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Abstract Background Breastfeeding should begin as soon as possible after birth and continue exclusively to 6 months of age. In Vietnam, as in many other countries, breastfeeding is decreasing because of modern lifestyles and the promotion of infant formula. It is important to provide mothers, family members, and the community with the knowledge and strategies to improve breastfeeding rates. Smartphones are almost ubiquitous in Vietnam and of the potential to provide information about breastfeeding. This study aimed to document the process of designing and developing a mobile app to increase breastfeeding rates in Vietnamese women. Methods We used a four-step mixed methods approach with a literature review, formative research (22 in-depth interviews and 49 self-administered online questionnaires), and testing of prototype apps (3 focus groups discussion and external experts). Formative research and focus group discussion involved 99 participants. Finally, the revisions of the app were tested. All of the formative research was undertaken in Hanoi in 2019–2020. Target behaviors followed by key determinants, to improve breastfeeding self-efficacy were studied and this information was then applied in developing the messages and library content. Barriers and facilitators to breastfeeding were identified from literature reviews and qualitative research. The messages were targeted at not only mothers but also included fathers, mothers-in-law, or families. Results Mothers were mostly concerned about the initiation of breastfeeding, preventing and reducing difficulties encountered during breastfeeding, and nutrition for breastfeeding mothers. Mental health and well-being in the postnatal period are also concerns. Three key features to be included in the app were identified from the formative research: (1) notifications; (2) an information library; and (3) a searching function. The research found that the app should be installed during pregnancy rather than after delivery (81% vs 17%, respectively). Notifications that convey breastfeeding messages should be sent 2–3 times per week. Conclusion The development of the app followed a best practice approach, including the involvement of stakeholders and grounding in behavior change theory. The next step is to evaluate the effectiveness of the BeBo mobile app in a well-conducted randomized controlled trial. Trial registration ACTRN12619000531112.

Building smart transportation services in urban cities has become a worldwide problem owing to the rapidly increasing global population and the development of Internet‐of‐Things applications. Traffic congestion and environmental concerns can be alleviated by sharing mobility, which reduces the number of vehicles on the road network. The taxi‐parcel sharing problem has been considered as an efficient planning model for people and goods flows. In this paper, we enhance the functionality of a current people‐parcel taxi sharing model. The adapted model analyzes the historical request data and predicts the current service demands. We then propose two novel online routing algorithms that construct optimal routes in real‐time. The objectives are to maximize (as far as possible) both the parcel delivery requests and ride requests while minimizing the idle time and travel distance of the taxis. The proposed online routing algorithms are evaluated on instances adapted from real Cabspotting datasets. After implementing our routing algorithms, the total idle travel distance per day was 9.64% to 12.76% lower than that of the existing taxi‐parcel sharing method. Our online routing algorithms can be incorporated into an efficient smart shared taxi system. [ABSTRACT FROM AUTHOR]

Standard tree-based genetic programming suffers from a structural difficulty problem in that it is unable to search effectively for solutions requiring very full or very narrow trees. This deficiency has been variously explained as a consequence of restrictions imposed by the tree structure or as a result of the numerical distribution of tree shapes. We show that by using a different tree-based representation and local (insertion and deletion) structural modification operators, that this problem can be almost eliminated even with trivial (stochastic hill-climbing) search methods, thus eliminating the above explanations. We argue, instead, that structural difficulty is a consequence of the large step size of the operators in standard genetic programming, which is itself a consequence of the fixed-arity property embodied in its representation. [ABSTRACT FROM AUTHOR]