Development of a Decision Support System for Post Mining Land Use on Abandoned Surface Coal Mines in Appalachia

Decision support systems are diverse and have been used to solve multiple problems ranging from the complex to the simple. With the complexity of environmental decisions today, these systems provide a logic based approach to evaluating and choosing environmental solutions. Abandoned mining lands (AML) are an issue for the environment in the Appalachian region. Given this a decision support system was designed using previously created frameworks and indices from other systems created. The system is comprised of two main sections, selecting the ideal post-mining land-use (PMLU), and maximizing the potential of land to be reclaimed under budgetary constraints. This system incorporates stakeholders, and takes into account the regulations governing reclamation of AML in Appalachia. The system could potentially be adjusted and used in other land use decision situations

Participation in multicriteria decision support - the case of conflicting water allocation in the Spree River basin

This discussion paper presents the Integrated Methodological Approach for participatory multi-criteria decision support under uncertainty (IMA), which emerged from the debates about participation, multi-criteria analysis (MCA) and benefit-cost analysis (BCA). It provides a framework for participatory and science-based evaluation processes with combined use of BCA and MCA to support large-scale public decisions. While IMA does not claim to realize an all-inclusive participation scheme, it offers the advantage to improve the quality of decision making through advances in competence and fairness. Its practical application with emphasis on its participatory elements is demonstrated by the case study on the water allocation conflict of the German Spree River, which involves the German capital of Berlin, an important wetland, and the needs to remediate a post-mining landscape. --Participation,Multi-criteria analysis,Cost-benefit analysis,River basin management,Integrated Assesment

Multi-Criteria Decision Analysis for Evaluating Transitional and Post-Mining Options—An Innovative Perspective from the EIT ReviRIS Project

grant agreement 19075: ReviRIS—Revitalising Post-Mining Regions, UIDB/04035/2020.In mine design and planning, identifying appropriate Post-Mining Land Use (PMLU) is necessary and crucial to achieving environmental quality and socioeconomic renewal. In this context, Multi-Criteria Decision Making (MCDM) methods are used to support decision-maker and stakeholder decisions. However, most studies regarding the application of MCDM methods to PMLU decisions do not favor their widespread use because they start from an already structured decisional problem. The structure they present may not apply to another PMLU decision. Therefore, the primary goal of this study is to present an innovative methodology and its corresponding framework to help decision-makers and stakeholders structure their PMLU decisions. This innovative methodology can be used from an early stage, with a low level of detail, until a later stage, with a high level of detail, and is composed of three main stages. The first stage is selecting the Transitional Post-Mining Landscape Profile, which guides the user to different Multi-Criteria Decision Analysis (MCDA) goals. The second stage is developing criteria and alternatives according to the MCDA goal, using topics representing essential dimensions that cannot be disregarded, and testing the MCDM methods. Finally, the third stage is the participatory process and final application of MCDM methods.publishersversionpublishe

Machine Learning Approach for Classifying Power Outage in Secondary Electric Distribution Network

Power outage is the problem that hinders social and economic development especially for developing countries like Tanzania. Frequent power outages damage electric equipment, and negatively affect the industrial production process. Power outages cannot be completely eradicated due to uncontrolled cause like natural calamities but technical challenges can be managed and hence reducing power outages. The existing manual methods used to locate power outage like customer calls is inefficient and time consuming. On the other hand, modern method like the Advanced Metering Infrastructure (AMI) still faces a challenge in effectively classifying power line outage due to the nature of imbalanced datasets. Therefore, there is a need to develop a Machine Learning (ML) model to accurately classify power line outage. In this study, machine learning models are constructed from ensemble algorithms and tested using outage AMI data from 2012 to 2019 with 2 hours interval records. We propose the following ensemble-based machine learning approach to enhance classification; data sampling, algorithm weighting and finally ensembling. Results show that the Hybrid Stacking Ensemble Classifier (HSEC) model outperforms the others by accuracy of 0.981 G-mean, followed by Extra tree with accuracy of 0.964 G-mean. This model can be used in power line outage classification in any Secondary Electrical Distribution Network (SEDN). This study can be extended to locate power outage to household

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

A Survey of Operations Research and Analytics Literature Related to Anti-Human Trafficking

Human trafficking is a compound social, economic, and human rights issue occurring in all regions of the world. Understanding and addressing such a complex crime requires effort from multiple domains and perspectives. As of this writing, no systematic review exists of the Operations Research and Analytics literature applied to the domain of human trafficking. The purpose of this work is to fill this gap through a systematic literature review. Studies matching our search criteria were found ranging from 2010 to March 2021. These studies were gathered and analyzed to help answer the following three research questions: (i) What aspects of human trafficking are being studied by Operations Research and Analytics researchers? (ii) What Operations Research and Analytics methods are being applied in the anti-human trafficking domain? and (iii) What are the existing research gaps associated with (i) and (ii)? By answering these questions, we illuminate the extent to which these topics have been addressed in the literature, as well as inform future research opportunities in applying analytical methods to advance the fight against human trafficking.Comment: 28 pages, 6 Figures, 2 Table

Agroforestry as a post-mining land-use approach for waste deposits in alluvial gold mining areas of Colombia

Alluvial gold mining generates a vast amount of extractive waste that completely covers the natural soil, destroys riparian ecosystems, and negatively impacts river beds and valleys. Since 2002, a gold mining company has striven to create agroforestry plots in the waste deposits as a post-mining management approach, where agricultural crops and livestock are combined to complement reforestation in the area. This research aims at supporting reclamation of waste deposits by providing a comprehensive understanding of processes to manage the transition of nutrient-poor and acidic deposition sites towards productive agroforestry-based systems. Major components of this research comprise (i) an analysis of environmental and social challenges of the gold mining sector in Colombia, and its potential opportunities to add value to affected communities, (ii) an assessment of management practices and decision-making processes of the farmers working on reclamation areas, (iii) an analysis of the sources of variability of waste deposits from the perspective of soil development and vegetation succession, (iv) an analysis of spatial variability of the physicochemical properties of waste deposits with a spatially explicit management scheme, and (v) an assessment of vegetation recovery in terms of biomass and plant community composition. Farmers who are currently working on areas undergoing reclamation rely mostly on their own local knowledge to respond to the challenges that the heavily disturbed conditions of the area pose to crop establishment. Therefore, increasing their awareness of the inherent heterogeneity of their fields, as well as the interdependencies between management practices and improvement of soil fertility, may increase the productivity of their farms. The analysis of sources of variability of the waste deposits generated by alluvial gold mining revealed that these deposits are primarily influenced by the parent material of the alluvial gold deposits and by the technology used for gold mining (bucket or suction dredges), which define the type of deposit formed (gravel or sand). Waste deposits can provide essential functions for rural areas such as woody biomass production and crop establishment if deposits are managed according to a specific purpose, and crop selection for each deposit is done based on physicochemical and structural soil properties. This finding is echoed by the spatial assessment of vegetation reestablishment through the combination of remote sensing with machine-learning techniques that show a high spatial variability of textural properties and nutrient contents of the deposits. A management approach is proposed with the use of delineated management zones, which can lead to an overall increased productivity by developing strategies suitable to the characteristics of each field and its potential uses.Agroforstwirtschaft als Landnutzungsansatz auf Abraumdeponien in alluvialen Goldabbaugebieten Kolumbiens Der Abbau von alluvialem Gold erzeugt eine große Menge mineralischen Abfalls, der den natürlichen Boden vollständig bedeckt, Uferökosysteme zerstört, und Flussbetten und -täler negativ beeinflusst. Von einem Goldminenbetreiber werden seit 2002, als ein Ansatz einer Postbergbaustrategie, Agroforstparzellen in Abraumdeponien angelegt. In diesen werden landwirtschaftliche Nutzpflanzen und Viehhaltung zur Aufforstung der Parzelle kombiniert eingesetzt. Diese Forschungsarbeit beabsichtigt die Rekultivierungsmaßnahmen in Agroforstparzellen durch ein umfassendes Verständnis der beteiligten Prozesse zu unterstützen und den Übergang von nährstoffarmen und sauren Abraumdeponien hin zu produktiven agroforstbasierten Systemen zu steuern. Die Hauptbestandteile dieser Arbeit umfassen (i) eine Analyse der ökologischen und sozialen Herausforderungen des Goldminensektors in Kolumbien und potenzielle Möglichkeiten einen Mehrwert für die betroffenen Gemeinden zu schaffen, (ii) eine Bewertung der Managementpraktiken und Entscheidungsprozesse der Landwirte im Rahmen der Rückgewinnung von Landnutzungsflächen, (iii) eine Analyse der Ursachen von Varianz zwischen Abfalldeponien aus der Perspektive der Boden- und Vegetationsentwicklung, (iv) eine Analyse der räumlichen Variabilität der physikochemischen Eigenschaften von mineralischen Abraumdeponien mit einem räumlich expliziten Managementschema und (v) eine Bewertung der Vegetationserholung im Sinne der Zusammensetzung von Biomasse und Pflanzengemeinschaften. Landwirte die in Gebieten arbeiten die gegenwärtig einer Rekultivierung unterzogen werden, verlassen sich größtenteils auf ihre lokalen Erfahrungswerte, um mit den Herausforderungen für die Nutzpflanzenproduktion umzugehen, die durch die stark gestörten Bodenbedingungen verursacht werden. Eine Steigerung des Bewusstseins der lokalen Farmer für die inhärente Heterogenität ihrer Felder, sowie der Interdependenzen zwischen Managementpraktiken und der Verbesserung der Bodenfruchtbarkeit, kann die Produktivität der Farmbetriebe erhöhen. Die Analyse der Variabilitätsquellen der durch den alluvialen Goldabbau entstandenen mineralischen Abfalllager ergab, dass diese Lagerstätten in erster Linie vom Grundgestein der alluvialen Goldlagerstätten und der verwendeten Abbautechnik (Schaufel- oder Saugbagger) beeinflusst werden. Diese Faktoren bestimmen die Art der gebildeten Ablagerung (Kies oder Sand). Abfalldeponien können wesentliche Funktionen für ländliche Gebiete wie die Produktion von Holzbiomasse und den Anbau von Nutzpflanzen ermöglichen, wenn die Lagerstätten einem bestimmten Zweck entsprechend bewirtschaftet werden und die Auswahl der Kulturen für jede Lagerstätte auf Grundlage der spezifischen physikochemischen und strukturellen Bodeneigenschaften erfolgt. Dieser Befund wird durch die räumliche Bewertung der Vegetationsneubildung durch die Kombination von Fernerkundung mit maschinellen Lerntechniken bestätigt, die eine hohe räumliche Variabilität der Textureigenschaften und Nährstoffgehalte der Deponien zeigt. Es wird ein Managementansatz vorgeschlagen, bei dem abgegrenzte Bewirtschaftungszonen unterteilt werden. Dies kann zu einer insgesamt höheren Produktivität führen, indem Strategien entwickelt werden, die den Eigenschaften jedes einzelnen Feldes und seiner potenziellen Nutzungsmöglichkeiten entsprechen

Applications of artificial intelligence in dentistry: A comprehensive review

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUAObjective: To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods: The comprehensive review was conducted in MEDLINE/ PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results: Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions: The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance: The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00 PGC2018-101904-A-100University of Granada project A.TEP. 280.UGR18Junta de Andalucia P20-00200Fapergs/Capes do Brasil grant 19/25510000928-3Universidad de Granada/CBU