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Midori is a lightweight block cipher designed by Banik et al. and presented at the ASIACRYPT 2015 conference. According to the block size, it consists of two algorithms, denoted as Midori-64 and Midori-128. Midori generates 8-bit S-Boxes from 4-bit S-Boxes and applies almost MDS matrices instead of MDS matrices. In this paper, we introduce the meet-in-the-middle fault attack model in the 4-round cell-oriented fault propagation trail and reduce the key space in the last round by $ 2^{45.71} $ and $ 2^{39.86} $ for Midori-64 and Midori-128, respectively. For Midori-64, we reduce the time complexity from $ 2^{80} $ to $ 2^{28} $, $ 2^{32} $ and $ 2^{56} $ for the different single fault injection approaches. For Midori-128, we provide a 4-round fault attack method, which slightly increases the complexity compared to previous attacks. Our results indicate that the first and last four rounds of Midori must be protected to achieve its security.

Let $ mathcal{L} = -Delta+V $ be the Schrödinger operators on $ mathbb{R}^n $ with nonnegative potential $ V $ belonging to the reverse Hölder class $ RH_q $ for some $ q geq frac{n}{2} $. We prove the boundedness of fractional integral operator $ mathcal{I}_alpha $ related to the Schrödinger operators $ mathcal{L} $ from strong and weak variable exponent Lebesgue spaces into suitable variable exponent Lipschitz type spaces.

Accurately predicting lane-changing behaviors (lane keeping, left lane change and right lane change) in real-time is essential for ensuring traffic safety, particularly in mixed-traffic environments with both autonomous and manual vehicles. This paper proposes a fused model that predicts vehicle lane-changing behaviors based on the road traffic environment and vehicle motion parameters. The model combines the ensemble learning XGBoost algorithm with the deep learning Bi-GRU neural network. The XGBoost algorithm first checks whether the present environment is safe for the lane change and then evaluates the likelihood that the target vehicle will make a lane change. Subsequently, the Bi-GRU neural network is used to accurately forecast the lane-changing behaviors of nearby vehicles using the feasibility of lane-changing and the vehicle's motion status as input features. The highD trajectory dataset was utilized for training and testing the model. The model achieved an accuracy of 98.82%, accurately predicting lane changes with an accuracy exceeding 87% within a 2-second timeframe. By comparing with other methods and conducting experimental validation, we have demonstrated the superiority of the proposed model, thus, the research achievement is of utmost significance for the practical application of autonomous driving technology.

With the development of unmanned ships, their use in production is becoming more and more common. However, the unmanned ship work cycle is long and the work environment is complex, and it is still very difficult to calculate the phased mission reliability without unmanned ship. We analyze the unmanned ship phased mission reliability based on the binary decision diagram. Moreover, redundancy is used as the unmanned ship reliability optimization scheme. Considering the resource limitation, and the capacity of unmanned ship, the redundancy allocation scheme of unmanned ship is established. The redundancy allocation scheme is solved by marginal optimization algorithm. Finally, a case study is established to analyze the effectiveness and practicality of the proposed method.