Search results for: sentiment mining

Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: William Wong Xiu Shun, Yoonjin Hyun, Mingyu Kim, Seongi Choi, Namgyu Kim

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Recently, the demand of extracting the R&D keywords from the issues and using them in retrieving R&D information is increasing rapidly. But it is hard to identify the related issues or to distinguish them. Although the similarity between the issues cannot be identified, but with the R&D lexicon, the issues that always shared the same R&D keywords can be determined. In details, the R&D keywords that associated with particular issue is implied the key technology elements that needed to solve the problem of the particular issue. Furthermore, the related issues that sharing the same R&D keywords can be showed in a more systematic way through the issue clustering constructed from the perspective of R&D. Thus, sharing of the R&D result and reusable of the R&D technology can be facilitated. Indirectly, the redundancy of investment on the same R&D can be reduce as the R&D information can be shared between those corresponding issues and reusability of the related R&D can be improved. Therefore, a methodology of constructing an issue clustering from the perspective of common R&D keywords is proposed to satisfy the demands mentioned.

Keywords: clustering, social network analysis, text mining, topic analysis

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Authors: Zakari Mustapha, Clinto Aigbavboa, Wellinton Didi Thwala

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Occupational Health and Safety (OHS) issues has been a major challenge to the Ghanaian government. The purpose of the study was to examine OHS practices in Ghana. The study looked at various views from different scholars about OHS practices in order to achieve the objective of the study. Literature review was conducted on OHS in Ghana. Findings from the study shows Ministry of Roads and Transport (MRT) and Ministry of Water Resources, Works and Housing (MWRWH) are two government ministries in charge of construction and implementation of the construction sector policy. The Factories, Offices and Shops Act 1970, Act 328 and the Mining Regulations 1970 LI 665 are the two major edicts. The study presents a strong background on OHS practices in Ghana and contribute to the body of knowledge on the solution to the current trends and challenges of OHS in the construction sector.

Keywords: ILO convention, OHS challenges, OHS practices, OHS improvement

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Authors: Marc Romero-Estonllo, Judith Ramos-Castro, Yaiza San Miguel, Beatriz Rodríguez-Garrido, Carmela Monterroso

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Mining activity is among the main sources of trace and heavy metal(loid) pollution worldwide, which is a hazard to human and environmental health. That is why several projects have been emerging for the remediation of such polluted places. Phytomanagement strategies draw good performances besides big side benefits. In this work, a glasshouse assay with trace element polluted soils from an old Cu mine ore (NW of Spain) which forms part of the PhytoSUDOE network of phytomanaged contaminated field sites (PhytoSUDOE Project (SOE1/P5/E0189)) was set. The objective was to evaluate improvements induced by the following phytoremediation-related treatments. Three increasingly complex amendments alone or together with plant growth (Populus nigra L. alone and together with Tripholium repens L.) were tested. And three different rhizosphere bioinocula were applied (Plant Growth Promoting Bacteria (PGP), mycorrhiza (MYC), or mixed (PGP+MYC)). After 110 days of growth, plants were collected, biomass was weighed, and tree length was measured. Physical-chemical analyses were carried out to determine pH, effective Cation Exchange Capacity, carbon and nitrogen contents, bioavailable phosphorous (Olsen bicarbonate method), pseudo total element content (microwave acid digested fraction), EDTA extractable metals (complexed fraction), and NH4NO3 extractable metals (easily bioavailable fraction). On plant material, nitrogen content and acid digestion elements were determined. Amendment usage, plant growth, and bioinoculation were demonstrated to improve soil fertility and/or plant health within the time span of this study. Particularly, pH levels increased from 3 (highly acidic) to 5 (acidic) in the worst-case scenario, even reaching 7 (neutrality) in the best plots. Organic matter and pH increments were related to polluting metals’ bioavailability decrements. Plants grew better both with the most complex amendment and the middle one, with few differences due to bioinoculation. Using the less complex amendment (just compost) beneficial effects of bioinoculants were more observable, although plants didn’t thrive very well. On unamended soils, plants neither sprouted nor bloomed. The scheme assayed in this study is suitable for phytomanagement of these kinds of soils affected by mining activity. These findings should be tested now on a larger scale.

Keywords: aided phytoremediation, mine pollution, phytostabilization, soil pollution, trace elements

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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

Keywords: IDS, DDoS, MLP, KDD

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Authors: Marwa Hussien Mohamed, Mohamed Helmy Khafagy

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Map-Reduce is a programming model which is widely used to extract valuable information from enormous volumes of data. Map-reduce designed to support heterogeneous datasets. Apache Hadoop map-reduce used extensively to uncover hidden pattern like data mining, SQL, etc. The most important operation for data analysis is joining operation. But, map-reduce framework does not directly support join algorithm. This paper explains and compares two-way and multi-way map-reduce join algorithms for map reduce also we implement MR join Algorithms and show the performance of each phase in MR join algorithms. Our experimental results show that map side join and map merge join in two-way join algorithms has the longest time according to preprocessing step sorting data and reduce side cascade join has the longest time at Multi-Way join algorithms.

Keywords: Hadoop, MapReduce, multi-way join, two-way join, Ubuntu

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Authors: Samhita Mummadi, Sree Divya Nagalli, Harshini Vemuri, Saketh Charan Nakka, Sumesh K. J.

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One of the most significant challenges faced by people in today’s digital era is an alarming increase in fraudulent activities on online platforms. The fascination with online shopping to avoid long queues in shopping malls, the availability of a variety of products, and home delivery of goods have paved the way for a rapid increase in vast online shopping platforms. This has had a major impact on increasing fraudulent activities as well. This loop of online shopping and transactions has paved the way for fraudulent users to commit fraud. For instance, consider a store that orders thousands of products all at once, but what’s fishy about this is the massive number of items purchased and their transactions turning out to be fraud, leading to a huge loss for the seller. Considering scenarios like these underscores the urgent need to introduce machine learning approaches to combat fraud in online shopping. By leveraging robust algorithms, namely KNN, Decision Trees, and Random Forest, which are highly effective in generating accurate results, this research endeavors to discern patterns indicative of fraudulent behavior within transactional data. Introducing a comprehensive solution to this problem in order to empower e-commerce administrators in timely fraud detection and prevention is the primary motive and the main focus. In addition to that, sentiment analysis is harnessed in the model so that the e-commerce admin can tailor to the customer’s and consumer’s concerns, feedback, and comments, allowing the admin to improve the user’s experience. The ultimate objective of this study is to ramp up online shopping platforms against fraud and ensure a safer shopping experience. This paper underscores a model accuracy of 84%. All the findings and observations that were noted during our work lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as technologies continue to evolve.

Keywords: behavior analysis, feature selection, Fraudulent pattern recognition, imbalanced classification, transactional anomalies

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Authors: Ghaleb Y. Abbasi, Israa Abu Rumman

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This paper modeled sales history archived from 2012 to 2015 bulked in monthly bins for five products for a medical supply company in Jordan. The sales forecasts and extracted consistent patterns in the sales demand history from the Enterprise Resource Planning (ERP) system were used to predict future forecasting and generate sales demand forecasting using time series analysis statistical technique called Auto Regressive Integrated Moving Average (ARIMA). This was used to model and estimate realistic sales demand patterns and predict future forecasting to decide the best models for five products. Analysis revealed that the current replenishment system indicated inventory overstocking.

Keywords: ARIMA models, sales demand forecasting, time series, R code

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Authors: Abdul Kader

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This paper attempts to investigate several contributory factors to recent volatility in the Islamic Banking sector of Bangladesh. In particular, the study explores corporate governance, credit management, credit regulations, inept board of directors, using religious sentiment as a means to deceive general people, and the degree of political interference as potential contributory factors. To find the correlation among different variables, semi-structured questionnaires were distributed among the clients, bank managers, some Banking scholars and ex-members of the board of directors of three Islamic Banks in Bangladesh. Later, ten interviews were collected from key informants to gain in-depth information about the present mismanagement of Islamic Banks in Bangladesh. After then, data were analyzed using statistical software and substantiated by secondary sources like newspapers, reports and investigative reports aired in screen media. The paper found a correlation between almost all contributory factors and recent unstable conditions in the Islamic banking sector. After performing regression analysis, this paper found a more significant relationship between some of the contributory factors with Banking volatility than others. For instance, credit management, inept board of directors, depriving customers of proving no profit in the name of business—no interest-- and political interference have a strong significant positive correlation with the present poor condition of Islamic Banking. This paper concludes that while internal management is important in recovering the losses, the government needs to ensure framing better policy for the Islamic Banking system, Central Bank needs to supervise and monitor all Islamic banks meticulously and loan receivers must go through the impartial evaluation and approved by the representatives of the Central Shariah Board. This paper also recommends that there is a need to strengthen the auditing system and improve regulatory oversight of the Islamic Banks in Bangladesh. Policy recommendations that this paper put forward could provide an outline for dealing with the existing challenging condition of Islamic Banks and these could be applied to similar problems in other countries where the Islamic Banking model exists.

Keywords: Islamic bank, volatility in banking sector, shariah law, credit management, political interference

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Authors: Syauqi Asfiya R.

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This study examines the constraints faced by Islamic boarding school (Pesantren) in Indonesia in effectively addressing physical violence within their educational institutions. The vulnerability to violence in the education sector remains pervasive, including in Pesantren, primarily due to the residential nature of the boarding school system, which necessitates round-the-clock interaction among students from diverse backgrounds. Additionally, environmental factors, parenting styles, individual characteristics, and media influences further complicate the conditions within Pesantren. Numerous cases of physical violence have been reported, underscoring the need to identify the constraints of violence prevention strategies implemented by Pesantren. Adopting a case study approach, this research focuses on a Modern Pesantren in Tangerang and utilizes interviews conducted with 20 victims of violence to explore the aspects of Pesantren's violence prevention strategies that may have been overlooked. The findings indicate that many students face a dilemma when reporting the violence they experience, as the imposed sanctions often prove excessively severe and carry the risk of exacerbating the violence perpetrated by the offenders. Consequently, numerous victims choose to remain silent, thereby enabling the perpetuation of violence. Moreover, senior students (mudabbir) are prohibited from giving punishment, but there are still many who punish other students based on their personal moods. Furthermore, violence is also perpetrated by religious teachers (ustadz), despite their responsibility for addressing such issues. The evaluation process often follows a unidirectional approach wherein the santri have limited freedom compared to the Mudabbir or ustadz when it comes to providing feedback. Additionally, sentiment within specific student generations is reinforced due to the segregation of dormitories based on cohorts. Lastly, the absence of psychologists to address the trauma experienced by victims further exacerbates the situation. This research sheds light on the constraints faced by Pesantren in effectively preventing physical violence and emphasizes the importance of implementing comprehensive measures to create safer and nurturing learning environments within these institutions.

Keywords: physical violence, islam, boarding school, constraint

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Authors: Ziad Abdallah, Mohamad Oueidat, Ali El-Zaart

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Image Multi-label Classification (IMC) assigns a label or a set of labels to an image. The big demand for image annotation and archiving in the web attracts the researchers to develop many algorithms for this application domain. The existing techniques for IMC have two drawbacks: The description of the elementary characteristics from the image and the correlation between labels are not taken into account. In this paper, we present an algorithm (MIML-HOGLPP), which simultaneously handles these limitations. The algorithm uses the histogram of gradients as feature descriptor. It applies the Label Priority Power-set as multi-label transformation to solve the problem of label correlation. The experiment shows that the results of MIML-HOGLPP are better in terms of some of the evaluation metrics comparing with the two existing techniques.

Keywords: data mining, information retrieval system, multi-label, problem transformation, histogram of gradients

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Authors: C. B. Abhilash, Kavi Mahesh

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Human beings are the most advanced species on earth; it's all because of the ability to communicate and share information via human language. In today's world, a huge amount of data is available on the web in text format. This has also resulted in the generation of big data in structured and unstructured formats. In general, the data is in the textual form, which is highly unstructured. To get insights and actionable content from this data, we need to incorporate the concepts of text mining and natural language processing. In our study, we mainly focus on Interesting data through which interesting facts are generated for the knowledge base. The approach is to derive the analytics from the text via the application of natural language processing. Using semantic web Resource framework descriptors (RDF), we generate the triple from the given data and derive the interesting patterns. The methodology also illustrates data integration using the RDF for reliable, interesting patterns.

Keywords: RDF, interestingness, knowledge base, semantic data

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: Hyun-Woo Cho

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Unexpected events may occur with serious impacts on industrial process. This work utilizes a data representation technique to model and to analyze process data pattern for the purpose of diagnosis. In this work, the use of triangular representation of process data is evaluated using simulation process. Furthermore, the effect of using different pre-treatment techniques based on such as linear or nonlinear reduced spaces was compared. This work extracted the fault pattern in the reduced space, not in the original data space. The results have shown that the non-linear technique based diagnosis method produced more reliable results and outperforms linear method.

Keywords: process monitoring, data analysis, pattern modeling, fault, nonlinear techniques

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Authors: Grecco M. Ante, Princess Rochelle O. Gan

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Plants that can absorb greater than 10,000 µg Ni/g dry mass in their stems and leaves are termed as ‘hypernickelophores’. Chelators are chemicals that make the metals in the soil more soluble, making them a potential enhancer for phytoextraction. This study aims to observe the effect of different concentrations of the chelating agent ethylene diamine tetraacetate (EDTA) on the metal uptake (or rate of phytoextraction) of Nickel by Phyllanthus sp. nov. The plant is found to be a hyperickelophore in normal conditions. The addition of EDTA increased the metal uptake of the plant. The increasing amount of the chelating agent causes a decrease in the phytoextraction of the plant but moves the onset of its peak of maximum nickel content in its tissue to an earlier time. The chelator-assisted phytoextraction of nickel by Phyllanthus sp. nov. is proven to be an efficient auxiliary mining operation for nickel laterite mines.

Keywords: phytomining, Phyllanthus sp. nov., EDTA, nickel, laterite

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Authors: Yaojun Wang, Yaoqing Wang

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Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio.

Keywords: case-based reasoning, decision tree, stock selection, machine learning

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Authors: Selifontova Darya Yurievna

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The study of the process of the emergence and evolution of socio-cultural ideas about the "Asian states" in the context of the development of US cinema in 1941-1945 will contribute both to the approbation of a new approach to the classical subject and will allow using the methodological tools of history, political science, philology, sociology for understanding modern military-political, historical, ideological, socio-cultural processes on a concrete example. This is especially important for understanding the process of constructing the image of the Japanese Empire in the USA. Assessments and images of China and Japan in World War II, created in American cinema, had an immediate impact on the media, public sentiment, and opinions. During the war, the US cinema created new myths and actively exploited old ones, combining them with traditional Hollywood cliches - all this served as a basis for creating the image of China and the Japanese Empire on the screen, which were necessary to solve many foreign policy and domestic political tasks related to the construction of two completely different, but at the same time, similar images of Asia (China and the Japanese Empire). In modern studies devoted to the history of wars, the study of the specifics of the information confrontation of the parties is in demand. A special role in this confrontation is played by propaganda through cinema, which uses images, historical symbols, and stable metaphors, the appeal to which can form a certain public reaction. Soviet documentaries of the war years are proof of this. The relevance of the topic is due to the fact that cinema as a means of propaganda was very popular and in demand during the Second World War. This period was the time of creation of real masterpieces in the field of propaganda films, in the documentary space of the cinema of 1941 – 1945. The traditions of depicting the Second World War were laid down. The study of the peculiarities of visualization and mythologization of the Second World War in Soviet cinema is the most important stage for studying the development of the specifics of propaganda methods since the methods and techniques of depicting the war formed in 1941-1945 are also significant at the present stage of the study of society.

Keywords: asian countries, politics, sociology, domestic politics, USA, cinema

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Authors: Marina Yurievna Aleksandrova

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Research of the Holocaust retains value not only for history but also for sociology and psychology. One of the most important fields of study is how people were coping during and after this traumatic event. The aim of this paper is to identify the main contexts of the topic of love and to determine which contexts are more characteristic for different groups of victims of the Holocaust (gender, nationality, age). In this research, transcripts of interviews with Holocaust victims that were collected during 1946 for the "Voices of the Holocaust" project were used as data. Main contexts were analyzed with methods of network analysis and latent semantic analysis and classified by gender, age, and nationality with random forest. The results show that love is articulated and described significantly differently for male and female informants, nationality is shown results with lower values of quality metrics, as well as the age.

Keywords: Holocaust, latent semantic analysis, network analysis, text-mining, random forest

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

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Authors: Joao Victor Borges Dos Santos, Thomas Richard, Yevhen Kovalyshen

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Down-the-hole (DTH) percussive drilling is an excavation method that is widely used in the mining industry due to its high efficiency in fragmenting hard rock formations. A DTH hammer system consists of a fluid driven (air or water) piston and a drill bit; the reciprocating movement of the piston transmits its kinetic energy to the drill bit by means of stress waves that propagate through the drill bit towards the rock formation. In the literature of percussive drilling, the existence of an optimum drilling state (Sweet Spot) is reported in some laboratory and field experimental studies. An optimum rate of penetration is achieved for a specific range of axial thrust (or weight-on-bit) beyond which the rate of penetration decreases. Several authors advance different explanations as possible root causes to the occurrence of the Sweet Spot, but a universal explanation or consensus does not exist yet. The experimental investigation in this work was initiated with drilling experiments conducted at a mining site. A full-scale drilling rig (equipped with a DTH hammer system) was instrumented with high precision sensors sampled at a very high sampling rate (kHz). Data was collected while two boreholes were being excavated, an in depth analysis of the recorded data confirmed that an optimum performance can be achieved for specific ranges of input thrust (weight-on-bit). The high sampling rate allowed to identify the bit penetration at each single impact (of the piston on the drill bit) as well as the impact frequency. These measurements provide a direct method to identify when the hammer does not fire, and drilling occurs without percussion, and the bit propagate the borehole by shearing the rock. The second stage of the experimental investigation was conducted in a laboratory environment with a custom-built equipment dubbed Woody. Woody allows the drilling of shallow holes few centimetres deep by successive discrete impacts from a piston. After each individual impact, the bit angular position is incremented by a fixed amount, the piston is moved back to its initial position at the top of the barrel, and the air pressure and thrust are set back to their pre-set values. The goal is to explore whether the observed optimum drilling state stems from the interaction between the drill bit and the rock (during impact) or governed by the overall system dynamics (between impacts). The experiments were conducted on samples of Calca Red, with a drill bit of 74 millimetres (outside diameter) and with weight-on-bit ranging from 0.3 kN to 3.7 kN. Results show that under the same piston impact energy and constant angular displacement of 15 degrees between impact, the average drill bit rate of penetration is independent of the weight-on-bit, which suggests that the sweet spot is not caused by intrinsic properties of the bit-rock interface.

Keywords: optimum drilling state, experimental investigation, field experiments, laboratory experiments, down-the-hole percussive drilling

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Authors: Sergey Nikitenko, Vladimir Klishin, Yury Malakhov, Elena Goosen

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Transition to renewable energy sources (solar, wind, bioenergy, etc.) and launching of alternative energy generation has weakened the role of coal as a source of energy. The Paris Agreement and assumption of obligations by many nations to orderly reduce CO₂ emissions by means of technological modernization and climate change adaptation has abridged coal demand yet more. This paper aims to assess current resilience of the coal industry to stress and to define prospects for coal production optimization using high technologies pursuant to global challenges and requirements of energy transition. Our research is based on the resilience concept adapted to the coal industry. It is proposed to divide the coal sector into segments depending on the prevailing value chains (VC). Four representative models of VC are identified in the coal sector. The most promising lines of upgrading VC in the coal industry include: •Elongation of VC owing to introduction of clean technologies of coal conversion and utilization; •Creation of parallel VC by means of waste management; •Branching of VC (conversion of a company’s VC into a production network). The upgrade effectiveness is governed in many ways by applicability of advanced coal processing technologies, usability of waste, expandability of production, entrance to non-rival markets and localization of new segments of VC in receiving regions. It is also important that upgrade of VC by means of formation of agile high-tech inter-industry production networks within the framework of operating surface and underground mines can reduce social, economic and ecological risks associated with closure of coal mines. Such promising route of VC upgrade is application of methanotrophic bacteria to produce protein to be used as feed-stuff in fish, poultry and cattle breeding, or in production of ferments, lipoids, sterols, antioxidants, pigments and polysaccharides. Closed mines can use recovered methane as a clean energy source. There exist methods of methane utilization from uncontrollable sources, including preliminary treatment and recovery of methane from air-and-methane mixture, or decomposition of methane to hydrogen and acetylene. Separated hydrogen is used in hydrogen fuel cells to generate power to feed the process of methane utilization and to supply external consumers. Despite the recent paradigm of carbon-free energy generation, it is possible to preserve the coal mining industry using the differentiated approach to upgrade of value chains based on flexible technologies with regard to specificity of mining companies.

Keywords: resilience, resilience concept, resilience indicator, resilience in the Russian coal industry, value chains

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Authors: Azadeh Askarinejad, Parham Hayati, Reza Parchami, Parisa Hayati

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One of the most important industries and building operations causing carbon dioxide emission is the cement and concrete related industries so that cement production (including direct fuel for mining and transporting raw material) consumes approximately 6 million Btus per metric-ton, and releases about 1 metric-ton of CO2. Reducing the consumption of cement with simultaneous utilizing waste materials as cement replacement is preferred for reasons of environmental protection. Blended cements consist of different supplementary cementitious materials (SCM), such as fly ash, silica fume, Ground Granulated Blast Furnace Slag (GGBFS), limestone, natural pozzolans, etc. these materials should be chemically activated to show effective cementitious properties. The present review article reports three different methods of nanofabrication that were used for activation of two types of SCMs.

Keywords: nanofabrication, cement replacement materials, activation, concrete

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Authors: Dina Kon Mushid, Sage Ngoie, Tshimbalanga Madiba, Kabutakapua Kakanda

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Slope stability assessment is still the biggest challenge in mining activities and civil engineering structures. The slope in an opencast mine frequently reaches multiple weak layers that lead to the instability of the pit. Faults and soft layers throughout the rock would increase weathering and erosion rates. Therefore, it is essential to investigate the stability of the complex strata to figure out how stable they are. In the Dikuluwe-Mashamba (DIMA) area, the lithology of the stratum is a set of metamorphic rocks whose parent rocks are sedimentary rocks with a low degree of metamorphism. Thus, due to the composition and metamorphism of the parent rock, the rock formation is different in hardness and softness, which means that when the content of dolomitic and siliceous is high, the rock is hard. It is softer when the content of argillaceous and sandy is high. Therefore, from the vertical direction, it appears as a weak and hard layer, and from the horizontal direction, it seems like a smooth and hard layer in the same rock layer. From the structural point of view, the main structures in the mining area are the Dikuluwe dipping syncline and the Mashamba dipping anticline, and the occurrence of rock formations varies greatly. During the folding process of the rock formation, the stress will concentrate on the soft layer, causing the weak layer to be broken. At the same time, the phenomenon of interlayer dislocation occurs. This article aimed to evaluate the stability of metasedimentary rocks of the Dikuluwe-Mashamba (DIMA) open-pit mine using limit equilibrium and stereographic methods Based on the presence of statistical structural planes, the stereographic projection was used to study the slope's stability and examine the discontinuity orientation data to identify failure zones along the mine. The results revealed that the slope angle is too steep, and it is easy to induce landslides. The numerical method's sensitivity analysis showed that the slope angle and groundwater significantly impact the slope safety factor. The increase in the groundwater level substantially reduces the stability of the slope. Among the factors affecting the variation in the rate of the safety factor, the bulk density of soil is greater than that of rock mass, the cohesion of soil mass is smaller than that of rock mass, and the friction angle in the rock mass is much larger than that in the soil mass. The analysis showed that the rock mass structure types are mostly scattered and fragmented; the stratum changes considerably, and the variation of rock and soil mechanics parameters is significant.

Keywords: slope stability, weak layer, safety factor, limit equilibrium method, stereography method

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Authors: Abdulaziz Alakeel, Roaa Alamri, Abdulrahman Alomair, Mohammed Fouda

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Introduction: Ibrutinib is a selective, potent, and irreversible small-molecule inhibitor of Bruton's tyrosine kinase (BTK). It forms a covalent bond with a cysteine residue (CYS-481) at the active site of Btk, leading to inhibition of Btk enzymatic activity. The drug is indicated to treat certain type of cancers such as mantle cell lymphoma (MCL), chronic lymphocytic leukaemia and Waldenström's macroglobulinaemia (WM). Cardiac failure is a condition referred to inability of heart muscle to pump adequate blood to human body organs. There are multiple types of cardiac failure including left and right-sided heart failure, systolic and diastolic heart failures. The aim of this review is to evaluate the risk of cardiac failure associated with the use of ibrutinib and to suggest regulatory recommendations if required. Methodology: Signal Detection team at the National Pharmacovigilance Center (NPC) of Saudi Food and Drug Authority (SFDA) performed a comprehensive signal review using its national database as well as the World Health Organization (WHO) database (VigiBase), to retrieve related information for assessing the causality between cardiac failure and ibrutinib. We used the WHO- Uppsala Monitoring Centre (UMC) criteria as standard for assessing the causality of the reported cases. Results: Case Review: The number of resulted cases for the combined drug/adverse drug reaction are 212 global ICSRs as of July 2020. The reviewers have selected and assessed the causality for the well-documented ICSRs with completeness scores of 0.9 and above (35 ICSRs); the value 1.0 presents the highest score for best-written ICSRs. Among the reviewed cases, more than half of them provides supportive association (four probable and 15 possible cases). Data Mining: The disproportionality of the observed and the expected reporting rate for drug/adverse drug reaction pair is estimated using information component (IC), a tool developed by WHO-UMC to measure the reporting ratio. Positive IC reflects higher statistical association while negative values indicates less statistical association, considering the null value equal to zero. The results of (IC=1.5) revealed a positive statistical association for the drug/ADR combination, which means “Ibrutinib” with “Cardiac Failure” have been observed more than expected when compared to other medications available in WHO database. Conclusion: Health regulators and health care professionals must be aware for the potential risk of cardiac failure associated with ibrutinib and the monitoring of any signs or symptoms in treated patients is essential. The weighted cumulative evidences identified from causality assessment of the reported cases and data mining are sufficient to support a causal association between ibrutinib and cardiac failure.

Keywords: cardiac failure, drug safety, ibrutinib, pharmacovigilance, signal detection

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Authors: Basit Shahzad, Abdullatif M. Abdullatif

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Recent advancements in Information and Communication Technologies (ICT) and easy access to Internet have made social media the first choice for information sharing related to any important events or news. On Twitter, trend is a common feature that quantifies the level of popularity of a certain news or event. In this work, we examine the impact of Twitter trends on real world events by hypothesizing that Twitter trends have an influence on print media in Pakistan. For this, Twitter is used as a platform and Twitter trends as a base line. We first collect data from two sources (Twitter trends and print media) in the period May to August 2016. Obtained data from two sources is analyzed and it is observed that social media is significantly influencing the print media and majority of the news printed in newspaper are posted on Twitter earlier.

Keywords: twitter trends, text mining, effectiveness of trends, print media

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Authors: Heejin Yun, Juanjuan Zang

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This study aims to analyze the text terms posted on an online community of people from the same hometown and to understand the topic and trend of nostalgia composed online. For this purpose, this study collected 144 writings which the natives of Yeongjong Island, Incheon, South-Korea have posted on an online community. And it analyzed association relations. As a result, online community texts means that just defining nostalgia as ‘a mind longing for hometown’ is not an enough explanation. Second, texts composed online have abstractness rather than persons’ individual stories. This study figured out the relationship that had the most critical and closest mutual association among the terms that constituted nostalgia through literature research and association rule concerning nostalgia. The result of this study has a characteristic that it summed up the core terms and emotions related to nostalgia.

Keywords: nostalgia, cultural memory, data mining, association rule

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Authors: S.V. Kleyn, S.Yu. Zagorodnov, А.А. Kokoulina

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Technogenic emissions of the mining and processing complex are characterized by a high content of chemical components and solid dust particles. However, each industrial enterprise and the surrounding area have features that require refinement and parameterization. Numerous studies have shown the negative impact of fine dust PM10 and PM2.5 on the health, as well as the possibility of toxic components absorption, including heavy metals by dust particles. The target of the study was the quantitative assessment of the fractional and particle size composition of ambient air dust in the area of impact by primary magnesium production complex. Also, we tried to describe the morphology features of dust particles. Study methods. To identify the dust emission sources, the analysis of the production process has been carried out. The particulate composition of the emissions was measured using laser particle analyzer Microtrac S3500 (covered range of particle size is 20 nm to 2000 km). Particle morphology and the component composition were established by electron microscopy by scanning microscope of high resolution (magnification rate - 5 to 300 000 times) with X-ray fluorescence device S3400N ‘HITACHI’. The chemical composition was identified by X-ray analysis of the samples using an X-ray diffractometer XRD-700 ‘Shimadzu’. Determination of the dust pollution level was carried out using model calculations of emissions in the atmosphere dispersion. The calculations were verified by instrumental studies. Results of the study. The results demonstrated that the dust emissions of different technical processes are heterogeneous and fractional structure is complicated. The percentage of particle sizes up to 2.5 micrometres inclusive was ranged from 0.00 to 56.70%; particle sizes less than 10 microns inclusive – 0.00 - 85.60%; particle sizes greater than 10 microns - 14.40% -100.00%. During microscopy, the presence of nanoscale size particles has been detected. Studied dust particles are round, irregular, cubic and integral shapes. The composition of the dust includes magnesium, sodium, potassium, calcium, iron, chlorine. On the base of obtained results, it was performed the model calculations of dust emissions dispersion and establishment of the areas of fine dust РМ 10 and РМ 2.5 distribution. It was found that the dust emissions of fine powder fractions PM10 and PM2.5 are dispersed over large distances and beyond the border of the industrial site of the enterprise. The population living near the enterprise is exposed to the risk of diseases associated with dust exposure. Data are transferred to the economic entity to make decisions on the measures to minimize the risks. Exposure and risks indicators on the health are used to provide named patient health and preventive care to the citizens living in the area of negative impact of the facility.

Keywords: dust emissions, еxposure assessment, PM 10, PM 2.5

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Igor A. Podgorny

Abstract:

TurboTax AnswerXchange is a social Q/A system supporting users working on federal and state tax returns. Content quality and popularity in the AnswerXchange can be predicted with propensity models using attributes of the question and answer. Using uplift modeling, we identify features of questions and answers that can be modified during the question-asking and question-answering experience in order to optimize the AnswerXchange content quality. We demonstrate that adding details to the questions always results in increased question popularity that can be used to promote good quality content. Responding to close-ended questions assertively improve content quality in the AnswerXchange in 90% of cases. Answering knowledge questions with web links increases the likelihood of receiving a negative vote from 60% of the askers. Our findings provide a rationale for employing the uplift modeling approach for AnswerXchange operations.

Keywords: customer relationship management, human-machine interaction, text mining, uplift modeling

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Authors: Bhawna Shivan

Abstract:

Indian monuments have been spoken of as for variety, extent, completeness and beauty unsurpassed perhaps unequaled in world. India’s monumental heritage is a part and parcel of India today. The underlying issue with the monumental heritage in contemporary times is that these monuments suffered many times with various degrees of threats/ pressures which hampered their beauty. In the current situation, the urbanization policies for sustainable development and tourism management pay no attention to the basic point of conservation and protection of these cultural heritages rather they focus more on profit earned from these sites. Many times rich heritage is found balancing between conflicting pressures of conservation of heritage elements with sustainability and local economic development. There is a need of a new attitude to India’s independent and democratic ideology. The paper will enquire about the historical perspective by analyzing and understanding the importance of Mughal Architecture while focusing on Humayun Tomb while assessing the value and sentiment people attach to these monuments. It will also put the focus on the future of these monuments in the era of globalization and urbanization. The role of public and private authorities for conservation and sustainable development of these monuments. As well as assessing other facilities like toilets, parking, eatery joint, Museum with Display of structural representation and display of books, and a mobile shop. The research will be helpful in assessing the importance of heritage buildings whether they are a tool of enhancing ‘Tourism Industry’ for Central and State Government or really there is still some future of these monuments. Can we still consider these heritage sites as the integral part of our society in this urbanized world? The study will also analyze the attitude of the central and state government towards a building when it declared as a ‘World Heritage Site’. The study will also examine how the Youth and other aged generations append their sentimental values towards them, say (what is their purpose of coming to a heritage site, what makes them coming here, how they view this particular monument) Apart from this, probing the factors such as rapid growth of cities and its population, increasing value of urban land and effect of globalization on urban growth pattern that are capable of producing a variety of threats and pressures on any monument for instance Humayun Tomb.

Keywords: globalization, monuments, tourism, urban heritage

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