Abstract: Lands are valuable & limited resource which constantly changes with the growth of the population. An efficient and good land management system is essential to avoid conflicts associated with lands. This paper aims to design the prototype model of a Mobile GIS Land use and Land Information System in real-time. Homagama Divisional Secretariat Division situated in the western province of Sri Lanka was selected as the study area. The prototype model was developed after reviewing related literature. The methodology was consisted of designing and modeling the prototype model into an application running on a mobile platform. The system architecture mainly consists of a Google mapping app for real-time updates with firebase support tools. Thereby, the method of implementation consists of front-end and back-end components. Software tools used in designing applications are Android Studio with JAVA based on GeoJSON File structure. Android Studio with JAVA in GeoJSON File Synchronize to Firebase was found to be the perfect mobile solution for continuously updating Land use and Land Information System (LIS) in real-time in the present scenario. The mobile-based land use and LIS developed in this study are multiple user applications catering to different hierarchy levels such as basic users, supervisory managers, and database administrators. The benefits of this mobile mapping application will help public sector field officers with non-GIS expertise to overcome the land use planning challenges with land use updated in real-time.
Abstract: This paper presents an approach to reduce some of its current flaws in the requirements phase inside the software development process. It takes the software requirements of an application, makes a conceptual modeling about it and formalizes it within JSON documents. This formal model is lodged in a NoSQL database which is document-oriented, that is, MongoDB, because of its advantages in flexibility and efficiency. In addition, this paper underlines the contributions of the detailed approach and shows some applications and benefits for the future work in the field of automatic code generation using model-driven engineering tools.
Abstract: High Performance Liquid Chromatography (HPLC)
method was developed and validated for simultaneous estimation of
6-Gingerol(6G) and 6-Shogaol(6S) in joint pain relief gel containing
ginger extract. The chromatographic separation was achieved by
using C18 column, 150 x 4.6mm i.d., 5μ Luna, mobile phase
containing acetonitrile and water (gradient elution). The flow rate
was 1.0 ml/min and the absorbance was monitored at 282 nm. The
proposed method was validated in terms of the analytical parameters
such as specificity, accuracy, precision, linearity, range, limit of
detection (LOD), limit of quantification (LOQ), and determined
based on the International Conference on Harmonization (ICH)
guidelines. The linearity ranges of 6G and 6S were obtained over 20-
60 and 6-18 μg/ml respectively. Good linearity was observed over the
above-mentioned range with linear regression equation Y= 11016x-
23778 for 6G and Y = 19276x-19604 for 6S (x is concentration of
analytes in μg/ml and Y is peak area). The value of correlation
coefficient was found to be 0.9994 for both markers. The limit of
detection (LOD) and limit of quantification (LOQ) for 6G were
0.8567 and 2.8555 μg/ml and for 6S were 0.3672 and 1.2238 μg/ml
respectively. The recovery range for 6G and 6S were found to be
91.57 to 102.36 % and 84.73 to 92.85 % for all three spiked levels.
The RSD values from repeated extractions for 6G and 6S were 3.43
and 3.09% respectively. The validation of developed method on
precision, accuracy, specificity, linearity, and range were also
performed with well-accepted results.
Abstract: This paper describes the tradeoffs and the design from
scratch of a self-contained, easy-to-use health dashboard software
system that provides customizable data tracking for patients in smart
homes. The system is made up of different software modules and
comprises a front-end and a back-end component. Built with HTML,
CSS, and JavaScript, the front-end allows adding users, logging into
the system, selecting metrics, and specifying health goals. The backend
consists of a NoSQL Mongo database, a Python script, and a
SimpleHTTPServer written in Python. The database stores user
profiles and health data in JSON format. The Python script makes use
of the PyMongo driver library to query the database and displays
formatted data as a daily snapshot of user health metrics against
target goals. Any number of standard and custom metrics can be
added to the system, and corresponding health data can be fed
automatically, via sensor APIs or manually, as text or picture data
files. A real-time METAR request API permits correlating weather
data with patient health, and an advanced query system is
implemented to allow trend analysis of selected health metrics over
custom time intervals. Available on the GitHub repository system,
the project is free to use for academic purposes of learning and
experimenting, or practical purposes by building on it.