Background There are over 2 million newly diagnosed patients with breast cancer worldwide with more than 10,000 cases in Taiwan each year. During 2017-2018, the National Yang-Ming University, the Taiwan University of Science and Technology, and the Taiwan Breast Cancer Prevention Foundation collaborated to develop a breast cancer self-management support (BCSMS) mHealth app for Taiwanese women with breast cancer. Objective The aim of this study was to investigate the quality of life (QoL) of women with breast cancer in Taiwan after using the BCSMS app. Methods After receiving a first diagnosis of breast cancer, women with stage 0 to III breast cancer, who were recruited from social networking sites or referred by their oncologists or oncology case managers, were randomized 1:1 into intervention and control groups. Intervention group subjects used the BCSMS app and the control group subjects received usual care. Two questionnaires—the European Organization for Research and Treatment of Cancer (EORTC) Quality-of-Life Questionnaire Core 30 (QLQ-C30) and the EORTC Breast Cancer-Specific Quality-of-Life Questionnaire (QLQ-BR23)—were distributed to subjects in both arms. Paper-based questionnaires were used at baseline; paper-based or Web-based questionnaires were used at 1.5-month and 3-month follow-up evaluations. All evaluations were self-assessed and anonymous, and participants were blinded to their allocation groups. Descriptive analysis, the Pearson chi-square test, analysis of variance, and the generalized estimating equation were used to analyze the data. Missing values, with and without multi-imputation techniques, were used for sensitivity analysis. Results A total of 112 women were enrolled and randomly allocated to either the experimental group (n=53) or control group (n=59). The follow-up completion rate was 89.3% (100/112). The demographic data showed homogeneity between the two groups in age (range 50-64 years), breast cancer stage (stage II), marital status (married), working status (employed), and treatment status (receiving treatments). The mean total QoL summary scores from the QLQ-C30 (83.45 vs 82.23, P=.03) and the QLQ-BR23 (65.53 vs 63.13, P=.04) were significantly higher among the experimental group versus the control group, respectively, at 3 months. Conclusions This research provides support for using a mobile health care app to promote the QoL among women in Taiwan after a first diagnosis of breast cancer. The BCSMS app could be used to support disease self-management, and further evaluation of whether QoL is sustained is warranted. Trial Registration ClinicalTrials.gov NCT004174248; https://clinicaltrials.gov/ct2/show/NCT04174248

Abstract-Current traffic engineering in SDN mostly focuses on unicast. By contrast, compared with individual unicast, multicast can effectively reduce network resources consumption to serve multiple clients jointly. Since many important applications require reliable transmissions, it is envisaged that reliable multicast plays a crucial role when an SDN operator plans to provide multicast services. However, the shortest-path tree (SPT) adopted in current Internet is not bandwidth-efficient, while the Steiner tree (ST) in Graph Theory is not designed to support reliable transmissions since the selection of recovery nodes is not examined. In this paper, therefore, we propose a new reliable multicast tree for SDN, named Recover-aware Steiner Tree (RST). The goal of RST is to minimize both tree and recovery costs, while finding an RST is very challenging. We prove that the RST problem is NPHard and inapproximable within k, which is the number of destination nodes. Thus, we design an approximate algorithm, called Recover Aware Edge Reduction Algorithm (RAERA), to solve the problem. The simulation results on real networks and large synthetic networks, together with the experiment on our SDN testbed with real YouTube traffic, all manifest that RST outperforms both SPT and ST. Also, the implementation of RAERA in SDN controllers shows that an RST can be returned within a few seconds and thereby is practical for SDN networks.

Background Evidence has shown that breast cancer self-management support from mobile health (mHealth) apps can improve the quality of life of survivors. Although many breast cancer self-management support apps exist, few papers have documented the procedure for the development of a user-friendly app from the patient’s perspective. Objective This study aimed to investigate the information needs of Taiwanese women with breast cancer to inform the development of a self-management support mHealth app. Methods A 5-step design thinking approach, comprising empathy, define, ideate, prototype, and test steps, was used in the focus groups and individual interviews conducted to collect information on the requirements and expectations of Taiwanese women with breast cancer with respect to the app. A thematic analysis was used to identify information needs. Results A total of 8 major themes including treatment, physical activity, diet, emotional support, health records, social resources, experience sharing, and expert consultation were identified. Minor themes included the desire to use the app under professional supervision and a trustworthy app manager to ensure the credibility of information. Conclusions The strengths of the design thinking approach were user-centered design and cultural sensitivity. The results retrieved from each step contributed to the development of the app and reduction of the gap between end users and developers. An mHealth app that addresses these 8 main themes can facilitate disease self-management for Taiwanese women with breast cancer.

Although Software-Defined Networking (SDN) enables flexible network resource allocations for traffic engineering, current literature mostly focuses on unicast communications. Compared to traffic engineering for multiple unicast flows, multicast traffic engineering for multiple trees is very challenging not only because minimizing the bandwidth consumption of a single multicast tree by solving the Steiner tree problem is already NP-Hard, but the Steiner tree problem does not consider the link capacity constraint for multicast flows and node capacity constraint to store the forwarding entries in Group Table of OpenFlow. In this paper, therefore, we first study the hardness results of scalable multicast traffic engineering in SDN. We prove that scalable multicast traffic engineering with only the node capacity constraint is NP-Hard and not approximable within δ, which is the number of destinations in the largest multicast group. We then prove that scalable multicast traffic engineering with both the node and link capacity constraints is NP-Hard and not approximable within any ratio. To solve the problem, we design a δ-approximation algorithm, named Multi-Tree Routing and State Assignment Algorithm (MTRSA), for the first case and extend it to the general multicast traffic engineering problem. The simulation and implementation results demonstrate that the solutions obtained by the proposed algorithm outperform the shortest-path trees and Steiner trees. Most importantly, MTRSA is computation-efficient and can be deployed in SDN since it can generate the solution with numerous trees in a short time.

Abstract-This paper investigates the issue of interference mitigation in wireless body area networks (BANs). Although several approaches have been proposed in BAN standard IEEE 802.15.6, they increase transmission latency or energy cost, and do not mitigate interference effectively. In order to avoid both intra-and inter-BAN interference, we present a MAC protocol with two-layer interference mitigation (2L-MAC) for BANs. Considering the QoS requirements of BANs, the proposed protocol not only avoids packet collisions but also reduces transmission delay and energy consumption in sensors. Moreover, channel switching is triggered whenever a BAN needs to acquire more bandwidth. Simulation results show that our protocol outperforms other protocols in terms of delivery rate, latency and energy saving.

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Recent years have seen a tremendous growth in the volume of video traffic in mobile settings. In this paper, we present the design of a mobile video-centric proxy cache, named iProxy, that offers improved performance in terms of both hit rates and streaming quality. Our thesis in designing iProxy is that we need to elevate the traditional view of caching from "data" to "information" in order to optimally meet the stringent requirements of video streaming in mobile settings. iProxy relies on recent advances on informationbound references (IBRs) to collapse multiple related cache entries into a single one, improving hitrate while lowering storage costs. iProxy incorporates a novel dynamic linear rate adaptation scheme to ensure high stream quality in face of channel diversity and device heterogeneity. Our evaluation of iProxy using realistic traffic traces shows that it can improve hitrate, but we need to use novel information-aware replacement policies for optimal performance. We show that our linear encoder can adapt well to changes in bandwidth, and yield better bit rates, lower buffering and lower start up delays than state-of-the-art schemes.

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