Search arXivSearch

arXiv · 1402.6282

Mobile GIS and Open Source Platform Based on Android: Technology for System Pregnant Women

Abstract

The statistic of World Health Organization shows at one year about 287000 women died most of them during and following pregnancy and childbirth in Africa and south Asia. This paper suggests system for serving pregnant women using open source based on Android technology, the proposed system works based on mobile GIS to select closest care centre or hospital maternity on Google map for the pregnant woman, which completed an online registration by sending SMS via GPRS network (or internet) contains her name and phone number and region (Longitude and Latitude) and other required information the server will save the information in server database then find the closest care centre and call her for first review at the selected care centre, the proposed system allowed the pregnant women from her location (home, market, etc) can send a help request in emergency cases (via SMS by click one button) contains the ID for this pregnant woman, and her coordinates (Longitude and Latitude) via GPRS network, then the server will locate the pregnant on Google map and retrieve the pregnant information from the database. This information will be used by the server to send succoring to pregnant woman at her location and at the same time notify the nearest hospital and moreover, the server will send SMS over IP to inform her husband and the hospital doctors. Implement and applied this proposed system of pregnant women shows more effective cost than other systems because it works in economic mode (SMS), and the services of proposed system are flexible (open source platform) as well as rapidly (mobile GIS based on Android) achieved locally registration, succoring in emergency cases, change the review date of pregnant woman, addition to different types of advising according to pregnancy. Index Terms: Build-in GPS; GPRS; Mobile GIS; SoIP; Open Source; Google Maps API ; Android Technology

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ayad Ghany Ismaeel, Nur Gaylan Hamead. 2014-02-25. Mobile GIS and Open Source Platform Based on Android: Technology for System Pregnant Women. https://doi.org/10.14299/000000

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A Brief AI Literacy Intervention Does Not Significantly Reduce Over-Reliance and Increases Under-Reliance on ChatGPT: A Randomized Study

In this study, we examined whether a brief AI literacy intervention influences high school students' reliance on recommendations from large language models (LLMs). In a randomized experiment, students were assigned to either a control group receiving a brief introduction to LLMs or an intervention group receiving additional information about how LLMs work, their limitations, and effective usage strategies. Participants then solved eight math puzzles with ChatGPT's advice, which was incorrect in half of the trials. Results indicated widespread over-reliance, with incorrect recommendations adopted in 52.1% of the trials. The intervention did not significantly reduce over-reliance. Instead, it led to an increase in under-reliance, as students were more likely to reject correct recommendations. These findings provide preliminary evidence that brief text-based interventions may be ineffective in fostering appropriate reliance. More comprehensive and interactive approaches may be required to meaningfully influence students' real-world reliance on LLMs.

cs.CY

Your Programming Students' Cognition with ChatGPT: Higher Performance, Lower Retention, and Reduced Ownership

Generative AI can improve students' programming performance, but successful task completion may not reflect what they retain. We examined performance, retention, cognitive load, and ownership in a controlled between-subjects experiment with 59 undergraduate computer science students, 55 were retained for analysis. Participants completed three introductory C programming tasks with access to ChatGPT-4.5 or conventional web search without generative AI. We measured task performance, self-reported mental effort and difficulty, pupillary responses, heart rate variability, and ownership, and assessed cued recall immediately and 48 hours later. ChatGPT-assisted students achieved higher coding scores (89% vs. 69%) but lower recall scores immediately (41% vs. 53%) and after 48 hours (39% vs. 52%). There was no significant difference in the loss of recall information over 48 hours between the groups. Self-reported mental effort increased less across tasks in the ChatGPT condition (Holm-adjusted p = .047), and students attributed less of the submitted code to themselves (45% vs. 81%). Confirmatory physiological tests did not detect significant differences in trajectories between conditions; substantial data loss limits their interpretation. These findings reveal a gap between assisted task performance and subsequent recall and sense of ownership in this setting. They motivate the need for assessment practices and AI learning tools that require students to explain, retrieve, and contribute to the work they submit as active participants in their education.

cs.CY

Open Platform Field Experiments: Expanding the Design Space of Experimental Research on Social Media

Despite a growing demand for causal evidence about social media, independent researchers remain severely constrained in their ability to conduct experiments directly on online platforms. To cope, multiple methodological workarounds have emerged - from controlled surveys and simulations to client-side overlays and platform partnerships - each requiring distinct trade-offs between desirable experimental properties. The recent emergence of open social media platforms offers a qualitatively different methodological opportunity. Here we propose a design space of social media experimentation and discuss Open Platform Field Experiments (OPFEs). OPFEs represent a distinct class of experimental approaches that enable independent researchers to directly intervene on functional platform components - such as clients, recommendation systems, and moderation services - within live social media environments. Through a comparative analysis of experimental archetypes, we show that OPFEs occupy a previously unexplored region of the design space. We then bridge theory and practice by characterizing the architectural and governance elements that enable OPFEs, mapping them onto Bluesky and the AT Protocol, and illustrating the end-to-end lifecycle of a complete OPFE design. Overall, this work establishes OPFEs as a practical methodological paradigm for independent, transparent, and ecologically grounded experimentation on open social media.

cs.CY