Search results for: emotion mining

Authors: Gholam Reza Mirzaei

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This research aimed to investigate the effectiveness of acceptance and commitment therapy (ACT) in reducing corona disease anxiety in the staff working at Shahid Beheshti Hospital of Shiraz. The current research was a quasi-experimental study having pre-test and post-test with two experimental and control groups. The statistical population of the research included all the staff of Shahid Beheshti Hospital of Shiraz in 2021. From among the statistical population, 30 participants (N =15 in the experimental group and N =15 in the control group) were selected by available sampling. The materials used in the study comprised the Cognitive Emotion Regulation Questionnaire (CERQ) and Corona Disease Anxiety Scale (CDAS). Following data collection, the participants’ scores were analyzed using SPSS 20 at both descriptive (mean and standard deviation) and inferential (analysis of covariance) levels. The results of the analysis of covariance (ANCOVA) showed that acceptance and commitment therapy (ACT) is effective in reducing Corona disease anxiety (mental and physical symptoms) in the staff working at Shahid Beheshti Hospital of Shiraz. The effectiveness of acceptance and commitment therapy (ACT) on reducing mental symptoms was 25.5% and on physical symptoms was 13.8%. The mean scores of the experimental group in the sub-scales of Corona disease anxiety (mental and physical symptoms) in the post-test were lower than the mean scores of the control group.

Keywords: acceptance and commitment therapy, corona disease anxiety, hospital staff, Shiraz

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Authors: Mesfin Angaw Tesfay

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Uridine-5’-diphosphate (UDP)-glucose is one of the most versatile building blocks within the metabolism of prokaryotes and eukaryotes serving as an activated sugar donor during the glycosylation of natural products. It is formed by the enzyme UDP-glucose pyrophosphorylase (UGPase) using uridine-5′-triphosphate (UTP) and α-d-glucose 1-phosphate as a substrate. Herein two UGPase genes from Lactobacillus kefiranofaciens ZW3 (LkUGPase) and Rhodococcus wratislaviensis IFP 2016 (RwUGPase) were identified through genome mining approaches. The LkUGPase and RwUGPase have 299 and 306 amino acids, respectively. Both UGPase has the conserved UTP binding site (G-X-G-T-R-X-L-P) and the glucose -1-phosphate binding site (V-E-K-P). The LkUGPase and RwUGPase were cloned in E. coli and SDS-PAGE analysis showed the expression of both enzymes forming about 36 KDa of protein band after induction. LkUGPase and RwUGPase have an activity of 1549.95 and 671.53 U/mg respectively. Currently, their kinetic properties are under investigation.

Keywords: UGPase, LkUGPase, RwUGPase, UDP-glucose, Glycosylation

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Authors: Nour Tawil, Sandra Weber, Kirsten K. Roessler, Martin Mau, Simone Kuhn

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The link between “quality” residential environments and human health and well-being has long been proposed. While the physical properties of a sound environment have been fairly defined, little focus has been given to the psychological impact of architectural elements. Recently, studies have investigated the response to architectural parameters, using measures of physiology, brain activity, and emotion. Results showed different aspects of interest: detailed and open versus blank and closed facades, patterns in perceiving different elements, and a visual bias for capturing faces in buildings. However, in the absence of a consensus on methodologies, the available studies remain unsystematic and face many limitations regarding the underpinning psychological mechanisms. To bridge some of these gaps, an online study was launched to investigate design features that influence the aesthetic judgement and emotional evaluation of house facades, using a well-controlled stimulus set of Canadian houses. A methodical modelling of design features will be performed to extract both high and low level image properties, in addition to segmentation of layout-related features. 300 participants from Canada, Denmark, and Germany will rate the images on twelve psychological dimensions representing appealing aspects of a house. Subjective ratings are expected to correlate with specific architectural elements while controlling for typicality and familiarity, and other individual differences. With the lack of relevant studies, this research aims to identify architectural elements of beneficial qualities that can inform design strategies for optimized residential spaces.

Keywords: architectural elements, emotions, psychological response, residential facades.

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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: 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: Begum Serim-Yildiz

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The aim of this study is to examine the role of parents' emotional and behavioral reactions on fears of children in the COVID-19 pandemic considering Rachman’s Three Pathways Theory. For this purpose, a phenomenological qualitative study was conducted. Thirteen participants living with their children were utilized through criterion and snowball sampling. In semi-structured interviews parents were asked about their own and their children’s beahavioral and emotional reactions in the COVID-19 pandemic, and they were expected to give detailed information about fears of their children before and in pandemic. Firstly, parents were asked about their behavioral and emotional reactions in the COVID-19 pandemic. As behavioral reactions, precautions taken by parents to protect the rest of the family from negative physical and emotional impact of the pandemic were mentioned, while emotional reactions were defined as acquisition of negative emotions like fear, anxiety, and worry. Secondly, parents were asked about their children’s behavioral and emotional reactions. Some of the parents talked about positive behavioral changes such as gaining self-control, while some others explained negative behavioral changes like increased time spent with technological tools. In the emotional changes section, all of the parents explained at least one negative emotion. All of the parents stated that their children had COVID-19 related fears. According to parents’ expressions, fears of children in pandemic were examined in two dimensions. Fears directly related to COVID-19 were fear of virus/microbes, illness or death of someone in family and death and fears. Fears indirectly related to COVID-19 were fear of going out, sleep alone at night, separation, touching stuff outside the home, and cold. Considering existing literature and based on the findings of this study, it can be concluded that children’s modelling experiences have impact on acquisition of negative emotions, especially fear, therefore, preventive interventions involving caregivers should be provided by mental health professionals working with children.

Keywords: children’s fears, COVID-19 pandemic, modelling experiences, parents’ reactions

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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: Mindy A. Brown, Emma E. Reardon, Jennifer Isenhour, Sheila E. Crowell, K. Lee Raby, Elisabeth Conradt

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The positive impact of maternal sensitivity on infant social and emotional development is well-known, as is the notion that stress may impair a mother’s ability to provide sensitive care for her infant. However, individual differences in susceptibility to parenting-related stress are less understood. This study explores how chronic prenatal stress moderates the effect of acute stressors on maternal sensitivity. Data were gathered from 110 mothers and their 7-month-old infants. Mothers were exposed to either an acute stress task or a control task, after which they engaged in the still-face paradigm, a face-to-face interaction where maternal sensitivity was measured. Chronic maternal stress was assessed using the UCLA Life Stress Interview during the third trimester of pregnancy. The results revealed that among mothers exposed to the stress condition, those with higher chronic stress levels in the previous six months displayed significantly lower sensitivity during the still-face paradigm compared to those with lower chronic stress. Notably, past stress levels had no effect on maternal sensitivity in the control condition. These findings suggest a moderating effect of chronic stress on maternal caregiving behavior, with higher prenatal stress diminishing a mother’s ability to cope with acute parenting-related stressors in the present. The mechanisms behind this may involve changes in stress reactivity pathways, such as the hypothalamic-pituitary-adrenal (HPA) axis or altered emotion regulation strategies developed in response to chronic stress. Understanding these pathways could guide targeted interventions for mothers who may be more vulnerable to stress, improving caregiving outcomes.

Keywords: acute stress, maternal stress, prenatal stress, still-face paradigm

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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: Yangwon Hyun

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Nowadays, business settings are becoming increasingly multilingual and multicultural under the conditions of globalization. When people from different linguistic and cultural background interact, their divergent cultural conceptualizations may result in communicative problems. Even though intercultural business communication between Thais and South Koreans has become increasingly significant along with the development of international trade relations, there have been very few attempts to conduct research on interactional problems in that context. Based on a Cultural Linguistics perspective, this study analyzes Thai cultural conceptualizations that underly interactional problems with Koreans at Korean business organizations where were located in Thailand. The data was gathered through in-depth interviews with ten Korean and ten Thai employees as well as through six months of field observations. The results indicate that there are three cultural pragmatic schemas that underly problems of interaction between the two groups; (1) the schema of kreng-jai as a communicative strategy of withholding one’s intentions in order to not hurt another’s emotion, (2) the schema of mai-pen-rai as a linguistic strategy of inducing interlocutors to not be concerned about substantial matters so as to avoid confrontations, and (3) the schema of jai-yen as a linguistic expression used for persuading interlocutors to maintain a calm state of mind in order to avoid conflicts. The linguistic behaviors of Thai employees in relation to these three cultural schemas had led to misunderstandings and interpersonal conflicts with Korean supervisors and co-workers. These interactional problems were caused not only because Koreans were unfamiliar with Thai cultural conceptualizations, but also because these conceptualizations were incompatible with goal-oriented Korean business discourse. The findings of this study can enhance mutual understandings between Thai and Korean employees. Thus, it contributes to the promotion of smooth intercultural communication in the workplace.

Keywords: business context, cultural conceptualizations, cultural linguistics, intercultural communication, thai culture

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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: Muñoz-Sanchez, Victor Manuel, Perez-Flores, Antonio Manuel

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The main objective of this paper is to establish the relationship between models of state organization and the various types of collective identity expressed by the Spanish. The question of nationalism and identity ascription in Spain has always been a topic of special importance due to the presence in that country of territories where the population emits very different opinions of nationalist sentiment than the rest of Spain. The current situation of sovereignty challenge of Catalonia to the central government exemplifies the importance of the subject matter. In order to analyze this process of interrelation, we use a secondary data mining by applying the multiple correspondence analysis technique (MCA). As a main result a typology of four types of expression of collective identity based on models of State organization are shown, which are connected with the party position on this issue.

Keywords: models of organization of the state, nationalism, collective identity, Spain, political parties

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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

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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

Procedia PDF Downloads 244

Authors: Yujun Liu, Maosheng Ran

Abstract:

Background: Chinese rural-to-urban migrant workers’ mental health problems raise attentions from different social sectors. However, situation of present mental health services provided to this population has not been discovered. This study attempts to describe the current mental health service situation, identify the gaps and give the future directions based on the quantitative data. Methods: Questionnaire surveys were conducted among 2017 rural-to-urban migrant workers in 13 cities and 100 social work service organizations in 5 cities in 2014. Data was collected by face-to-face structured interview by trained interviewers. Findings: Migrant workers’ mental health status was not good. Compared to the severity of mental distress, mental health service for this population was lacking and insufficient, which accounted for only 14.4% of all services in our sample. And the group work and case work were the most frequently-used methods. By estimating a series of regression models, we revealed that life experiences and working conditions were significantly associated with migrant workers’ mental health status. Therefore, the macro social work practices aimed at this whole group were advocated to promote their mental wellbeing. That is, practitioners should not only focus on the improvement of migrant workers’ emotion management capacity, but also pay attention to raise awareness and improve their living and working condition; not only concentrate on the solving of individuals’ dilemma, but also promote gradual reformation of present labor regime and hukou system in China.

Keywords: Chinese rural-to-urban migrant workers, macro social work practice, mental health service needs, mental health status

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Authors: Saadoun Abd Errazak, Hafssaoui Abdallah, Fredj Mohamed

Abstract:

Mining operations open induce risks of instability that can cause landslides and collapse at the bleachers slope. These risks may occur both during and after the operation phase. The magnitude of these risks depends on the mechanical and physical characteristics of the rock mass, the geometrical dimensions of ore bodies, their spatial arrangement, and the state of the operated area. If security and technology measures are not taken into account for this purpose, the environment will be affected. The main objective of this work is to assess these risks by analytical and numerical methods. The study is based on the geological, hydrogeological and geotechnical rock mass of the open pit quarry of Chouf Amar M'sila. The results obtained have allowed us to obtain an acceptable factor of safety and stability study of the open pit.

Keywords: stability, land sliding, numerical modeling, safety factor, open-pit quarry

Procedia PDF Downloads 375

Authors: Ismene Ithai Bras-Ruiz

Abstract:

Learning Analytics (LA) is employed by universities not only as a tool but as a specialized ground to enhance students and professors. However, not all the academic programs apply LA with the same goal and use the same tools. In fact, LA is formed by five main fields of study (academic analytics, action research, educational data mining, recommender systems, and personalized systems). These fields can help not just to inform academic authorities about the situation of the program, but also can detect risk students, professors with needs, or general problems. The highest level applies Artificial Intelligence techniques to support learning practices. LA has adopted different techniques: statistics, ethnography, data visualization, machine learning, natural language process, and data mining. Is expected that any academic program decided what field wants to utilize on the basis of his academic interest but also his capacities related to professors, administrators, systems, logistics, data analyst, and the academic goals. The Open and Distance University System (SUAYED in Spanish) of the University National Autonomous of Mexico (UNAM), has been working for forty years as an alternative to traditional programs; one of their main supports has been the employ of new information and communications technologies (ICT). Today, UNAM has one of the largest network higher education programs, twenty-six academic programs in different faculties. This situation means that every faculty works with heterogeneous populations and academic problems. In this sense, every program has developed its own Learning Analytic techniques to improve academic issues. In this context, an investigation was carried out to know the situation of the application of LA in all the academic programs in the different faculties. The premise of the study it was that not all the faculties have utilized advanced LA techniques and it is probable that they do not know what field of study is closer to their program goals. In consequence, not all the programs know about LA but, this does not mean they do not work with LA in a veiled or, less clear sense. It is very important to know the grade of knowledge about LA for two reasons: 1) This allows to appreciate the work of the administration to improve the quality of the teaching and, 2) if it is possible to improve others LA techniques. For this purpose, it was designed three instruments to determinate the experience and knowledge in LA. These were applied to ten faculty coordinators and his personnel; thirty members were consulted (academic secretary, systems manager, or data analyst, and coordinator of the program). The final report allowed to understand that almost all the programs work with basic statistics tools and techniques, this helps the administration only to know what is happening inside de academic program, but they are not ready to move up to the next level, this means applying Artificial Intelligence or Recommender Systems to reach a personalized learning system. This situation is not related to the knowledge of LA, but the clarity of the long-term goals.

Keywords: academic improvements, analytical techniques, learning analytics, personnel expertise

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Authors: Radhia Toujani, Jalel Akaichi

Abstract:

Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

Procedia PDF Downloads 365

Authors: Sukla Saha, Alok Sinha

Abstract:

Acid mine drainage (AMD) refers to the production of acidified water from abandoned mines and active mines as well. The reason behind the generation of this kind of acidified water is the oxidation of pyrites present in the rocks in and around mining areas. Thiobacillus ferrooxidans, which is a sulfur oxidizing bacteria, helps in the oxidation process. AMD is extremely acidic in nature, (pH 2-3) with high concentration of several trace and heavy metals such as Fe, Al, Zn, Mn, Cu and Co and anions such as chloride and sulfate. AMD has several detrimental effect on aquatic organism and environment. It can directly or indirectly contaminate the ground water and surface water as well. The present study considered the treatment of AMD with metallurgical slag, which is a waste material. Slag helped to enhance the pH of AMD to 8.62 from 1.5 with 99% removal of trace metals such as Fe, Al, Mn, Cu and Co. Metallurgical slag was proven as efficient neutralizing material for the treatment of AMD.

Keywords: acid mine drainage, Heavy metals, metallurgical slag, Neutralization

Procedia PDF Downloads 187