieves an accuracy of 82.81% and a sensitivity of 86.63%, surpassing the state‐of‐the‐art method by 3.86% and 5.1%, respectively. These findings demonstrate the effectiveness of ACP‐DPE for ACP prediction and highlight its potential as a valuable tool in cancer treatment research.

Driven by emerging wireless applications, numerous smart objects will be deployed, forming things networks interconnected by the Internet of Things (IoT). Many of these networks follow the low-power multi-hop wireless network (LPMWN) paradigm due to the good scalability of the distributed schemes adopted by LPMWNs. Although distributed schemes may operate satisfactorily most of the time, they are often complex and costly to manage. To simplify network management, Long Range (LoRa) wireless technology has been introduced into LPMWNs for centralized network control. Unfortunately, due to LoRa’s inherent limitation that a downlink packet must be in response to a precedent uplink packet, the existing protocols typically require every node to periodically send an uplink packet to the gateway for downlink network command dissemination. This causes intensive uplink collisions and quickly depletes LoRa bandwidth, especially when the network is large. In light of these pitfalls, we propose a Cross-interface Downlink Relay (CDR) scheme, which leverages the ZigBee communications that are already available in LPMWNs to relay LoRa downlink data traffic for bandwidth-efficient and delay-guaranteed data dissemination. CDR determines the optimal network topology for bandwidth-minimized data relay while adjusting system parameters to ensure each node’s delay requirement under network dynamics. A prototype system is implemented by integrating LoRa and ZigBee into an IoT platform. Extensive outdoor experiments show that the bandwidth consumption of CDR is 39.3% lower than a state-of-the-art LoRa data dissemination protocol for ZigBee-based LPMWNs under a moderate data traffic and delay requirement. Besides, CDR improves energy efficiency and network lifetime significantly.