Search results for: mining legacy

Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: Paballo M. Moshupya, Tamiru A. Abiye, Ian Korir

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Mining and processing of naturally occurring radioactive materials could result in elevated levels of natural radionuclides in the environment. The aim of this study was to evaluate the radioactivity levels on a large scale in the West Rand District in South Africa, which is dominated by abandoned gold mine tailings and the consequential radiological exposures to members of the public. The activity concentrations of ²³⁸U, ²³²Th and 40K in mine tailings, soil and rocks were assessed using the BGO Super-Spec (RS-230) gamma spectrometer. The measured activity concentrations for ²³⁸U, ²³²Th and 40K in the studied mine tailings were found to range from 209.95 to 2578.68 Bq/kg, 19.49 to 108.00 Bq/kg and 31.30 to 626.00 Bq/kg, respectively. In surface soils, the overall average activity concentrations were found to be 59.15 Bq/kg, 34.91 and 245.64 Bq/kg for 238U, ²³²Th and 40K, respectively. For the rock samples analyzed, the mean activity concentrations were 32.97 Bq/kg, 32.26 Bq/kg and 351.52 Bg/kg for ²³⁸U, ²³²Th and 40K, respectively. High radioactivity levels were found in mine tailings, with ²³⁸U contributing significantly to the overall activity concentration. The external gamma radiation received from surface soil in the area is generally low, with an average of 0.07 mSv/y. The highest annual effective doses were estimated from the tailings dams and the levels varied between 0.14 mSv/y and 1.09 mSv/y, with an average of 0.51 mSv/y. In certain locations, the recommended dose constraint of 0.25 mSv/y from a single source to the average member of the public within the exposed population was exceeded, indicating the need for further monitoring and regulatory control measures specific to these areas to ensure the protection of resident members of the public.

Keywords: activity concentration, gold mine tailings, in-situ gamma spectrometry, radiological exposures

Procedia PDF Downloads 116

Authors: Wajid Rehman, Sirajul Haq, Bakhtiar Muhammad, Syed Fahad Hassan, Amin Badshah, Muhammad Waseem, Fazal Rahim, Obaid-Ur-Rahman Abid, Farzana Latif Ansari, Umer Rashid

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Five novel triorganotin (IV) compounds have been synthesized and characterized. The tin atom is penta-coordinated to assume trigonal-bipyramidal geometry. Using in silico derived parameters; the objective of our study is to design and synthesize promiscuous antibacterials potent enough to combat resistance. Among various synthesized organotin (IV) complexes, compound 5 was found as potent antibacterial agent against various bacterial strains. Further lead optimization of drug-like properties was evaluated through in silico predictions. Data mining and computational analysis were utilized to derive compound promiscuity phenomenon to avoid drug attrition rate in designing antibacterials. Xanthine oxidase and human glucose- 6-phosphatase were found as only true positive off-target hits by ChEMBL database and others utilizing similarity ensemble approach. Propensity towards a-3 receptor, human macrophage migration factor and thiazolidinedione were found as false positive off targets with E-value 1/4> 10^-4 for compound 1, 3, and 4. Further, displaying positive drug-drug interaction of compound 1 as uricosuric was validated by all databases and docked protein targets with sequence similarity and compositional matrix alignment via BLAST software. Promiscuity of the compound 5 was further confirmed by in silico binding to different antibacterial targets.

Keywords: antibacterial activity, drug promiscuity, ADMET prediction, metallo-pharmaceutical, antimicrobial resistance

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Authors: Ho-Ting Wu, Kai-Wei Ke, Bo-Yu Huang, Liang-Lin Yan, Chun-Ting Lin

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With the advances in advanced technology, wireless sensor network (WSN) has become one of the most promising candidates to implement the wireless industrial internet of things (IIOT) architecture. However, the legacy IEEE 802.15.4 based WSN technology such as Zigbee system cannot meet the stringent QoS requirement of low powered, real-time, and highly reliable transmission imposed by the IIOT environment. Recently, the IEEE society developed IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) access mode to serve this purpose. Furthermore, the IETF 6TiSCH working group has proposed standards to integrate IEEE 802.15.4e with IPv6 protocol smoothly to form a complete protocol stack for IIOT. In this work, we develop key network technologies for IEEE 802.15.4e based wireless IIoT architecture, focusing on practical design and system implementation. We realize the OpenWSN-based wireless IIOT system. The system architecture is divided into three main parts: web server, network manager, and sensor nodes. The web server provides user interface, allowing the user to view the status of sensor nodes and instruct sensor nodes to follow commands via user-friendly browser. The network manager is responsible for the establishment, maintenance, and management of scheduling and topology information. It executes centralized scheduling algorithm, sends the scheduling table to each node, as well as manages the sensing tasks of each device. Sensor nodes complete the assigned tasks and sends the sensed data. Furthermore, to prevent scheduling error due to packet loss, a schedule inspection mechanism is implemented to verify the correctness of the schedule table. In addition, when network topology changes, the system will act to generate a new schedule table based on the changed topology for ensuring the proper operation of the system. To enhance the system performance of such system, we further propose dynamic bandwidth allocation and distributed scheduling mechanisms. The developed distributed scheduling mechanism enables each individual sensor node to build, maintain and manage the dedicated link bandwidth with its parent and children nodes based on locally observed information by exchanging the Add/Delete commands via two processes. The first process, termed as the schedule initialization process, allows each sensor node pair to identify the available idle slots to allocate the basic dedicated transmission bandwidth. The second process, termed as the schedule adjustment process, enables each sensor node pair to adjust their allocated bandwidth dynamically according to the measured traffic loading. Such technology can sufficiently satisfy the dynamic bandwidth requirement in the frequently changing environments. Last but not least, we propose a packet retransmission scheme to enhance the system performance of the centralized scheduling algorithm when the packet delivery rate (PDR) is low. We propose a multi-frame retransmission mechanism to allow every single network node to resend each packet for at least the predefined number of times. The multi frame architecture is built according to the number of layers of the network topology. Performance results via simulation reveal that such retransmission scheme is able to provide sufficient high transmission reliability while maintaining low packet transmission latency. Therefore, the QoS requirement of IIoT can be achieved.

Keywords: IEEE 802.15.4e, industrial internet of things (IIOT), scheduling mechanisms, wireless sensor networks (WSN)

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Authors: Chen Chou, Feng-Tyan Lin

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Big Data has been attracted a lot of attentions in many fields for analyzing research issues based on a large number of maternal data. Electronic Toll Collection (ETC) is one of Intelligent Transportation System (ITS) applications in Taiwan, used to record starting point, end point, distance and travel time of vehicle on the national freeway. This study, taking advantage of ETC big data, combined with urban planning theory, attempts to explore various phenomena of inter-city transportation activities. ETC, one of government's open data, is numerous, complete and quick-update. One may recall that living area has been delimited with location, population, area and subjective consciousness. However, these factors cannot appropriately reflect what people’s movement path is in daily life. In this study, the concept of "Living Area" is replaced by "Influence Range" to show dynamic and variation with time and purposes of activities. This study uses data mining with Python and Excel, and visualizes the number of trips with GIS to explore influence range of Tainan city and the purpose of trips, and discuss living area delimited in current. It dialogues between the concepts of "Central Place Theory" and "Living Area", presents the new point of view, integrates the application of big data, urban planning and transportation. The finding will be valuable for resource allocation and land apportionment of spatial planning.

Keywords: Big Data, ITS, influence range, living area, central place theory, visualization

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Authors: K. G. Sorokozherdyev, E. A. Efimov

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The socio-economic development of regions and countries is an important research issue. Today, in the face of many negative events in the global and regional economies, it is especially important to identify those areas that can serve as sources of economic growth and the basis for the well-being of the population. This study aims to identify the most important sectors of the economy of the Arkhangelsk region that can contribute to the socio-economic development of the region as a whole. For research, the Arkhangelsk region was taken as one of the typical Russian regions that do not have significant reserves of hydrocarbons nor there are located any large industrial complexes. In this regard, the question of possible origins of economic growth seems especially relevant. The basis of this study constitutes the distributed lag regression model (ADL model) developed by the authors, which is based on quarterly data on the socio-economic development of the Arkhangelsk region for the period 2004-2016. As a result, we obtained three equations reflecting the dynamics of three indicators of the socio-economic development of the region -the average wage, the regional GRP, and the birth rate. The influencing factors are the shares in GRP of such sectors as agriculture, mining, manufacturing, construction, wholesale and retail trade, hotels and restaurants, as well as the financial sector. The study showed that the greatest influence on the socio-economic development of the region is exerted by such industries as wholesale and retail trade, construction, and industrial sectors. The study can be the basis for forecasting and modeling the socio-economic development of the Arkhangelsk region in the short and medium term. It also can be helpful while analyzing the effectiveness of measures aimed at stimulating those or other industries of the region. The model can be used in developing a regional development strategy.

Keywords: regional economic development, regional sectoral structure, ADL model, Arkhangelsk region

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Authors: Bouayachi Ali, Alikouss Saida, Baroudi Zouhir, Zerhouni Youssef, Zouhair Mohammed, El Idrissi Assia, Essalhi Mourad

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The world-class Imiter silver deposit is located on the northern flank of the Precambrian Imiter buttonhole. This deposit is formed by epithermal veins hosted in the sandstone-pelite formations of the lower complex and in the basic conglomerates of the upper complex, these veins are controlled by a regional scale fault cluster, oriented N70°E to N90°E. The present work on the contribution of remote sensing on the geological mapping of the Imiter buttonhole and application to the mineralized structures of the Principal Corps B3. Mapping on satellite images is a very important tool in mineral prospecting. It allows the localization of the zones of interest in order to orientate the field missions by helping the localization of the major structures which facilitates the interpretation, the programming and the orientation of the mining works. The predictive map also allows for the correction of field mapping work, especially the direction and dimensions of structures such as dykes, corridors or scrapings. The use of a series of processing such as SAM, PCA, MNF and unsupervised and supervised classification on a Landsat 8 satellite image of the study area allowed us to highlight the main facies of the Imite area. To improve the exploration research, we used another processing that allows to realize a spatial distribution of the alteration mineral indices, and the application of several filters on the different bands to have lineament maps.

Keywords: principal corps B3, teledetection, Landsat 8, Imiter II, silver mineralization, lineaments

Procedia PDF Downloads 90

Authors: D. T. Chabalala, J. M. Ndambuki, M. F. Ilunga

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Sedimentation includes the processes of erosion, transportation, deposition, and the compaction of sediment. Sedimentation in reservoir results in a decrease in water storage capacity, downstream problems involving aggregation and degradation, blockage of the intake, and change in water quality. A study was conducted in Caledon River catchment in the upstream of Welbedacht Reservoir located in the South Eastern part of Free State province, South Africa. The aim of this research was to investigate and develop a model for an Integrated Catchment Modelling of Sedimentation processes and management for the Welbedacht reservoir. Revised Universal Soil Loss Equation (RUSLE) was applied to determine sources of sediment at different areas of the catchment. The model has been also used to determine the impact of changes from management practice on erosion generation. The results revealed that the main sources of sediment in the watershed are cultivated land (273 ton per hectare), built up and forest (103.3 ton per hectare), and grassland, degraded land, mining and quarry (3.9, 9.8 and 5.3 ton per hectare) respectively. After application of soil conservation practices to developed Revised Universal Soil Loss Equation model, the results revealed that the total average annual soil loss in the catchment decreased by 76% and sediment yield from cultivated land decreased by 75%, while the built up and forest area decreased by 42% and 99% respectively. Thus, results of this study will be used by government departments in order to develop sustainable policies.

Keywords: Welbedacht reservoir, sedimentation, RUSLE, Caledon River

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Authors: Deepika Verma, Runa Sarkar

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One of the prominent debates in the social entrepreneurship literature has been to establish whether entrepreneurial work for social well-being by for-profit organizations can be classified as social entrepreneurship or not. Of late, the scholarship has reached a consensus. It concludes that there seems little sense in confining social entrepreneurship to just non-profit organizations. Boosted by this research, increasingly a lot of businesses engaged in filling the social infrastructure gaps in developing countries are calling themselves social enterprise. These organizations are diverse in their ownership, size, objectives, operations and business models. The lack of a comprehensive definition of social enterprise leads to three issues. Firstly, researchers may face difficulty in creating a database for social enterprises because the choice of an entity as a social enterprise becomes subjective or based on some pre-defined parameters by the researcher which is not replicable. Secondly, practitioners who use ‘social enterprise’ in their vision/mission statement(s) may find it difficult to adjust their business models accordingly especially during the times when they face the dilemma of choosing social well-being over business viability. Thirdly, social enterprise and social entrepreneurship attract a lot of donor funding and venture capital. In the paucity of a comprehensive definitional guide, the donors or investors may find assigning grants and investments difficult. It becomes necessary to develop an exhaustive and objective definition of social enterprise and examine whether the understanding of the academicians and practitioners about social enterprise match. This paper develops a dictionary of words often associated with social enterprise or (and) social entrepreneurship. It further compares two lexicographic definitions of social enterprise imputed from the abstracts of academic journal papers and trade publications extracted from the EBSCO database using the ‘tm’ package in R software.

Keywords: EBSCO database, lexicographic definition, social enterprise, text mining

Procedia PDF Downloads 383

Authors: Guo-Hui Lu, Jing-Yi Cai, Ke-Yan Tan, Xiao-Cai Yin, Yu Zheng, Peng-Wei Shao, Yong-Liang Yang

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Soil erosion around abandoned mines is one of the important geological agents for pollutant diffuses to the lower reaches of the local river basin system. River loading of pollutants is an important parameter for remediation of abandoned mines. In order to obtain information on pollutant transport and diffusion downstream in mining area, the small tributary system of the Xiaxi River in Wanshan District of Guizhou Province was selected as the research area. Sediment and suspended matter samples were collected and determined for Pb, As, Hg, Zn, Co, Cd, Cu, Ni, Cr, and Mn by inductively coupled plasma mass spectrometry (ICP-MS) and atomic fluorescence spectrometry (AFS) with the pretreatment of wet digestion. Discussions are made for pollution status and spatial distribution characteristics. The total Hg content in the sediments ranged from 0.45 to 16.0 g/g (dry weight) with an average of 5.79 g/g, which was ten times higher than the limit of Class II soil for mercury by the National Soil Environmental Quality Standard. The maximum occurred at the intersection of the Jin River and the Xiaxi River. The potential ecological hazard index (RI) was used to evaluate the ecological risk of heavy metals in the sediments. The average RI value for the whole study area suggests the high potential ecological risk level. High Cd potential ecological risk was found at individual sites.

Keywords: heavy metal, risk assessment, sediment, suspended matter, Wanshan mercury mine, small tributary system

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Authors: Mauricio Gómez, Esmeralda López, Ian Becar, Jaime Pizarro, Paula A. Zapata

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A biodegradable material is developed with adsorptive capacity for metals ion for intended use in mining tailings mitigating the environmental impact with economic retribution, two types of fibers were elaborated by electrospinning: (1) a cellulose acetate (CA) matrix and (2) a cellulose acetate (CA)/chitosan (CH) matrix evaluating the effect of CH in CA on its physicochemical properties. Through diffuse reflectance infrared fourier transform spectroscopy (DRIFTS) the incorporation of chitosan in the matrix was identified, observing the band of the amino group at 1500 - 1600 [cm-1]. By scanning electron microscopy (SEM), Hg porosimetry, and CO2 isotherm at 273 [K], the intrafiber microporosity and interfiber macroporosity were identified, with an increase in the distribution of macropores for CA/CH fibers. In the tensile test, CH into the matrix produces a more ductile and tenacious behavior, where the % elongation at break increased by 33% with the other parameters constant. Thermal analysis by differential scanning calorimetry (DSC) and Thermogravimetric Analysis (TGA) showed that the incorporation of chitosan produces higher retention of water molecules due to the functional groups (amino groups (- NH3)), but there is a decrease in the specific heat and thermoplastic properties of the matrix since the glass transition temperature and softening temperature disappear. The effect of the optimum pH for CA and CA/CH fibers were studied in a batch system. In the adsorption kinetic study, the best isotherm model adapted to the experimental results corresponds to the Sips model and the kinetics corresponds to pseudo-second order

Keywords: environmental materials, wastewater treatment, electrospun fibers, biopolymers (cellulose acetate/chitosan), metals recovery

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

Procedia PDF Downloads 239

Authors: Gabriel K. Nzulu, Lina Rostorm, Hans Högberg, Jun Liu, per Eklund, Lars Hultman, Martin Magnuson

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Altered and gangue quartz in hydrothermal veins from the Kubi Gold deposit in Dunkwa on Offin in the central region of Ghana are investigated for possible Au associated pathfinder minerals and to provide understanding and increase the knowledge of the mineral hosting and alteration processes in quartz. X-ray diffraction, air annealing furnace, differential scanning calorimetry, energy dispersive X-ray spectroscopy, and transmission electron microscopy have been applied on different quartz types outcropping from surface and bed rocks at the Kubi Gold Mining to reveal the material properties at different temperatures. From the diffraction results of the fresh and annealed quartz samples, we find that the samples contain pathfinder and the impurity minerals FeS₂, biotite, TiO₂, and magnetite. These minerals, under oxidation process between 574-1400 °C temperatures experienced hematite alterations and a transformation from α-quartz to β-quartz and further to cristobalite as observed from the calorimetry scans for hydrothermally exposed materials. The energy dispersive spectroscopy revealed elemental species of Fe, S, Mg, K, Al, Ti, Na, Si, O, and Ca contained in the samples and these are attributed to the impurity phase minerals observed in the diffraction. The findings also suggest that during the hydrothermal flow regime, impurity minerals and metals can be trapped by voids and faults. Under favorable temperature conditions the trapped minerals can be altered to change color at different depositional stages by oxidation and reduction processes leading to hematite alteration which is a useful pathfinder in mineral exploration.

Keywords: quartz, hydrothermal, minerals, hematite, x-ray diffraction, crystal-structure, defects

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Authors: M. Gómez-Ramírez, A. Rivas-Castillo, I. Rodríguez-Pozos, R. A. Avalos-Zuñiga, N. G. Rojas-Avelizapa

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Among the diverse types of pollutants produced by anthropogenic activities, metals represent a serious threat, due to their accumulation in ecosystems and their elevated toxicity. The mine tailings of abandoned mines contain high levels of metals such as arsenic (As), zinc (Zn), copper (Cu), and lead (Pb), which do not suffer any degradation process, they are accumulated in environment. Abandoned mine tailings potentially could contaminate rivers and aquifers representing a risk for human health due to their high metal content. In an attempt to remove the metals and thereby mitigate the environmental pollution, an environmentally friendly and economical method of bioremediation has been introduced. Bioleaching has been actively studied over the last several years, and it is one of the bioremediation solutions used to treat heavy metals contained in sewage sludge, sediment and contaminated soil. Acidithiobacillus thiooxidans, an extremely acidophilic, chemolithoautotrophic, gram-negative, rod shaped microorganism, which is typically related to Cu mining operations (bioleaching), has been well studied for industrial applications. The sulfuric acid produced plays a major role in bioleaching. Specifically, Acidithiobacillus thiooxidans strain DSM 26636 has been able to leach Al, Ni, V, Fe, Mg, Si, and Ni contained in slags from coal combustion wastes. The present study reports the ability of A. thiooxidans DSM 26636 for the bioleaching of metals contained in two different mine tailing samples (MT1 and MT2). It was observed that Al, Fe, and Mn were removed in 36.3±1.7, 191.2±1.6, and 4.5±0.2 mg/kg for MT1, and in 74.5±0.3, 208.3±0.5, and 20.9±0.1 for MT2. Besides, < 1.5 mg/kg of Au and Ru were also bioleached from MT1; in MT2, bioleaching of Zn was observed at 55.7±1.3 mg/kg, besides removal of < 1.5 mg/kg was observed for As, Ir, Li, and 0.6 for Os in this residue. These results show the potential of strain DSM 26636 for the bioleaching of metals that came from different mine tailings.

Keywords: A. thiooxidans, bioleaching, metals, mine tailings

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the easy creation of an institutional risk profile for endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support risk factors set up with just the most important values that are important for a particular organisation. Subsequently, the risk profile employs fuzzy models and associated configurations for the file format metadata aggregator to support digital preservation experts with a semi-automatic estimation of endangerment level for file formats. Our goal is to make use of a domain expert knowledge base aggregated from a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation and analysis of risk factors for a requried dimension. The proposed methods improve the visibility of risk factor information and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and automatically aggregated file format metadata from linked open data sources. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: digital information management, file format, endangerment analysis, fuzzy models

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Authors: John Okanda Okwaro

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Carbon (IV) oxide (CO₂) is emitted majorly from fossil fuel combustion and industrial production. The sources of interest of carbon (IV) oxide in the study area are mining activities, transport systems, and industrial processes. This study is aimed at building models that will help in monitoring the emissions within the study area. Three scenarios were discussed, namely: pessimistic scenario, business-as-usual scenario, and optimistic scenario. The result showed that there was a reduction in carbon dioxide concentration by approximately 50.5 ppm between March 2020 and January 2021 inclusive. This is majorly due to reduced human activities that led to decreased consumption of energy. Also, the CO₂ concentration trend follows the business-as-usual scenario (BAU) path. From the models, the pessimistic, business-as-usual, and optimistic scenarios give CO₂ concentration of about 545.9 ppm, 408.1 ppm, and 360.1 ppm, respectively, on December 31st, 2021. This research helps paint the picture to the policymakers of the relationship between energy sources and CO₂ emissions. Since the reduction in CO₂ emission was due to decreased use of fossil fuel as there was a decrease in economic activities, then if Kenya relies more on green energy than fossil fuel in the post-COVID-19 period, there will be more CO₂ emission reduction. That is, the CO₂ concentration trend is likely to follow the optimistic scenario path, hence a reduction in CO₂ concentration of about 48 ppm by the end of the year 2021. This research recommends investment in solar energy by energy-intensive companies, mine machinery and equipment maintenance, investment in electric vehicles, and doubling tree planting efforts to achieve the 10% cover.

Keywords: forecasting, greenhouse gas, green energy, hierarchical data format

Procedia PDF Downloads 157

Authors: Lucero Luciano, Cesar Celis, Jose Ramos

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Polygeneration improves energy efficiency and reduce both energy consumption and pollutant emissions compared to conventional generation technologies. A polygeneration system is a variation of a cogeneration one, in which more than two outputs, i.e., heat, power, cooling, water, energy or fuels, are accounted for. In particular, polygeneration systems integrating solar energy and water desalination represent promising technologies for energy production and water supply. They are therefore interesting options for coastal regions with a high solar potential, such as those located in southern Peru and northern Chile. Notice that most of the Peruvian and Chilean mining industry operations intensive in electricity and water consumption are located in these particular regions. Accordingly, this work focus on the design and integration of a polygeneration system producing industrial heating, cooling, electrical power and water for an industrial plant. The design procedure followed in this work involves integer linear programming modeling (MILP), operational planning and dynamic operating conditions. The technical and economic feasibility of integrating renewable energy technologies (photovoltaic and solar thermal, PV+CPS), thermal energy store, power and thermal exchange, absorption chillers, cogeneration heat engines and desalination technologies is particularly assessed. The polygeneration system integration carried out seek to minimize the system total annual cost subject to CO2 emissions restrictions. Particular economic aspects accounted for include investment, maintenance and operating costs.

Keywords: desalination, design and integration, polygeneration systems, renewable energy

Procedia PDF Downloads 117

Authors: González A. Julio, Ramírez L. Leonardo, Puerta A. Gabriel

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Telemedicine services use a large amount of data, most of which are diagnostic images in Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7) formats. Metadata is generated from each related image to support their identification. This study presents the use of decision trees for the optimization of information search processes for diagnostic images, hosted on the cloud server. To analyze the performance in the server, the following quality of service (QoS) metrics are evaluated: delay, bandwidth, jitter, latency and throughput in five test scenarios for a total of 26 experiments during the loading and downloading of DICOM images, hosted by the telemedicine group server of the Universidad Militar Nueva Granada, Bogotá, Colombia. By applying decision trees as a data mining technique and comparing it with the sequential search, it was possible to evaluate the search times of diagnostic images in the server. The results show that by using the metadata in decision trees, the search times are substantially improved, the computational resources are optimized and the request management of the telemedicine image service is improved. Based on the experiments carried out, search efficiency increased by 45% in relation to the sequential search, given that, when downloading a diagnostic image, false positives are avoided in management and acquisition processes of said information. It is concluded that, for the diagnostic images services in telemedicine, the technique of decision trees guarantees the accessibility and robustness in the acquisition and manipulation of medical images, in improvement of the diagnoses and medical procedures in patients.

Keywords: cloud storage, decision trees, diagnostic image, search, telemedicine

Procedia PDF Downloads 195

Authors: Sang Ho Lee, Tae Heon Moon, Youn Taik Leem, Kwang Woo Nam

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Knowledge and IT inputs to other industrial production have become more important as a key factor for the competitiveness of national and regional economies, such as knowledge economies in smart cities. Knowledge and IT industries lead the industrial innovation and technical (r)evolution through low cost, high efficiency in production, and by creating a new value chain and new production path chains, which is referred as knowledge and IT dynamics. This study aims to investigate the knowledge and IT dynamics in Korea, which are analyzed through the input-output model and structural path analysis. Twenty-eight industries were reclassified into seven categories; Agriculture and Mining, IT manufacture, Non-IT manufacture, Construction, IT-service, Knowledge service, Non-knowledge service to take close look at the knowledge and IT dynamics. Knowledge and IT dynamics were analyzed through the change of input output coefficient and multiplier indices in terms of technical innovation, as well as the changes of the structural paths of the knowledge and IT to other industries in terms of new production value creation from 1985 and 2010. The structural paths of knowledge and IT explain not only that IT foster the generation, circulation and use of knowledge through IT industries and IT-based service, but also that knowledge encourages IT use through creating, sharing and managing knowledge. As a result, this paper found the empirical investigation on the knowledge and IT dynamics of the Korean economy. Knowledge and IT has played an important role regarding the inter-industrial transactional input for production, as well as new industrial creation. The birth of the input-output production path has mostly originated from the knowledge and IT industries, while the death of the input-output production path took place in the traditional industries from 1985 and 2010. The Korean economy has been in transition to a knowledge economy in the Smart City.

Keywords: knowledge and IT industries, input-output model, structural path analysis, dynamics of knowledge and it, knowledge economy, knowledge city and smart city

Procedia PDF Downloads 328

Authors: S. M. Javad Davoodi, Mojtaba Shourian

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Groundwater table close to the ground surface causes major problems in construction and mining operation. One of the methods to control groundwater in such cases is using pumping wells. These pumping wells remove excess water from the site project and lower the water table to a desirable value. Although the efficiency of this method is acceptable, it needs high expenses to apply. It means even small improvement in a design of pumping wells can lead to substantial cost savings. In order to minimize the total cost in the method of pumping wells, a simulation-optimization approach is applied. The proposed model integrates MODFLOW as the simulation model with Firefly as the optimization algorithm. In fact, MODFLOW computes the drawdown due to pumping in an aquifer and the Firefly algorithm defines the optimum value of design parameters which are numbers, pumping rates and layout of the designing wells. The developed Firefly-MODFLOW model is applied to minimize the cost of the dewatering project for the ancient mosque of Kerman city in Iran. Repetitive runs of the Firefly-MODFLOW model indicates that drilling two wells with the total rate of pumping 5503 m3/day is the result of the minimization problem. Results show that implementing the proposed solution leads to at least 1.5 m drawdown in the aquifer beneath mosque region. Also, the subsidence due to groundwater depletion is less than 80 mm. Sensitivity analyses indicate that desirable groundwater depletion has an enormous impact on total cost of the project. Besides, in a hypothetical aquifer decreasing the hydraulic conductivity contributes to decrease in total water extraction for dewatering.

Keywords: groundwater dewatering, pumping wells, simulation-optimization, MODFLOW, firefly algorithm

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Mónica Arroyo

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South America shows increasing decline in regional export values in the last years, after a strong growth of trade flows especially with China up to 2013. This change is due to the end of the commodity price boom, the slowing of the Chinese economy and the effects of the 2008 economic crisis. This paper examines the integration of regional economies in this context, particularly the situation in Brazil and Argentina. Based on transformations over the last two decades, the analysis is focused on the spatial circuits of production linked to foreign markets, contributing to the understanding of the different uses of territory and the within-country inequality. The South American regional exports, consisting basically of raw materials, are concentrated in a few companies. Large areas are intended for the production of agriculture and mining commodities, under the command of major economic groups, both domestic and foreign, relegating the local population to less productive places or, in most cases, forcing them to change their activity and to migrate to other regions in search of some source of income. On the other hand, the dynamics of these commodities’ spatial circuits of production print requirements in territories in terms of infrastructure and regulation. Capturing this movement requires understanding businesses and government’s role in territorial regulation, and consequently how regional systems are changing – for instance, economic specialisation, growing role of services, investment in roads, railways, ports, and airports. This paper aims to highlight topics for discussion on regional economic dynamics and their different degrees of internationalisation. The intention is to contribute to the debate about the relations between trade, globalization, and development.

Keywords: regional inequality, international trade, developing world, South America

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Authors: Rachael Sullivan, Joshua Berman

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The number of satellites orbiting earth are in the thousands now. Commercial launches are increasing, and civilians are venturing into the outer reaches of the atmosphere. As the space industry continues to grow and evolve, so too will the demand on resources, the disparities amongst socio-economic groups, and space company governance standards. Outside of just ensuring that space operations are compliant with government regulations, export controls, and international sanctions, companies should also keep in mind the impact their operations will have on society and the environment. Those looking to expand their operations into outer space should remain mindful of both the opportunities and challenges that they could encounter along the way. From commercial launches promoting civilian space travel—like the recent launches from Blue Origin, Virgin Galactic, and Space X—to regulatory and policy shifts, the commercial landscape beyond the Earth's atmosphere is evolving. But practices will also have to become sustainable. Through a review and analysis of space industry trends, international government regulations, and empirical data, this research explores how Environmental, Social, and Governance (ESG) reporting and investing will manifest within a fast-changing space industry.Institutions, regulators, investors, and employees are increasingly relying on ESG. Those working in the space industry will be no exception. Companies (or investors) that are already engaging or plan to engage in space operations should consider 1) environmental standards and objectives when tackling space debris and space mining, 2) social standards and objectives when considering how such practices may impact access and opportunities for different socioeconomic groups to the benefits of space exploration, and 3) how decision-making and governing boards will function ethically, equitably, and sustainably as we chart new paths and encounter novel challenges in outer space.

Keywords: climate, environment, ESG, law, outer space, regulation

Procedia PDF Downloads 138

Authors: Teja Sai Puligadda

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Satellite images in detail can serve an important role in the geographic study. Quantitative and qualitative information provided by the satellite and remote sensing images minimizes the complexity of work and time. Data/images are captured at regular intervals by satellite remote sensing systems, and the amount of data collected is often enormous, and it expands rapidly as technology develops. Interpreting remote sensing images, geographic data mining, and researching distinct vegetation types such as agricultural and forests are all part of satellite image categorization. One of the biggest challenge data scientists faces while classifying satellite images is finding the best suitable classification algorithms based on the available that could able to classify images with utmost accuracy. In order to categorize satellite images, which is difficult due to the sheer volume of data, many academics are turning to deep learning machine algorithms. As, the CNN algorithm gives high accuracy in image recognition problems and automatically detects the important features without any human supervision and the ANN algorithm stores information on the entire network (Abhishek Gupta., 2020), these two deep learning algorithms have been used for satellite image classification. This project focuses on remote sensing through Deep Neural Networks i.e., ANN and CNN with Deep Sat (SAT-4) Airborne dataset for classifying images. Thus, in this project of classifying satellite images, the algorithms ANN and CNN are implemented, evaluated & compared and the performance is analyzed through evaluation metrics such as Accuracy and Loss. Additionally, the Neural Network algorithm which gives the lowest bias and lowest variance in solving multi-class satellite image classification is analyzed.

Keywords: artificial neural network, convolutional neural network, remote sensing, accuracy, loss

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Comfort Adeoye, Anthony Eneji

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Lead pollution by mining, industrial dumping, and other anthropogenic uses are corroding the environment. Efforts being made to control it include physical, chemical and biological methods. The failure of the aforementioned methods are largely due to the fact that they are cumbersome, expensive, and not eco-friendly. Some plant species can be used for remediation of these pollutants. The objective of this work is to investigate the abilities of two native weed species to remediate two lead-polluted soils: a) Battery dumpsite and, (b) Naturally occurring lead mine. Soil samples were taken from the two sites: a) Kumapayi in Ibadan, a battery dumpsite, (b) Zamfara, a natural lead mine. Screen house experiment in Complete Randomized Design (CRD) replicated three times was carried out at I.I.T.A. Unpolluted soils were collected and polluted with various rates of lead concentrations of 0, 0.1, 0.2, and 0.5%. These were planted with weed species. Plant growth parameters were monitored for twelve weeks, after which the plants were harvested. Dry weight and plant uptake of the lead were taken. Analysis of data was carried out using, Genstat, Excel and descriptive statistics. Relative concentration of lead (Pb) in the above and below ground parts of Gomphrena celusoides revealed that a higher amount of Pb is taken up in the root compared with the shoots at different levels of Pb pollution. However, lead uptake at 0.5% > 0.2% > 0.1% > Control. In essence, phytoremediation of Gomphrena is highest at soil pollution of 0.5% and its retention is greater in the root than the shoot.In S. pyramidalis, soil retention ranges from 0.1% > 0.5% > 0.2% > control. Uptake is highest at 0.5% > 0.1% > 0.2 in stem. Uptake in leaves is highest at 0.2%, but none in the 0.5% pollution. Therefore, different plant species exhibited different accumulative mode probably due to their physiological and rooting systems. Gomphrena spp. rooting system is tap root,while that of S.pyramidalis is fibrous.

Keywords: grass, lead, phytoremediation, pollution

Procedia PDF Downloads 317

Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

Procedia PDF Downloads 155

Authors: Joel Rookwood

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Sports mega events have become increasingly significant in sporting, political and economic terms, with analysis often focusing on issues including resource expenditure, development, legacy and sustainability. Transnational tournaments can inspire interest from a variety of demographics, and the operational management of such events can involve contributions from a range of personnel. In addition to television audiences events also attract attending spectators, and in football contexts the temporary migration of fans from potentially rival nations and teams can present event organising committees and security personnel with various challenges in relation to crowd management. The behaviour, interaction and control of supporters has previously led to incidents of disorder and hooliganism, with damage to property as well as injuries and deaths proving significant consequences. The Heysel tragedy at the 1985 European Cup final in Brussels is a notable example, where 39 fans died following crowd disorder and mismanagement. Football disasters and disorder, particularly in the context of international competition, have inspired responses from police, law makers, event organisers, clubs and associations, including stadium improvements, legislative developments and crowd management practice to improve the effectiveness of spectator safety. The growth and internationalisation of fandom and developments in event management and tourism have seen various responses to the evolving challenges associated with hosting large numbers of visiting spectators at mega events. In football contexts ‘fan parks’ are a notable example. Since the first widespread introduction in European football competitions at the 2006 World Cup finals in Germany, these facilities have become a staple element of such mega events. This qualitative, longitudinal, multi-continent research draws on extensive semi-structured interview and observation data. As a frame of reference, this work considers football events staged before and after the development of fan parks. Research was undertaken at four World Cup finals (Japan 2002, Germany 2006, South Africa 2010 and Brazil 2014), four European Championships (Portugal 2004, Switzerland/Austria 2008, Poland/Ukraine 2012 and France 2016), four other confederation tournaments (Ghana 2008, Qatar 2011, USA 2011 and Chile 2015), and four European club finals (Istanbul 2005, Athens 2007, Rome 2009 and Basle 2016). This work found that these parks are typically temporarily erected, specifically located zones where supporters congregate together irrespective of allegiances to watch matches on large screens, and partake in other forms of organised on-site entertainment. Such facilities can also allow organisers to control the behaviour, confine the movement and monitor the alcohol consumption of supporters. This represents a notable shift in policy from previous football tournaments, when the widely assumed causal link between alcohol and hooliganism which frequently shaped legislative and police responses to disorder, also dissuaded some authorities from permitting fans to consume alcohol in and around stadia. It also reflects changing attitudes towards modern football fans. The work also found that in certain contexts supporters have increasingly engaged with such provision which impacts fan behaviour, but that this is relative to factors including location, facilities, management and security.

Keywords: event, facility, fan, management, park

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Authors: Calvin Stephen, Biswajit Basu, Aonghus McNabola

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Market liberalization in the power sector has led to the emergence of micro-hydropower schemes that are dependent on the use of pumps-as-turbines in applications that were not suitable as potential hydropower sites in earlier years. These applications include energy recovery in water supply networks, sewage systems, irrigation systems, alcohol breweries, underground mining and desalination plants. As a result, there has been an accelerated adoption of pumpsas-turbine technology due to the economic advantages it presents in comparison to the conventional turbines in the micro-hydropower space. The performance of this machines under cavitation conditions, however, is not well understood as there is a deficiency of knowledge in literature focused on their turbine mode of operation. In hydraulic machines, cavitation is a common occurrence which needs to be understood to safeguard them and prolong their operation life. The overall purpose of this study is to investigate the effects of cavitation on the performance of a pumps-as-turbine system over its entire operating range. At various operating speeds, the cavitating region is identified experimentally while monitoring the effects this has on the power produced by the machine. Initial results indicate occurrence of cavitation at higher flow rates for lower operating speeds and at lower flow rates at higher operating speeds. This implies that for cavitation free operation, low speed pumps-as-turbine must be used for low flow rate conditions whereas for sites with higher flow rate conditions high speed turbines should be adopted. Such a complete understanding of pumps-as-turbine suction performance can aid avoid cavitation induced failures hence improved reliability of the micro-hydropower plant.

Keywords: cavitation, micro-hydropower, pumps-as-turbine, system design

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Authors: Adrian Ionita, Ana-Maria Ghimes

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The lack of features, design and the lack of promoting an integrated booking application are some of the reasons why most online travel platforms only offer automation of old booking processes, being limited to the integration of a smaller number of services without addressing the user experience. This paper represents a practical study on how to improve travel applications creating user-profiles through data-mining based on neural networks and genetic algorithms. Choices made by users and their ‘friends’ in the ‘social’ network context can be considered input data for a recommendation engine. The purpose of using these algorithms and this design is to improve user experience and to deliver more features to the users. The paper aims to highlight a broader range of improvements that could be applied to travel applications in terms of design and service integration, while the main scientific approach remains the technical implementation of the neural network solution. The motivation of the technologies used is also related to the initiative of some online booking providers that have made the fact that they use some ‘neural network’ related designs public. These companies use similar Big-Data technologies to provide recommendations for hotels, restaurants, and cinemas with a neural network based recommendation engine for building a user ‘DNA profile’. This implementation of the ‘profile’ a collection of neural networks trained from previous user choices, can improve the usability and design of any type of application.

Keywords: artificial intelligence, big data, cloud computing, DNA profile, genetic algorithms, machine learning, neural networks, optimization, recommendation system, user profiling

Procedia PDF Downloads 155