Search results for: mining

Authors: Abrehet Kahsay Mehari

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Aquatic plants provide the physical structure of wetlands and increase their habitat complexity and heterogeneity, and as such, have a profound influence on other biotas. In this study, we investigated how human disturbance activities influenced the species richness and community composition of aquatic plants in the wetlands of Lake Tana, Ethiopia. Twelve wetlands were selected: four lacustrine, four river mouths, and four riverine papyrus swamps. Data on aquatic plants, environmental variables, and human activities were collected during the dry and wet seasons of 2018. A linear mixed effect model and a distance-based Redundancy Analysis (db-RDA) were used to relate aquatic plant species richness and community composition, respectively, to human activities and environmental variables. A total of 113 aquatic plant species, belonging to 38 families, were identified across all wetlands during the dry and wet seasons. Emergent species had the maximum area covered at 73.45 % and attained the highest relative abundance, followed by amphibious and other forms. The mean taxonomic richness of aquatic plants was significantly lower in wetlands with high overall human disturbance scores compared to wetlands with low overall human disturbance scores. Moreover, taxonomic richness showed a negative correlation with livestock grazing, tree plantation, and sand mining. The community composition also varied across wetlands with varying levels of human disturbance and was primarily driven by turnover (i.e., replacement of species) rather than nestedness resultant(i.e., loss of species). Distance-based redundancy analysis revealed that livestock grazing, tree plantation, sand mining, waste dumping, and crop cultivation were significant predictors of variation in aquatic plant communities’ composition in the wetlands. Linear mixed effect models and distance-based redundancy analysis also revealed that water depth, turbidity, conductivity, pH, sediment depth, and temperature were important drivers of variations in aquatic plant species richness and community composition. Papyrus swamps had the highest species richness and supported different plant communities. Conservation efforts should therefore focus on these habitats and measures should be taken to restore the highly disturbed and species poor wetlands near the river mouths.

Keywords: species richness, community composition, aquatic plants, wetlands, Lake Tana, human disturbance activities

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Authors: Della Saddam Housseyn

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Gold, the quintessential noble metal, is one of the most popular metals today, given its ever-increasing cost in the international market. The Amesmessa gold deposit is one of the gold-producing deposits. The first step in our job is to analyze the ore (considered rich ore). Mineralogical and chemical analysis has shown that the general constitution of the ore is quartz in addition to other phases such as Al2O3, Fe2O3, CaO, dolomite. The second step consists of all the leaching tests carried out in rolling bottles. These tests were carried out on 14 samples to determine the maximum recovery rate and the optimum consumption of reagent (NaCN and CaO). Tests carried out on a pulp density at 50% solid, 500 ppm cyanide concentration and particle size less than 0.6 mm at alkaline pH gave a recovery rate of 94.37%.

Keywords: cyanide, DRX, FX, gold, leaching, rate of recovery, SAA

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Authors: Hoda Rahmani, Gary Weckman

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The body of research examining the integration of drones into various industries is expanding rapidly. Despite progress made in addressing the cybersecurity concerns for commercial drones, knowledge deficits remain in determining potential occupational hazards and risks of drone use to employees’ well-being and health in the workplace. This creates difficulty in identifying key approaches to risk mitigation strategies and thus reflects the need for raising awareness among employers, safety professionals, and policymakers about workplace drone-related accidents. The purpose of this study is to investigate the prevalence of and possible risk factors for drone-related mishaps by comparing the application of drones in construction with manufacturing industries. The chief reason for considering these specific sectors is to ascertain whether there exists any significant difference between indoor and outdoor flights since most construction sites use drones outside and vice versa. Therefore, the current research seeks to examine the causes and patterns of workplace drone-related mishaps and suggest possible ergonomic interventions through data collection. Potential ergonomic practices to mitigate hazards associated with flying drones could include providing operators with professional pieces of training, conducting a risk analysis, and promoting the use of personal protective equipment. For the purpose of data analysis, two data mining techniques, the random forest and association rule mining algorithms, will be performed to find meaningful associations and trends in data as well as influential features that have an impact on the occurrence of drone-related accidents in construction and manufacturing sectors. In addition, Spearman’s correlation and chi-square tests will be used to measure the possible correlation between different variables. Indeed, by recognizing risks and hazards, occupational safety stakeholders will be able to pursue data-driven and evidence-based policy change with the aim of reducing drone mishaps, increasing productivity, creating a safer work environment, and extending human performance in safe and fulfilling ways. This research study was supported by the National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant #T42OH008432.

Keywords: commercial drones, ergonomic interventions, occupational safety, pattern recognition

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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The aim of this study was to develop an Artificial Neural Network0 s recommendation model for an online process using the complexity of load, performance, and average servicing time of the resources. Here, the proposed model investigates the resource performance using stochastic gradient decent method for learning ranking function. A probabilistic cost function is implemented to identify the optimal θ values (load) on each resource. Based on this result the recommendation of resource suitable for performing the currently executing task is made. The test result of CoSeLoG project is presented with an accuracy of 72.856%.

Keywords: ADALINE, neural network, gradient decent, process mining, resource behaviour, polynomial regression model

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Authors: Hussaini Garba Mohammed

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Background/Significance: The lack of data gathering is affecting NGOs world-wide in general to have good data information about educational and health related issues among communities in any country and around the world. For example, HIV/AIDS smoking (Tuberculosis diseases) and COVID-19 virus carriers is becoming a serious public health problem, especially among old men and women. But there is no full details data survey assessment from communities, villages, and rural area in some countries to show the percentage of victims and patients, especial with this world COVID-19 virus among the people. These data are essential to inform programming targets, strategies, and priorities in getting good information about data gathering in any society.

Keywords: reliable information, data assessment, data mining, data communication

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Authors: M. Turmanauli, T. Todua, O. Gvaberidze, R. Javakhadze, N. Chkhaidze, N. Khatiashvili

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Background: Mining process has the significant influence on human health and quality of life. In recent years the events in Georgia were reflected on the industry working process, especially minimal requirements of labor safety, hygiene standards of workplace and the regime of work and rest are not observed. This situation is often caused by the lack of responsibility, awareness, and knowledge both of workers and employers. The control of working conditions and its protection has been worsened in many of industries. Materials and Methods: For evaluation of the current situation the prospective epidemiological study by face to face interview method was conducted at Georgian “Manganese Zestafoni Ferroalloy Plant” in 2011-2013. 65.7% of employees (1428 bulletin) were surveyed and the incidence rates of temporary disability days were studied. Results: The average length of a temporary disability single accident was studied taking into consideration as sex groups as well as the whole cohort. According to the classes of harmfulness the following results were received: Class 2.0-10.3%; 3.1-12.4%; 3.2-35.1%; 3.3-12.1%; 3.4-17.6%; 4.0-12.5%. Among the employees 47.5% and 83.1% were tobacco and alcohol consumers respectively. According to the age groups and years of work on the base of previous experience ≥50 ages and ≥21 years of work data prevalence respectively. The obtained data revealed increased morbidity rate according to age and years of work. It was found that the bone and articulate system and connective tissue diseases, aggravation of chronic respiratory diseases, ischemic heart diseases, hypertension and cerebral blood discirculation were the leading among the other diseases. High prevalence of morbidity observed in the workplace with not satisfactory labor conditions from the hygienic point of view. Conclusion: According to received data the causes of morbidity are the followings: unsafety labor conditions; incomplete of preventive medical examinations (preliminary and periodic); lack of access to appropriate health care services; derangement of gathering, recording, and analysis of morbidity data. This epidemiological study was conducted at the JSC “Manganese Ferro Alloy Plant” according to State program “ Prevention of Occupational Diseases” (Program code is 35 03 02 05).

Keywords: occupational health, mining process, morbidity level, cerebral blood discirculation

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Authors: Avelyno D'Costa, S. K. Shyama, M. K. Praveen Kumar

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The coast of Goa, India receives constant anthropogenic stress through its major rivers which carry mining rejects of iron and manganese ores from upstream mining sites and petroleum hydrocarbons from shipping and harbor-related activities which put the aquatic fauna such as bivalves at risk. The present study reports the pollution status of the Goan coast by the above xenobiotics employing genotoxicity studies. This is further supplemented by the quantification of total petroleum hydrocarbons (TPHs) and various trace metals (iron, manganese, copper, cadmium, and lead) in gills of the estuarine clam, Meretrix ovum as well as from the surrounding water and sediment, over a two-year sampling period, from January 2013 to December 2014. Bivalves were collected from a probable unpolluted site at Palolem and a probable polluted site at Vasco, based upon the anthropogenic activities at these sites. Genotoxicity was assessed in the gill cells using the comet assay and micronucleus test. The quantity of TPHs and trace metals present in gill tissue, water and sediments were analyzed using spectrofluorometry and atomic absorption spectrophotometry (AAS), respectively. The statistical significance of data was analyzed employing Student’s t-test. The relationship between DNA damage and pollutant concentrations was evaluated using multiple regression analysis. Significant DNA damage was observed in the bivalves collected from Vasco which is a region of high industrial activity. Concentrations of TPHs and trace metals (iron, manganese, and cadmium) were also found to be significantly high in gills of the bivalves collected from Vasco compared to those collected from Palolem. Further, the concentrations of these pollutants were also found to be significantly high in the water and sediments at Vasco compared to that of Palolem. This may be due to the lack of industrial activity at Palolem. A high positive correlation was observed between the pollutant levels and DNA damage in the bivalves collected from Vasco suggesting the genotoxic nature of these pollutants. Further, M. ovum can be used as a bioindicator species for monitoring the level of pollution of the estuarine/coastal regions by TPHs and trace metals.

Keywords: comet assay, metals, micronucleus test, total petroleum Hydrocarbons

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Authors: Erin Hea-Jin Kim

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This study aims to investigate scholarly communication patterns in Korean studies, which focuses on all aspects of Korea, including history, culture, literature, politics, society, economics, religion, and so on. It is called ‘national study or home study’ as the subject of the study is itself, whereas it is called ‘area study’ as the subject of the study is others, i.e., outside of Korea. Understanding of the structure of scholarly communication in Korean studies is important since the motivations, procedures, results, or outcomes of individual studies may be affected by the cooperative relationships that appear in the communication structure. To this end, we collected 1,798 articles with the (author or index) keyword ‘Korean’ published in 2018 from the Scopus database and extracted the institution and country of the authors using a text mining technique. A total of 96 countries, including South Korea, was identified. Then we constructed a co-authorship network based on the countries identified. The indicators of social network analysis (SNA), co-occurrences, and cluster analysis were used to measure the activity and connectivity of participation in collaboration in Korean studies. As a result, the highest frequency of collaboration appears in the following order: S. Korea with the United States (603), S. Korea with Japan (146), S. Korea with China (131), S. Korea with the United Kingdom (83), and China with the United States (65). This means that the most active participants are S. Korea as well as the USA. The highest rank in the role of mediator measured by betweenness centrality appears in the following order: United States (0.165), United Kingdom (0.045), China (0.043), Japan (0.037), Australia (0.026), and South Africa (0.023). These results show that these countries contribute to connecting in Korean studies. We found two major communities among the co-authorship network. Asian countries and America belong to the same community, and the United Kingdom and European countries belong to the other community. Korean studies have a long history, and the study has emerged since Japanese colonization. However, Korean studies have never been investigated by digital content analysis. The contributions of this study are an analysis of co-authorship in Korean studies with a global perspective based on digital content, which has not attempted so far to our knowledge, and to suggest ideas on how to analyze the humanities disciplines such as history, literature, or Korean studies by text mining. The limitation of this study is that the scholarly data we collected did not cover all domestic journals because we only gathered scholarly data from Scopus. There are thousands of domestic journals not indexed in Scopus that we can consider in terms of national studies, but are not possible to collect.

Keywords: co-authorship network, Korean studies, Koreanology, scholarly communication

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Authors: Trupti Diwan

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Predicting student failure at school has changed into a difficult challenge due to both the large number of factors that can affect the reduced performance of students and the imbalanced nature of these kinds of data sets. This paper surveys the two elements needed to make prediction on Students’ Academic Performances which are parameters and methods. This paper also proposes a framework for predicting the performance of engineering students. Genetic programming can be used to predict student failure/success. Ranking algorithm is used to rank students according to their credit points. The framework can be used as a basis for the system implementation & prediction of students’ Academic Performance in Higher Learning Institute.

Keywords: classification, educational data mining, student failure, grammar-based genetic programming

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Authors: Kanika Gupta, Ashok Kumar

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Biofilms are dense, highly hydrated cell clusters that are irreversibly attached to a substratum, to an interface or to each other, and are embedded in a self-produced gelatinous matrix composed of extracellular polymeric substances. Research in biofilm field has become very significant, as biofilm has shown high mechanical resilience and resistance to antibiotic treatment and constituted as a significant problem in both healthcare and other industry related to microorganisms. The massive information both stated and hidden in the biofilm literature are growing exponentially therefore it is not possible for researchers and practitioners to automatically extract and relate information from different written resources. So, the current work proposes and discusses the use of text mining techniques for the extraction of information from biofilm literature corpora containing 34306 documents. It is very difficult and expensive to obtain annotated material for biomedical literature as the literature is unstructured i.e. free-text. Therefore, we considered unsupervised approach, where no annotated training is necessary and using this approach we developed a system that will classify the text on the basis of growth and development, drug effects, radiation effects, classification and physiology of biofilms. For this, a two-step structure was used where the first step is to extract keywords from the biofilm literature using a metathesaurus and standard natural language processing tools like Rapid Miner_v5.3 and the second step is to discover relations between the genes extracted from the whole set of biofilm literature using pubmed.mineR_v1.0.11. We used unsupervised approach, which is the machine learning task of inferring a function to describe hidden structure from 'unlabeled' data, in the above-extracted datasets to develop classifiers using WinPython-64 bit_v3.5.4.0Qt5 and R studio_v0.99.467 packages which will automatically classify the text by using the mentioned sets. The developed classifiers were tested on a large data set of biofilm literature which showed that the unsupervised approach proposed is promising as well as suited for a semi-automatic labeling of the extracted relations. The entire information was stored in the relational database which was hosted locally on the server. The generated biofilm vocabulary and genes relations will be significant for researchers dealing with biofilm research, making their search easy and efficient as the keywords and genes could be directly mapped with the documents used for database development.

Keywords: biofilms literature, classifiers development, text mining, unsupervised learning approach, unstructured data, relational database

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Authors: Jeongyeob Hong, Myeonghoon Park, Taeson Yoon

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After the advent of Achaeans which utilize different metabolism pathway and contain conspicuously different cellular structure, they have been recognized as possible materials for developing quality of human beings. Among diverse Achaeans, in this paper, we compared 16s RNA Sequences of four different species of Thermococcus: Achaeans genus specialized in sulfur-dealing metabolism. Four Species, Barophilus, Kodakarensis, Hydrothermalis, and Onnurineus, live near the hydrothermal vent that emits extreme amount of sulfur and heat. By comparing ribosomal sequences of aforementioned four species, we found similarities in their sequences and expressed protein, enabling us to expect that certain ribosomal sequence or proteins are vital for their survival. Apriori algorithms and Decision Tree were used. for comparison.

Keywords: Achaeans, Thermococcus, apriori algorithm, decision tree

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Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Abdelhalem Shahen

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The world is now experiencing a digital revolution and greatly accelerated technological developments, in addition to the transition from the economy in its traditional form to the digital economy, which has resulted in the emergence of new tools that are appropriate to those developments, and from this, this paper attempts to explore the economic characteristics of the bitcoin currency that circulated recently. Due to the many advantages that distinguish it from money in its traditional forms, which have a range of economic effects. The study found that Bitcoin is among the technological innovations, which contain a set of characteristics that are worth studying, those that make it the focus of attention, such as the digital currency, the peer-to-peer property, Lower and Faster Transaction Costs, transparency, decentralized control, privacy, and Double-Spending, as well as security and Cryptographic, and finally mining.

Keywords: Digital Economics, Digital Currencies, Bitcoin, Features of Bitcoin

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Authors: Atsuko Okazaki, Jurg Ott

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Some genetic diseases (‘digenic traits’) are due to the interaction between two DNA variants. For example, certain forms of Retinitis Pigmentosa (a genetic form of blindness) occur in the presence of two mutant variants, one in the ROM1 gene and one in the RDS gene, while the occurrence of only one of these mutant variants leads to a completely normal phenotype. Detecting such digenic traits by genetic methods is difficult. A common approach to finding disease-causing variants is to compare 100,000s of variants between individuals with a trait (cases) and those without the trait (controls). Such genome-wide association studies (GWASs) have been very successful but hinge on genetic effects of single variants, that is, there should be a difference in allele or genotype frequencies between cases and controls at a disease-causing variant. Frequent pattern mining (FPM) methods offer an avenue at detecting digenic traits even in the absence of single-variant effects. The idea is to enumerate pairs of genotypes (genotype patterns) with each of the two genotypes originating from different variants that may be located at very different genomic positions. What is needed is for genotype patterns to be significantly more common in cases than in controls. Let Y = 2 refer to cases and Y = 1 to controls, with X denoting a specific genotype pattern. We are seeking association rules, ‘X → Y’, with high confidence, P(Y = 2|X), significantly higher than the proportion of cases, P(Y = 2) in the study. Clearly, generally available FPM methods are very suitable for detecting disease-associated genotype patterns. We use fpgrowth as the basic FPM algorithm and built a framework around it to enumerate high-frequency digenic genotype patterns and to evaluate their statistical significance by permutation analysis. Application to a published dataset on opioid dependence furnished results that could not be found with classical GWAS methodology. There were 143 cases and 153 healthy controls, each genotyped for 82 variants in eight genes of the opioid system. The aim was to find out whether any of these variants were disease-associated. The single-variant analysis did not lead to significant results. Application of our FPM implementation resulted in one significant (p < 0.01) genotype pattern with both genotypes in the pattern being heterozygous and originating from two variants on different chromosomes. This pattern occurred in 14 cases and none of the controls. Thus, the pattern seems quite specific to this form of substance abuse and is also rather predictive of disease. An algorithm called Multifactor Dimension Reduction (MDR) was developed some 20 years ago and has been in use in human genetics ever since. This and our algorithms share some similar properties, but they are also very different in other respects. The main difference seems to be that our algorithm focuses on patterns of genotypes while the main object of inference in MDR is the 3 × 3 table of genotypes at two variants.

Keywords: digenic traits, DNA variants, epistasis, statistical genetics

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Authors: Yayun Hsu, Henry Horng-Shing Lu

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This paper proposes a data-driven, biology-inspired neural segmentation method of 3D drosophila Brainbow images. We use Bayesian Sequential Partitioning algorithm for probabilistic modeling, which can be used to detect somas and to eliminate cross talk effects. This work attempts to develop an automatic methodology for neuron image segmentation, which nowadays still lacks a complete solution due to the complexity of the image. The proposed method does not need any predetermined, risk-prone thresholds since biological information is inherently included in the image processing procedure. Therefore, it is less sensitive to variations in neuron morphology; meanwhile, its flexibility would be beneficial for tracing the intertwining structure of neurons.

Keywords: brainbow, 3D imaging, image segmentation, neuron morphology, biological data mining, non-parametric learning

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Authors: Saeed Bagherifam, Trevor Brown, Chris Fellows, Ravi Naidu

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Over the last century, rapid urbanization, industrial emissions, and mining activities have resulted in widespread contamination of the environment by heavy metal(loid)s. Arsenic (As) is a toxic metalloid belonging to group 15 of the periodic table, which occurs naturally at low concentrations in soils and the earth’s crust, although concentrations can be significantly elevated in natural systems as a result of dispersion from anthropogenic sources, e.g., mining activities. Bioavailability is the fraction of a contaminant in soils that is available for uptake by plants, food chains, and humans and therefore presents the greatest risk to terrestrial ecosystems. Numerous attempts have been made to establish in situ and ex-situ technologies of remedial action for remediation of arsenic-contaminated soils. In situ stabilization techniques are based on deactivation or chemical immobilization of metalloid(s) in soil by means of soil amendments, which consequently reduce the bioavailability (for biota) and bioaccessibility (for humans) of metalloids due to the formation of low-solubility products or precipitates. This study investigated the effectiveness of two different types of synthetic manganese and iron oxides (birnessite and goethite) for stabilization of As in a soil spiked with 1000 mg kg⁻¹ of As and treated with 10% dosages of soil amendments. Birnessite was made using HCl and KMnO₄, and goethite was synthesized by the dropwise addition of KOH into Fe(NO₃) solution. The resulting contaminated soils were subjected to a series of chemical extraction studies including sequential extraction (BCR method), single-step extraction with distilled (DI) water, 2M HNO₃ and simplified bioaccessibility extraction tests (SBET) for estimation of bioaccessible fractions of As in two different soil fractions ( < 250 µm and < 2 mm). Concentrations of As in samples were measured using inductively coupled plasma mass spectrometry (ICP-MS). The results showed that soil with birnessite reduced bioaccessibility of As by up to 92% in both soil fractions. Furthermore, the results of single-step extractions revealed that the application of both birnessite and Goethite reduced DI water and HNO₃ extractable amounts of arsenic by 75, 75, 91, and 57%, respectively. Moreover, the results of the sequential extraction studies showed that both birnessite and goethite dramatically reduced the exchangeable fraction of As in soils. However, the amounts of recalcitrant fractions were higher in birnessite, and Goethite amended soils. The results revealed that the application of both birnessite and goethite significantly reduced bioavailability and the exchangeable fraction of As in contaminated soils, and therefore birnessite and Goethite amendments might be considered as promising adsorbents for stabilization and remediation of As contaminated soils.

Keywords: arsenic, bioavailability, in situ stabilisation, metalloid(s) contaminated soils

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Authors: Pavol Tanuska, Pavel Vazan, Michal Kebisek, Dominika Jurovata

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The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system, thus, the proposed solution has been verified. The paper documents how it is possible to apply new discovery knowledge to be used in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.

Keywords: hierarchical process control, knowledge discovery from databases, neural network, process control

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Authors: Hing Hao Chan, Thomas Richard, Masood Mostofi

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The mining industry relies on an estimate of rock strength at several stages of a mine life cycle: mining (excavating, blasting, tunnelling) and processing (crushing and grinding), both very energy-intensive activities. An effective comminution design that can yield significant dividends often requires a reliable estimate of the material rock strength. Common laboratory tests such as rod, ball mill, and uniaxial compressive strength share common shortcomings such as time, sample preparation, bias in plug selection cost, repeatability, and sample amount to ensure reliable estimates. In this paper, the authors present a methodology to derive an estimate of the rock strength from drilling data recorded while coring with a diamond core head. The work presented in this paper builds on a phenomenological model of the bit-rock interface proposed by Franca et al. (2015) and is inspired by the now well-established use of the scratch test with PDC (Polycrystalline Diamond Compact) cutter to derive the rock uniaxial compressive strength. The first part of the paper introduces the phenomenological model of the bit-rock interface for a diamond core head that relates the forces acting on the drill bit (torque, axial thrust) to the bit kinematic variables (rate of penetration and angular velocity) and introduces the intrinsic specific energy or the energy required to drill a unit volume of rock for an ideally sharp drilling tool (meaning ideally sharp diamonds and no contact between the bit matrix and rock debris) that is found well correlated to the rock uniaxial compressive strength for PDC and roller cone bits. The second part describes the laboratory drill rig, the experimental procedure that is tailored to minimize the effect of diamond polishing over the duration of the experiments, and the step-by-step methodology to derive the intrinsic specific energy from the recorded data. The third section presents the results and shows that the intrinsic specific energy correlates well to the uniaxial compressive strength for the 11 tested rock materials (7 sedimentary and 4 igneous rocks). The last section discusses best drilling practices and a method to estimate the rock strength from field drilling data considering the compliance of the drill string and frictional losses along the borehole. The approach is illustrated with a case study from drilling data recorded while drilling an exploration well in Australia.

Keywords: bit-rock interaction, drilling experiment, impregnated diamond drilling, uniaxial compressive strength

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Authors: Shengnan Chen, Shuhua Wang

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Successful production of hydrocarbon from unconventional tight oil reserves has changed the energy landscape in North America. The oil contained within these reservoirs typically will not flow to the wellbore at economic rates without assistance from advanced horizontal well and multi-stage hydraulic fracturing. Efficient and economic development of these reserves is a priority of society, government, and industry, especially under the current low oil prices. Meanwhile, society needs technological and process innovations to enhance oil recovery while concurrently reducing environmental impacts. Recently, big data analysis and artificial intelligence become very popular, developing data-driven insights for better designs and decisions in various engineering disciplines. However, the application of data mining in petroleum engineering is still in its infancy. The objective of this research aims to apply intelligent data analysis and data-driven models to exploit unconventional oil reserves both efficiently and economically. More specifically, a comprehensive database including the reservoir geological data, reservoir geophysical data, well completion data and production data for thousands of wells is firstly established to discover the valuable insights and knowledge related to tight oil reserves development. Several data analysis methods are introduced to analysis such a huge dataset. For example, K-means clustering is used to partition all observations into clusters; principle component analysis is applied to emphasize the variation and bring out strong patterns in the dataset, making the big data easy to explore and visualize; exploratory factor analysis (EFA) is used to identify the complex interrelationships between well completion data and well production data. Different data mining techniques, such as artificial neural network, fuzzy logic, and machine learning technique are then summarized, and appropriate ones are selected to analyze the database based on the prediction accuracy, model robustness, and reproducibility. Advanced knowledge and patterned are finally recognized and integrated into a modified self-adaptive differential evolution optimization workflow to enhance the oil recovery and maximize the net present value (NPV) of the unconventional oil resources. This research will advance the knowledge in the development of unconventional oil reserves and bridge the gap between the big data and performance optimizations in these formations. The newly developed data-driven optimization workflow is a powerful approach to guide field operation, which leads to better designs, higher oil recovery and economic return of future wells in the unconventional oil reserves.

Keywords: big data, artificial intelligence, enhance oil recovery, unconventional oil reserves

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Authors: Khadidja Belbachir, Hafida Belbachir

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subsequently the expansion of the physical supports storage and the needs ceaseless to accumulate several data, the sequential algorithms of associations’ rules research proved to be ineffective. Thus the introduction of the new parallel versions is imperative. We propose in this paper, a parallel version of a sequential algorithm “Partition”. This last is fundamentally different from the other sequential algorithms, because it scans the data base only twice to generate the significant association rules. By consequence, the parallel approach does not require much communication between the sites. The proposed approach was implemented for an experimental study. The obtained results, shows a great reduction in execution time compared to the sequential version and Count Distributed algorithm.

Keywords: association rules, distributed data mining, partition, parallel algorithms

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Authors: Bambang Antoro, Lasito Soebari, Geoffrey de Jong, Fernandy Meiriyanto, Michael Siahaan, Eko Wibowo, Pormando Silalahi, Ruswanto, Adi Budirumantyo

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The Ertsberg East Skarn System (EESS) is located in the Ertsberg Mining District, Papua, Indonesia. EESS is a sub-vertical zone of copper-gold mineralization hosted in both diorite (vein-style mineralization) and skarn (disseminated and vein style mineralization). Deep Mill Level Zone (DMLZ) is a mining zone in the lower part of East Ertsberg Skarn System (EESS) that product copper and gold. The Deep Mill Level Zone deposit is located below the Deep Ore Zone deposit between the 3125m to 2590m elevation, measures roughly 1,200m in length and is between 350 and 500m in width. DMLZ planned start mined on Q2-2015, being mined at an ore extraction rate about 60,000 tpd by the block cave mine method (the block cave contain 516 Mt). Mineralization and associated hydrothermal alteration in the DMLZ is hosted and enclosed by a large stock (The Main Ertsberg Intrusion) that is barren on all sides and above the DMLZ. Late porphyry dikes that cut through the Main Ertsberg Intrusion are spatially associated with the center of the DMLZ hydrothermal system. DMLZ orebody hosted in diorite and skarn, both dominantly by vein style mineralization. Percentage Material Mined at DMLZ compare with current Reserves are diorite 46% (with 0.46% Cu; 0.56 ppm Au; and 0.83% EqCu); Skarn is 39% (with 1.4% Cu; 0.95 ppm Au; and 2.05% EqCu); Hornfels is 8% (with 0.84% Cu; 0.82 ppm Au; and 1.39% EqCu); and Marble 7 % possible mined waste. Correlation between Ertsberg intrusion, major structure, and vein style mineralization is important to determine characteristic orebody in DMLZ Mine. Generally Deep Mill Level Zone has 2 type of vein filling mineralization from both hosted (diorite and skarn), in diorite hosted the vein system filled by chalcopyrite-bornite-quartz and pyrite, in skarn hosted the vein filled by chalcopyrite-bornite-pyrite and magnetite without quartz. Based on orientation the stockwork vein at diorite hosted and shallow vein in skarn hosted was generally NW-SE trending and NE-SW trending with shallow-moderate dipping. Deep Mill Level Zone control by two main major faults, geologist founded and verified local structure between major structure with NW-SE trending and NE-SW trending with characteristics slickenside, shearing, gauge, water-gas channel, and some has been re-healed.

Keywords: copper-gold, DMLZ, skarn, structure

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Authors: Victorita Radulescu

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Introduction: The sewage treatment plants, in the second stage, are formed by tanks having as main purpose the formation of the suspensions with high possible solid concentration values. The paper presents a solution to produce a rapid concentration of the slurry and sludge, having as main purpose the minimization as much as possible the size of the tanks. The solution is based on a rotor with bars, tested into two different areas of industrial activity: the remediation of the wastewater from the oil industry and, in the last year, into the mining industry. Basic Methods: It was designed, realized and tested a thickening system with vertical bars that manages to reduce sludge moisture content from 94% to 87%. The design was based on the hypothesis that the streamlines of the vortices detached from the rotor with vertical bars accelerate, under certain conditions, the sludge thickening. It is moved at the lateral sides, and in time, it became sediment. The formed vortices with the vertical axis in the viscous fluid, under the action of the lift, drag, weight, and inertia forces participate at a rapid aggregation of the particles thus accelerating the sludge concentration. Appears an interdependence between the Re number attached to the flow with vortex induced by the vertical bars and the size of the hydraulic compaction phenomenon, resulting from an accelerated process of sedimentation, therefore, a sludge thickening depending on the physic-chemical characteristics of the resulting sludge is projected the rotor's dimensions. Major findings/ Results: Based on the experimental measurements was performed the numerical simulation of the hydraulic rotor, as to assure the necessary vortices. The experimental measurements were performed to determine the optimal height and the density of the bars for the sludge thickening system, to assure the tanks dimensions as small as possible. The time thickening/settling was reduced by 24% compared to the conventional used systems. In the present, the thickeners intend to decrease the intermediate stage of water treatment, using primary and secondary settling; but they assume a quite long time, the order of 10-15 hours. By using this system, there are no intermediary steps; the thickening is done automatically when are created the vortices. Conclusions: The experimental tests were carried out in the wastewater treatment plant of the Refinery of oil from Brazi, near the city Ploiesti. The results prove its efficiency in reducing the time for compacting the sludge and the smaller humidity of the evacuated sediments. The utilization of this equipment is now extended and it is tested the mining industry, with significant results, in Lupeni mine, from the Jiu Valley.

Keywords: experimental tests, hydrodynamic modeling, rotor efficiency, wastewater treatment

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Authors: Agostinho Antunes

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The completion of the human genome sequencing in 2003 opened a new perspective into the importance of whole genome sequencing projects, and currently multiple species are having their genomes completed sequenced, from simple organisms, such as bacteria, to more complex taxa, such as mammals. This voluminous sequencing data generated across multiple organisms provides also the framework to better understand the genetic makeup of such species and related ones, allowing to explore the genetic changes underlining the evolution of diverse phenotypic traits. Here, recent results from our group retrieved from comparative evolutionary genomic analyses of selected marine animal species will be considered to exemplify how gene novelty and gene enhancement by positive selection might have been determinant in the success of adaptive radiations into diverse habitats and lifestyles.

Keywords: comparative genomics, adaptive evolution, bioinformatics, phylogenetics, genome mining

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Authors: K. Raghu, M. N. Vathhsala, Naveen Aradya, Sharth

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River sand is the most preferred fine aggregate for mortar and concrete. River sand is a product of natural weathering of rocks over a period of millions of years and is mined from river beds. Sand mining has disastrous environmental consequences. The excessive mining of river bed is creating an ecological imbalance. This has lead to have restrictions imposed by ministry of environment on sand mining. Driven by the acute need for sand, stone dust or manufactured sand prepared from the crushing and screening of coarse aggregate is being used as sand in the recent past. However manufactured sand is also a natural material and has quarrying and quality issues. To reduce the burden on the environment, alternative materials to be used as fine aggregates are being extensively investigated all over the world. Looking to the quantum of requirements, quality and properties there has been a global consensus on a material – Granulated slags. Granulated slag has been proven as a suitable material for replacing natural sand / crushed fine aggregates. In developed countries, the use of granulated slag as fine aggregate to replace natural sand is well established and is in regular practice. In the present paper Granulated slag has been experimented for usage in mortar. Slags are the main by-products generated during iron and steel production in the steel industry. Over the past decades, the steel production has increased and, consequently, the higher volumes of by-products and residues generated which have driven to the reuse of these materials in an increasingly efficient way. In recent years new technologies have been developed to improve the recovery rates of slags. Increase of slags recovery and use in different fields of applications like cement making, construction and fertilizers help in preserving natural resources. In addition to the environment protection, these practices produced economic benefits, by providing sustainable solutions that can allow the steel industry to achieve its ambitious targets of “zero waste” in coming years. Slags are generated at two different stages of steel production, iron making and steel making known as BF(Blast Furnace) slag and steel slag respectively. The slagging agent or fluxes, such as lime stone, dolomite and quartzite added into BF or steel making furnaces in order to remove impurities from ore, scrap and other ferrous charges during smelting. The slag formation is the result of a complex series of physical and chemical reactions between the non-metallic charge(lime stone, dolomite, fluxes), the energy sources(coal, coke, oxygen, etc.) and refractory materials. Because of the high temperatures (about 15000 C) during their generation, slags do not contain any organic substances. Due to the fact that slags are lighter than the liquid metal, they float and get easily removed. The slags protect the metal bath from atmosphere and maintain temperature through a kind of liquid formation. These slags are in liquid state and solidified in air after dumping in the pit or granulated by impinging water systems. Generally, BF slags are granulated and used in cement making due to its high cementious properties, and steel slags are mostly dumped due to unfavourable physio-chemical conditions. The increasing dump of steel slag not only occupies a plenty of land but also wastes resources and can potentially have an impact on the environment due to water pollution. Since BF slag contains little Fe and can be used directly. BF slag has found a wide application, such as cement production, road construction, Civil Engineering work, fertilizer production, landfill daily cover, soil reclamation, prior to its application outside the iron and steel making process.

Keywords: steel slag, river sand, granulated slag, environmental

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Authors: Aigerim Uyzbayeva, Valeriya Tyo, Nurlan Ibrayev

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Kazakhstan is currently one of the dynamically developing states in its region. The stable growth in all sectors of the economy leads to a corresponding increase in energy consumption. Thus, country consumes a significant amount of energy due to the high level of industralisation and the presence of energy-intensive manufacturing such as mining and metallurgy which in turn leads to low energy efficiency. With allowance for this the Government has set several priorities to adopt a transition of Republic of Kazakhstan to a “green economy”. This article provides an overview of Kazakhstan’s energy efficiency situation in for the period of 1991-2014. First, the dynamics of production and consumption of conventional energy resources are given. Second, the potential of renewable energy sources is summarised, followed by the description of GHG emissions trends in the country. Third, Kazakhstan’ national initiatives, policies and locally implemented projects in the field of energy efficiency are described.

Keywords: energy efficiency in Kazakhstan, greenhouse gases, renewable energy, sustainable development

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Authors: Yu Wang

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Nowadays, Internet enables its users to share the information online and to interact with others. Facing with numerous information, these Internet users are confused and begin to rely on the opinion leaders’ recommendations. The online opinion leaders are the individuals who have professional knowledge, who utilize the online channels to spread word-of-mouth information and who can affect the attitudes or even the behavior of their followers to some degree. Because utilizing the online opinion leaders is seen as an important approach to affect the potential consumers, how to identify them has become one of the hottest topics in the related field. Hence, in this article, the concepts and characteristics are introduced, and the researches related to identifying opinion leaders are collected and divided into three categories. Finally, the implications for future studies are provided.

Keywords: online opinion leaders, user attributes analysis, text mining analysis, network structure analysis

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Félix David Suárez Bonilla

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A sentiment analysis service to detect polarity (positive, neural, and negative), based on transfer learning, was built using a Spanish version of BERT and applied to tweets written in Spanish. The dataset that was used consisted of 11975 reviews, which were extracted from Google Play using the google-play-scrapper package. The BETO trained model used: the AdamW optimizer, a batch size of 16, a learning rate of 2x10⁻⁵ and 10 epochs. The system was tested using tweets of three presidential pre-candidates from Costa Rica. The system was finally validated using human labeled examples, achieving an accuracy of 83.3%.

Keywords: NLP, transfer learning, BERT, sentiment analysis, social media, opinion mining

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: Mohsen Rabbani, Trista McLaughlin, Ehsan Vahidi

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the remediation of polluted sites with heavy metals, such as rare earth elements (REEs), has been a primary concern of researchers to decontaminate the soil. Among all developed methods to address this concern, phytoremediation has been established as efficient, cost-effective, easy-to-use, and environmentally friendly way, providing a long-term solution for addressing this global concern. Furthermore, this technology has another great potential application in the metals production sector through returning metals buried in soil via metals cropping. Considering the significant metal concentration in hyper-accumulators, the utilization of bioaccumulated metals to extract metals from plant matter has been proposed as a sub-economic area called phytomining. As a recent, more advanced technology to eliminate such pollutants from the soil and produce critical metals, bioharvesting (phytomining/agromining) has been considered another compromising way to produce metals and meet the global demand for critical/target metals. The bio-ore obtained from phytomining can be safely disposed of or introduced to metal production pathways to obtain the most demanded metals, such as REEs. It is well-known that some hyperaccumulators, e.g., fern Dicranopteris linearis, can be used to absorb REE metals from the polluted soils and accumulate them in plant organs, such as leaves and stems. After soil remediation, the plant species can be harvested and introduced to the downstream steps, namely crushing/grinding, leaching, and purification processes, to extract REEs from plant matter. This novel interdisciplinary field can fill the gap between agriculture, mining, metallurgy, and the environment. Despite the advantages of agromining for the REEs production industry, key issues related to the environmental sustainability of the entire life cycle of this new concept have not been assessed yet. Hence, a comparative life cycle assessment (LCA) study was conducted to quantify the environmental footprints of REEs phytomining. The current LCA study aims to estimate and calculate environmental effects associated with phytomining by considering critical factors, such as climate change, land use, and ozone depletion. The results revealed that phytomining is an easy-to-use and environmentally sustainable approach to either eliminate REEs from polluted sites or produce REEs, offering a new source of such metals production. This LCA research provides guidelines for researchers active in developing a reliable relationship between agriculture, mining, metallurgy, and the environment to encounter soil pollution and keep the earth green and clean.

Keywords: phytoremediation, phytomining, life cycle assessment, environmental impacts, rare earth elements, hyperaccumulator

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