Decision Support Information Research Articles

Identifying Plume Mode via WSR-88D Observations of Wildland Fire Convective Plumes and Proposed Tactical Decision Support Applications

To date, the use of Doppler radar (WSR-88D) in wildland fire operations has been limited, with tactical applications focused on analyzing ambient atmospheric features. This paper presents geographically diverse analysis of radar-observed wildland fire convective plumes to determine indicators of plume mode for tactical decision support. Through the visualization of buoyancy via thermal bubbles and vertical plumes, plume mode is revealed via WSR-88D interrogation of three Southern Great Plains grass/shrub fires and two timber fires in Texas and California. Analogous to thunderstorm convective modes, past research has identified two distinct plume modes of wildland fire: multicell and intense convective plume. Multicell plume mode is characterized by a series of shallow discrete cells that move away from the fire’s main buoyancy source, with successive cells rising, expanding, and replacing cells from the updraft source. This process, known as the thermal bubble concept, occurs most notably in strong vertical wind profile environments with a strong advection component. These cells or thermal bubbles are observed via WSR-88D data for three Southern Great Plains cases. Intense convective plumes are observed to be vertical with the low-level reflectivity maximum and maximum echo top juxtaposed and occurrence is confined to weak wind environments; these plume structures are identified in the two timber fire cases. An important WSR-88D signature, the back-sheared convective plume (hereafter BSCP), is identified in terms of transverse vortices and vortex rings, which may imply enhanced combustion rates due to increased turbulent mixing. Determination of plume convective mode via radar offers meteorologists the ability to detect changes in plume mode and to provide important tactical decision support information about fire behavior.

Web-Based Decision Support Information System Design of Lecturer Performance Assessment at the Faculty of Islamic Studies, Islamic University Attahiriyah

Faculty of Islamic Attahiriyah Islamic University (UNIAT) Jakarta is one of the private universities under the auspices of the Region I Jakarta Coordinator of Islamic Religion (KOPERTAIS), In the process of evaluating the performance of lecturers in the Islamic Faculty of Islamic University of Attahiri, every year. Lecturers who are selected as lecturers by having excellent performance appraisals will later get an award from the Faculty leadership (Dean of the Faculty of Islamic Religion). From this process, it takes a long time to process the calculation of who the lecturer is by assessing his excellent performance and also documents stored in the form of files stored on the archive shelf. One problem solving to find a solution from the system that runs above is by designing a decision support system application for lecturer performance assessment using a web-based Weighted Product (WP) method so that the lecturer performance appraisal can be done in real time and efficiently. Software The application of lecturer performance appraisal decision support can be applied directly as a problem solving solution, carry out maintenance on the application regularly every month so that something or damage occurs so that if a problem occurs it can restore the database again.

Application for Employee Performance Assessment Using Profile Matching Method

Human resources an important role for the agency. Good employee performance can provide a good image for the company. Many companies give rewards or rewards to their employees for their work performance. The assessment is done in addition to giving or appreciation as well as motivation for employees to work better. Problems that often occur in the employee appraisal process are the large number of employees and the criteria assessed and data processing are still in conventional process so that takes a long time and the results of the assessment are still not objective. To overcome, application was built could simplify the employee performance appraisal process. The method used is Profile Matching to assess and determine employees who excel. Factors or criteria used in the form of performance, discipline, honesty, years of service, cooperation. Profile matching is broadly the process of comparing the actual data value of a profile to be assessed with the expected profile value. Application is built based on web with PHP programming and MYSQL. To help the process of employee performance appraisal at Universitas Prima Indonesia. Collecting data in research using literature studies, observations, interviews, and sampling. Result the research is Profile Matching Method can be use for Decision Support System in determining employee achievement, with the highest calculation results in the sample data obtained by A3 and the lowest position obtained by A1. In academic, research can be an enrichment of teaching materials especially in subject of Decision Support Systems and information systems in general.

County-level indoor radon concentration mapping and uncertainty assessment in South Korea using geostatistical simulation and environmental factors

This paper presents a geostatistical simulation approach to not only map the county-level indoor radon concentration (IRC) distributions in South Korea, but also quantify the uncertainty that can be used as decision-supporting information. For county-level IRC mapping in South Korea, environmental factors including geology, radium concentration in surface soil, gravel content in subsoil, and fault line density, which are known to be associated with the source and migration of radon gas, were incorporated into IRC measurements using multi-Gaussian kriging with local means. These four environmental factors could account for about 36% of the variability of noise-filtered IRCs, implying that regional variations of IRCs were affected by these factors. Sequential Gaussian simulation was then applied to generate alternative realizations of county-level IRC distributions. By summarizing the multiple simulation results, we identified some counties that lay on the great limestone series showed elevated IRCs. In addition, there were some counties in which the proportion of grids exceeding the recommended level was high but the uncertainty was also large according to the analysis of several uncertainty measures, which indicates that additional sampling is required for these counties. From the local cluster analysis in conjunction with simulation results, we found that the counties with higher levels of IRC belonged to the statistically significant clusters of high values, and these counties should be the prime targets for radon management and in-depth survey. The geographical distributions of IRC and uncertainty measures presented in this study provide guidance for effective radon management if they are consistently combined with both future IRC measurements and a geogenic radon potential map.

Bio-inspired weighed quantum particle swarm optimization and smooth support vector machine ensembles for identification of abnormalities in medical data

Knowledge mining is an emerging field where various patterns, rules, etc. can be generated which helps us in the analysis of the result. Medical information systems in modern hospitals and medical institutions become larger and larger, it causes great difficulties in extracting useful information for decision support. Especially, when traditional manual data analysis has become inefficient and methods for efficient computer-based analysis are indispensable. Decision support system is a suitable tool for medical application which helps in the extraction of useful data. A new hybrid model, weighted-quantum particle swarm optimization (WQPSO) for data clustering in sequence with smooth support vector machine (SSVM) is proposed for classification. The parameters considered to evaluate the clustering methods are inter-cluster distance, intra-cluster distance, and validation index. Experiments are performed in MATLAB and the performance analysis, comparisons are made with real-world datasets that are retrieved from UC Irvine Machine Learning Repository. The proposed approach overcomes the drawbacks of existing algorithms in terms of accuracy, performance and complexity. To validate the proposed algorithm, experiments are conducted on two data sets, i.e., liver disorders dataset and Wisconsin Breast Cancer Diagnosis (WBCD) dataset. The accuracy of proposed WQPSO-SSVM classification methodology is 83.76% for liver disorder, 98.42% for WBCD, 95.21% of mammography mass data. Among the considered algorithms such as WPSO-SVM, fuzzy, K-means, and fuzzy C-means methods, WQPSO-SSVM is found to yield a better convergence and provide an improved optimal solution.

КОНЦЕПЦИЯ СИСТЕМЫ ПОДДЕРЖКИ ПРИНЯТИЯ РЕШЕНИЯ В ПРЕДВАРИТЕЛЬНОМ СЛЕДСТВИИ

Based on the developed network ontology of the subject area Preliminary investigation a conceptual model of the information and analytical decision support system is proposed as a response to the challenge of the increasing need for the analysis of big data in the course of a crime investigation. It is demonstrated the importance of automated collection and processing of a variety of information obtained during the individual investigations based on the analysis of network interaction of persons involved in 12 criminal cases.

ESE Framework Verification by MBSE

Enterprise system engineering (ESE) is a practice emerging in the last decades, aiming to support the enterprise systems engineers in generating and developing enterprise architecture and improving enterprise operational processes. However, ESE differs significantly from systems engineering (SE) due to the enterprise's unique property as a system of unsynchronized arrays of systems. This property might lead to severe issues, such as cross-enterprise failures, lack of decision-support information for cross-enterprise decisions, long delays in integration tests, and unexpected emergent behavior. Enterprise Processes Integrative Collaboration (EPIC) framework is an ESE framework, aims to address this property by generating better coordination between unsynchronized arrays of systems within the enterprise and even further across several enterprises. Previous research dealt with the validation of this framework by implementation in two large-scale enterprises from the aerospace and transportation disciplines. The objective of this study was to verify the EPIC framework completeness and integrity, as an example of ESE framework verification. A model-based SE methodology was used to encapsulate EPIC, defining the ESE activities, required resources, and products. This encapsulation allows tracing the engineering activities, verifying the model completeness, traceability, integrity, and interoperability of the ESE processes collaboration.

New trends and opportunities in post-disaster relief optimization problems

Goal: Post-disaster operations are a challenging issue, which becomes very complex due to the high density of population in urban areas. Thus, efficient relief operations are very relevant in attenuating the impacts of disasters on the population. For this purpose, optimizing post disaster operations plays a key role, and such issues are focused on this contribution.
 Design / Methodology / Approach: Optimization problems appearing in the aftermath of disasters, related to accessibility, distribution and facility location are summarized. They were addressed using real data and the core of such problems was identified in a bottom-up approach, by analyzing the situation on the ground.
 Results: Examples of results are presented by means of maps with decision support information. A case study on an addressed location problem is described for the 2015 Kathmandu earthquake. In addition, new trends and opportunities are pointed out, taking into account technological tools, the endogenous characteristics of disasters and emerging applications.
 Limitations of the investigation: Regarding the Kathmandu case study, two issues are left for future studies: the uncertain data associated with the affected population and the elasticity of demands.
 Practical implications: The data treatment to produce inputs for the algorithms and suitable outputs for the relief teams highlight a collective effort and a real opportunity to use the results practically for humanitarian logistics.
 Originality / Value: The optimization models were progressively approached to the situation on the ground, which permits us to identify difficult aspects of the problems, while remaining pragmatic enough to solve real issues.

Severe Fire Danger Index: A Forecastable Metric to Inform Firefighter and Community Wildfire Risk Management

Despite major advances in numerical weather prediction, few resources exist to forecast wildland fire danger conditions to support operational fire management decisions and community early-warning systems. Here we present the development and evaluation of a spatial fire danger index that can be used to assess historical events, forecast extreme fire danger, and communicate those conditions to both firefighters and the public. It uses two United States National Fire Danger Rating System indices that are related to fire intensity and spread potential. These indices are normalized, combined, and categorized based on a 39-yr climatology (1979–2017) to produce a single, categorical metric called the Severe Fire Danger Index (SFDI) that has five classes; Low, Moderate, High, Very High, and Severe. We evaluate the SFDI against the number of newly reported wildfires and total area burned from agency fire reports (1992–2017) as well as daily remotely sensed numbers of active fire pixels and total daily fire radiative power for large fires (2003–2016) from the Moderate-Resolution Imaging Spectroradiometer (MODIS) across the conterminous United States. We show that the SFDI adequately captures geographic and seasonal variations of fire activity and intensity, where 58% of the eventual area burned reported by agency fire records, 75.2% of all MODIS active large fire pixels, and 81.2% of all fire radiative power occurred when the SFDI was either Very High or Severe (above the 90th percentile). We further show that SFDI is a strong predictor of firefighter fatalities, where 97 of 129 (75.2%) burnover deaths from 1979 to 2017 occurred when SFDI was either Very High or Severe. Finally, we present an operational system that uses short-term, numerical weather predictions to produce daily SFDI forecasts and show that 76.2% of all satellite active fire detections during the first 48 h following the ignition of nine high-profile case study fires in 2017 and 2018 occurred under Very High or Severe SFDI conditions. The case studies indicate that the extreme weather events that caused tremendous damage and loss of life could be mapped ahead of time, which would allow both wildland fire managers and vulnerable communities additional time to prepare for potentially dangerous conditions. Ultimately, this simple metric can provide critical decision support information to wildland firefighters and fire-prone communities and could form the basis of an early-warning system that can improve situational awareness and potentially save lives.

Information Support of Decision-Making in Waste Management Problems on the Basis of Mathematical and Geoinformation Modeling

Information Support of Decision-Making in Waste Management Problems on the Basis of Mathematical and Geoinformation Modeling

Using multiple composite fingerprints to quantify source contributions and uncertainties in an arid region

Developing targeted protection measures at a watershed scale requires spatially distributed information of sediment sources. Therefore, the objectives of this study are to (1) test and evaluate the ability of multiple composite fingerprints (MCF) to quantify sediment provenance using multiple particle size classes in an arid region; (2) quantify uncertainty of the estimated proportional contributions of sediment sources; and (3) provide decision support information for sediment control in the Danghe Reservoir Watershed. In total, 66 samples were collected from north alluvial fan, south alluvial fan, and high mountains, and all samples were divided into six particle size groups. A multistep test was used to remove the tracers that were non-conservative, unable to differentiate sources, or highly variable within a source. Based on geochemical properties of distributed source samples and a linear mixing model, a MCF method with multiple particle size tracking was used to estimate proportions of three potential source contributions. More importantly, the uncertainty of sediment source contributions was quantified using the Gaussian first-order approximation. The results showed that the MCF method with multiple particle size tracking could obtain relatively accurate estimates of the contributions with an overall mean absolute relative error of 3.5% and a relatively narrow 95% confidence interval. The major contributions were consistently coming from the high mountains for all six particle groups. During these runoff events, the overall estimated mean proportions were 49.0%, 26.5%, and 24.5% from the high mountains, south alluvial fan, and north alluvial fan, respectively. Furthermore, the Gaussian first-order approximation revealed that more than 60% of the total uncertainty contribution was a byproduct of the downstream sediment mixture, while each individual sediment source produced less than 15% of the absolute uncertainty. Acquiring watershed scale sediment source information is challenging and the MCF method proved accurate. A majority of the contribution uncertainties were associated with the downstream sediment mixture, which is because the sediment sink inherited both spatial and temporal variations of all contributing sources. Consequently, a larger sample size is recommended for sediment mixtures, compared to each sediment source, in order to increase the accuracy of the source proportion estimation.

Integrated Rural Decision Support System Model Based on Android

The village needs to develop the independence and welfare of the community by increasing knowledge, attitudes, skills, behaviors, abilities, awareness, and utilizing resources through the establishment of policies, programs, activities and mentoring in accordance with the essence of the problems and priorities of the needs of the village community, related to the implementation of The Village Law Number 6 of 2014, one of which is about an integrated village-based village potential information system to open information on the implementation of village governance to the public, with the opening of public information the citizens can be more active together with the government to build the village. Information that needs to be known, among others:, Village administration potential, population administration, Village profile administration. The object of research was Juli Tambotanjong Village, Alue Buya Term Village, Abeuk Budi Village with different Village characteristics, so that the formation of an integrated Android-based Village Database utilizing Cloud Computing and Mobile Computing, which made it easier for users (communities) to access information in making decisions. -that. And the purpose of the Integrated Rural Decision Support Information System Model Based on Android.

Parallel particle swarm optimization classification algorithm variant implemented with Apache Spark

SummaryWith the rapid development of technologies such as the internet, the amount of data that are collected or generated in many areas such as in the agricultural, biomedical, and finance sectors poses challenges to the scientific community because of the volume and complexity of the data. Furthermore, the need of analysis tools that extract useful information for decision support has been receiving more attention in order for researchers to find a scalable solution to traditional algorithms. In this paper, we proposed a scalable design and implementation of a particle swarm optimization classification (SCPSO) approach that is based on the Apache Spark framework. The main idea of the SCPSO algorithm is to find the optimal centroid for each target label using particle swarm optimization and then assign unlabeled data points to the closest centroid. Two variants of SCPSO, SCPSO‐F1 and SCPSO‐F2, were proposed based on different fitness functions, which were tested on real data sets in order to evaluate their scalability and performance. The experimental results revealed that SCPSO‐F1 and SCPSO‐F2 scale very well with increasing data set sizes and the speedup of SCPSO‐F2 is almost identical to the linear speedup while the speedup of SCPSO‐F1 is very close to the linear speedup. Thus, SCPSO‐F1 and SCPSO‐F2 can be efficiently parallelized using the Apache Spark framework.

Development of methods and tools to support regional management in the Arctic zone of the Russian Federation based on cognitive interfaces

This article discusses the creation of methods and tools for decision-making information support for regional management systems in the context of ensuring environmental and technological security in the Arctic region. To implement information support for regional management, we propose to use information technology for spatial data visualization. This technology is used to synthesize cognitive interfaces of multi-subject information systems supporting regional management in the Arctic zone of the Russian Federation. The article describes the methodological support, composition and structure of information technology to identify implicit conflicts of environmental management based on the use of methods of conceptual and ontological modeling of the subject area, system analysis and theory of management of complex systems. The key elements of the technology are the conceptual model of the conflict situation of environmental management, the ontology of the subject area, the knowledge base and the cognitive user interface of the multi-subject information regional management support system for the Arctic territories. A brief classification of conflicts among the stakeholders of the Arctic region (using the example of the Murmansk region) is also described. The proposed technology allows to identify potential conflicts of environmental management, including the detection and identification of environmental and technological safety problems in the Arctic regions of the Russian Federation.

Old School and High Tech: A Comparison of Methods to Quantify Ashe Juniper Biomass as Fuel or Forage

• Ashe juniper invasion is a widespread issue on Texas and Oklahoma rangelands. Increased densities of Ashe juniper trees increase the risk of wildfire and decrease herbaceous forage production. • Browsing animals, such as goats, are one tool that can be used to effectively reduce juniper fuel. • In order to estimate the available biomass, allometric measurements were compared against three-dimensional Light Detection and Ranging (LiDAR) scans of whole juniper plants. • Accurate measurements of standing juniper browse and fuel load can be vital information for decision support of grazing management and wildland fire mitigation, especially in the ever-growing wildland-urban interface.

A stream computing approach for live environmental models using a spatial data infrastructure with a waterlogging model case study

Traditional environmental model simulations often use archived data as inputs. Recent advancement of Sensor Web technologies in Spatial Data Infrastructures (SDIs) allows real-time observations to be fed into models to generate “live” models. A key challenge is how to efficiently process observation streams in models, which is particularly important in time-critical cases like disaster management. This paper presents an observation stream computing model for live modelling, which couples Sensor Web and models in stream computing environment to provide timely decision-support information. Observation Streams are proposed as information models to deal with observation stream processing. The approach shows how MapReduce and Apache Spark stream processing can be leveraged to support coupling of observation streams and models. The approach is applied in a disaster management case, where in-situ observation streams are processed to compute the waterlogging information in near real time. The results illustrate applicability and effectiveness of the approach.

Information support of decision-making in computer-aided reliability-oriented design

The paper proposes a modified form and procedure for automated processing of information about failures based on the analysis of the existing forms for recording statistical data on operating results and laboratory tests (developed on the basis of state standards). The procedure is intended for informational support of decision-making in the development of measures aimed at eliminating the causes of failures. The proposed form with fault categories and repeatability will provide not only information support in making decisions about the development of the missing measures, but also the effectiveness evaluation of previously completed improvements. The proposed procedure for automated processing of information about failures speeds up the decision-making process, based on automated calculation of the repeatability index. The implementation of statistical data processing in computer-aided design systems makes it possible to reduce the time taken to develop corrective measures and take into account the effect of operating factors when creating new projects.

Multi-objective strategic waste transfer station planning

The production of mixed municipal waste changes due to the increase of separation rate, urbanization and other factors. The future changes in the legislation and technology development influences the way the waste is being treated. Thus the method, location and capacity of processing sites are unknown. The realization of future projects can be supported by the developing of transportation infrastructure. Such a feature may be represented by a robust transfer station grid, which can be designed to handle all possible future realizations and technological solutions (establishment of waste treatment facilities). The paper presents an approach utilising a mathematical model for the design of transfer stations. It is formulated as a multi-objective two-stage mixed-integer stochastic programming problem, where the trade-off between the environmental aspect and the economic viability is considered. The model is tested through a case study for the Czech Republic, where the waste treatment of over 6,000 municipalities is analysed. The solutions for different preferences are assessed throughout the principle called out-of-sample stability with 10,000 scenarios. The optimal decision consists of the robust transfer station grid with selected locations and their respective capacity. The particular solution with respect to the potential trade-off suggests to save 1.148 mil of travelled kilometres with only 0.77 mil EUR. The output in the form of decision support can serve possible stakeholders from the field of waste management to plan more sustainable projects.

The inherently democratic nature of technology assessment

AbstractTechnology assessment (TA) emerged more than fifty years ago to provide information supply, decision support, and orientation for democratic processes and institutions in many democratic countries. This historical observation alone, however, does not justify speaking of an inherent relationship between TA and democracy. The latter requires taking a conceptual view. Arguments supporting the thesis of the inherently democratic nature of TA will be given based on pragmatist approaches developed by John Dewey and Jürgen Habermas. This perspective on TA has specific implications for the inclusion of the knowledge and perspectives of stakeholders, people affected and citizens involved in TA processes, as well as the necessity to develop or strengthen thinking in alternative options. Furthermore, it makes clear that in the current crisis of democracy in many countries, TA cannot take a distant and neutral position.

Asthma care based on Chronic Care Model in an aging Asian community

To cope with the higher prevalence of asthma and other non-communicable diseases without compromising on quality of care, a Singapore public primary care institution has adopted the Chronic Care Model (CCM). This retrospective cohort study aimed to describe the proportion of patients with well-controlled asthma (based on Asthma Control Test score ≥20) between 2010 and 2016 in association with their management based on the CCM (which covers the polyclinic clinical information system, self-management measures, system re-design and decision support). Data were retrieved from the Singapore National Asthma Programme (SNAP) and institutional clinical quality databases of eight local polyclinics in eastern and southern Singapore. The data were aggregated, analysed and presented in proportions against monthly polyclinic attendances for asthma. From 2010 to 2016, the total asthma attendances increased by 31% from 27,345 to 35,731, with the highest rise among patients aged ≥60 years. The proportion of patients with good asthma control rose from 71.4% to 80.9%; those who received rescue therapy for acute exacerbations fell from 15.8% to 11.7% and those referred to emergency departments after failed rescue therapy decreased from 0.7% to 0.6%. The proportion of patients with updated asthma action plans increased from 66.7% to 73.4% (proxy for self-management). The overall health and process outcomes of asthma seemed to have improved with multiplex of system-based interventions relating to the introduction of CCM in a public primary healthcare institution in Singapore.