Search results for: predictive mining

Authors: Zhidong Zhang, Zhi-Chao Zhang, Georgianna Duarte

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The social problems of early adolescents in Northeast China were examined with the instrument of Achenbach System of Empirically Based Assessment (ASEBA). In this study, the data consisted of 2532 early adolescents. The relevant variables such as sports activities, hobbies, chores and the number of close friends, as independent variables have been included in this study. The stratified sampling method was used to collect data from 2532 participants. The analysis results indicated that sports activities, hobbies, chores and the number of close friends, as predictors can be used in a predictive model, which significantly predict the social problem T-score.

Keywords: social problems, ASEBA, early adolescents, predictive Model

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Authors: Irtaza M. Syed, Kaamran Raahemifar

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This paper presents a model predictive control (MPC) of a utility interactive (UI) single phase inverter (SPI) for a photovoltaic (PV) system at residential/distribution level. The proposed model uses single-phase phase locked loop (PLL) to synchronize SPI with the grid and performs MPC control in a dq reference frame. SPI model consists of boost converter (BC), maximum power point tracking (MPPT) control, and a full bridge (FB) voltage source inverter (VSI). No PI regulators to tune and carrier and modulating waves are required to produce switching sequence. Instead, the operational model of VSI is used to synthesize sinusoidal current and track the reference. Model is validated using a three kW PV system at the input of UI-SPI in Matlab/Simulink. Implementation and results demonstrate simplicity and accuracy, as well as reliability of the model.

Keywords: phase locked loop, voltage source inverter, single phase inverter, model predictive control, Matlab/Simulink

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Authors: Ilona Mewes, Helena Jenzer, Farshideh Einsele

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Objective: The objective of the study was to integrate two big databases from Swiss nutrition national survey (menuCH) and Swiss health national survey 2012 for data mining purposes. Each database has a demographic base data. An integrated Swiss database is built to later discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Design: Swiss nutrition national survey (menuCH) with approx. 2000 respondents from two different surveys, one by Phone and the other by questionnaire along with Swiss health national survey 2012 with 21500 respondents were pre-processed, cleaned and finally integrated to a unique relational database. Results: The result of this study is an integrated relational database from the Swiss nutritional and health databases.

Keywords: health informatics, data mining, nutritional and health databases, nutritional and chronical databases

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Authors: Yifu Ding, Vijay Avinash, Malcolm McCulloch

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As the proliferation of decentralized energy systems, the UK power system allows small-scale entities such as microgrids (MGs) to tender multiple energy services including energy arbitrage and frequency responses (FRs). However, its operation requires the balance between the uncertain renewable generations and loads in real-time and has to fulfill their provision requirements of contract services continuously during the time window agreed, otherwise it will be penalized for the under-delivered provision. To hedge against risks due to uncertainties and maximize the economic benefits, we propose a stochastic model predictive control (SMPC) framework to optimize its operation for the multi-service provision. Distinguished from previous works, we include a detailed economic-degradation model of the lithium-ion battery to quantify the costs of different service provisions, as well as accurately describe the changing dynamics of the battery. Considering a branch of load and generation scenarios and the battery aging, we formulate a risk-averse cost function using conditional value at risk (CVaR). It aims to achieve the maximum expected net revenue and avoids severe losses. The framework will be performed on a case study of a PV-battery grid-tied microgrid in the UK with real-life data. To highlight its performance, the framework will be compared with the case without the degradation model and the deterministic formulation.

Keywords: model predictive control (MPC), battery degradation, frequency response, microgrids

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Authors: Ahmad Afif, Teuku Yuri M. Zagloel

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Sustainable supply chain management is a new pattern that has emerged recently in industry and companies. The procurement process is one of the key factors for efficiency in supply chain management practices. Partnership is one of the procurement strategies for strategic items. The success factors of the partnership must be determined to avoid things that endanger the financial and operational status of the company. The current supplier partnership research focuses on the selection of general criteria and sustainable supplier selection. Currently, there is still limited research on the success factors of supplier partnerships that focus on strategic items in the coal mining industry. Meanwhile, the procurement of coal mining has its own characteristics, and there are regulations related to the procurement of goods. Therefore, this research was conducted to determine the categories of goods that are included in the strategic items and to design the success factors of supplier partnerships. The main factors studied are general, financial, production, reputation, synergies, and sustainable. The research was conducted using the Kraljic method to determine the categories of goods that are included in the strategic items. To design a supplier partnership success factor using the Hybrid Multi Criteria Decision Making method. Integrated Fuzzy AHP-Fuzzy TOPSIS is used to determine the weight of the success factors of supplier partnerships and to rank suppliers on the factors used.

Keywords: supplier, partnership, strategic item, success factors, and coal mining industry

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Authors: Ayoub Moufid, Najib Bennis, Soumia El Hani

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The objective of greenhouse climate control is to improve the culture development and to minimize the production costs. A greenhouse is an open system to external environment and the challenge is to regulate the internal climate despite the strong meteorological disturbances. The internal state of greenhouse considered in this work is defined by too relevant and coupled variables, namely inside temperature and hygrometry. These two variables are chosen to describe the internal state of greenhouses due to their importance in the development of plants and their sensitivity to external climatic conditions, sources of weather disturbances. A multivariable model is proposed and validated by considering a greenhouse as black-box system and the least square method is applied to parameters identification basing on collected experimental measures. To regulate the internal climate, we propose a Model Predictive Control (MPC) scheme. This one considers the measured meteorological disturbances and the physical and operational constraints on the control and state variables. A successful feasibility study of the proposed controller is presented, and simulation results show good performances despite the high interaction between internal and external variables and the strong external meteorological disturbances. The inside temperature and hygrometry are tracking nearly the desired trajectories. A comparison study with an On/Off control applied to the same greenhouse confirms the efficiency of the MPC approach to inside climate control.

Keywords: climate control, constraints, identification, greenhouse, model predictive control, optimization

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Authors: Lindie Grebe

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This paper reports on a study that explored the strategizing practices of professional accountants in the mining industry, based on Jarratt and Stiles’ dominant strategizing practice models framework. Drawing on a strategy-as-practice perspective, the paper recognises qualified professional accountants in strategic management such as Chief Executive Officers, as strategy practitioners that perform their strategizing practices and praxis within a specific context. The main findings of this paper were produced through semi-structured individual interviews with accountants that perform strategy on a business level in the South African mining industry. Qualitative data were analysed through conversation analysis over two coding-cycles. Findings describe accountant strategists as practitioners who challenge the dominant business model when a disconnect seems to exist between international corporate level strategy and business level strategy in the South African mining industry. Accountant strategy practitioners described their dominant strategizing practice model as incremental change during strategic planning and as a lived experience during strategy implementation. Findings portrayed these strategists as taking initiative as strategy leaders in a dynamic and volatile environment to combine their accounting background with strategic management and challenge the dominant business model. Understanding how accountant strategists perform strategizing offers insight into the social practice of strategic management. This understanding contributes to the body of knowledge on strategizing in the South African mining industry. In addition, knowledge on the transformation of accountants as strategists could provide valuable practice relevant insights for accounting educators and the accounting profession alike.

Keywords: accountant strategists, dominant strategizing practice models framework, mining industry, strategy-as-practice

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Authors: Ben Salah Nahla

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The problem of water in Tunisia affects the quality and quantity. Tunisia is in a situation of water shortage. It was estimated that 4.6 Mm3/an. Moreover, the quality of water in Tunisia is also mediocre. In fact, 50% of the water has a high salinity (> 1.5g/l). There are several parameters which affect water quality such as sodium, fluoride. An excess of this parameter may induce some human health. Furthermore, the mining basin area has a problem of industrial waste. This problem may affect the water quality of the groundwater. Therefore, the purpose of this work is to assess the water quality in Basin Mining and the impact of fluorine. For this research, some water samples were done in the field and specific water analysis was implemented in the laboratory. Sampling is carried out on eight drilling in the area of the mining region. In the following, we will look at water view composition, physical and chemical quality. A physical-chemical analysis of water from a survey of the Mining area of Tunisia was performed and showed an excess for the following items: fluorine, sodium, sulfate. So many chemicals may be present in water. However, only a small number of them immediately concern in terms of health in all circumstances. Fluorine (F) is one particular chemical that is considered both necessary for the human body, but an excess of the rate of this chemical causes serious diseases. Sodium fluoride and sodium silicofluoride are more soluble and may spread in animals and plants where their toxicity largest organizations. The more complex particles such as cryolite and fluorite, almost insoluble, are more stable and less toxic. Thereafter, we will study the problem of excess fluorine in the water. The latter intended for human consumption must always comply with the limits for microbiological quality parameters and physical-chemical parameters defined by European standards (1.5 mg/l) and Tunisian (2 mg/l).

Keywords: water, minier basin, fluorine, silicofluoride

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Authors: Tania Vasquez Mata, Luis A. Herrera, Cristian Arriaga Canon

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Background: Locally advanced breast cancer of the luminal B phenotype, poses challenges due to its variable response to neoadjuvant chemotherapy. A predictive biomarker is needed to identify patients who will not respond to treatment, allowing for alternative therapies. This study aims to validate the use of the lncRNA GATA3-AS1, as a predictive biomarker using RNA in situ hybridization. Research aim: The aim of this study is to determine if GATA3-AS1 can serve as a biomarker for resistance to neoadjuvant chemotherapy in patients with locally advanced luminal B breast cancer. Methodology: The study utilizes RNA in situ hybridization with predesigned probes for GATA3-AS1 on Formalin-Fixed Paraffin-Embedded tissue sections. The samples underwent pretreatment and protease treatment to enable probe penetration. Chromogenic detection and signal evaluation were performed using specific criteria. Findings: Patients who did not respond to neoadjuvant chemotherapy showed a 3+ score for GATA3-AS1, while those who had a complete response had a 1+ score. Theoretical importance: This study demonstrates the potential clinical utility of GATA3-AS1 as a biomarker for resistance to neoadjuvant chemotherapy. Identifying non-responders early on can help avoid unnecessary treatment and explore alternative therapy options. Data collection and analysis procedures: Tissue samples from patients with locally advanced luminal B breast cancer were collected and processed using RNA in situ hybridization. Signal evaluation was conducted under a microscope, and scoring was based on specific criteria. Questions addressed: Can GATA3-AS1 serve as a predictive biomarker for neoadjuvant chemotherapy response in locally advanced luminal B breast cancer? Conclusion: The lncRNA GATA3-AS1 can be used as a biomarker for resistance to neoadjuvant chemotherapy in patients with locally advanced luminal B breast cancer. Its identification through RNA in situ hybridization of tissue obtained from the initial biopsy can aid in treatment decision-making.

Keywords: biomarkers, breast neoplasms, genetics, neoadjuvant therapy, tumor

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Authors: Galo D. Soria, Jenny S. Casame, Eddy F. Pazmino

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In Ecuador, mining operations have significantly impacted water sources. Artisanal mining extensively relies in mercury amalgamation. Mercury is a neurotoxic substance even at low concentrations. The objective of this investigation is to exploit Hg-removal capacity of sulfur-limonene polysulfide (SLP), which is a low-cost polymer, in order to prepare granular media (sand) coated with SLP to be used in laboratory scale column-filtration systems. Preliminary results achieved 85% removal of Hg⁺⁺ from synthetic effluents using 20-cm length and 5-cm diameter columns at 119m/day average pore water velocity. During elution of the column, the SLP-coated sand indicated that Hg⁺⁺ is permanently fixed to the collector surface, in contrast, uncoated sand showed reversible retention in Hg⁺⁺ in the solid phase. Injection of 50 pore volumes decreased Hg⁺⁺ removal to 46%. Ongoing work has been focused in optimizing the synthesis of SLP and the polymer content in the porous media coating process to improve Hg⁺⁺ removal and extend the lifetime of the column-filter.

Keywords: column-filter, mercury, mining, polysulfide, water treatment

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Authors: M. Belén Prendes-Gero, Celestino González-Nicieza, M. Inmaculada Alvarez-Fernández

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Roof collapse is one of the problems with a higher frequency in most of the mines of all countries, even now. There are many reasons that may cause the roof to collapse, namely the mine stress activities in the mining process, the lack of vigilance and carelessness or the complexity of the geological structure and irregular operations. This work is the result of the analysis of one accident produced in the “Mary” coal exploitation located in northern Spain. In this accident, the roof of a crossroad of excavated galleries to exploit the “Morena” Layer, 700 m deep, collapsed. In the paper, the work done by the forensic team to determine the causes of the incident, its conclusions and recommendations are collected. Initially, the available documentation (geology, geotechnics, mining, etc.) and accident area were reviewed. After that, laboratory and on-site tests were carried out to characterize the behaviour of the rock materials and the support used (metal frames and shotcrete). With this information, different hypotheses of failure were simulated to find the one that best fits reality. For this work, the software of finite differences in three dimensions, FLAC 3D, was employed. The results of the study confirmed that the detachment was originated as a consequence of one sliding in the layer wall, due to the large roof span present in the place of the accident, and probably triggered as a consequence of the existence of a protection pillar insufficient. The results allowed to establish some corrective measures avoiding future risks. For example, the dimensions of the protection zones that must be remained unexploited and their interaction with the crossing areas between galleries, or the use of more adequate supports for these conditions, in which the significant deformations may discourage the use of rigid supports such as shotcrete. At last, a grid of seismic control was proposed as a predictive system. Its efficiency was tested along the investigation period employing three control equipment that detected new incidents (although smaller) in other similar areas of the mine. These new incidents show that the use of explosives produces vibrations which are a new risk factor to analyse in a next future.

Keywords: forensic analysis, hypothesis modelling, roof failure, seismic monitoring

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Authors: Arie Pujiwati, Kengo Nakamura, Noriaki Watanabe, Takeshi Komai

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Coal mining is well known to cause considerable environmental impacts, including trace element contamination of soil. This study aimed to assess the trace element (As, Cd, Co, Cu, Ni, Pb, Sb, and Zn) contamination of soil in the vicinity of coal mining activities, using the case study of Asam-asam River basin, South Kalimantan, Indonesia, and to assess the human health risk, incorporating total and bioavailable (water-leachable and acid-leachable) concentrations. The results show the enrichment of As and Co in soil, surpassing the background soil value. Contamination was evaluated based on the index of geo-accumulation, I_geo and the pollution index, PI. I_geo values showed that the soil was generally uncontaminated (I_geo ≤ 0), except for elevated As and Co. Mean PI for Ni and Cu indicated slight contamination. Regarding the assessment of health risks, the Hazard Index, HI showed adverse risks (HI > 1) for Ni, Co, and As. Further, Ni and As were found to pose unacceptable carcinogenic risk (risk > 1.10^-5). Farming, settlement, and plantation were found to present greater risk than coal mines. These results show that coal mining activity in the study area contaminates the soils by particular elements and may pose potential human health risk in its surrounding area. This study is important for setting appropriate countermeasure actions and improving basic coal mining management in Indonesia.

Keywords: coal mine, risk, trace elements, soil

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Authors: Sadiq J. Abou-Loukh, Hanan M. Habbi

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This paper presents a steganography to hide the transmitted information without excite suspicious and also illustrates the level of secrecy that can be increased by using cryptography techniques. The proposed system has been implemented firstly by encrypted image file one time pad key and secondly encrypted message that hidden to perform encryption followed by image embedding. Then the new image file will be created from the original image by using four triangles operation, the new image is processed by one of two image processing techniques. The proposed two processing techniques are thresholding and differential predictive coding (DPC). Afterwards, encryption or decryption keys are generated by functional key generator. The generator key is used one time only. Encrypted text will be hidden in the places that are not used for image processing and key generation system has high embedding rate (0.1875 character/pixel) for true color image (24 bit depth).

Keywords: encryption, thresholding, differential predictive coding, four triangles operation

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Authors: Ndivhudzannyi Rembuluwani, Francis Dacosta, Emmanuel Mhlongo

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The small-scale rock aggregate sector of the mining industry is a major source of employment for a significant number of people, particularly in remote rural areas, where alternative livelihoods are rare. It contributes to local economy by generating income and producing major and essential materials for the building, construction, and other industries. However, the sector is confronted with many challenges that hamper productivity and growth. The problems that confront this sector includes: health and safety, environmental impacts, low production and low adherence to mining legislations. This study investigated the challenges confronting selected small-scale rock aggregate mines in the Vhembe District of Limpopo province of South Africa, assesses the health, safety, low production and environmental impacts associated with aggregate production and to develop an integrated approach of addressing the multi-faceted challenges.

Keywords: health and safety, legislative framework, productivity, rock aggregate, small-scale mining

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Authors: Waheeda Jabbar, Ali Gul, Rida Noor, Sania Kurd, Saba Gulzar

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Hundreds of miners die from mining accidents each year due to poisonous gases found underground mining areas. This paper proposed an idea to protect the precious lives of mining workers. A supervising system is designed which is based on ZigBee wireless technique along with the smart protective helmets to detect real-time surveillance and it gives early warnings on presence of different poisonous gases in order to save mineworkers from any danger caused by these poisonous gases. A wireless sensor network is established using ZigBee wireless technique by integrating sensors on the helmet, apart from this helmet have embedded heartbeat sensor to detect the pulse rate and be aware of the physical or mental strength of a mineworker to increase the potential safety. Radio frequency identification (RFID) technology is used to find the location of workers. A ZigBee based base station is set-upped to control the communication. The idea is implemented and results are verified through experiment.

Keywords: Arduino, gas sensor (MQ7), RFID, wireless ZigBee

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Authors: Zhu Weimin, Allegorico Carmine, Ruggiero Gionata

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During decades of Baker Hughes (BH) iCenter experience, it is demonstrated that in addition to conventional insights on equipment steady operation conditions, insights on transient conditions can add significant and exclusive value for anomaly detection, downtime saving, and predictive maintenance. Our work shows examples from the BH iCenter experience to introduce the advantages and features of using transient condition analytics: (i) Operation under critical engine conditions: e.g., high level or high change rate of temperature, pressure, flow, vibration, etc., that would not be reachable in normal operation, (ii) Management of dedicated sub-systems or components, many of which are often bottlenecks for reliability and maintenance, (iii) Indirect detection of anomalies in the absence of instrumentation, (iv) Repetitive sequences: if data is properly processed, the engineering features of transients provide not only anomaly detection but also problem characterization and prognostic indicators for predictive maintenance, (v) Engine variables accounting for fatigue analysis. iCenter has been developing and deploying a series of analytics based on transient conditions. They are contributing to exclusive value adding in the following areas: (i) Reliability improvement, (ii) Startup reliability improvement, (iii) Predictive maintenance, (iv) Repair/overhaul cost down. Illustrative examples for each of the above areas are presented in our study, focusing on challenges and adopted techniques ranging from purely statistical approaches to the implementation of machine learning algorithms. The obtained results demonstrate how the value is obtained using transient condition analytics in the BH iCenter experience.

Keywords: analytics, diagnostics, monitoring, turbomachinery

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Authors: Mina Jafari, Kobra Hamraee, Saied Hossein Hosseini

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The prediction of breast cancer disease is one of the challenges in medicine. In this paper we collected 528 records of women’s information who live in Kabul including demographic, life style, diet and pregnancy data. There are many classification algorithm in breast cancer prediction and tried to find the best model with most accurate result and lowest error rate. We evaluated some other common supervised algorithms in data mining to find the best model in prediction of breast cancer disease among afghan women living in Kabul regarding to momography result as target variable. For evaluating these algorithms we used Cross Validation which is an assured method for measuring the performance of models. After comparing error rate and accuracy of three models: Decision Tree, Naive Bays and Rule Induction, Decision Tree with accuracy of 94.06% and error rate of %15 is found the best model to predicting breast cancer disease based on the health care records.

Keywords: decision tree, breast cancer, probability, data mining

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Authors: Hasan Basaran, Emre Unal

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Formation flying of satellites is a mission that involves a relative position keeping of different satellites in the constellation. In this study, different control algorithms are compared with one another in terms of ΔV, velocity increment, and tracking error. Various control methods, covering continuous and impulsive approaches are implemented and tested for satellites flying in low Earth orbit. Feedback linearization, sliding mode control, and model predictive control are designed and compared with an impulsive feedback law, which is based on mean orbital elements. Feedback linearization and sliding mode control approaches have identical mathematical models that include second order Earth oblateness effects. The model predictive control, on the other hand, does not include any perturbations and assumes circular chief orbit. The comparison is done with 4 different initial errors and achieved with velocity increment, root mean square error, maximum steady state error, and settling time. It was observed that impulsive law consumed the least ΔV, while produced the highest maximum error in the steady state. The continuous control laws, however, consumed higher velocity increments and produced lower amounts of tracking errors. Finally, the inversely proportional relationship between tracking error and velocity increment was established.

Keywords: chief-deputy satellites, feedback linearization, follower-leader satellites, formation flight, fuel consumption, model predictive control, rendezvous, sliding mode

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Authors: Tran Gia Khanh, Dao Phuong Nam, Do Trong Tan, Nguyen Van Huong, Mai Xuan Sinh

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This work presents the problem of tube-based robust model predictive controller for a class of continuous-time systems in the presence of input disturbances. The main objective is to point out the state trajectory of closed system being maintained inside a sequence of tubes. An estimation of attraction region of the closed system is pointed out based on input state stability (ISS) theory and linearized model in each time interval. The theoretical analysis and simulation results demonstrate the performance of the proposed algorithm for a wheeled inverted pendulum system.

Keywords: input state stability (ISS), tube-based robust MPC, continuous-time nonlinear systems, wheeled inverted pendulum

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Authors: Yusif Habib

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The Volta River Basin (VRB) is a transboundary resource shared by Six (6) the West African States. It’s utilization spans across irrigation, hydropower generation, domestic/household water use, transportation, industrial processing, among others. Simultaneously, mineral resources such as gold are mined within the VRB catchment. Typically, the extraction/mining operation is earth-surface excavation; known as Artisanal and Small-scale mining. We developed a conceptual framework in the context of Water-Energy-Food (WEF) Nexus to delineate the trade-offs and synergies between the mineral extractive operation’s impact on Agricultural systems, specifically, cereal crops (e.g. Maize, Millet, and Rice) and the environment (water and soil quality, deforestation, etc.) on the VRB. Thus, the study examined the trade-offs and synergies through the WEF nexus lens to explore the extent of an eventual overarching mining preference for gold exploration with high economic returns as opposed to the presumably low yearly harvest and household income from food crops production to inform intervention prioritization. Field survey (household, expert, and stakeholder consultation), bibliometric analysis/literature review, scenario, and simulation models, including land-use land cover (LULC) analyses, were conducted. The selected study area(s) in Ghana was the location where the mineral extractive operation’s presence and impact are widespread co-exist with the Agricultural systems. Overall, the study proposes mechanisms of the virtuous cycle through FEW Nexus instead of the presumably existing vicious cycle to inform decision making and policy implementation.

Keywords: agriculture, environmental sustainability, gold Mining, synergies, trade-off, water-energy-food nexus

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Authors: Zhi Hao Chow, Cian Mulligan, Jack Walsh, Antonio Garzon Vico, Dimitar Krastev

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Drug development is resource-intensive, time-consuming, and increasingly expensive with each developmental stage. The success rates of drug development are also relatively low, and the resources committed are wasted with each failed candidate. As such, a reliable method of predicting the success of drug development is in demand. The hypothesis was that some examples of failed drug candidates are pushed through developmental pipelines based on false confidence and may possess common linguistic features identifiable through sentiment analysis. Here, the concept of using text analysis to discover such features in research publications and investor reports as predictors of success was explored. R studios were used to perform text mining and lexicon-based sentiment analysis to identify affective phrases and determine their frequency in each document, then using SPSS to determine the relationship between our defined variables and the accuracy of predicting outcomes. A total of 161 publications were collected and categorised into 4 groups: (i) Cancer treatment, (ii) Neurodegenerative disease treatment, (iii) Vaccines, and (iv) Others (containing all other drugs that do not fit into the 3 categories). Text analysis was then performed on each document using 2 separate datasets (BING and AFINN) in R within the category of drugs to determine the frequency of positive or negative phrases in each document. A relative positivity and negativity value were then calculated by dividing the frequency of phrases with the word count of each document. Regression analysis was then performed with SPSS statistical software on each dataset (values from using BING or AFINN dataset during text analysis) using a random selection of 61 documents to construct a model. The remaining documents were then used to determine the predictive power of the models. Model constructed from BING predicts the outcome of drug performance in clinical trials with an overall percentage of 65.3%. AFINN model had a lower accuracy at predicting outcomes compared to the BING model at 62.5% but was not effective at predicting the failure of drugs in clinical trials. Overall, the study did not show significant efficacy of the model at predicting outcomes of drugs in development. Many improvements may need to be made to later iterations of the model to sufficiently increase the accuracy.

Keywords: data analysis, drug development, sentiment analysis, text-mining

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Authors: Ariungerel Jargal

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: Cave mining is a bulk underground mining method, which allows large low-grade deposits to be mined underground. This method involves undermining the orebody to make it collapse under its own weight into a series of chambers from which the ore extracted. It is a useful technique to extend the life of large deposits previously mined by open pits, and it is a method increasingly proposed for new mines around the world. We plan to conduct a feasibility study using Electrical impedance tomography (EIT) technology to show how much subsidence there is at the intersection with the cave mining surface. EIT is an imaging technique which uses electrical measurements at electrodes attached on the body surface to yield a cross-sectional image of conductivity changes within the object. EIT has been developed in several different applications areas as a simpler, cheaper alternative to many other imaging methods. A low frequency current is injected between pairs of electrodes while voltage measurements are collected at all other electrode pairs. In the difference EIT, images are reconstructed of the change in conductivity distribution (σ) between the acquisition of the two sets of measurements. Image reconstruction in EIT requires the solution of an ill-conditioned nonlinear inverse problem on noisy data, typically requiring make simpler assumptions or regularization. It is noted that the ratio of current to voltage represents a complex value according to Ohm’s law, and that it is theoretically possible to re-express EIT. The results of the experiment were presented on the simulation, and it was concluded that it is possible to conduct further real experiments. Drill a certain number of holes in the top wall of the cave to attach the electrodes, flow a current through them, and measure and acquire the potential through these electrodes. Appropriate values should be selected depending on the distance between the holes, the frequency and duration of the measurements, the surface characteristics and the size of the study area using an EIT device.

Keywords: impedance tomography, cave mining, soil, EIT device

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Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

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Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

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Authors: Z. Abdullah, A. R. Hamdan

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This paper is meant to analyze the ranking of University of Malaysia Terengganu, UMT’s website in the World Wide Web. There are only few researches have been done on comparing the ranking of universities’ websites so this research will be able to determine whether the existing UMT’s website is serving its purpose which is to introduce UMT to the world. The ranking is based on hub and authority values which are accordance to the structure of the website. These values are computed using two web-searching algorithms, HITS and SALSA. Three other universities’ websites are used as the benchmarks which are UM, Harvard and Stanford. The result is clearly showing that more work has to be done on the existing UMT’s website where important pages according to the benchmarks, do not exist in UMT’s pages. The ranking of UMT’s website will act as a guideline for the web-developer to develop a more efficient website.

Keywords: algorithm, ranking, website, web structure mining

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Authors: Akshay Agrawal, Anand K. Srivastava

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The increasing amount of XML datasets available to casual users increases the necessity of investigating techniques to extract knowledge from these data. Data mining is widely applied in the database research area in order to extract frequent correlations of values from both structured and semi-structured datasets. The increasing availability of heterogeneous XML sources has raised a number of issues concerning how to represent and manage these semi structured data. In recent years due to the importance of managing these resources and extracting knowledge from them, lots of methods have been proposed in order to represent and cluster them in different ways.

Keywords: XML, similarity measure, clustering, cluster quality, semantic clustering

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Authors: Khaled Benyounes

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Determination of the rock mechanical properties such as unconfined compressive strength UCS, Young’s modulus E, and tensile strength by the Brazilian test Rtb is considered to be the most important component in drilling and mining engineering project. Research related to establishing correlation between strength and physical parameters of rocks has always been of interest to mining and reservoir engineering. For this, many rock blocks of limestone were collected from the quarry located in Meftah(Algeria), the cores were crafted in the laboratory using a core drill. This work examines the relationships between mechanical properties and some physical properties of limestone. Many empirical equations are established between UCS and physical properties of limestone (such as dry bulk density, velocity of P-waves, dynamic Young’s modulus, alteration index, and total porosity). Others correlations UCS-tensile strength, dynamic Young’s modulus-static Young’s modulus have been find. Based on the Mohr-Coulomb failure criterion, we were able to establish mathematical relationships that will allow estimating the cohesion and internal friction angle from UCS and indirect tensile strength. Results from this study can be useful for mining industry for resolve range of geomechanical problems such as slope stability.

Keywords: limestone, mechanical strength, Young’s modulus, porosity

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Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

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With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

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Authors: Mohamed El Horri, Amine Mouden, Reda Messaoudi, Mohamed Chekkal, Driss Benlaldj, Malika Baghdadi, Lahcene Benmahdi, Fatima Seghier

Abstract:

Background/aim: Recently, the Von Willebrand factor antigen (vWF-Ag)has been identified as a new marker of portal hypertension (PH) and its complications. Few studies talked about its role in the prediction of esophageal varices. VWF-Ag is considered a non-invasive approach, In order to avoid the endoscopic burden, cost, drawbacks, unpleasant and repeated examinations to the patients. In our study, we aimed to evaluate the ability of this marker in the prediction of another complication of portal hypertension, which is portal hypertensive gastropathy (PHG), the one that is diagnosed also by endoscopic tools. Patients and methods: It is about a prospective study, which include 124 cirrhotic patients with no history of bleeding who underwent screening endoscopy for PH-related complications like esophageal varices (EVs) and PHG. Routine biological tests were performed as well as the VWF-Ag testing by both ELFA and Immunoturbidimetric techniques. The diagnostic performance of our marker was assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curves. Results: 124 patients were enrolled in this study, with a mean age of 58 years [CI: 55 – 60 years] and a sex ratio of 1.17. Viral etiologies were found in 50% of patients. Screening endoscopy revealed the presence of PHG in 20.2% of cases, while for EVsthey were found in 83.1% of cases. VWF-Ag levels, were significantly increased in patients with PHG compared to those who have not: 441% [CI: 375 – 506], versus 279% [CI: 253 – 304], respectively (p <0.0001). Using the area under the receiver operating characteristic curve (AUC), vWF-Ag was a good predictor for the presence of PHG. With a value higher than 320% and an AUC of 0.824, VWF-Ag had an 84% sensitivity, 74% specificity, 44.7% positive predictive value, 94.8% negative predictive value, and 75.8% diagnostic accuracy. Conclusion: VWF-Ag is a good non-invasive low coast marker for excluding the presence of PHG in patients with liver cirrhosis. Using this marker as part of a selective screening strategy might reduce the need for endoscopic screening and the coast of the management of these kinds of patients.

Keywords: von willebrand factor, portal hypertensive gastropathy, prediction, liver cirrhosis

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Authors: Goudjil kamel, Boukhatem Ghania, Jlailia Djihene

Abstract:

This paper delves into the development of a sophisticated desktop application designed to calculate soil bearing capacity and predict limit pressure. Drawing from an extensive review of existing methodologies, the study meticulously examines various approaches employed in soil bearing capacity calculations, elucidating their theoretical foundations and practical applications. Furthermore, the study explores the burgeoning intersection of artificial intelligence (AI) and geotechnical engineering, underscoring the transformative potential of AI- driven solutions in enhancing predictive accuracy and efficiency.Central to the research is the utilization of cutting-edge machine learning techniques, including Artificial Neural Networks (ANN), XGBoost, and Random Forest, for predictive modeling. Through comprehensive experimentation and rigorous analysis, the efficacy and performance of each method are rigorously evaluated, with XGBoost emerging as the preeminent algorithm, showcasing superior predictive capabilities compared to its counterparts. The study culminates in a nuanced understanding of the intricate dynamics at play in geotechnical analysis, offering valuable insights into optimizing soil bearing capacity calculations and limit pressure predictions. By harnessing the power of advanced computational techniques and AI-driven algorithms, the paper presents a paradigm shift in the realm of geotechnical engineering, promising enhanced precision and reliability in civil engineering projects.

Keywords: limit pressure of soil, xgboost, random forest, bearing capacity

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Authors: Juliette Harper, Yu Yang

Abstract:

Biofuels have gained significant attention recently due to the new regulations and agreements regarding fossil fuels and greenhouse gases being made by countries around the globe. One of the most common types of biofuels is biodiesel, primarily made via the transesterification reaction. We model this nonlinear process in MATLAB using the standard kinetic equations. Then, a nonlinear Model predictive control (NMPC) was developed to regulate this process due to its capability to handle process constraints. The feeding flow uncertainty and kinetic disturbances are further incorporated in the model to capture the real-world operating conditions. The simulation results will show that the proposed NMPC can guarantee the final composition of fatty acid methyl esters (FAME) above the target threshold with a high chance by adjusting the process temperature and flowrate. This research will allow further understanding of NMPC under uncertainties and how to design the computational strategy for larger process with more variables.

Keywords: NMPC, biodiesel, uncertainties, nonlinear, MATLAB

Procedia PDF Downloads 97