A Hoffa fracture is an uncommon clinical entity typically seen in adults after high-energy trauma. Nonunion of a Hoffa fracture appears to be even more uncommon. To our knowledge, only three cases of nonunion of a Hoffa fracture have been documented in the literature to date, including two children and one adult. This article presents a case of an adult who had nonunion of a Hoffa fracture for 27 years and was treated by open reduction and internal fixation, and the varus deformity corrected with xenogenous bone graft. An excellent result has been achieved to date. This unusual case reminds us that we cannot neglect the possibility of nonunion of a cancellous bone fracture, especially the Hoffa fractures of the medial femoral condyle if they are treated nonoperatively. It also demonstrates that internal fixation with bone graft is effective, even for the 27-year Hoffa fracture.

With the development of information technology, the demand for information security is increasing. For more convenient and safer needs, the encryption technology based on biometrics has developed rapidly. Among them, iris technology has become an important research object of information security research due to the stability of iris characteristics and its difficulty in forgery. In this paper, the iris feature encryption technology based on the iris is studied by using the method of deep learning as the feature classification method and the iris feature as the research object. The simulation experiment is carried out by using the common iris database. The results show that the method can greatly improve the consistency of iris encryption and improve the security of encryption and decryption process.

As the Internet of Things (IoT) is predicted to deal with different problems based on big data, its applications have become increasingly dependent on visual data and deep learning technology, and it is a big challenge to find a suitable method for IoT systems to analyze image data. Traditional deep learning methods have never explicitly taken the color differences of data into account, but from the experience of human vision, colors play differently significant roles in recognizing things. This paper proposes a weight initialization method for deep learning in image recognition problems based on RGB influence proportion, aiming to improve the training process of the learning algorithms. In this paper, we try to extract the RGB proportion and utilize it in the weight initialization process. We conduct several experiments on different datasets to evaluate the effectiveness of our proposal, and it is proven to be effective on small datasets. In addition, as for the access to the RGB influence proportion, we also provide an expedient approach to get the early proportion for the following usage. We assume that the proposed method can be used for IoT sensors to securely analyze complex data in the future.

Nowadays, the aviation industry pays more attention to emission reduction toward the net-zero carbon goals. However, the volume of global passengers and baggage is exponentially increasing, which leads to challenges for sustainable airports. A baggage-free airport terminal is considered a potential solution in solving this issue. Removing the baggage operation away from the passenger terminals will reduce workload for airport operators and promote passengers to use public transport to airport terminals. As a result, it will bring a significant impact on energy and the environment, leading to a reduction of fuel consumption and mitigation of carbon emission. This paper studies a baggage collection network design problem using vehicle routing strategies and augmented reality for baggage-free airport terminals. We use a spreadsheet solver tool, based on the integration of the modified Clark and Wright savings heuristic and density-based clustering algorithm, for optimizing the location of logistic hubs and planning the vehicle routes for baggage collection. This tool is applied for the case study at London City Airport to analyze the impacts of the strategies on carbon emission quantitatively. The result indicates that the proposed baggage collection network can significantly reduce 290.10 tonnes of carbon emissions annually.

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