A Method for 3-D Printing Patient-Specific Prosthetic Arms With High Accuracy Shape and Size

Limb amputation creates serious emotional and functional damage to the one who lost a limb. For some upper limb prosthesis users, comfort and appearance are among the desired features. The objective of this paper is to develop a streamlined methodology for prosthesis design by recreating the shape and size of an amputated arm with high accuracy through 3-D printing and silicone casting. To achieve this, the computer tomography (CT) images of the patient’s affected and non-affected arms were scanned. Next, the geometry of the socket and the prosthetic arm were designed according the mirrored geometry of the non-affected arm through computer-aided design software. The support structure and the moulds were 3-D printed, and the prosthetic arm was casted with a silicone material. To validate the replication, the shape of the socket and prosthetic arm were quantitatively compared with respect to the source CT scan from the patient. The prosthetic arm was found to have high accuracy on the basis of the Dice Similarity Coefficient (DSC; 0.96), percent error (0.67%), and relative mean distance (0.34 mm, SD = 0.48 mm). Likewise, the socket achieved high accuracy based on those measures: DSC (0.95), percent error (2.97%), and relative mean distance (0.46 mm, SD = 1.70 mm) The liner, socket, and prosthetic arm were then shipped to the patient for fitting. The patient found the fit of the socket and the replication of the shape and the size of the prosthesis to be desirable. Overall, this paper demonstrates that CT imaging, computed-aided design, desktop 3-D printing, and silicone casting can achieve patient-specific cosmetic prosthetic arms with high accuracy.

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Linguistic Feature Based Filtering Mechanism for Recommending Posts in a Social Networking Group

Online social networks have spawned myriads of online social groups, where people can interact and exchange their ideas. However, the major issues that interfere with the user security and comfort are privacy breach, groups without opt-in options, clutter created out of numerous groups in which a user is a member of and difficulty in managing group principles. This can be lessened to an extent by an automated filtering mechanism capable of categorizing members within a group based on their pattern of response. In our proposed method, the posts within a group are clustered based on stylistic, thematic, emotional, sentimental, and psycholinguistic aspects. Then the members of the group are categorized based on their response to the posts belonging to different aspects as mentioned above. This results in categories of individuals within a group, who are like minded. The categorization caters to the most important issues related to soft security , such as the clutter associated with irrelevant notifications received from multiple groups, by suggesting the users, posts that are likely to be of interest to them. It also helps to identify the group members intended towards spreading posts that violate group policies. The categorization exhibits satisfiable performance in case of large number of candidate members in a populous group by performing clustering based on linguistic features. The double level of clustering, based on the posts and response of users based on the aspects of the posts, enhances the performance of the system, hence outperforming traditional recommender systems. The system has been tested on Facebook group data, where it offers a significant solution to an unaddressed problem associated with social networking groups.

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An Open Framework for Participatory PM2.5 Monitoring in Smart Cities

As the population in cities continues to increase rapidly, air pollution becomes a serious issue from public health to social economy. Among all pollutants, fine particulate matters (PM2.5) directly related to various serious health concerns, e.g., lung cancer, premature death, asthma, and cardiovascular and respiratory diseases. To enhance the quality of urban living, sensors are deployed to create smart cities. In this paper, we present a participatory urban sensing framework for PM2.5 monitoring with more than 2500 devices deployed in Taiwan and 29 other countries. It is one of the largest deployment project for PM2.5 monitor in the world as we know until May 2017. The key feature of the framework is its open system architecture, which is based on the principles of open hardware, open source software, and open data. To facilitate the deployment of the framework, we investigate the accuracy issue of low-cost particle sensors with a comprehensive set of comparison evaluations to identify the most reliable sensor. By working closely with government authorities, industry partners, and maker communities, we can construct an effective eco-system for participatory urban sensing of PM2.5 particles. Based on our deployment achievements to date, we provide a number of data services to improve environmental awareness, trigger on-demand responses, and assist future government policymaking. The proposed framework is highly scalable and sustainable with the potential to facilitate the Internet of Things, smart cities, and citizen science in the future.

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A Multi Criteria-Based Approach for Virtual Machines Consolidation to Save Electrical Power in Cloud Data Centers

Consolidation of virtual machines is used to reduce the power consumed in cloud computing systems. In consolidation, some virtual machines are migrated from some source servers to other destination servers and source servers are turned off. Most current consolidation approaches depend on the utilization of servers to determine both source and destination servers. In this paper, a consolidation approach that depends on multiple criteria is proposed and evaluated. The approach has one algorithm for determining source servers and another algorithm for determining destination servers. Simulations experiments show relevant improvements over utilization-based approach in terms of throughput, power consumption, monetary cost, and scalability by 21%, 12%, 24%, and 37%, respectively.

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