The Review of using Unified BPM Cycle for Public Credit Recovery Activites

Business Process Management (BPM) is a combination of Information Technology and management science, which applies to improve business process in order to improve operational excellence and business performances [1] leading to process automation. This review article based is on a case study in application of business governance in public sector organization, which was conducted by Abimael R. Do Nascimento, Roquemar de Lima Baldam, Lourenço Costa and Thalmo de Paiva Coelho Junior in 2018. The article analyzes the implementation of unified BPM in operational activities in a federal public advocacy body with evaluating corporate governance practices of the process. The study used mix method approach to gather data and to analyze them. Findings revealed the requirement of corporate governance practices, prioritizing BMP and auditing process

Automatic conversion of activity diagrams into flexible smart home apps

Despite the availability of a large number of sensor and actuator devices designed to co-perform in a smart home, only a few of these devices are easily integrated into a single smart home unit. However, as devices become more advanced and feature-rich, the need for smart software to orchestrate these devices to offer complex smart home services has risen. The research focus of this thesis is designing and deploying software (or apps) that works with different and changing, sensor-actuator configurations in smart-homes. A systematic literature review was used to identify a visual design modeling framework for designing smart home apps. Behavioral models, specifically UML Activity Diagrams were identified as the most appropriate app design model due to high usability and similarity with flowcharts. The literature review also informed the key qualities of an end-to-end solution to design and deploy these smart home apps. Subsequently, we design and develop an automatic translation tool to address some key usability and deployment challenges. This tool offers a customized and fully-featured UML Activity Diagram Editor that allows non-experts to model any smart home system, such as a smart lighting system. The compiler offered by the Automatic Translation Tool accepts UML Activity Diagrams as input and generates executable Java code which can be deployed into any smart home application. An evaluation using a representative a set of case studies shows that the Automatic Translation Tool features high usability, availability, and performance

GPS assisted traffic alerting and road congestion reduction mechanism

Traffic congestion is one of the major problems which almost all people face in their day-to-day life. Resources such as time, fuel and money are wasted because of this problem. Both government and non-government parties have taken many actions to reduce this problem but it remains as it was. There are some solutions which provide non-live traffic alerts through mobile phones such as Dialog SatNav, Mobitel T-Navi. Alternative Traffic Alert (ANT) system provides real time traffic alerts to road travellers, giving them the best condition, cost beneficial and optimal alternative route to use at a time of a traffic jam. ANT Solution is implemented with the use of Global Positioning System (GPS) based vehicle tracking, vehicle motion based traffic condition evaluation, mobile and web technologies. This ANT approach supports any mobile phone while other available systems only limited to SMART phones. In addition ANT also performs cost, distance and velocity calculations before determining the best and optimal alternative route

Global dataset of soil organic carbon in tidal marshes.

Funder: The Nature Conservancy through the Bezos Earth Fund and other donor supportFunder: Nelson Mandela UniversityFunder: State Research Agency of Spain (AEI; CGL2007-64915), the Mancomunidad de los Canales del Taibilla (MCT), and the Science and Technology Agency of the Murcia Region (Seneca Foundation; 00593/PI/04 & 08739/PI/08).Funder: Scottish Government and UK Natural Environment Research Council C-SIDE project (grant NE/R010846/1)Funder: COOLSTYLE/CARBOSTORE projectFunder: New Zealand Ministry for Business, Innovation and Employment Contract #C01X2109Funder: Portuguese national funds from FCT - Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020, LA/P/0101/2020, and 2020.03825.CEECINDFunder: German Research Foundation (DFG project number: GI 171/25-1)Funder: State Research Agency of Spain (AEI; CGL2007-64915), the Mancomunidad de los Canales del Taibilla (MCT), the Science and Technology Agency of the Murcia Region (Seneca Foundation; 00593/PI/04 & 08739/PI/08), and a Ramón y Cajal contract from the Spanish Ministry of Science and Innovation (RYC2020-029322-I)Funder: Velux foundation (#28421, Blå Skove – Havets Skove som kulstofdræn)Funder: LIFE ADAPTA BLUES project Ref. LIFE18 CCA/ES/001160Funder: LIFE ADAPTA BLUES project Ref. LIFE18 CCA/ES/001160, support of national funds through Fundação para a Ciência e Tecnologia, I.P. (FCT), under the projects UIDB/04292/2020, UIDP/04292/2020, granted to MARE, and LA/P/0069/2020, granted to the Associate Laboratory ARNETFunder: Financial support provided by the Welsh Government and Higher Education Funding Council for Wales through the Sêr Cymru National Research Network for Low Carbon, Energy and Environment; as well as the Spanish Ministry of Science and Innovation (project PID2020-113745RB-I00) and FEDERFunder: South African Department of Science and Innovation (DSI)—National Research Foundation (NRF) Research Chair in Shallow Water Ecosystems (UID: 84375), and the Nelson Mandela UniversityFunder: I+D+i projects RYC2019-027073-I and PIE HOLOCENO 20213AT014 funded by MCIN/AEI/10.13039/501100011033 and FEDERFunder: Funding support from the Scottish Government and UK Natural Environment Research Council C-SIDE project (grant NE/R010846/1)Funder: Xunta de Galicia (GRC project IN607A 2021-06)Funder: U.S. Army Engineering, Research and Development Center (ACTIONS project, W912HZ2020070)Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha-1 in the top 30 cm and 231 ± 134 Mg SOC ha-1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies

Global dataset of soil organic carbon in tidal marshes

Funding: W.E.N.A. and C.S. would like to acknowledge funding support from the Scottish Government and UK Natural Environment Research Council C-SIDE project (grant NE/R010846/1).Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2±38.1 Mg SOC ha−1 in the top 30cm and 231±134 Mg SOC ha−1 in the top 1m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.Publisher PDFPeer reviewe

Database: Tidal Marsh Soil Organic Carbon (MarSOC) Dataset

The repository is formatted in the following structure: - README.md: markdown file with repository description - MarSOC-Dataset.Rproj: R project file - useful when using RStudio - Maxwell_MarSOC_dataset.csv: .csv file containing the final dataset. The data structure is described in the metadata file. It contains 17,454 records distributed amongst 29 countries. - Maxwell_MarSOC_dataset_metadata.csv: .csv file containing the main data file metadata (equivalent to Table 1). - data_paper/: folder containing the list of studies included in the dataset, as well as figures for this data paper (generated from the following R script: ‘reports/04_data_process/scripts/04_data-paper_data_clean.R’). - reports/01_litsearchr/: folder containing .bib files with references from the original naive search, a .Rmd document describing the litsearchr analysis using nodes to go from the naive search to the final search string, and the .bib files from this final search, which were then imported into sysrev for abstract screening. - reports/02_sysrev/: folder with .csv files exported from sysrev after abstract screening. These files contain the included studies with their various labels. - reports/03_data_format/: folder containing all original data, associated scripts, and exported data. - reports/04_data_process/: folder containing data processing scripts to bind and clean the exported data, as well as a script testing the different models for predicting soil organic carbon from organic matter and finalising the equation using all available data. A script testing and removing outliers is also included