Route Optimization of MSW Collection and Transport Using a GIS-Based Analysis on the Tourism Island

The Municipal Solid Waste (MSW) management on Si Chang Island is challenging in terms of its limited land resources, high cost of waste treatment, and seasonal fluctuations in waste volumes from tourists and shipping activities. There are sufficient waste bins available to cover MSW production on the island. The downside of the management is an inappropriate open dumping site that is prone to environmental pollution and health risk. However, resilience is shown in the implementation of an integrated approach of waste separation, composting, and incineration. This study developed a complete road network and applied a network analyst extension, which was useful in the area of optimization of MSW collection and transport. Two optimal routes were shown for MSW collection. Two vehicles were utilized to collect about 10 tons of MSW per day in two trips. A total travel distance for one-day transportation was 38.5 km. Carbon dioxide (CO2) emission from vehicles during MSW collection and transport was 0.85 g/km, accounting for 119 t CO2/yr

Land Use Change Monitoring and Modelling using GIS and Remote Sensing Data for Watershed Scale in Thailand

Landsat 7 Enhanced Thematic Mapper (ETM), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images obtained in 1991, 2005 and 2014 with maps, and field survey data were used to classify land use and land cover (LULC) changes over 23 years and predict soil erosion risk locations in the Khlong Kui watershed (73,700 ha), Prachuap Khiri Khan province, Thailand. Classified images together with soil features, slope and rainfall data were used to identify potential risk areas of soil erosion. Based on field check data, the overall classification accuracy was accessed from random samples that resulted as 80% for 1991, 83% for 2005 and 86% for 2014. The study discovered that rice field and rangeland increased by 1.12 and 2.81%, respectively, deciduous forest, and on the other hand, it decreased by 8.28%. GIS analysis identified the potential risk areas of soil erosion as 46,431 ha (0.63%) at very high risk

Evaluating search key distribution impact on searching performance in large data streams

The distribution pattern of search keys is assessed in this study by contrasting four methods of index searching on large-scale JSON files with data streams. The Adelson-Velskii and Landis (AVL) tree, binary search tree (BST), linear search (LS), and binary search (BS) are among the search strategies. We look at the normal distribution, left-skewed distribution, and right-skewed distribution of search-key distributions. According to the results, LS performs the slowest, averaging 653.166 milliseconds, whereas AVL tree performs better than the others in dense index, with an average search time of 0.005 milliseconds. With 0.011 milliseconds per keyword for sparse index, BS outperforms LS, which averages 1007.848 milliseconds. For dense indexing, an AVL tree works best; for sparse indexing, BS is recommended

Phuket mascot design from based on cultural value

Designing with cultural, wisdom, way of life, beliefs, and other values that have been passed down through generations. Let's produce a creative product that is entertaining and varied. One of them is to design a mascot to be used in the media as a character and representation of something, and to imply that this mascot will be able to act on behalf of those representatives. It is clear that Phuket has an exceptional and valuable cultural capital. In addition, there are numerous traditions, rituals, beliefs, customs, arts, and civilizations that are indigenous to the local population. It was thought that this study did a good job of figuring out how to use cultural values to make characters by applying design theory or design science to creativity. It also did a good job of figuring out how applicable the methods and design approaches were

Exploring spatial patterns and hotspots of diarrhea in Chiang Mai, Thailand

<p>Abstract</p> <p>Background</p> <p>Diarrhea is a major public health problem in Thailand. The Ministry of Public Health, Thailand, has been trying to monitor and control this disease for many years. The methodology and the results from this study could be useful for public health officers to develop a system to monitor and prevent diarrhea outbreaks.</p> <p>Methods</p> <p>The objective of this study was to analyse the epidemic outbreak patterns of diarrhea in Chiang Mai province, Northern Thailand, in terms of their geographical distributions and hotspot identification. The data of patients with diarrhea at village level and the 2001–2006 population censuses were collected to achieve the objective. Spatial analysis, using geographic information systems (GIS) and other methods, was used to uncover the hidden phenomena from the data. In the data analysis section, spatial statistics such as quadrant analysis (QA), nearest neighbour analysis (NNA), and spatial autocorrelation analysis (SAA), were used to identify the spatial patterns of diarrhea in Chiang Mai province. In addition, local indicators of spatial association (LISA) and kernel density (KD) estimation were used to detect diarrhea hotspots using data at village level.</p> <p>Results</p> <p>The hotspot maps produced by the LISA and KD techniques showed spatial trend patterns of diarrhea diffusion. Villages in the middle and northern regions revealed higher incidences. Also, the spatial patterns of diarrhea during the years 2001 and 2006 were found to represent spatially clustered patterns, both at global and local scales.</p> <p>Conclusion</p> <p>Spatial analysis methods in GIS revealed the spatial patterns and hotspots of diarrhea in Chiang Mai province from the year 2001 to 2006. To implement specific and geographically appropriate public health risk-reduction programs, the use of such spatial analysis tools may become an integral component in the epidemiologic description, analysis, and risk assessment of diarrhea.</p

Enhancing data retrieval efficiency in large-scale javascript object notation datasets by using indexing techniques

The use of javascript object notation (JSON) format as a not only structured query language (NoSQL) storage solution has grown in popularity, but has presented technical challenges, particularly in indexing large-scale JSON files. This has resulted in slow data retrieval, especially for larger datasets. In this study, we propose the use of JSON datasets to preserve data in resource survey processes. We conducted experiments on a 32-gigabyte dataset containing 1,000,000 transactions in JSON format and implemented two indexing methods, dense and sparse, to improve retrieval efficiency. Additionally, we determined the optimal range of segment sizes for the indexing methods. Our findings revealed that adopting dense indexing reduced data retrieval time from 15,635 milliseconds to 55 milliseconds in one-to-one data retrieval, and from 38,300 milliseconds to 1 millisecond in the absence of keywords. In contrast, using sparse indexing reduced data retrieval time from 33,726 milliseconds to 36 milliseconds in one-to-many data retrieval and from 47,203 milliseconds to 0.17 milliseconds when keywords were not found. Furthermore, we discovered that the optimal segment size range was between 20,000 and 200,000 transactions for both dense and sparse indexing

Web access monitoring mechanism via Android WebView for threat analysis

Many Android apps employ WebView, a component that enables the display of web content in the apps without redirecting users to web browser apps. However, WebView might also be used for cyberattacks. Moreover, to the best of our knowledge, although some countermeasures based on access control have been reported for attacks exploiting WebView, no mechanism for monitoring web access via WebView has been proposed and no analysis results focusing on web access via WebView are available. In consideration of this limitation, we propose a web access monitoring mechanism for Android WebView to analyze web access via WebView and clarify attacks exploiting WebView. In this paper, we present the design and implementation of this mechanism by modifying Chromium WebView without any modifications to the Android framework or Linux kernel. The evaluation results of the performance achieved on introducing the proposed mechanism are also presented here. Moreover, the result of threat analysis of displaying a fake virus alert while browsing websites on Android is discussed to demonstrate the effectiveness of the proposed mechanism

Spatially-explicit and spectral soil carbon modeling in Florida.

Profound shifts have occurred over the last three centuries in which human actions have become the main driver to global environmental change. In this new epoch, the Anthropocene, human-driven changes such as population growth, climate and land use change, are pushing the Earth system well outside its normal operating range causing severe and abrupt environmental change. In this context, we present research highlights from Florida (150,000 km2) showing how anthropogenic-induced changes have had major impacts on carbon dynamics in soils, including (i) modeling of carbon and nutrient dynamics and soil carbon sequestration impacted by climate and land use change; (ii) geospatial assessment of soil carbon stocks and pools, and (iii) spectral-based soil carbon modeling. Our research is embedded in the STEP-AWBH modeling concept which explicitly incorporates Human forcings and time-dependent evolution of Atmospheric, Water, and Biotic factors into the modeling process. Spatially-explicit soil carbon observations were fused with ancillary environmental data and various statistical and geostatistical methods were used to upscale soil carbon across the region. Our results suggest that soil hydrologic and taxonomic, biotic (vegetation and land use), and climatic properties show complex interactions explaining the variation of soil carbon within this heterogeneous subtropical landscape

The Terrestrial Carbon (Terra C) Information System to facilitate carbon synthesis across heterogeneous landscapes.

There are urgent needs to better synthesize knowledge and data across large regions and time periods to address global climate change, conduct soil/terrestrial carbon accounting, model carbon dynamics, assess carbon sequestration, and develop strategies for mitigation and adaptation. To address these needs we developed the Terrestrial Carbon (TerraC) Information System dedicated to advance soil/terrestrial carbon science. TerraC offers user-friendly tools to upload, store, manage, query, analyze, and download lab and field data characterizing carbon in soils, plants/biomass, atmosphere, water, and whole ecosystems. The purpose of TerraC is three-fold to: (i) advance carbon science through sharing of carbon and ancillary environmental data; (ii) facilitate environmental synthesis; and (iii) enhance collaboration among students, faculty, scientists, and extension specialists through shared resources. Data and metadata stored in TerraC can be shared privately among selected users (groups) or publicly with any user. We integrated various spatially-explicit soil carbon and ancillary environmental data collected in Florida representing different time periods, and conducted a synthesis analysis on soil carbon that will be presented as a case study. Detailed information about TerraC and data sharing options are available at: http://TerraC.ifas.ufl.edu