PEAT VOLUME LOSS IN THE DITCHES OF NORTHERN MINNESOTA

Peatlands play an important role in carbon (C) storage and are estimated to contain 30% of global soil C, despite occupying only 3% of global land area. Historic management of peatlands has led to widespread degradation and loss of important ecosystem services including C- and fresh water storage. Legacy drainage features in the peatlands of northern Minnesota were studied to assess the volume of peat that has been lost in the ~100 years since drainage. Using high-resolution Light Detection and Ranging (LiDAR) data, we measured elevation changes along the margins of legacy ditches to model preditch surface areas, which were used to calculate peat volume loss. We established relationships between volume loss and site characteristics from existing Geographic Information Systems (GIS) datasets and used those relationships to scale volume loss to the length of peatland ditches in northern Minnesota. It is estimated that 165.3 ± 8.6 million m3 of peat have been lost throughout peatland ditches that extend almost 4,000 km. Peat loss on the upslope side of the ditch was significantly less than peat loss on the downslope side of the ditch (P-1 yr-1, respectively. Our framework can be used as a decision support tool to guide preliminary management decisions with the objective of protecting C on natural Minnesota landscapes

Contribution of low impact development practices-bioretention systems towards urban flood resilience: case study of Novi Sad, Serbia

Bioretention systems are globally the most accepted Low Impact Development (LID) practices. In this study, we simulated bioretention performances for four locations in the city of Novi Sad, with RECARGA modelling software. The primary objective of the research was to evaluate potential of bioretention systems for runoff reduction. The second research objective was to suggest RECARGA model as a support for future decision-making processes. Analysis of the sensitivity of bioretention design parameters on bioretention performances, involved variations related to different sizes of bioretention systems, application of an underdrain, the difference in soil texture and changes in the depth of each individual bioretention layer. The total average volume of retained runoff by bioretention systems ranged from 43.33 to 93.84%, while some single simulation results were 100%. Among all tested design parameters, bioretention size and the native soil hydraulic conductivity have shown the greatest influence on the runoff reduction rate. This study provides information about the developing a site-specific bioretention solutions needed to prevent urban flooding in the area of research where this systems are still not sufficiently applied in practice. The obtained methodology can be applied for other locations and also it can be extended to other cities with similar urban flooding problems

Performance Evaluation of Apache Spark MLlib Algorithms on an Intrusion Detection Dataset

The increase in the use of the Internet and web services and the advent of the fifth generation of cellular network technology (5G) along with ever-growing Internet of Things (IoT) data traffic will grow global internet usage. To ensure the security of future networks, machine learning-based intrusion detection and prevention systems (IDPS) must be implemented to detect new attacks, and big data parallel processing tools can be used to handle a huge collection of training data in these systems. In this paper Apache Spark, a general-purpose and fast cluster computing platform is used for processing and training a large volume of network traffic feature data. In this work, the most important features of the CSE-CIC-IDS2018 dataset are used for constructing machine learning models and then the most popular machine learning approaches, namely Logistic Regression, Support Vector Machine (SVM), three different Decision Tree Classifiers, and Naive Bayes algorithm are used to train the model using up to eight number of worker nodes. Our Spark cluster contains seven machines acting as worker nodes and one machine is configured as both a master and a worker. We use the CSE-CIC-IDS2018 dataset to evaluate the overall performance of these algorithms on Botnet attacks and distributed hyperparameter tuning is used to find the best single decision tree parameters. We have achieved up to 100% accuracy using selected features by the learning method in our experimentsComment: Journal of Computing and Security (Isfahan University, Iran), Vol. 9, No.1, 202

Usage habits of business information system in Hungary

The IT functions of the companies can be executed in different ways in-house solution, outsourcing, in sourcing, formation a spin-off company. Predominantly this function is provided within the company in Hungary. The larger a company is; it is more likely that a separate IT manager will be entrusted for the supervision of IT functions. Only a very small number of small-sized enterprises said that they paid special attention to formulating an IT strategy, while it was not considered important by microenterprises at all

Results of Environmental Scanning Applied to the Design of a Deer Management Decision Support System (DSS) For The United States and California

Using freely available internet search tools for environmental scanning, information related to deer management was collected, categorized, and evaluated with the goal of providing public decision support. Key issues raised in the public debate discovered by the search are addressed with relevant information formatted as output for a decision support system – dashboard elements. A graph addresses contradictory reports about the current direction of the deer population; the trend since 2006 appears to be down. Another graph illustrates the approximate longterm population trend; the current U.S. white-tailed deer population is about the same as in 1500. A table summarizes profiles of state deer issues and strategies. Only eleven states are trying to reduce their deer population. A graph illustrates the rise and fall of the California population, the most dramatic population decline in the U.S. over the past 100 years. Hunting pressure and herd demographic management are found to be related to the decline, making these candidate variables for attention in the decision support system. This case application is designed to illustrate methods the author has learned in creating a variety of decision support applications for technology companies

Decision support system for the long-term city metabolism planning problem

A Decision Support System (DSS) tool for the assessment of intervention strategies (Alternatives) in an Urban Water System (UWS) with an integral simulation model called “WaterMet²” is presented. The DSS permits the user to identify one or more optimal Alternatives over a fixed long-term planning horizon using performance metrics mapped to the TRUST sustainability criteria (Alegre et al., 2012). The DSS exposes lists of in-built intervention options and system performance metrics for the user to compose new Alternatives. The quantitative metrics are calculated by the WaterMet² model and further qualitative or user-defined metrics may be specified by the user or by external tools feeding into the DSS. A Multi-Criteria Decision Analysis (MCDA) approach is employed within the DSS to compare the defined Alternatives and to rank them with respect to a pre-specified weighting scheme for different Scenarios. Two rich, interactive Graphical User Interfaces, one desktop and one web-based, are employed to assist with guiding the end user through the stages of defining the problem, evaluating and ranking Alternatives. This mechanism provides a useful tool for decision makers to compare different strategies for the planning of UWS with respect to multiple Scenarios. The efficacy of the DSS is demonstrated on a northern European case study inspired by a real-life urban water system for a mixture of quantitative and qualitative criteria. The results demonstrate how the DSS, integrated with an UWS modelling approach, can be used to assist planners in meeting their long-term, strategic level sustainability objectives

Information systems and healthcare XXIV: Factors affecting the EAI adoption in the healthcare sector

Recent developments in the field of integration technologies like Enterprise Application Integration (EAI) have emerged to support organizations towards improving the quality of services and reducing integration costs. Despite the importance of EAI, there is limited empirical research reported on its adoption in the healthcare sector. Khoumbati et al. [2006] developed a model for the evaluation of EAI in healthcare organizations. In doing so, the causal interrelationship of EAI adoption factors was identified by using fuzzy cognitive mapping. This paper is a progression of previous work in the area and seeks to contribute by validating the model through a different case environment. Thus, this paper contributes by deriving and proposing the MAESTRO model for EAI adoption. MAESTRO identifies a set of factors that influence EAI adoption and it is evaluated through a real-life case study. It provides an understanding of the EAI adoption process through its grounding on empirical data. In doing so, the MAESTRO model supports the management of healthcare organizations during the decision-making process for EAI adoption

Deer Herd Management Using the Internet: A Comparative Study of California Targeted By Data Mining the Internet

An ongoing project to investigate the use of the internet as an information source for decision support identified the decline of the California deer population as a significant issue. Using Google Alerts, an automated keyword search tool, text and numerical data were collected from a daily internet search and categorized by region and topic to allow for identification of information trends. This simple data mining approach determined that California is one of only four states that do not currently report total, finalized deer harvest (kill) data online and that it is the only state that has reduced the amount of information made available over the internet in recent years. Contradictory information identified by the internet data mining prompted the analysis described in this paper indicating that the graphical information presented on the California Fish and Wildlife website significantly understates the severity of the deer population decline over the past 50 years. This paper presents a survey of how states use the internet in their deer management programs and an estimate of the California deer population over the last 100 years. It demonstrates how any organization can use the internet for data collection and discovery