Search results for: mining methods

Authors: Eman AbuKhousa, Marwan Z. Bataineh

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The 21^st century higher education and globalization challenge new faculty members to build effective professional networks and partnership with industry in order to accelerate their growth and success. This creates the need for community of practice (CoP)-oriented development approaches that focus on cognitive apprenticeship while considering individual predisposition and future career needs. This work adopts data mining, clustering analysis, and social networking technologies to present the CoP-Network as a virtual space that connects together similar career-aspiration individuals who are socially influenced to join and engage in a process for domain-related knowledge and practice acquisitions. The CoP-Network model can be integrated into higher education to extend traditional graduate and professional development programs.

Keywords: clustering analysis, community of practice, data mining, higher education, new faculty challenges, social network, social influence, professional development

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Authors: Yahya Almurtadha, Mukhtar Ghaleb, Ahmed M. Shamsan Saleh

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The world witnessed a fierce attack from the Covid-19 virus, which affected public life socially, economically, healthily and psychologically. The world's governments tried to confront the pandemic by imposing a number of precautionary measures such as general closure, curfews and social distancing. Scientists have also made strenuous efforts to develop an effective vaccine to train the immune system to develop antibodies to combat the virus, thus reducing its symptoms and limiting its spread. Artificial intelligence, along with researchers and medical authorities, has accelerated the vaccine development process through big data processing and simulation. On the other hand, one of the most important negatives of the impact of Covid 19 was the state of anxiety and fear due to the blowout of rumors through social media, which prompted governments to try to reassure the public with the available means. This study aims to proposed using Sentiment Analysis (AKA Opinion Mining) and deep learning as efficient artificial intelligence techniques to work on retrieving the tweets of the public from Twitter and then analyze it automatically to extract their opinions, expression and feelings, negatively or positively, about the symptoms they may feel after vaccination. Sentiment analysis is characterized by its ability to access what the public post in social media within a record time and at a lower cost than traditional means such as questionnaires and interviews, not to mention the accuracy of the information as it comes from what the public expresses voluntarily.

Keywords: deep learning, opinion mining, natural language processing, sentiment analysis

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Authors: Lintang Putra Sadewa, Ilham Prasetya Budhi

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Slope monitoring program has now become a mandatory campaign for any open pit mines around the world to operate safely. Utilizing various slope monitoring instruments and strategies, miners are now able to deliver precise decisions in mitigating the risk of slope failures which can be catastrophic. Currently, the most sophisticated slope monitoring technology available is the Slope Stability Radar (SSR), whichcan measure wall deformation in submillimeter accuracy. One of its eminent features is that SSRcan provide a timely warning by automatically raise an alarm when a predetermined rate-of-movement threshold is reached. However, establishing proper alarm thresholds is arguably one of the onerous challenges faced in any slope monitoring program. The difficulty mainly lies in the number of considerations that must be taken when generating a threshold becausean alarm must be effectivethat it should limit the occurrences of false alarms while alsobeing able to capture any real wall deformations. In this sense, experience shows that a site-specific alarm thresholdtendsto produce more reliable results because it considers site distinctive variables. This study will attempt to determinealarming thresholds for safety-critical monitoring based on an empirical model of slope deformation behaviour that is defined statistically fromdeformation data captured by the Slope Stability Radar (SSR). The study area comprises of upright-dipping highwall setting in a coal mining area with intense mining activities, andthe deformation data used for the study were recorded by the SSR throughout the year 2022. The model is site-specific in nature thus, valuable information extracted from the model (e.g., time-to-failure, onset-of-acceleration, and velocity) will be applicable in setting up site-specific alarm thresholds and will give a clear understanding of how deformation trends evolve over the area.

Keywords: safety-critical monitoring, alarming strategy, slope deformation behaviour model, coal mining

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Authors: Sudipto Shanker Dasgupta

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One of the largest mining companies wanted to look at health analytics for their driverless trucks. These trucks were the key to their supply chain logistics. The automated trucks had multi-level sub-assemblies which would send out sensor information. The use case that was worked on was to capture the sensor signal from the truck subcomponents and analyze the health of the trucks from repair and replacement purview. Open source software was used to stream the data into a clustered Hadoop setup in Amazon Web Services cloud and Apache Spark SQL was used to analyze the data. All of this was achieved through a 10 node amazon 32 core, 64 GB RAM setup real-time analytics was achieved on ‘300 million records’. To check the scalability of the system, the cluster was increased to 100 node setup. This talk will highlight how Open Source software was used to achieve the above use case and the insights on the high data throughput on a cloud set up.

Keywords: streaming analytics, data science, big data, Hadoop, high throughput, sensor data

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Authors: A. T. Muruviwa

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To-date, the discourse on corporate social responsibility (CSR) has primarily centred on the actions and inactions of corporations; hence, the dominant focus on CSR has been on impacts and outcomes. The obscuring effect of this approach has, arguably, resulted in the emergence of what may be termed a ‘Northern’ agenda on CSR theory and practice, in contrast to an emergency ‘Southern’ discourse, which appears to highlight the crucial issues of poverty reduction, infrastructure development and the broader questions of social provisioning and community empowerment. Some scholars have explicitly called for a CSR research agenda that focuses on the 'reciprocal duties' of the stakeholders in the CSR process rather than fixate on the actions and inactions of business. It is against the backdrop of these contestations that this study assesses the reciprocal relationships amongst CSR stakeholders in a Zimbabwean platinum mining town, with a view to demonstrating how such relationships – and the expectations and obligations embedded in them – impact on the success or failure of CSR initiatives. The existence of mutual relations between the corporation and its stakeholders signifies the successes of CSR processes and hence the outcomes. The company is Zimplats Mining Company; the community is Mhondoro-Ngezi, and the stakeholders are clearly identified in the study. The study utilised a triangulated design, with data collected using a mini survey, focus groups, in-depth interview and observation. The key findings are that the CSR process in the study community is dominated by the mining company. Despite the existence of a CSR framework that recognises government, local leaders and community members as legitimate stakeholders, there is little evidence of concrete contributions made by these stakeholders towards the realisation of CSR objectives. As a result, the community development process – in so far as CSR is concerned – fails to address the developmental concerns of the various stakeholders. On the basis of these findings, the study concludes that there is a crisis of reciprocity in the CSR process in Mhondoro-Ngezi, and that a situation where the conceptualisation of local development needs and the deployment of specific development tools seems to be driven by one stakeholder almost to the exclusion of all others, can only present contradictory development outcomes. The significance of this study is that it allows for the development of a more nuanced and robust CSR discourse. Rather than focusing on the corporate and stakeholder perspectives and outcomes of CSR initiatives, this study examines the CSR- development nexus by interrogating the idea of reciprocal responsibility as a sin qua non to CSR success. This analytical strategy and focus allow the researcher to gain a clear understanding of how stakeholder relationships and duties influence CSR processes and also the overall outcome. At a more practical level, the findings of the study should help to shape the policy on corporate community relationships with a view to enhancing the role of mining in development.

Keywords: community development, processes, reciprocity, stakeholders

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Authors: Julian Wise

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Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

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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: Anyimc, C. J. Aneke, J. O. Orji, O. Nworie, U. C. C. Egbule

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The microbial examination and antimicrobial susceptibility profile of microorganism isolated from the salt mining site in Ebonyi state were evaluated in the present study using a standard microbiological technique. A total of 300 samples were randomly collected in three sample groups (A, B, and C) of 100 each. Isolation, Identification and characterization of organization present on the soil samples were determined by culturing, gram-staining and biochemical technique. The result showed the following organisms were isolated with their frequency as follow: Bacillus species (37.3%) and Staphylococcus species(23.5%) had the highest frequency in the whole Sample group A and B while Klebsiella specie (15.7%), Pseudomonas species(13.7%), and Erwinia species (9.8%) had the least. Rhizopus species (42.0%) and Aspergillus species (26.0%) were the highest fungi isolated, followed by Penicillum species (20.0%) while Mucor species (4.0%), and Fusarium species (8.0%) recorded the least. Sample group C showed high microbial population of all the microbial isolates when compared to sample group A and B. Disc diffusion method was used to determine the susceptibility of isolated bacteria to various antibiotics (oxfloxacin, pefloxacin, ciprorex, augumentin, gentamycin, ciproflox, septrin, ampicillin), while agar well diffusion method was used to determine the susceptibility of isolated fungi to some antifungal drugs (metronidazole, ketoconazole, itraconazole fluconazole). The antibacterial activity of the antibiotics used showed that ciproflux has the best inhibitory effect on all the test bacteria. Ketoconazole showed the highest inhibitory effect on the fungal isolates, followed by itraconazole, while metronidazole and fluconazole showed the least inhibitory effect on the entire test fungal isolates. Hence, the multiple drug resistance of most isolates to appropriate drugs of choice are of great public health concern and cells for periodic monitoring of antibiograms to detect possible changing patterns. Microbes isolated in the salt mining site can also be used as a source of gene(s) that can increase salt tolerance in different crop species through genetic engineering.

Keywords: microorganisms, antibacterial, antifungal, resistance, salt mining site, Ebonyi State

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Authors: Arturo Reyes Roman, Daniza Castillo Godoy, Francisca Balarezo Olivares, Francisco Arriagada Castro, Miguel Maulen Tapia

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Mining waste–based geopolymers are a sustainable alternative to conventional cement materials due to their contribution to the valorization of mining wastes as well as to the new construction materials with reduced fingerprints. The objective of this study was to determine the potential of leaching gravel (LG) from hydrometallurgical copper processing to be used as a raw material in the manufacture of geopolymer. NaOH, Na2SiO3 (modulus 1.5), and LG were mixed and then wetted with an appropriate amount of tap water, then stirred until a homogenous paste was obtained. A liquid/solid ratio of 0.3 was used for preparing mixtures. The paste was then cast in cubic moulds of 50 mm for the determination of compressive strengths. The samples were left to dry for 24h at room temperature, then unmoulded before analysis after 28 days of curing time. The compressive test was conducted in a compression machine (15/300 kN). According to the laser diffraction spectroscopy (LDS) analysis, 90% of LG particles were below 500 μm. The X-ray diffraction (XRD) analysis identified crystalline phases of albite (30 %), Quartz (16%), Anorthite (16 %), and Phillipsite (14%). The X-ray fluorescence (XRF) determinations showed mainly 55% of SiO2, 13 % of Al2O3, and 9% of CaO. ICP (OES) concentrations of Fe, Ca, Cu, Al, As, V, Zn, Mo, and Ni were 49.545; 24.735; 6.172; 14.152, 239,5; 129,6; 41,1;15,1, and 13,1 mg kg-1, respectively. The geopolymer samples showed resistance ranging between 2 and 10 MPa. In comparison with the raw material composition, the amorphous percentage of materials in the geopolymer was 35 %, whereas the crystalline percentage of main mineral phases decreased. Further studies are needed to find the optimal combinations of materials to produce a more resistant and environmentally safe geopolymer. Particularly are necessary compressive resistance higher than 15 MPa are necessary to be used as construction unit such as bricks.

Keywords: mining waste, geopolymer, construction material, alkaline activation

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Authors: Ahmed Elwaleed, Huiho Jeong, Ali H. Abdelbagi, Nguyen Thi Quynh, Koji Arizono, Yasuhiro Ishibashi

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Artisanal and small-scale gold mining (ASGM) poses substantial risks to both human health and the environment, particularly through contamination of soil, water, and air. Prolonged exposure to ASGM-contaminated soils can lead to acute or chronic mercury toxicity. This study assesses the human health risks associated with mercury-contaminated soils and tailings in the Alebediah mining community in Sudan. Soil samples were collected from various locations within Alebediah, including ASGM areas, farmlands, and residential areas, along with tailings samples commonly found within ASGM sites. The evaluation of potential health risks to humans included the computation of the estimated daily intake (AvDI), the hazard quotient (HQ), and the hazard index (HI) for both adults and children. The primary exposure route identified as potentially posing a significant health risk was the volatilization of mercury from tailings samples, where mercury concentrations reached up to 25.5 mg/kg. In contrast, other samples within the ASGM area showed elevated mercury levels but did not present significant health risks, with HI values below 1. However, all areas indicated HI values above 1 for the remaining exposure routes. The study observed a decrease in mercury concentration with increasing distance from the ASGM community. Additionally, soil samples revealed elevated mercury levels exceeding background values, prompting an assessment of contamination levels using the enrichment factor (EF). The findings indicated that farmlands and residential areas exhibited depleted EF, while areas surrounding the ASGM community showed none to moderate pollution. In contrast, ASGM areas exhibited significant to extreme pollution. A GIS map was generated to visually depict the extent of mercury pollution, facilitating communication with stakeholders and decision-makers.

Keywords: mercury pollution, artisanal and small-scale gold mining, health risk assessment, hazard index, soil and tailings, enrichment factor

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Authors: Suchhanda Ghosh, A. K. Paul

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Nickel, an industrially important metal is not mined in India, due to the lack of its primary mining resources. But, the chromite deposits occurring in the Sukinda and Baula-Nuasahi region of Odhisa, India, is reported to contain around 0.99% of nickel entrapped in the goethite matrix of the lateritic iron rich ore. Weathering of the dumped chromite mining overburden often leads to the contamination of the ground as well as the surface water with toxic nickel. Microbes inherent to this metal contaminated environment are reported to be capable of removal as well as detoxification of various metals including nickel. Nickel resistant fungal isolates obtained in pure form from the metal rich overburden were evaluated for their potential to biosorb nickel by using their dried biomass. Penicillium simplicissimum SAU203 was the best nickel biosorbant among the 20 fungi tested and was capable to sorbing 16.85 mg Ni/g biomass from a solution containing 50 mg/l of Ni. The identity of the isolate was confirmed using 18S rRNA gene analysis. The sorption capacity of the isolate was further standardized following Langmuir and Freundlich adsorption isotherm models and the results reflected energy efficient sorption. Fourier-transform infrared spectroscopy studies of the nickel loaded and control biomass in a comparative basis revealed the involvement of hydroxyl, amine and carboxylic groups in Ni binding. The sorption process was also optimized for several standard parameters like initial metal ion concentration, initial sorbet concentration, incubation temperature and pH, presence of additional cations and pre-treatment of the biomass by different chemicals. Optimisation leads to significant improvements in the process of nickel biosorption on to the fungal biomass. P. simplicissimum SAU203 could sorb 54.73 mg Ni/g biomass with an initial Ni concentration of 200 mg/l in solution and 21.8 mg Ni/g biomass with an initial biomass concentration of 1g/l solution. Optimum temperature and pH for biosorption was recorded to be 30°C and pH 6.5 respectively. Presence of Zn and Fe ions improved the sorption of Ni(II), whereas, cobalt had a negative impact. Pre-treatment of biomass with various chemical and physical agents has affected the proficiency of Ni sorption by P. simplicissimum SAU203 biomass, autoclaving as well as treatment of biomass with 0.5 M sulfuric acid and acetic acid reduced the sorption as compared to the untreated biomass, whereas, NaOH and Na₂CO₃ and Twin 80 (0.5 M) treated biomass resulted in augmented metal sorption. Hence, on the basis of the present study, it can be concluded that P. simplicissimum SAU203 has the potential for the removal as well as detoxification of nickel from contaminated environments in general and particularly from the chromite mining areas of Odhisa, India.

Keywords: nickel, fungal biosorption, Penicillium simplicissimum SAU203, Indian chromite mines, mining overburden

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Authors: G. Martino, F. Silva, E. Marchal

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The control over delivered iron ore blend characteristics is one of the most important aspects of the mining business. The iron ore price is a function of its composition, which is the outcome of the beneficiation process. So, end-to-end integrated planning of mine operations can reduce risks of penalties on the iron ore price. In a standard iron mining company, the production chain is composed of mining, ore beneficiation, and client supply. When mine planning and client supply decisions are made uncoordinated, the beneficiation plant struggles to deliver the best blend possible. Technological improvements in several fields allowed bridging the gap between departments and boosting integrated decision-making processes. Clusterization and classification algorithms over historical production data generate reasonable previsions for quality and volume of iron ore produced for each pile of run-of-mine (ROM) processed. Mathematical modeling can use those deterministic relations to propose iron ore blends that better-fit specifications within a delivery schedule. Additionally, a model capable of representing the whole production chain can clearly compare the overall impact of different decisions in the process. This study shows how flexibilization combined with a planning optimization model between the mine and the ore beneficiation processes can reduce risks of out of specification deliveries. The model capabilities are illustrated on a hypothetical iron ore mine with magnetic separation process. Finally, this study shows ways of cost reduction or profit increase by optimizing process indicators across the production chain and integrating the different plannings with the sales decisions.

Keywords: clusterization and classification algorithms, integrated planning, mathematical modeling, optimization, penalty minimization

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Authors: A. T. Muruviwa

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Empirical studies of the current CSR agenda have been dominated by literature from the North at the expense of the nations from the South where most TNCs are located. Therefore, owing to the limitations of the current discourse that is dominated by Western ideas such as voluntarism, philanthropy, business case and economic gains, scholars have been calling for a new CSR agenda that is South-centred and addresses the needs of developing nations. The development theme has dominated in the recent literature as scholars concerned with the relationship between business and society have tried to understand its relationship with CSR. Despite a plethora of literature on the roles of corporations in local communities and the impact of CSR initiatives, there is lack of adequate empirical evidence to help us understand the nexus between CSR and development. For all the claims made about the positive and negative consequences of CSR, there is surprisingly little information about the outcomes it delivers. This study is a response to these claims made about the developmental aspect of CSR in developing countries. It offers some empirical bases for assessing the major CSR projects that have been fulfilled by a major mining company, Zimplats in Mhondoro-Ngezi Zimbabwe. The neo-liberal idea of capitalism and market dominations has empowered TNCs to stamp their authority in the developing countries. TNCs have made their mark in developing nations as they stamp their global private authority, rivalling or implicitly challenging the state in many functions. This dominance of corporate power raises great concerns over their tendencies of abuses in terms of environmental, social and human rights concerns as well as how to make them increasingly accountable. The hegemonic power of TNCs in the developing countries has had a tremendous impact on the overall CSR practices. While TNCs are key drivers of globalization they may be acting responsibly in their Global Northern home countries where there is a combination of legal mechanisms and the fear of civil society activism associated with corporate scandals. Using a triangulated approach in which both qualitative and quantitative methods were used the study found out that most CSR projects in Zimbabwe are dominated and directed by Zimplats because of the power it possesses. Most of the major CSR projects are beneficial to the mining company as they serve the business plans of the mining company. What was deduced from the study is that the infrastructural development initiatives by Zimplats confirm that CSR is a tool to advance business obligations. This shows that although proponents of CSR might claim that business has a mandate for social obligations to society, we need not to forget the dominant idea that the primary function of CSR is to enhance the firm’s profitability.

Keywords: hegemonic power, projects, reciprocity, stakeholders

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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: Ramona Zharfpeykan, Paul Rouse

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Organizational sustainability is important to both organizations themselves and their stakeholders. Despite its increasing popularity and increasing numbers of organizations reporting sustainability, research on evaluating and comparing the sustainability performance of companies is limited. The aim of this study was to develop models to measure sustainability performance for both cross-sectional and longitudinal comparisons across companies in the same or different industries. A secondary aim was to see if sustainability reports can be used to evaluate sustainability performance. The study used both a content analysis of Australian sustainability reports in mining and metals and financial services for 2011-2014 and a survey of Australian and New Zealand organizations. Two methods ranging from a composite index using uniform weights to data envelopment analysis (DEA) were employed to analyze the data and develop the models. The results show strong statistically significant relationships between the developed models, which suggests that each model provides a consistent, systematic and reasonably robust analysis. The results of the models show that for both industries, companies that had sustainability scores above or below the industry average stayed almost the same during the study period. These indices and models can be used by companies to evaluate their sustainability performance and compare it with previous years, or with other companies in the same or different industries. These methods can also be used by various stakeholders and sustainability ranking companies such as the Global Reporting Initiative (GRI).

Keywords: data envelopment analysis, sustainability, sustainability performance measurement system, sustainability performance index, global reporting initiative

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Authors: F. Al-Sheikh, C. Moralejo, M. Pritzker, W. A. Anderson, A. Elkamel

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Ammonia in mining wastewater is a significant problem, and treatment can be especially difficult in cold climates where biological treatment is not feasible. An adsorption process is one of the alternative processes that can be used to reduce ammonia concentrations to acceptable limits, and therefore a LEWATIT resin strongly acidic H+ form ion exchange resin and a Bowie Chabazite Na form AZLB-Na zeolite were tested to assess their effectiveness. For these adsorption tests, two packed bed columns (a mini-column constructed from a 32-cm long x 1-cm diameter piece of glass tubing, and a 60-cm long x 2.5-cm diameter Ace Glass chromatography column) were used containing varying quantities of the adsorbents. A mining wastewater with ammonia concentrations of 22.7 mg/L was fed through the columns at controlled flowrates. In the experimental work, maximum capacities of the LEWATIT ion exchange resin were 0.438, 0.448, and 1.472 mg/g for 3, 6, and 9 g respectively in a mini column and 1.739 mg/g for 141.5 g in a larger Ace column while the capacities for the AZLB-Na zeolite were 0.424, and 0.784 mg/g for 3, and 6 g respectively in the mini column and 1.1636 mg/g for 38.5 g in the Ace column. In the theoretical work, Thomas, Adams-Bohart, and Yoon-Nelson models were constructed to describe a breakthrough curve of the adsorption process and find the constants of the above-mentioned models. In the regeneration tests, 5% hydrochloric acid, HCl (v/v) and 10% sodium hydroxide, NaOH (w/v) were used to regenerate the LEWATIT resin and AZLB-Na zeolite with 44 and 63.8% recovery, respectively. In conclusion, continuous flow adsorption using a LEWATIT ion exchange resin and an AZLB-Na zeolite is efficient when using a co-flow technique for removal of the ammonia from wastewater. Thomas, Adams-Bohart, and Yoon-Nelson models satisfactorily fit the data with R² closer to 1 in all cases.

Keywords: AZLB-Na zeolite, continuous adsorption, Lewatit resin, models, regeneration

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Authors: Kim Dongwon, Yu Songjin

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This study identifies the business value of VOC (Voice of Customer) on the business. Precisely, we intend to demonstrate how much negative and positive sentiment of VOC has an influence on car sales market share in the unites states. We extract 7 emotions such as sadness, shame, anger, fear, frustration, delight and satisfaction from the VOC data, 23,204 pieces of opinions, that had been posted on car-related on-line community from 2007 to 2009(a part of data collection from 2007 to 2015), and intend to clarify the correlation between negative and positive sentimental keywords and contribution to market share. In order to develop a lexicon for each category of negative and positive sentiment, we took advantage of Corpus program, Antconc 3.4.1.w and on-line sentimental data, SentiWordNet and identified the part of speech(POS) information of words in the customers' opinion by using a part-of-speech tagging function provided by TextAnalysisOnline. For the purpose of this present study, a total of 45,741 pieces of customers' opinions of 28 car manufacturing companies had been collected including titles and status information. We conducted an experiment to examine whether the inclusion, frequency and intensity of terms with negative and positive emotions in each category affect the adoption of customer opinions for vehicle organizations' market share. In the experiment, we statistically verified that there is correlation between customer ideas containing negative and positive emotions and variation of marker share. Particularly, "Anger," a domain of negative domains, is significantly influential to car sales market share. The domain "Delight" and "Satisfaction" increased in proportion to growth of market share.

Keywords: data mining, opinion mining, sentiment analysis, VOC

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Authors: Kristýna Weissová

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This study is focused on two selected model taxa of invertebrates - Hymenoptera: Aculeata and Lepidopterawithnocturnalactivity, recordedatthesitesof lignite dumps and their surroundings in the North Bohemian Lignite Basin, Czech Republic. There search was conducted on 10 landfills, 3 study areas were determined on each landfill - primary and secondary succession and recultivation. A total of 3,202 individuals belonging to 232 species and 17 families of sagebrushinsects were collected. died, 74%of the species occurred on the primary succes sionare as, that is 2x more species than on the reclaimed areas. Of the total number of species and on all areas, 60 rare species were recorded - 29 vulnerable, 21 endangered, 8 critically endangered, and 2 extinct The areas of primary succession were again confirmed to be the richest in terms of rare species, hosting 39 rare species of Hymenoptera: Aculeata. In addition, bothextinct species were represented only on plots of primary succession. The family Crabronidae had the largestre presentation of species on theareasofleft primary succession, the family Halictidae was the most represented on the reclaimed areas and areas of secondary succession. A total of 3,634 moths were collected, assigned to 262 species and 10 families. A similar number of species occurred on the primary succession and reclaimed areas, but the reclaimed area had a greater abundance. Secondary successionsiteshostedha lf as many species and alsocontainedlow abundance compared to other management types. The results show that there claimed areas host a numerically larger group and more species of moths than the successionalareas. Rare species did not occur at any site. A higher number of days in locations without water bodies, wetland vegetation, and locations with a highre presentation of woody species. It is advisable to combine individual types of landscape management in such a way as to create a colorfulmosaic that supports biodiversity. In particular, we recommend incorporating natural successionintoreclamation plans, which is a refuge for many rare species of invertebrates, which has not yetbeenroutinely and purposefully practiced.

Keywords: hymenoptera: aculeata, lepidoptera, reclamation, succession, post-mining ara

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Authors: Silvano Salvador, Maryam Zarrinderakht, Alan Martin

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Open pits, which are the result of mining, are filled by water over time until the water reaches the elevation of the local water table and generates mine pit lakes. There are several specific regulations about the water quality of pit lakes, and mining operations should keep the quality of groundwater above pre-defined standards. Therefore, an accurate, acceptable numerical model predicting pit lakes’ water balance and water quality is needed in advance of mine excavation. We carry on analyzing and developing the model introduced by Crusius, Dunbar, et al. (2002) for pit lakes. This model, called “PitMod”, simulates the physical and geochemical evolution of pit lakes over time scales ranging from a few months up to a century or more. Here, a lake is approximated as one-dimensional, horizontally averaged vertical layers. PitMod calculates the time-dependent vertical distribution of physical and geochemical pit lake properties, like temperature, salinity, conductivity, pH, trace metals, and dissolved oxygen, within each model layer. This model considers the effect of pit morphology, climate data, multiple surface and subsurface (groundwater) inflows/outflows, precipitation/evaporation, surface ice formation/melting, vertical mixing due to surface wind stress, convection, background turbulence and equilibrium geochemistry using PHREEQC and linking that to the geochemical reactions. PitMod, which is used and validated in over 50 mines projects since 2002, incorporates physical processes like those found in other lake models such as DYRESM (Imerito 2007). However, unlike DYRESM PitMod also includes geochemical processes, pit wall runoff, and other effects. In addition, PitMod is actively under development and can be customized as required for a particular site.

Keywords: pit lakes, mining, modeling, hydrology

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Authors: J. P. Chollom, G. M. Kumleng, S. Longwap

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The search for higher order A-stable linear multi-step methods has been the interest of many numerical analysts and has been realized through either higher derivatives of the solution or by inserting additional off step points, supper future points and the likes. These methods are suitable for the solution of stiff differential equations which exhibit characteristics that place a severe restriction on the choice of step size. It becomes necessary that only methods with large regions of absolute stability remain suitable for such equations. In this paper, high order block implicit multi-step methods of the hybrid form up to order twelve have been constructed using the multi-step collocation approach by inserting one or more off step points in the multi-step method. The accuracy and stability properties of the new methods are investigated and are shown to yield A-stable methods, a property desirable of methods suitable for the solution of stiff ODE’s. The new High Order Block Implicit Multistep methods used as block integrators are tested on stiff differential systems and the results reveal that the new methods are efficient and compete favourably with the state of the art Matlab ode23 code.

Keywords: block linear multistep methods, high order, implicit, stiff differential equations

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Authors: Komol Phaisarn, Anuphan Suttimarn, Vitchanan Keawtong, Kittisak Thongyoun, Chaiyos Jamsawang

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This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible.

Keywords: decision tree, data mining, customers, life insurance pay package

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Authors: Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari, Chang Wook Ahn

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Feature selection and attribute reduction are crucial problems, and widely used techniques in the field of machine learning, data mining and pattern recognition to overcome the well-known phenomenon of the Curse of Dimensionality. This paper presents a feature selection method that efficiently carries out attribute reduction, thereby selecting the most informative features of a dataset. It consists of two components: 1) a measure for feature subset evaluation, and 2) a search strategy. For the evaluation measure, we have employed the fuzzy-rough dependency degree (FRFDD) of the lower approximation-based fuzzy-rough feature selection (L-FRFS) due to its effectiveness in feature selection. As for the search strategy, a modified version of a binary shuffled frog leaping algorithm is proposed (B-SFLA). The proposed feature selection method is obtained by hybridizing the B-SFLA with the FRDD. Nine classifiers have been employed to compare the proposed approach with several existing methods over twenty two datasets, including nine high dimensional and large ones, from the UCI repository. The experimental results demonstrate that the B-SFLA approach significantly outperforms other metaheuristic methods in terms of the number of selected features and the classification accuracy.

Keywords: binary shuffled frog leaping algorithm, feature selection, fuzzy-rough set, minimal reduct

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Authors: Remy Mpulumba Badiambile, Paul Musa Obadia, Malick Useni Mutayo, Jeef Numbi Mukanya, Patient Nkulu Banza, Tony Kayembe Kitenge, Erik Smolders, Jean-François Picron, Vincent Haufroid, Célestin Banza Lubaba Nkulu, Benoit Nemery

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Background: In July 2021, the Tshikapa river became heavily polluted by mining wastes from a diamond mine in neighboring Angola, leading to massive killing of fish, as well as disease and even deaths among residents living along the Tshikapa and Kasai rivers, a major contributory of the Congo river. The exact nature of the pollutants was unknown. Methods: In a cross-sectional study conducted in the city of Tshikapa in August 2021, we enrolled by opportunistic sampling 65 residents (11 children < 16y) living alongside the polluted rivers and 65 control residents (5 children) living alongside a non-affected portion of the Kasai river (upstream from the Tshikapa-Kasai confluence). We administered a questionnaire and obtained spot urine samples for measurements of thiocyanate (a metabolite of cyanide) and 26 trace metals (by ICP-MS). Metals (and pH) were also measured in samples of river water. Results: Participants from both groups consumed river water. In the area affected by the pollution, most participants had eaten dead fish. Prevalences of reported health symptoms were higher in the exposed group than among controls: skin rashes (52% vs 0%), diarrhea (40% vs 8%), abdominal pain (8% vs 3%), nausea (3% vs 0%). In polluted water, concentrations [median (range)] were only higher for nickel [(2.2(1.4–3.5)µg/L] and uranium [78(71–91)ng/L] than in non-polluted water [0.8(0.6–1.9)µg/L; 9(7–19)ng/L]. In urine, concentrations [µg/g creatinine, median(IQR)] were significantly higher in the exposed group than in controls for lithium [19.5(12.4–27.3) vs 6.9(5.9–12.1)], thallium [0.41(0.31–0.57) vs 0.19(0.16–0.39)], and uranium [0.026(0.013–0.037)] vs 0.012(0.006–0.024)]. Other elements did not differ between the groups, but levels were higher than reference values for several metals (including manganese, cobalt, nickel, and lead). Urinary thiocyanate concentrations did not differ. Conclusion: This study, after an ecological disaster in the DRC, has documented contamination of river water by nickel and uranium and high urinary levels of some trace metals among affected riverine populations. However, the exact cause of the massive fish kill and disease among residents remains elusive. The capacity to rapidly investigate toxic pollution events must be increased in the area.

Keywords: metal contaminants, river water and human urine, pollution by mining wastes, DR Congo

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Authors: Martina Perinkova, Lenka Kolarcikova, Marketa Twrda

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Brownfields are one of the most important problems that must be solved by today's cities. The topic of this article is description of developing a comprehensive transformation of post-industrial area of the former iron factory national cultural heritage Lower Vítkovice. City of Ostrava used to be industrial superpower of the Czechoslovak Republic, especially in the area of coal mining and iron production, after declining industrial production and mining in the 80s left many unused areas of former factories generally brownfields and backfields. Since the late 90s we are observing how the city officials or private entities seeking to remedy this situation. Regeneration of brownfields is a very expensive and long-term process. The area is now rebuilt for tourists and residents of the city in the entertainment, cultural, and social center. It was necessary do the reconstruction of the industrial monuments. Equally important was the construction of new buildings, which helped reusing of the entire complex. This is a unique example of transformation of technical monuments and completion of necessary new objects, so that the area could start working again and reintegrate back into the urban system.

Keywords: brown fields, conversion, historical and industrial buildings, reconstruction

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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Authors: Rubén Manrique, Delio Balcázar, José Parrado, Sebastián Rodríguez

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Hand in hand with the evolution of technology, education systems have moved to virtual environments to provide increased coverage and facilitate the access to education. However, measuring student performance in virtual environments presents significant challenges to ensure students are acquiring the expected skills. In this study, the confidence and performance of engineering students in virtual environments is analyzed through different evaluation strategies. The effect of the assessment strategy in student confidence is identified using educational data mining techniques. Four assessment strategies were used. First, a conventional multiple choice test; second, a multiple choice test with feedback; third, a multiple choice test with a second chance; and fourth; a multiple choice test with feedback and second chance. Our results show that applying testing with online feedback strategies can influence positively student confidence.

Keywords: assessment strategies, educational data mining, student performance, student confidence

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Authors: Vineetha Bettaiah, Heggere S. Ranganath

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This paper presents a Hierarchical Piecewise Linear Approximation (HPLA) for the representation of time series data in which the time series is treated as a curve in the time-amplitude image space. The curve is partitioned into segments by choosing perceptually important points as break points. Each segment between adjacent break points is recursively partitioned into two segments at the best point or midpoint until the error between the approximating line and the original curve becomes less than a pre-specified threshold. The HPLA representation achieves dimensionality reduction while preserving prominent local features and general shape of time series. The representation permits course-fine processing at different levels of details, allows flexible definition of similarity based on mathematical measures or general time series shape, and supports time series data mining operations including query by content, clustering and classification based on whole or subsequence similarity.

Keywords: data mining, dimensionality reduction, piecewise linear representation, time series representation

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Authors: Saima Nazir, Mustansar Ali Ghazanfar, Sanay Muhammad Umar Saeed, Muhammad Awais Azam, Saad Ali Alahmari

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Terrorism is an important and challenging concern. The entire world is threatened by only few sophisticated terrorist groups and especially in Gulf Region and Pakistan, it has become extremely destructive phenomena in recent years. Predicting the pattern of attack type, attack group and target type is an intricate task. This study offers new insight on terrorist group’s attack type and its chosen target. This research paper proposes a framework for prediction of terrorist attacks using the historical data and making an association between terrorist group, their attack type and target. Analysis shows that the number of attacks per year will keep on increasing, and Al-Harmayan in Saudi Arabia, Al-Qai’da in Gulf Region and Tehreek-e-Taliban in Pakistan will remain responsible for many future terrorist attacks. Top main targets of each group will be private citizen & property, police, government and military sector under constant circumstances.

Keywords: data mining, counter terrorism, machine learning, SVM

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Authors: Jay N. Vyas, Sachin Daxini

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Numerical simulation techniques are widely used now in product development and testing instead of expensive, time-consuming and sometimes dangerous laboratory experiments. Numerous numerical methods are available for performing simulation of physical problems of different engineering fields. Grid based methods, like Finite Element Method, are extensively used in performing various kinds of static, dynamic, structural and non-structural analysis during product development phase. Drawbacks of grid based methods in terms of discontinuous secondary field variable, dealing fracture mechanics and large deformation problems led to development of a relatively a new class of numerical simulation techniques in last few years, which are popular as Meshless methods or Meshfree Methods. Meshless Methods are expected to be more adaptive and flexible than Finite Element Method because domain descretization in Meshless Method requires only nodes. Present paper introduces Meshless Methods and differentiates it with Finite Element Method in terms of following aspects: Shape functions used, role of weight function, techniques to impose essential boundary conditions, integration techniques for discrete system equations, convergence rate, accuracy of solution and computational effort. Capabilities, benefits and limitations of Meshless Methods are discussed and concluded at the end of paper.

Keywords: numerical simulation, Grid-based methods, Finite Element Method, Meshless Methods

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Authors: Shinen B., Bavor J., Dorjkhand B., Suvd B., Maitsetseg B.

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A number of remediation techniques are available for the treatment of soils and sediments contaminated by heavy metals. However, some of these techniques are expensive and environmentally disruptive. Nanomaterials are used in the environment as environmental catalysts to convert toxic substances from water, soil, and sediment into environmentally benign compounds. This study was carried out to scrutinize the feasibility of vivianite nanoparticles for remediation of soils contaminated with heavy metals. Column experiments were performed in the laboratory to examine nanoparticle sequestration of metal in soil amended with vivianite nanoparticle suspension. The effect of environmental parameters such as temperature, pH and redox potential on metal leachability and bioavailability of soil amended with nanoparticle suspension was examined and compared with non-amended soils. The vivianite was effective in reducing the leachability of metals in soils. It is suggested that vivianite nanoparticles could be applied for the remediation of contaminated sites polluted by heavy metals due to mining activities, particularly in Mongolia, where mining industries have been developing rapidly in the last decade.

Keywords: bioavailability, heavy metals, nanoparticles, remediation

Procedia PDF Downloads 165