Wing-diesel engine-powered hybrid ships can effectively reduce fuel consumption and CO₂ emissions by using wind energy as the auxiliary driving power. The energy optimization management of the hybrid system can further improve the ship's energy efficiency. To achieve this purpose, it is significant to establish an effective energy consumption model for the energy optimization management of the hybrid system. Therefore, an energy consumption model is established based on the energy conversion analysis of the hybrid power system in this paper. This model can effectively describe the energy consumption of the hybrid ship under different navigational environmental conditions. Then, a joint optimization method of the wing attack angle and of the sailing speed for the hybrid ship is proposed by adopting a swarm intelligence optimization algorithm, in order to reduce energy consumption and CO₂ emissions of the hybrid ship under different navigational environmental conditions. Finally, the energy consumption optimization potentials by adopting the hybrid power system and the proposed joint optimization method are analyzed. The results show that the energy consumption and CO₂ emissions along a typical route can be reduced by about 4.5%. This study provides an important basis for future practical operations of wing-diesel engine-powered hybrid ships.

Classification-based tracking strategies often face more challenges from intra-class discrimination than from inter-class separability. Even for deep convolutional neural networks that have been widely proven to be effective in various vision tasks, their intra-class discriminative capability is still limited by the weakness of softmax loss, especially for targets not seen in the training dataset. By taking intrinsic attributes of training samples into account, in this paper, we propose a position-sensitive loss coupled with softmax loss to achieve intra-class compactness and inter-class explicitness. Particularly, two additive margins are introduced to encode the position attribute for decision boundary maximization, which is also utilized with the proposed loss to supervise the fine-tuned features on the pre-trained model. With the nearest neighbor ranking measurement in the feature embedding domain, the whole scheme is able to reach an optimized balance between the feature-level inter-class semantic separability and instance-level intra-class relative distance ranking. We evaluate the proposed work on different popular benchmarks, and experimental results demonstrate that our tracking strategy performs favorably against most of the state-of-the-art trackers in the comparison of accuracy and robustness.

High temperature radiation damage in binary bcc Fe alloys containing 1 atomic % Au or Cu due to Fe ion irradiation at 550 °C to a peak dose of 2.8 and 8.3 dpa is studied. The precipitation behavior of gold and copper and its correlation to the irradiation-induced defects is studied by transmission electron microscopy and variable energy positron annihilation spectroscopy (VEPAS). The increase of S parameters from VEPAS indicates the formation of open volume defects upon irradiation. Disc-shaped Au precipitates, grown from the irradiation induced dislocations, are observed in the Fe-Au alloy. In the Fe-Cu alloy, spherical Cu particles are formed but no direct connection between Cu precipitates and radiation damage is detected. For the Fe-Au alloy, the surface hardness dramatically increases for a dose of 2.8 dpa, with a slight decrease as the irradiation dose is enhanced to 8.3 dpa. In the Fe-Cu alloy, radiation hardening increases continuously.

In this paper, we propose an efficient revocable Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme. We base on the direct revocation approach, by embedding the revocation list into ciphertext. However, since the revocation list will grow longer as time goes by, we further leverage this by proposing a secret key time validation technique so that users will have their keys expired on a date and the revocation list only needs to include those user keys revoked before their intended expired date (e.g. those user keys which have been stolen before expiry). These keys can be removed from the revocation list after their expiry date in order to keep the revocation list short, as these keys can no longer be used to decrypt ciphertext generated after their expiry time. This technique is derived from Hierarchical Identity-based Encryption (HIBE) mechanism and thus time periods are in hierarchical structure: year, month, day. Users with validity of the whole year can decrypt any ciphertext associated with time period of any month or any day within the year. By using this technique, the size of public parameters and user secret key can be greatly reduced. A bonus advantage of this technique is the support of discontinuity of user validity (e.g. taking no-paid leave).

Ciphertext-policy attribute-based encryption (CP-ABE) is a well-known cryptographic technology for guaranteeing data confidentiality but also fine-grained data access control. It enables data owners to define flexible access policy for cloud-based data sharing. However, the user revocation and attribute update problems existing in CP-ABE systems that are long-standing unsolved in the literature. In this paper, we propose the first access control (CP-ABE) scheme supporting user revocability and attribute update. Specifically, the user revocation is defined in the identity-based setting that does not conflict our attribute-based design. The cost brought by attribute update is efficient in the sense that we only concentrate on the update of the ciphertexts associated with the corresponding updated attribute. Moreover, the security analysis shows that the proposed scheme is secure under the decisional Bilinear Diffie-Hellman assumption.