Search results for: salt mining site

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: Billel Kenidra, Mohamed Benmohammed

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Data mining technique used in the field of clustering is a subject of active research and assists in biological pattern recognition and extraction of new knowledge from raw data. Clustering means the act of partitioning an unlabeled dataset into groups of similar objects. Each group, called a cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Several clustering methods are based on partitional clustering. This category attempts to directly decompose the dataset into a set of disjoint clusters leading to an integer number of clusters that optimizes a given criterion function. The criterion function may emphasize a local or a global structure of the data, and its optimization is an iterative relocation procedure. The K-Means algorithm is one of the most widely used partitional clustering techniques. Since K-Means is extremely sensitive to the initial choice of centers and a poor choice of centers may lead to a local optimum that is quite inferior to the global optimum, we propose a strategy to initiate K-Means centers. The improved K-Means algorithm is compared with the original K-Means, and the results prove how the efficiency has been significantly improved.

Keywords: microarray data mining, biological pattern recognition, partitional clustering, k-means algorithm, centroid initialization

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Authors: Juergen Orasche, Vesta Kohlmeier, George C. Dragan, Gert Jakobi, Patricia Forbes, Ralf Zimmermann

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Underground mining poses a difficult environment for both man and machines. At more than 1000 meters underneath the surface of the earth, ores and other mineral resources are still gained by conventional and motorised mining. Adding to the hazards caused by blasting and stone-chipping, the working conditions are best described by the high temperatures of 35-40°C and high humidity, at low air exchange rates. Separate ventilation shafts lead fresh air into a mine and others lead expended air back to the surface. This is essential for humans and machines working deep underground. Nevertheless, mines are widely ramified. Thus the air flow rate at the far end of a tunnel is sensed to be close to zero. In recent years, conventional mining was supplemented by mining with heavy diesel machines. These very flat machines called Load Haul Dump (LHD) vehicles accelerate and ease work in areas favourable for heavy machines. On the other hand, they emit non-filtered diesel exhaust, which constitutes an occupational hazard for the miners. Combined with a low air exchange, high humidity and inorganic dust from the mining it leads to 'black smog' underneath the earth. This work focuses on the air quality in mines employing LHDs. Therefore we performed personal sampling (samplers worn by miners during their work), stationary sampling and aethalometer (Microaeth MA200, Aethlabs) measurements in a platinum mine in around 1000 meters under the earth’s surface. We compared areas of high diesel exhaust emission with areas of conventional mining where no diesel machines were operated. For a better assessment of health risks caused by air pollution we applied a separated gas-/particle-sampling tool (or system), with first denuder section collecting intermediate VOCs. These multi-channel silicone rubber denuders are able to trap IVOCs while allowing particles ranged from 10 nm to 1 µm in diameter to be transmitted with an efficiency of nearly 100%. The second section is represented by a quartz fibre filter collecting particles and adsorbed semi-volatile organic compounds (SVOC). The third part is a graphitized carbon black adsorber – collecting the SVOCs that evaporate from the filter. The compounds collected on these three sections were analyzed in our labs with different thermal desorption techniques coupled with gas chromatography and mass spectrometry (GC-MS). VOCs and IVOCs were measured with a Shimadzu Thermal Desorption Unit (TD20, Shimadzu, Japan) coupled to a GCMS-System QP 2010 Ultra with a quadrupole mass spectrometer (Shimadzu). The GC was equipped with a 30m, BP-20 wax column (0.25mm ID, 0.25µm film) from SGE (Australia). Filters were analyzed with In-situ derivatization thermal desorption gas chromatography time-of-flight-mass spectrometry (IDTD-GC-TOF-MS). The IDTD unit is a modified GL sciences Optic 3 system (GL Sciences, Netherlands). The results showed black carbon concentrations measured with the portable aethalometers up to several mg per m³. The organic chemistry was dominated by very high concentrations of alkanes. Typical diesel engine exhaust markers like alkylated polycyclic aromatic hydrocarbons were detected as well as typical lubrication oil markers like hopanes.

Keywords: diesel emission, personal sampling, aethalometer, mining

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Authors: Imran Dayan, Ashiqul Khan

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Internal corporate fraud, which is fraud carried out by internal stakeholders of a company, affects the well-being of the organisation just like its external counterpart. Even if such an act is carried out for the short-term benefit of a corporation, the act is ultimately harmful to the entity in the long run. Internal fraud is often carried out by relying upon aberrations from usual business processes. Business processes are the lifeblood of a company in modern managerial context. Such processes are developed and fine-tuned over time as a corporation grows through its life stages. Modern corporations have embraced technological innovations into their business processes, and Enterprise Resource Planning (ERP) systems being at the heart of such business processes is a testimony to that. Since ERP systems record a huge amount of data in their event logs, the logs are a treasure trove for anyone trying to detect any sort of fraudulent activities hidden within the day-to-day business operations and processes. This research utilises the ERP systems in place within corporations to assess the likelihood of prospective internal fraud through developing a framework for measuring the risks of fraud through Process Mining techniques and hence finds risky designs and loose ends within these business processes. This framework helps not only in identifying existing cases of fraud in the records of the event log, but also signals the overall riskiness of certain business processes, and hence draws attention for carrying out a redesign of such processes to reduce the chance of future internal fraud while improving internal control within the organisation. The research adds value by applying the concepts of Process Mining into the analysis of data from modern day applications of business process records, which is the ERP event logs, and develops a framework that should be useful to internal stakeholders for strengthening internal control as well as provide external auditors with a tool of use in case of suspicion. The research proves its usefulness through a few case studies conducted with respect to big corporations with complex business processes and an ERP in place.

Keywords: enterprise resource planning, fraud risk framework, internal corporate fraud, process mining

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Authors: Henni Mansour Abdelwaheb

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This study discusses the design of a bounded position/force feedback controller developed to ensure position and force tracking for bilateral teleoperation arms operating with variable delay, and actuator saturation. Also, an adaptive robust Radial Basis Function (RBF) neural network is used to estimate the environment torque. The parameters of the environment torque are then sent from the slave site to the master site as a non-power signal to avoid passivity problems. Moreover, a nonlinear function is applied to each controller term as a smooth saturation function, providing a bounded control signal and preserving the system’s actuators. Lastly, the Lyapunov approach demonstrates the global stability of the controlled system, and numerical experiment results further confirm the validity of the presented strategy.

Keywords: teleoperation manipulators system, time-varying delay, actuator saturation, adaptive robust rbf neural network approximation, uncertainties

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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Clustering is a well known data mining technique used in pattern recognition and information retrieval. The initial dataset to be clustered can either contain categorical or numeric data. Each type of data has its own specific clustering algorithm. In this context, two algorithms are proposed: the k-means for clustering numeric datasets and the k-modes for categorical datasets. The main encountered problem in data mining applications is clustering categorical dataset so relevant in the datasets. One main issue to achieve the clustering process on categorical values is to transform the categorical attributes into numeric measures and directly apply the k-means algorithm instead the k-modes. In this paper, it is proposed to experiment an approach based on the previous issue by transforming the categorical values into numeric ones using the relative frequency of each modality in the attributes. The proposed approach is compared with a previously method based on transforming the categorical datasets into binary values. The scalability and accuracy of the two methods are experimented. The obtained results show that our proposed method outperforms the binary method in all cases.

Keywords: clustering, unsupervised learning, pattern recognition, categorical datasets, knowledge discovery, k-means

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Authors: Abdulmalik Altuwayjiri, Milad Pirhadi, Sina Taghvaee, Constantinos Sioutas

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A significant portion of PM₂.₅ mass concentration is carbonaceous matter (CM), which majorly exists in the form of organic carbon (OC). Ambient OC originates from a multitude of sources and plays an important role in global climate effects, visibility degradation, and human health. In this study, positive matrix factorization (PMF) was utilized to identify and quantify the long-term contribution of PM₂.₅ sources to total OC mass concentration in central Los Angeles (CELA) and Riverside (i.e., receptor site), using the chemical speciation network (CSN) database between 2005 and 2015, a period during which several state and local regulations on tailpipe emissions were implemented in the area. Our PMF resolved five different factors, including tailpipe emissions, non-tailpipe emissions, biomass burning, secondary organic aerosol (SOA), and local industrial activities for both sampling sites. The contribution of vehicular exhaust emissions to the OC mass concentrations significantly decreased from 3.5 µg/m³ in 2005 to 1.5 µg/m³ in 2015 (by about 58%) at CELA, and from 3.3 µg/m³ in 2005 to 1.2 µg/m³ in 2015 (by nearly 62%) at Riverside. Additionally, SOA contribution to the total OC mass, showing higher levels at the receptor site, increased from 23% in 2005 to 33% and 29% in 2010 and 2015, respectively, in Riverside, whereas the corresponding contribution at the CELA site was 16%, 21% and 19% during the same period. The biomass burning maintained an almost constant relative contribution over the whole period. Moreover, while the adopted regulations and policies were very effective at reducing the contribution of tailpipe emissions, they have led to an overall increase in the fractional contributions of non-tailpipe emissions to total OC in CELA (about 14%, 28%, and 28% in 2005, 2010 and 2015, respectively) and Riverside (22%, 27% and 26% in 2005, 2010 and 2015), underscoring the necessity to develop equally effective mitigation policies targeting non-tailpipe PM emissions.

Keywords: PM₂.₅, organic carbon, Los Angeles megacity, PMF, source apportionment, non-tailpipe emissions

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Authors: A. Kavita Murugkar, B. Anurag Kashyap

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With the Rights for Persons with Disabilities Act (RPWD Act) 2016, the Indian government has made it mandatory for all establishments, including Heritage Sites, to be accessible for People with Disabilities. However, recent access audit surveys done under the Accessible India Campaign by Ministry of Culture indicate that there are very few accessibility measures provided in the Heritage sites for people with disabilities. Though there are some measures for the mobility impaired, surveys brought out that there are almost no provisions for people with vision impairment (PwVI) in heritage sites thus depriving them of a reasonable physical & intellectual access that facilitates an enjoyable experience and enriching interpretation of the Heritage Site. There is a growing need to develop multisensory interpretative tools that can help the PwVI in perceiving heritage sites in the absence of vision. The purpose of this research was to examine the usability of an audio-tactile model as a haptic and sound-based strategy for augmenting the perception and experience of PwVI in a heritage site. The first phase of the project was a multi-stage phenomenological experimental study with visually impaired users to investigate the design parameters for developing an audio-tactile model for PwVI. The findings from this phase included user preferences related to the physical design of the model such as the size, scale, materials, details, etc., and the information that it will carry such as braille, audio output, tactile text, etc. This was followed by the second phase in which a working prototype of an audio-tactile model is designed and developed for a heritage site based on the findings from the first phase of the study. A nationally listed heritage site from the author’s city was selected for making the model. The model was lastly tested by visually impaired users for final refinements and validation. The prototype developed empowers People with Vision Impairment to navigate independently in heritage sites. Such a model if installed in every heritage site, can serve as a technological guide for the Person with Vision Impairment, giving information of the architecture, details, planning & scale of the buildings, the entrances, location of important features, lifts, staircases, and available, accessible facilities. The model was constructed using 3D modeling and digital printing technology. Though designed for the Indian context, this assistive technology for the blind can be explored for wider applications across the globe. Such an accessible solution can change the otherwise “incomplete’’ perception of the disabled visitor, in this case, a visually impaired visitor and augment the quality of their experience in heritage sites.

Keywords: accessibility, architectural perception, audio tactile model , inclusive heritage, multi-sensory perception, visual impairment, visitor experience

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Authors: Seyed Abolhasan Naeini, Saeideh Mohammadi

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Rapid urban development leads to the construction of urban tunnels for transport. Passage of tunnels under the surface structures and utilities prompted the changes in the site conditions and hence alteration of the dynamic response of surface structures. Therefore, in this study, the effect of the interaction of tunnel-superstructure on the site response is investigated numerically. For this purpose, Fast Lagrangian Analysis of Continua (FLAC 2D) is used, and stratification and properties of soil layers are selected based on the line No 7 of Tehran subway. The superstructure is modeled both as an equivalent surcharge and the actual structure, and the results are compared. A comparison of the results shows that consideration of structure geometry is necessary for dynamic analysis and it leads to the changes in displacements and accelerations. Consequently, the geometry of the superstructure should be modeled completely instead of the application of an equivalent load. The effect of tunnel diameter and depth on the settlement of superstructures is also studied. Results show that when the tunnel depth and diameter grow, the settlements increase considerably.

Keywords: tunnel, FLAC2D, settlement, dynamic analysis

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Authors: Geevika J. Ganegama Arachchi, Steve H. Flint, Lynn McIntyre, Cristina D. Cruz, Beatrice M. Dias-Wanigasekera, Craig Billington, J. Andrew Hudson, Anthony N. Mutukumira

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In seafood processing plants, Listeriamonocytogenes(L. monocytogenes)likely exists in a metabolically stressed state due to the nutrient-deficient environment, processing treatments such as heating, curing, drying, and freezing, and exposure to detergents and disinfectants. Stressed L. monocytogenes cells have been shown to be as pathogenic as unstressed cells. This study investigated lytic efficacy of (LiMN4L, LiMN4p, and LiMN17) which were previouslycharacterized as virulent against physiologically stressed cells of three seafood borne L. monocytogenesstrains (19CO9, 19DO3, and 19EO3).Physiologically compromised cells ofL. monocytogenesstrains were prepared by aging cultures in TrypticaseSoy Broth at 15±1°C for 72 h; heat injuringcultures at 54±1 - 55±1°C for 40 - 60 min;salt-stressing cultures in Milli-Q water were incubated at 25±1°C in darkness for three weeks; and incubating cultures in 9% (w/v) NaCl at 15±1°C for 72 h. Low concentrations of physiologically compromised cells of three L. monocytogenesstrainswere challenged in vitrowith high titre of three phages in separate experiments using Fish Broth medium (aqueous fish extract) at 15 °C in order to mimic the environment of seafood processing plant. Each phage, when present at ≈9 log10 PFU/ml, reduced late exponential phase cells of L. monocytogenes suspended in fish protein broth at ≈2-3 log10 CFU/ml to a non-detectable level (< 10 CFU/ml). Each phage, when present at ≈8.5 log10 PFU/ml, reduced both heat-injured cells present at 2.5-3.6 log10 CFU/ml and starved cells that were showed coccoid shape, present at ≈2-3 log10 CFU/ml to < 10 CFU/ml after 30 min. Phages also reduced salt-stressed cellspresent at ≈3 log10 CFU/ml by > 2 log10. L. monocytogenes (≈8 log10 CFU/ml) were reduced to below the detection limit (1 CFU/ml) by the three successive phage infections over 16 h, indicating that emergence of spontaneous phage resistance was infrequent. The three virulent phages showed high decontamination potential for physiologically stressed L. monocytogenes strains from seafood processing environments.

Keywords: physiologically stressed L. monocytogenes, heat injured, seafood processing environment, virulent phage

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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With the popularity of data-driven decision making on the rise, this study focuses on providing an alternative outlook towards the process of decision-making. Combining quantitative and qualitative methods rooted in the social sciences, an integrated framework is presented with a focus on delivering a much more robust and efficient approach towards the concept of data-driven decision-making with respect to not only Big data but also 'Thick data', a new form of qualitative data. In support of this, an example from the retail sector has been illustrated where the framework is put into action to yield insights and leverage business intelligence. An interpretive approach to analyze findings from both kinds of quantitative and qualitative data has been used to glean insights. Using traditional Point-of-sale data as well as an understanding of customer psychographics and preferences, techniques of data mining along with qualitative methods (such as grounded theory, ethnomethodology, etc.) are applied. This study’s final goal is to establish the framework as a basis for providing a holistic solution encompassing both the Big and Thick aspects of any business need. The proposed framework is a modified enhancement in lieu of traditional data-driven decision-making approach, which is mainly dependent on quantitative data for decision-making.

Keywords: big data, customer behavior, customer experience, data mining, qualitative methods, quantitative methods, thick data

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Authors: Meng-Tzu Cheng, Louisa Rosenheck, Chen-Yen Lin, Eric Klopfer

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The purpose of the research is to explore some of the ways in which gameplay data can be analyzed to yield results that feedback into the learning ecosystem. Back-end data for all users as they played an MMOG, The Radix Endeavor, was collected, and this study reports the analyses on a specific genetics quest by using the data mining techniques, including the decision tree method. In the study, different reasons for quest failure between participants who eventually succeeded and who never succeeded were revealed. Regarding the in-game tools use, trait examiner was a key tool in the quest completion process. Subsequently, the results of decision tree showed that a lack of trait examiner usage can be made up with additional Punnett square uses, displaying multiple pathways to success in this quest. The methods of analysis used in this study and the resulting usage patterns indicate some useful ways that gameplay data can provide insights in two main areas. The first is for game designers to know how players are interacting with and learning from their game. The second is for players themselves as well as their teachers to get information on how they are progressing through the game, and to provide help they may need based on strategies and misconceptions identified in the data.

Keywords: MMOG, decision tree, genetics, gaming-learning interaction

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Authors: Jaehyun Choi, Hyunjung Kim, Namho Kim

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Earthwork is one of the critical civil construction operations that require large-quantities of resources due to its intensive dependency upon construction equipment. Therefore, efficient construction equipment management can highly contribute to productivity improvements and cost savings. Earthwork operation utilizes various combinations of construction equipment in order to meet project requirements such as time and cost. Identification of site condition and construction methods should be performed in advance in order to develop a proper execution plan. The factors to be considered include capacity of equipment assigned, the method of construction, the size of the site, and the surrounding condition. In addition, optimal combination of various construction equipment should be selected. However, in real world practice, equipment utilization plan is performed based on experience and intuition of management. The researchers evaluated the efficiency of various alternatives of construction equipment combinations by utilizing the process simulation model, validated the model from a case study project, and presented a methodology to find optimized plan among alternatives.

Keywords: earthwork operation, construction equipment, process simulation, optimization

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Authors: Hamed K. Esfahani, Bithin Datta

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Transport of reactive chemical contaminant species in groundwater aquifers is a complex and highly non-linear physical and geochemical process especially for real life scenarios. Simulating this transport process involves solving complex nonlinear equations and generally requires huge computational time for a given aquifer study area. Development of optimal remediation strategies in aquifers may require repeated solution of such complex numerical simulation models. To overcome this computational limitation and improve the computational feasibility of large number of repeated simulations, Genetic Programming based trained surrogate models are developed to approximately simulate such complex transport processes. Transport process of acid mine drainage, a hazardous pollutant is first simulated using a numerical simulated model: HYDROGEOCHEM 5.0 for a contaminated aquifer in a historic mine site. Simulation model solution results for an illustrative contaminated aquifer site is then approximated by training and testing a Genetic Programming (GP) based surrogate model. Performance evaluation of the ensemble GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. The results show the efficiency and feasibility of using ensemble GP surrogate models as approximate simulators of complex hydrogeologic and geochemical processes in a contaminated groundwater aquifer incorporating uncertainties in historic mine site.

Keywords: geochemical transport simulation, acid mine drainage, surrogate models, ensemble genetic programming, contaminated aquifers, mine sites

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Authors: Wang Yang

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Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.

Keywords: POI, satellite remote sensing, the population distribution, urban heat island thermal map

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Authors: Mohamed Bekheit Gad Khaleil

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The Maadi was a civilization .It is considered one of the oldest civilizations in the world and an area of prehistoric times, especially the civilization (Nakada 1&2 ) It contains the oldest stone house in the history. Many excavations have been done in this area. This report was prepared under my supervision and in cooperation with the German institute .The stone building was redocumented, photographed and drawn once again . The stone building has been built carefully. The measurements for this building are (8m x 4m).and the depth of this building is 2m underground and an entrance located at the eastern part of the northern wall and it has three huge pits in the middle of the building seem to have contained wooden posts, most probably to support the roof. The use of the building is unclear. Circular impressions in front of the north wall and in the south-eastern part of the floor indicate that much of it was a storehouse for numerous vessels such as unique feature may have not only served for private domestic purposes. Before starting work in any site, instruction must be followed :- 1-Gather as much information about this place as possible . (Historical background - previous excavations - maps - pictures) 2-Writing, recording, describing and documenting 3- Draw a map of the site showing the place’s division system (trenches) 4- Safe ( Workers & The Place )

Keywords: photographing, excavations, documentation, registration

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Authors: Mário Ernesto Sitoe, Orlando Zacarias

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University education is influenced by several factors that range from the adoption of strategies to strengthen the whole process to the academic performance improvement of the students themselves. This work uses data mining techniques to develop a predictive model to identify students with a tendency to evasion and retention. To this end, a database of real students’ data from the Department of University Admission (DAU) and the Department of Mathematics and Informatics (DMI) was used. The data comprised 388 undergraduate students admitted in the years 2009 to 2014. The Weka tool was used for model building, using three different techniques, namely: K-nearest neighbor, random forest, and logistic regression. To allow for training on multiple train-test splits, a cross-validation approach was employed with a varying number of folds. To reduce bias variance and improve the performance of the models, ensemble methods of Bagging and Stacking were used. After comparing the results obtained by the three classifiers, Logistic Regression using Bagging with seven folds obtained the best performance, showing results above 90% in all evaluated metrics: accuracy, rate of true positives, and precision. Retention is the most common tendency.

Keywords: evasion and retention, cross-validation, bagging, stacking

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Authors: Sandhya Baskaran, Hari Kumar Nagabushanam

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Sarcasm sentiment recognition is an area of natural language processing that is being probed into in the recent times. Even with the advancements in NLP, typical translations of words, sentences in its context fail to provide the exact information on a sentiment or emotion of a user. For example, if something bad happens, the statement ‘That's just what I need, great! Terrific!’ is expressed in a sarcastic tone which could be misread as a positive sign by any text-based analyzer. In this paper, we are presenting a unique real time ‘word with its tone’ spotting technique which would provide the sentiment analysis for a tone or pitch of a voice in combination with the words being expressed. This hybrid approach increases the probability for identification of special sentiment like sarcasm much closer to the real world than by mining text or speech individually. The system uses a tone analyzer such as YIN-FFT which extracts pitch segment-wise that would be used in parallel with a speech recognition system. The clustered data is classified for sentiments and sarcasm score for each of it determined. Our Simulations demonstrates the improvement in f-measure of around 12% compared to existing detection techniques with increased precision and recall.

Keywords: sarcasm recognition, tone-word spotting, natural language processing, pitch analyzer

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Authors: Budoor Al Abid

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Educational systems are increasingly provided as open online services, providing guidance and support for individual learners. To adapt the learning systems, a proper evaluation must be made. This paper builds the evaluation model Fuzzy C Means Adaptive System (FCMAS) based on data mining techniques to assess the difficulty of the questions. The following steps are implemented; first using a dataset from an online international learning system called (slepemapy.cz) the dataset contains over 1300000 records with 9 features for students, questions and answers information with feedback evaluation. Next, a normalization process as preprocessing step was applied. Then FCM clustering algorithms are used to adaptive the difficulty of the questions. The result is three cluster labeled data depending on the higher Wight (easy, Intermediate, difficult). The FCM algorithm gives a label to all the questions one by one. Then Random Forest (RF) Classifier model is constructed on the clustered dataset uses 70% of the dataset for training and 30% for testing; the result of the model is a 99.9% accuracy rate. This approach improves the Adaptive E-learning system because it depends on the student behavior and gives accurate results in the evaluation process more than the evaluation system that depends on feedback only.

Keywords: machine learning, adaptive, fuzzy logic, data mining

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Authors: M. A. Hasan, M. R. Islam, R. A. Tarefder

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This study investigates how the site specific traffic data differs from the Mechanistic Empirical Pavement Design Software default values. Two Weigh-in-Motion (WIM) stations were installed in Interstate-40 (I-40) and Interstate-25 (I-25) to developed site specific data. A computer program named WIM Data Analysis Software (WIMDAS) was developed using Microsoft C-Sharp (.Net) for quality checking and processing of raw WIM data. A complete year data from November 2013 to October 2014 was analyzed using the developed WIM Data Analysis Program. After that, the vehicle class distribution, directional distribution, lane distribution, monthly adjustment factor, hourly distribution, axle load spectra, average number of axle per vehicle, axle spacing, lateral wander distribution, and wheelbase distribution were calculated. Then a comparative study was done between measured data and AASHTOWare default values. It was found that the measured general traffic inputs for I-40 and I-25 significantly differ from the default values.

Keywords: AASHTOWare, traffic, weigh-in-motion, axle load distribution

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Authors: Pavel Shcherban, Vlad Golovanov

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Nowadays, the depletion of hydrocarbon deposits on the land of the Kaliningrad region leads to active geological exploration and development of oil and natural gas reserves in the southeastern part of the Baltic Sea. LLC 'Lukoil-Kaliningradmorneft' implements a comprehensive program for the development of the region's shelf in 2014-2023. Due to heterogeneity of reservoir rocks in various open fields, as well as with ambiguous conclusions on the contours of deposits, additional geological prospecting and refinement of the recoverable oil reserves are carried out. The key element is use of an effective technique of computer stock modeling at the first stage of processing of the received data. The following step uses information for the cluster analysis, which makes it possible to optimize the field development approaches. The article analyzes the effectiveness of various methods for reserves' calculation and computer modelling methods of the offshore hydrocarbon fields. Cluster analysis allows to measure influence of the obtained data on the development of a technical and economic model for mining deposits. The relationship between the accuracy of the calculation of recoverable reserves and the need of modernization of existing mining infrastructure, as well as the optimization of the scheme of opening and development of oil deposits, is observed.

Keywords: cluster analysis, computer modelling of deposits, correction of the feasibility study, offshore hydrocarbon fields

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Authors: L. Angiuli, L. Trizio, R. Giua, A. Digilio, M. Tutino, P. Dambruoso, F. Mazzone, C. M. Placentino

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In the winter of 2014 a series of measurements were performed to evaluate the behavior of real-time PAHs and black carbon analyzers in a coke oven plant located in Taranto, a city of Southern Italy. Data were collected both insides than outside the plant, at air quality monitoring sites. Contemporary measures of PM2.5 and PM1 were performed. Particle-bound PAHs were measured by two methods: (1) aerosol photoionization using an Ecochem PAS 2000 analyzer, (2) PM2.5 and PM1 quartz filter collection and analysis by gas chromatography/mass spectrometry (GC/MS). Black carbon was determined both in real-time by Magee Aethalometer AE22 analyzer than by semi-continuous Sunset Lab EC/OC instrument. Detected PM2.5 and PM1 levels were higher inside than outside the plant while PAHs real-time values were higher outside than inside. As regards PAHs, inside the plant Ecochem PAS 2000 revealed concentrations not significantly different from those determined on the filter during low polluted days, but at increasing concentrations the automated instrument underestimated PAHs levels. At the external site, Ecochem PAS 2000 real-time concentrations were steadily higher than those on the filter. In the same way, real-time black carbon values were constantly lower than EC concentrations obtained by Sunset EC/OC in the inner site, while outside the plant real-time values were comparable to Sunset EC values. Results showed that in a coke plant real-time analyzers of PAHs and black carbon in the factory configuration provide qualitative information, with no accuracy and leading to the underestimation of the concentration. A site specific calibration is needed for these instruments before their installation in high polluted sites.

Keywords: black carbon, coke oven plant, PAH, PAS, aethalometer

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Authors: Fatmah Almathkour

Abstract:

Managing and controlling the construction supply chain (CSC) are very important components of effective construction project execution. The goals of managing the CSC are to reduce uncertainty and optimize the performance of a construction project by improving efficiency and reducing project costs. The heart of much SC activity is addressing risk, and the CSC is no different. The delivery and consumption of construction materials is highly variable due to the complexity of construction operations, rapidly changing demand for certain components, lead time variability from suppliers, transportation time variability, and disruptions at the job site. Current notions of managing and controlling CSC, involve focusing on one project at a time with a push-based material ordering system based on the initial construction schedule and, then, holding a tremendous amount of inventory. A two-stage methodology was proposed to coordinate the feed-forward control of advanced order placement with a supplier to a feedback local control in the form of adding the ability to transship materials between projects to improve efficiency and reduce costs. It focused on the single supplier integrated production and transshipment problem with multiple products. The methodology is used as a design tool for the CSC because it includes an external storage site not associated with one of the projects. The idea is to add this feature to a highly constrained environment to explore its effectiveness in buffering the impact of variability and maintaining project schedule at low cost. The methodology uses deterministic optimization models with objectives that minimizing the total cost of the CSC. To illustrate how this methodology can be used in practice and the types of information that can be gleaned, it is tested on a number of cases based on the real example of multiple construction projects in Kuwait.

Keywords: construction supply chain, inventory control supply chain, transshipment

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Authors: Carrie Ho, Tan Yee Yong

Abstract:

Urban runoff quality for a mixed residential-commercial land use catchment in Miri, Sarawak was investigated for three storm events in 2011. Samples from the three storm events were tested for five water quality parameters, Namely, TSS, COD, BOD5, TP, and Pb. Concentration of the pollutants were found to vary significantly between storms, but were generally influenced by the length of antecedent dry period and the strength of rainfall intensities. Runoff from the study site showed a significant level of pollution for all the parameters investigated. Based on the National Water Quality Standards for Malaysia (NWQS), stormwater quality from the study site was polluted and exceeded class III water for TSS and BOD5 with maximum EMCs of 177 and 24 mg/L, respectively. Design pollutant load based on a design storm of 3-month average recurrence interval (ARI) for TSS, COD, BOD5, TP, and Pb were estimated to be 40, 9.4, 5.4, 1.7, and 0.06 kg/ha, respectively. The design pollutant load for the pollutants can be used to estimate loadings from similar catchments within Miri City.

Keywords: mixed land-use, urban runoff, pollutant load, national water quality

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Authors: N. V. Kazmiruk, Y. R. Nartsissov

Abstract:

Phosphate activated glutaminase (GA, E.C. 3.5.1.2) plays a key role in glutamine/glutamate homeostasis in mammalian brain, catalyzing the hydrolytic deamidation of glutamine to glutamate and ammonium ions. GA is mainly localized in mitochondria, where it has the catalytically active form on the inner mitochondrial membrane (IMM) and the other soluble form, which is supposed to be dormant. At present time, the exact localization of the membrane glutaminase active site remains a controversial and an unresolved issue. The first hypothesis called c-side localization suggests that the catalytic site of GA faces the inter-membrane space and products of the deamidation reaction have immediate access to cytosolic metabolism. According to the alternative m-side localization hypothesis, GA orients to the matrix, making glutamate and ammonium available for the tricarboxylic acid cycle metabolism in mitochondria directly. In our study, we used a multi-compartment kinetic approach to simulate metabolism of glutamate and glutamine in the astrocytic cytosol and mitochondria. We used physiologically important ratio between the concentrations of glutamine inside the matrix of mitochondria [Glnₘᵢₜ] and glutamine in the cytosol [Glncyt] as a marker for precise functioning of the system. Since this ratio directly depends on the mitochondrial glutamine carrier (MGC) flow parameters, key observation was to investigate the dependence of the [Glnmit]/[Glncyt] ratio on the maximal velocity of MGC at different initial concentrations of mitochondrial glutamate. Another important task was to observe the similar dependence at different inhibition constants of the soluble GA. The simulation results confirmed the experimental c-side localization hypothesis, in which the glutaminase active site faces the outer surface of the IMM. Moreover, in the case of such localization of the enzyme, a 3-fold decrease in ammonium production was predicted.

Keywords: glutamate metabolism, glutaminase, kinetic approach, mitochondrial membrane, multi-compartment modeling

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Authors: Khizar Samiullah, Khurram Faroz, Riffat Yasin, Mehwish Iftekhar, Saleem Akhtar

Abstract:

The fossil site Lava is highly fossiliferous locality in the Chinji Formation, Lower Siwalik Hills of Pakistan. The studied mammalian fossil fauna from this locality consists of Suids, Giraffids and Bovids. The presence of these groups indicates that this Miocene locality has age of approximately 14-11 Ma. Sedimentologically this site is characterized by sandstone and reddish shale which also represents Chinji Formation of the Siwaliks, it consists of shales, siltstones, sandstones and there sediments show large variations in their degree of cementation. Few scientists worked at this locality, as it was first time discovered in 2011. The outcrops of lava locality were selected to explore in detail and comparison with European mammalian assemblages. The main focus was on artiodactyl’s mammalian fauna and four different species have been recovered during field work, in which Giraffokeryx punjabiensis is dominant. Different aspect like biogeographic distribution, evolution and taxonomy of discovered fossils fauna has been discussed in detail in this research work.

Keywords: fossil remains, lava, Chinji Formation, Pakistan

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Authors: Mehran Asadi, Afrand Agah

Abstract:

In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Kisik Song, Sungjoo Lee

Abstract:

With the growing importance of intangible assets recently, the impact of patent infringement on the business of a company has become more evident. Accordingly, it is essential for firms to estimate the risk of patent infringement risk before developing a technology and create new technology ideas to avoid the risk. Recognizing the needs, several attempts have been made to help develop new technology opportunities and most of them have focused on identifying emerging vacant technologies from patent analysis. In these studies, the IPC (International Patent Classification) system or keywords from text-mining application to patent documents was generally used to define vacant technologies. Unlike those studies, this study adopted F-term, which classifies patent documents according to the technical features of the inventions described in them. Since the technical features are analyzed by various perspectives by F-term, F-term provides more detailed information about technologies compared to IPC while more systematic information compared to keywords. Therefore, if well utilized, it can be a useful guideline to create a new technology idea. Recognizing the potential of F-term, this paper aims to suggest a novel approach to developing new technology ideas to avoid patent infringement based on F-term. For this purpose, we firstly collected data about F-term and then applied text-mining to the descriptions about classification criteria and attributes. From the text-mining results, we could identify other technologies with similar technical features of the existing one, the patented technology. Finally, we compare the technologies and extract the technical features that are commonly used in other technologies but have not been used in the existing one. These features are presented in terms of “purpose”, “function”, “structure”, “material”, “method”, “processing and operation procedure” and “control means” and so are useful for creating new technology ideas that help avoid infringing patent rights of other companies. Theoretically, this is one of the earliest attempts to adopt F-term to patent analysis; the proposed methodology can show how to best take advantage of F-term with the wealth of technical information. In practice, the proposed methodology can be valuable in the ideation process for successful product and service innovation without infringing the patents of other companies.

Keywords: patent infringement, new technology ideas, patent analysis, F-term

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Authors: Ved Kulkarni, Karthik Kini

Abstract:

This experiment aims to understand how the media affects the power markets in the mainland United States and study the duration of reaction time between news updates and actual price movements. it have taken into account electric utility companies trading in the NYSE and excluded companies that are more politically involved and move with higher sensitivity to Politics. The scrapper checks for any news related to keywords, which are predefined and stored for each specific company. Based on this, the classifier will allocate the effect into five categories: positive, negative, highly optimistic, highly negative, or neutral. The effect on the respective price movement will be studied to understand the response time. Based on the response time observed, neural networks would be trained to understand and react to changing market conditions, achieving the best strategy in every market. The stock trader would be day trading in the first phase and making option strategy predictions based on the black holes model. The expected result is to create an AI-based system that adjusts trading strategies within the market response time to each price movement.

Keywords: data mining, language processing, artificial neural networks, sentiment analysis

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Authors: Manar Al-Ateeq

Abstract:

The building construction industry plays a major role in the economy of the word and the state of Kuwait. This paper evaluates existing new civil site engineers, describes a new system for improvement and insures the importance of prequalifying and developing for new engineers. In order to have a strong base in engineering, educational institutes and workplaces should be responsible to continuously train engineers and update them with new methods and techniques in engineering. As to achieve that, school of engineering should constantly update computational resources to be used in the professions. A survey was prepared for graduated Engineers based on stated objectives to understand the status of graduate engineers in both the public and private sector. Interviews were made with different sectors in Kuwait, and several visits were made to different training centers within different workplaces in Kuwait to evaluate training process and try to improve it. Virtual Reality (VR) technology could be applied as a complement to three-dimensional (3D) modeling, leading to better communication whether in job training, in education or in professional practice. Techniques of 3D modeling and VR can be applied to develop the models related to the construction process. The 3D models can support rehabilitation design as it can be considered as a great tool for monitoring failure and defaults in structures; also it can support decisions based on the visual analyses of alternative solutions. Therefore, teaching computer-aided design (CAD) and VR techniques in school will help engineering students in order to prepare them to site work and also will assist them to consider these technologies as important supports in their later professional practice. This teaching technique will show how the construction works developed, allow the visual simulation of progression of each type of work and help them to know more about the necessary equipment needed for tasks and how it works on site.

Keywords: three dimensional modeling (3DM), civil engineers (CE), professional practice (PP), virtual reality (VR)

Procedia PDF Downloads 176