Organic Matter Fractions and Quality of the Surface Layer of a Constructed and Vegetated Soil After Coal Mining. II - Physical Compartments and Carbon Management Index

Soils constructed after mining often have low carbon (C) stocks and low quality of organic matter (OM). Cover crops are decisive for the recovery process of these stocks, improving the quality of constructed soils. Therefore, the goal of this study was to evaluate the effect of cover crops on total organic C (TOC) stocks, C distribution in physical fractions of OM and the C management index (CMI) of a soil constructed after coal mining. The experiment was initiated in 2003 with six treatments: Hemarthria altissima (T1), Paspalum notatum (T2), Cynodon dactylon (T3), Urochloa brizantha (T4), bare constructed soil (T5), and natural soil (T6). Soil samples were collected in 2009 from the 0.00-0.03 m layer, and the TOC and C stocks in the physical particle size fractions (carbon in the coarse fraction - CCF, and mineral-associated carbon - MAC) and density fractions (free light fraction - FLF; occluded light fraction - OLF, and heavy fraction - HF) of OM were determined. The CMI components: carbon pool index (CPI), lability (L) and lability index (LI) were estimated by both fractionation methods. No differences were observed between TOC, CCF and MAC stocks. The lowest C stocks in FLF and OLF fractions were presented by T2, 0.86 and 0.61 Mg ha-1, respectively. The values of TOC stock, C stock in physical fractions and CMI were intermediate, greater than T5 and lower than T6 in all treatments, indicating the partial recovery of soil quality. As a result of the better adaptation of the species Hemarthria and Brizantha, resulting in greater accumulation of labile organic material, the CPI, L, LI and CMI values were higher in these treatments, suggesting a greater potential of these species for recovery of constructed soils.

Application of text-mining for updating protein post-translational modification annotation in UniProtKB.

BACKGROUND: The annotation of protein post-translational modifications (PTMs) is an important task of UniProtKB curators and, with continuing improvements in experimental methodology, an ever greater number of articles are being published on this topic. To help curators cope with this growing body of information we have developed a system which extracts information from the scientific literature for the most frequently annotated PTMs in UniProtKB. RESULTS: The procedure uses a pattern-matching and rule-based approach to extract sentences with information on the type and site of modification. A ranked list of protein candidates for the modification is also provided. For PTM extraction, precision varies from 57% to 94%, and recall from 75% to 95%, according to the type of modification. The procedure was used to track new publications on PTMs and to recover potential supporting evidence for phosphorylation sites annotated based on the results of large scale proteomics experiments. CONCLUSIONS: The information retrieval and extraction method we have developed in this study forms the basis of a simple tool for the manual curation of protein post-translational modifications in UniProtKB/Swiss-Prot. Our work demonstrates that even simple text-mining tools can be effectively adapted for database curation tasks, providing that a thorough understanding of the working process and requirements are first obtained. This system can be accessed at http://eagl.unige.ch/PTM/.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Electric Motor and Frequency Converter Markets in Latin America and Especially in the Mining Industry of Chile

Latinalaisen Amerikan osuus maailmantaloudesta on pieni verrattuna sen maantieteelliseen kokoon, väkilukuun ja luonnonvaroihin. Aluetta pidetään kuitenkin yhtenä tulevaisuuden merkittävistä kasvumarkkinoista. Useissa Latinalaisen Amerikan maissa on teollisuutta, joka hyödyntää luonnonvaroja ja tuottaa raaka-aineita sekä kotimaan että ulkomaiden markkinoille. Tällaisia tyypillisiä teollisuudenaloja Latinalaisessa Amerikassa ovat kaivos- ja metsäteollisuus sekä öljyn ja maakaasun tuotanto. Näiden teollisuudenalojen tuotantolaitteiden ja koneiden valmistusta ei Latinalaisessa Amerikassa juurikaan ole. Ne tuodaan yleensä Pohjois-Amerikasta ja Euroopasta. Tässä diplomityössä tutkitaan sähkömoottorien ja taajuusmuuttajien markkinapotentiaalia Latinalaisessa Amerikassa. Tutkimuksessa perehdytään Latinalaisen Amerikan maiden kansantalouksien tilaan sekä arvioidaan sähkömoottorien ja taajuusmuuttajien markkinoiden kokoa tullitilastojen avulla. Chilen kaivosteollisuudessa arvioidaan olevan erityistä potentiaalia. Diplomityössä selvitetään ostoprosessin kulkua Chilen kaivosteollisuudessa ja eri asiakastyyppien roolia siinä sekä tärkeimpiä päätöskriteerejä toimittaja- ja teknologiavalinnoissa.

Educational Data Mining: cerrando el círculo del proceso de aprendizaje en entornos virtuales

Development of methods to explore data from educational settings, to understand better the learning process.

NATURAL REGENERATION STRATUM AS AN INDICATOR OF RESTORATION IN AREA OF ENVIRONMENTAL COMPENSATION FOR MINING LIMESTONE, MUNICIPALITY OF BARROSO, MG, BRAZIL

ABSTRACT This study was conducted in a forest under restoration process, which belongs to the company Holcim Brasil S/A, in the municipality of Barroso, state of Minas Gerais (21º00'to 22º00'S and 43º00' to 44º00'W), where 40 plots (2 x 2 m) were set, spaced at 10 m, forming eight strata parallel to the watercourse present in the area. Floristic composition and natural regeneration stratum were characterized, and the formed strata allowed evaluating whether the riparian vegetation and watercourse influence on the local regeneration. It was found 162 individuals of 13 families, 18 genera and 22 species, and 10,125 individuals/ha were estimated. Successional classes from pioneer and early secondary and zoochory dispersion syndrome prevailed among species and individuals. The watercourse and riparian vegetation did not exercise significant influence (p> 0.05) on the number of species and regenerating individuals among the different strata of the forest. The diversity index of Shannon-Wiener (H') and equability of Pielou (J') were 2.691 and 0.870, respectively. The species Psidium guajava and Myrtaceae families presented the highest VI (value of importance). Natural regeneration analysis showed the low floristic diversity in the area, suggesting that corrective management actions should be adopted.

Assessing the Environmental Value of Water Treatment Solutions in the Mining Industry

Environmental accountability has become a major source of competitive advantage for industrial companies, because customers consider it as relevant buying criterion. However, in order to leverage their environmental responsibility, industrial suppliers have to be able to demonstrate the environmental value of their products and services, which is also the aim of Kemira, a global water chemistry company considered in this study. The aim of this thesis is to develop a tool which Kemira can use to assess the environmental value of their solutions for the customer companies in mining industry. This study answers to questions on what kinds of methods to assess environmental impacts exist, and what kind of tool could be used to assess the environmental value of Kemira’s water treatment solutions. The environmental impacts of mining activities vary greatly between different mines. Generally the major impacts include the water related issues and wastes. Energy consumption is also a significant environmental aspect. Water related issues include water consumption and impacts in water quality. There are several methods to assess environmental impacts, for example life cycle assessment, eco-efficiency tools, footprint calculations and process simulation. In addition the corresponding financial value may be estimated utilizing monetary assessment methods. Some of the industrial companies considered in the analysis of industry best practices use environmental and sustainability assessments. Based on the theoretical research and conducted interviews, an Excel based tool utilizing reference data on previous customer cases and customer specific test results was considered to be most suitable to assess the environmental value of Kemira’s solutions. The tool can be used to demonstrate the functionality of Kemira’s solutions in customers’ processes, their impacts in other process parameters and their environmental and financial aspects. In the future, the tool may be applied to fit also Kemira’s other segments, not only mining industry.

Developing Service-based Solutions for Water Treatment in Finnish Mining Industry

The aim of this master’s thesis is to analyze the mining industry customers' current and future needs for the water treatment services and discover new business development opportunities in the context of mine water treatment. In addition, the study focuses on specifying service offerings needed and evaluate suitable revenue generation models for them. The main research question of the study is: What kind of service needs related to water treatment can be identified in the Finnish mining industry? The literature examined in the study focused on industrial service classification and new service development process as well as the revenue generation of services. A qualitative research approach employing a case study method was chosen for the study. The present study uses customer and expert interviews as primary data source, complemented by archival data. The primary data was gathered by organizing total of 13 interviews, and the interviews were analyzed by using qualitative content analysis. The abductive-logic was chosen as the way of conducting scientific reasoning in this study. As a result, new service proposals were developed for Finnish mine industry suppliers. The main areas of development were on asset efficiency services and process support services. The service needs were strongly associated with suppliers’ know-how of water treatment process optimization, cost-effectiveness as well as on alternative technologies. The study provides an insight for managers that wish to pursue a water treatment services as a part of their business offering.

Maximizing of water recovery in nanofiltration of mine process waters by using antiscalants

Reverse osmosis and nanofiltration are among the most effective and widely used desalination and water softening technologies. They can also be used to treat mining wastewaters and are capable of producing water of extremely high purity, regardless of the high concentrations of toxic heavy metals and extreme pH and salinity. However, challenges with recovering the salts and metals from mining wastewaters in exploitable form, as well as problems with scaling still limit the process efficiency and the ratio of purified water recoverable from process waters. To address the problem of membrane scaling caused by calcium sulfate, batch filtration experiments with the Desal-5 DL nanofiltration membrane, three commercial antiscalants and actual mine process water from a copper mine were performed. The aim of these experiments was to find process conditions where maximum water recovery would be achieved before significant scaling or irreversible membrane fouling would occur and to further improve water recovery by addition of antiscalants. Water recovery of 70 % was reached with the experimental setups by optimizing process conditions. PC-504T antiscaling agent was determined to be the most effective of the three antiscalants used and the addition of 5 ppm of PC-504T allowed the water recovery to be further increased from 70 % to 85 % before major scaling was observed. In these conditions 92 % calcium rejection was achieved.

Heat Demand Forecasting Models’ Development: Use of Data Mining Tools in SQL Server Analysis Services

This thesis introduces heat demand forecasting models which are generated by using data mining algorithms. The forecast spans one full day and this forecast can be used in regulating heat consumption of buildings. For training the data mining models, two years of heat consumption data from a case building and weather measurement data from Finnish Meteorological Institute are used. The thesis utilizes Microsoft SQL Server Analysis Services data mining tools in generating the data mining models and CRISP-DM process framework to implement the research. Results show that the built models can predict heat demand at best with mean average percentage errors of 3.8% for 24-h profile and 5.9% for full day. A deployment model for integrating the generated data mining models into an existing building energy management system is also discussed.

Investigating the potential of thecamoebians (testate amoebae) as bio-indicators of impact of oil sands mining operations on freshwater environments in Northeastern Alberta, Canada

Thecamoebian (testate amoeba) species diversity and assemblages in reclamation wetlands and lakes in northeastern Alberta respond to chemical and physical parameters associated with oil sands extraction. Ecosystems more impacted by OSPM (oil sands process-affected material) contain sparse, low-diversity populations dominated by centropyxid taxa and Arcella vulgaris. More abundant and diverse thecamoebian populations rich in difflugiid species characterize environments with lower OSPM concentrations. These shelled protists respond quickly to environmental change, allowing year-to-year variations in OSPM impact to be recorded. Their fossil record thus provides corporations with interests in the Athabasca Oil Sands with a potential means of measuring the progression of highlyimpacted aquatic environments to more natural wetlands. Development of this metric required investigation of controls on their fossil assemblage (e.g. seasonal variability, fossilization potential) and their biogeographic distribution, not only in the constructed lakes and wetlands on the oil sands leases, but also in natural environments across Alberta.

A Data Mining Approach for the Identification of Adverse Drug Events (ADE) resulting from Drug-Drug Interactions (DDI) to improve pharmacovigilance

In the current study, epidemiology study is done by means of literature survey in groups identified to be at higher potential for DDIs as well as in other cases to explore patterns of DDIs and the factors affecting them. The structure of the FDA Adverse Event Reporting System (FAERS) database is studied and analyzed in detail to identify issues and challenges in data mining the drug-drug interactions. The necessary pre-processing algorithms are developed based on the analysis and the Apriori algorithm is modified to suit the process. Finally, the modules are integrated into a tool to identify DDIs. The results are compared using standard drug interaction database for validation. 31% of the associations obtained were identified to be new and the match with existing interactions was 69%. This match clearly indicates the validity of the methodology and its applicability to similar databases. Formulation of the results using the generic names expanded the relevance of the results to a global scale. The global applicability helps the health care professionals worldwide to observe caution during various stages of drug administration thus considerably enhancing pharmacovigilance

Trace element partitioning and soil particle characterisation around mining and smelting areas at Tharsis, Riotinto and Huelva, SW Spain

Trace elements may present an environmental hazard in the vicinity of mining and smelting activities. However, the factors controlling trace element distribution in soils around ancient and modem mining and smelting areas are not always clear. Tharsis, Riotinto and Huelva are located in the Iberian Pyrite Belt in SW Spain. Tharsis and Riotinto mines have been exploited since 2500 B.C., with intensive smelting taking place. Huelva, established in 1970 and using the Flash Furnace Outokumpu process, is currently one of the largest smelter in the world. Pyrite and chalcopyrite ore have been intensively smelted for Cu. However, unusually for smelters and mines of a similar size, the elevated trace element concentrations in soils were found to be restricted to the immediate vicinity of the mines and smelters, being found up to a maximum of 2 kin from the mines and smelters at Tharsis, Riotinto and Huelva. Trace element partitioning (over 2/3 of trace elements found in the residual immobile fraction of soils at Tharsis) and soil particles examination by SEM-EDX showed that trace elements were not adsorbed onto soil particles, but were included within the matrix of large trace element-rich Fe silicate slag particles (i.e. 1 min circle divide at least 1 wt.% As, Cu and Zn, and 2 wt.% Pb). Slag particle large size (I mm 0) was found to control the geographically restricted trace element distribution in soils at Tharsis, Riotinto and Huelva, since large heavy particles could not have been transported long distances. Distribution and partitioning indicated that impacts to the environment as a result of mining and smelting should remain minimal in the region. (c) 2006 Elsevier B.V. All rights reserved.

Dynamic load balancing for the distributed mining of molecular structures

In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable for large-scale, multi-domain, heterogeneous environments, such as computational grids.

High performance subgraph mining in molecular compounds

Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.