Search results for: association mining

Authors: H. Ju, M. Jones, G. D. Mishra

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Introduction: Limited longitudinal studies have examined the relationship between BMI and dysmenorrhea, resulting in mixed results. This study aims to investigate the long-term association between BMI and dysmenorrhea. Methods: 9,688 women from Australian Longitudinal Study on Women’s Health (ALSWH), a prospective population-based cohort study, were followed for 13 years. Data were collected through self-reported questionnaires repeatedly on all variables, including dysmenorrhea, weight and height. The longitudinal association between dysmenorrhea and BMI or BMI transition (change of BMI categories between two successive surveys) was investigated by generalized estimating equations. Results: When the women were aged 22 to 27 years, approximately 11% were obese, 7% underweight, and 25% reported dysmenorrhea. Over the study period, the prevalence of obesity doubled whereas that of underweight declined substantially. The prevalence of dysmenorrhea remained relatively stable. Compared to women with a normal weight, significantly higher odds of reporting dysmenorrhea were detected for both women who were underweight (odds ratio (OR) 1.25, 95% confidence interval (CI) 1.09, 1.43) and obese (OR 1.20, 95% CI 1.10, 1.31). Being overweight was not associated with increased risk of dysmenorrhea. Compared to women who remained at normal weight or overweight over time, significant risk was detected for women who: remained underweight or obese (OR 1.35, 95% CI 1.23, 1.49), were underweight but became normal or overweight (OR 1.29, 95% CI 1.11, 1.50), became underweight (OR 1.24, 95% CI 1.01, 1.52). However, the higher risk among obese women disappeared when they lost weight and became normal weight or overweight (OR 1.07, 95% CI 0.87, 1.30). Conclusions: A U-shaped association was revealed between dysmenorrhea and BMI, revealing higher risk of dysmenorrhea for both underweight and obese women. Further, the risk disappeared when obese women lost weight and acquired a healthier BMI. However obesity certainly poses a greater burden of disease from the public health perspective, thus requires greater effort to tackle the increasing problem at the population level. It is important to maintain a healthy weight over time for women to enjoy a better reproductive health.

Keywords: body mass index, dysmenorrhea, obesity, painful period, underweight

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Authors: N. E. Gallagher, C. E. Neville, N. Lyner, J. Yarnell, C. C. Patterson, J. E. Gallacher, Y. Ben-Shlomo, A. Fehily, J. V. Woodside

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Hearing loss is highly prevalent in older people and can reduce quality of life substantially. Emerging research suggests that potentially modifiable risk factors, including risk factors previously related to cardiovascular disease risk, may be associated with a decreased or increased incidence of hearing loss. This has prompted investigation into the possibility that certain nutrients, foods or dietary patterns may also be associated with incidence of hearing loss. The aim of this study was to determine any associations between dietary patterns and hearing loss in men enrolled in the Caerphilly study. The Caerphilly prospective cohort study began in 1979-1983 with recruitment of 2512 men aged 45-59 years. Dietary data was collected using a self-administered, semi-quantitative, 56-item food frequency questionnaire (FFQ) at baseline (1979-1983), and 7-day weighed food intake (WI) in a 30% sub-sample, while pure-tone unaided audiometric threshold was assessed at 0.5, 1, 2 and 4 kHz, between 1984 and 1988. Principal components analysis (PCA) was carried out to determine a posteriori dietary patterns and multivariate linear and logistic regression models were used to examine associations with hearing level (pure tone average (PTA) of frequencies 0.5, 1, 2 and 4 kHz in decibels (dB)) for linear regression and with hearing loss (PTA>25dB) for logistic regression. Three dietary patterns were determined using PCA on the FFQ data- Traditional, Healthy, High sugar/Alcohol avoider. After adjustment for potential confounding factors, both linear and logistic regression analyses showed a significant and inverse association between the Healthy pattern and hearing loss (P<0.001) and linear regression analysis showed a significant association between the High sugar/Alcohol avoider pattern and hearing loss (P=0.04). Three similar dietary patterns were determined using PCA on the WI data- Traditional, Healthy, High sugar/Alcohol avoider. After adjustment for potential confounding factors, logistic regression analyses showed a significant and inverse association between the Healthy pattern and hearing loss (P=0.02) and a significant association between the Traditional pattern and hearing loss (P=0.04). A Healthy dietary pattern was found to be significantly inversely associated with hearing loss in middle-aged men in the Caerphilly study. Furthermore, a High sugar/Alcohol avoider pattern (FFQ) and a Traditional pattern (WI) were associated with poorer hearing levels. Consequently, the role of dietary factors in hearing loss remains to be fully established and warrants further investigation.

Keywords: ageing, diet, dietary patterns, hearing loss

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Authors: Nidesh Sapkota, Garima Pudasaini, Dikshya Agrawal, Binav Baral, Umesh Bhagat, Dharanidhar Baral

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Background: Medical and Dental students are vulnerable to higher levels of Psychological distress than other age matched peers. Many studies reveals that there is high prevalence of psychoactive substance use and Psychiatric co-morbidities among them. Objectives: -To study the prevalence of substance use among medical and dental students of a Medical University. -To study the prevalence of depression and anxiety in medical and dental students of a Medical University. Materials and Method: A cross-sectional descriptive study in which simple random sampling was done. Semi-structured questionnaire, AUDIT for alcohol use, Fagerstrom test for Nicotine dependence, Cannabis screening test (CAST), Beck’s Depression Inventory (BDI), Beck’s Anxiety Inventory (BAI) were used for the assessment. Results: Total sample size was 588 in which the mean age of participants was 22±2years. Among them the prevalence of alcohol users was 47.75%(281) in which 32%(90) were harmful users. Among 19.55%(115) nicotine users 56.5%(65), 37.4%(43), 6.1%(7) had low, low to moderate and moderate dependence respectively. The prevalence of cannabis users was 9%(53) with 45.3%(24), 18.9%(10) having low and high addiction respectively. Depressive symptoms were recorded in 25.3%(149) out of which 12.6%(74), 6.5%(38), 5.3%(31), 0.5%(3), 0.5%(3) had mild, borderline, moderate, severe and extreme depressive symptoms respectively. Similarly anxiety was recorded among 7.8%(46) students with 42 having moderate and 4 having severe anxiety symptoms. Among them 6.3%(37) had suicidal thoughts and 4(0.7%) of them had suicide attempt in last one year. Statistically significant association was noted with harmful alcohol users, Depression and suicidal attempts. Similar association was noted between Depression and suicide with moderate use of nicotine. Conclusion: There is high prevalence of Psychoactive substance use and psychiatric co-morbidities noted in the studies sample. Statistically significant association was noted with Psychiatric co-morbidities and substance use.

Keywords: alcohol, cannabis, dependence, depression, medical students

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Authors: Billel Kenidra, Mohamed Benmohammed

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

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

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

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

Keywords: diesel emission, personal sampling, aethalometer, mining

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Authors: Imran Dayan, Ashiqul Khan

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

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

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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

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

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Authors: Ibrahim Mohammed

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Background: Diabetes mellitus is a chronic disease that can cause devastating secondary complications, reducing the quality and length of life as well as increasing medical costs for the patient and society. The guidelines recommend both clinical and preventive strategies for diabetes management and are regularly updated. The aim of the study is to assess the level of adherence of physicians to American Diabetes Association Guidelines. Method: Observational multicenter retrospective study will be conducted among different hospitals in the central region. Patient data will be collected from the records of the last three years (2017- 2020). Records will be selected randomly after a complete randomized design. The study focuses on the management of type 2 according to ADA not changed in the last three updating; those standards; all patients should be taking Metformin 1500 to 2000 mg/day as recommended dose and should be received a high dose of statin if the high risk to ASCVD or moderate statin if not at risk, patients with hypertension and diabetes should taking ACE or ARBS. Result: The study aimed to evaluate the commitment of physicians in the central region to the ADA. Out of the 153 selected patients, only 17 % were able to control their diabetes with an average A1c below 7. ADA stated that to reach the minimum benefit of using Metformin, the daily dose should be between 1500 and 2000 mg. Results showed that 110 patients were on Metformin, where 68% of them were on the recommended dose. ADA recommended the intake of high statin for diabetic patients with ASCVD risk, while diabetic patients without ASCVD risk should be on a moderate statin. Results showed that 61.5% of patients with ASCVD risk were at high statin while only 36% of patients without ASCVD risk were at moderate statin. Results showed that 89 patients have hypertension, and 80% of them are getting ACE/ARBs as recommended by the ADA. Recommendation: It is necessary to implement periodic training courses for some physicians to enhance and update their knowledge.

Keywords: American Diabetic Association, diabetes mellitus, atherosclerotic cardiovascular disease, ACE inhibitors

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Authors: Roberta S. Ilinsky, Livia Eisler, Gustavo Mota, Kurt Faltin Jr., Cristina Lucia Feijó Ortolani

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The aim of the present study was to evaluate the presence of occlusal disorders associated with bottle feeding habits in children aged 4-6 years old. A cross-sectional study was performed in a sample of 466 preschool children aged 4-6 years, attending state preschools in the city of Sao Paulo, Brazil. Parents and caregivers answered a questionnaire about children’s oral habits, including bottle-feeding habit, and signed the Informed Consent form. The students underwent an oral examination to evaluate occlusal disorders. Data were analyzed by the SPSS 2.2 program (IBM, USA) and treated with non-parametric chi-square tests and multiple logistic regression with a significance level of p < 0.05. There was association between bottle-feeding and occlusal disorders (OR = 3.058, 95% CI = 1.561-5.991, PI < 0.001), with a higher significance for anterior open bite (OR = 2.855, 95% CI = 1.769-4.606, PI < 0.001) and canine class II (OR = 0.667, 95% CI = 0.449-0.990, PI < 0.045). There was no relationship between bottle-feeding habit and other occlusal disorders examined. It was possible to conclude that children who were bottle fed during childhood are more likely to develop occlusal disorders, especially anterior open bite and canine class II.

Keywords: anterior open-bite, bottle-feeding, habits, malocclusion

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Authors: G. Nampa, M. Ndlovu

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Mutualistic relationships amongst organisms drive diversity in terrestrial ecosystems. Yet, few mutual associations have been documented in the semi-arid savannas of Africa. The levels and benefits of association between Carissa bispinosa, a medium-sized evergreen thorny shrub, and Trinervitermes trinervoides, an ecosystem engineer termite species, were studied at a semi-arid savanna setting in Nylsvley nature reserve, South Africa. It was hypothesized that there would be a close plant-insect association since termite mounds provide nutrients for plant growth and, in return, the thorny shrubs protect mounds from predation and also provide a temperature buffer. Comparative plant and mounds measurements were taken from associated and isolated occurrences seasonally. Soil particle size, macro- and micronutrients were also evaluated from mounds and the adjacent topsoil matrix General Additive Mixed Models were used to assess internal mound temperatures in relation to prevailing ambient and plant shade temperatures. Findings revealed that plants growing on mounds were significantly taller with a wider canopy and remained greener in the dry season with more fruits. On the other hand, termite mounds under plants were less prone to be damaged by aardvarks and pangolins and had a significantly wider diameter than exposed mounds. All soil macronutrients except for calcium and phosphorous were enriched in mounds relative to the matrix. Only Manganese was enriched in mounds while the other micronutrients (Cu, Fe, Zn and B) were not. Termite mounds under plants maintained a better constant and higher mean internal temperature during winter compared to exposed mounds. To our best knowledge, the study has revealed a previously undocumented survival mechanism that termites use to escape extreme temperatures and predation in semi-arid savannas.

Keywords: mound, mutualism, soil nutrients, termites, thermoregulation

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Authors: Jonathan R. Olsen, Natalie Nicholls, Jenna Panter, Hannah Burnett, Michael Tornow, Richard Mitchell

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The 20-minute neighborhood is a policy priority for governments worldwide and a key feature of this policy is providing access to natural space within 800 meters of home. The study aims were to (1) examine the association between distance to nearest natural space and frequent use over time and (2) examine whether frequent use and changes in use were patterned by income and housing tenure over time. Bi-annual Scottish Household Survey data were obtained for 2013 to 2019 (n:42128 aged 16+). Adults were asked the walking distance to their nearest natural space, the frequency of visits to this space and their housing tenure, as well as age, sex and income. We examined the association between distance from home of nearest natural space, housing tenure, and the likelihood of frequent natural space use (visited once a week or more). Two-way interaction terms were further applied to explore variation in the association between tenure and frequent natural space use over time. We found that 87% of respondents lived within 10 minute walk of a natural space, meeting the policy specification for a 20-minute neighbourhood. Greater proximity to natural space was associated with increased use; individuals living a 6 to 10 minute walk and over 10 minute walk were respectively 53% and 78% less likely to report frequent natural space use than those living within a 5 minute walk. Housing tenure was an important predictor of frequent natural space use; private renters and homeowners were more likely to report frequent natural space use than social renters. Our findings provide evidence that proximity to natural space is a strong predictor of frequent use. Our study provides important evidence that time-based access measures alone do not consider deep-rooted socioeconomic variation in use of Natural space. Policy makers should ensure a nuanced lens is applied to operationalising and monitoring the 20-minute neighbourhood to safeguard against exacerbating existing inequalities.

Keywords: natural space, housing, inequalities, 20-minute neighbourhood, urban design

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Authors: Nikita Valluri, Vatcharaporn Esichaikul

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

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

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

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

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

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Authors: Wang Yang

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

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

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Authors: Yi-Shuan ShaoShao, Yen-An Tsai, Chia-Huang Chang, Kai-Wei Liao, Ming-Song Tsai, Mei-Lien Chen

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Organophosphate pesticides (OPs) is an environmental hormone with proven endocrine-disrupting effects that may affect the growth and development in human. A large amount of organophosphate pesticides (OPs) is used throughout Taiwan, and human may be exposed through dietary intake or residential use. During pregnancy, OPs can be transferred to the blood stream reaching the fetus through the placenta. The aim of this study was to explore the association between maternal OPs exposure levels and fetal developments and birth outcomes. A birth cohort was follow-up. Maternal urine sample were collected at the first, second, and third gestational trimester. Fetal growth characteristics were measured by ultrasonic scan and birth outcomes were assessed by pediatrician. Urinary metabolite of organophosphate pesticides were assessed using gas chromatography-mass spectrometry. The analytes included dimethylphosphate (DMP), dimethylthiophosphate (DMTP), dimethyldithiophosphates (DMDTP), diethylphosphate (DEP), diethylthiophosphate (DETP), and diethyldithiophosphate (DEDTP). We found that all of urine samples in each trimester were detected at least one metabolite for dialkyl phosphate (DAP). The detection rate range of OP urinary metabolites were from the lowest 22% DEDTP to the highest 100% DMP and DMTP. And to compared geometric means (GM) of urinary metabolites with three trimesters, that third trimester had the highest concentration for DMPs, DEPs, and DAPs in pregnant women were 368.01, 169.85 and 543.75 nmol/g creatinine, respectively. We observed that DAPs concentration in first and second trimester were significantly negative association with head circumference. DMPs in first trimester was significantly negative association with thoracic circumference (p=0.05) by spearman correlation. Our results support associations with prenatal OPs exposure with fetal head circumference and thoracic circumference. It provided that maternal OPs exposure might affect birth outcomes. Thus, prenatal exposure to OPs and health risk worthy of attention and concern.

Keywords: DAPs, birth outcomes, organophosphate pesticides, prenatal

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Authors: Mário Ernesto Sitoe, Orlando Zacarias

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

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

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Authors: Deepa Gandhi, Pravin K. Naoghare, Amit Bafana, Krishnamurthi Kannan, Saravanadevi Sivanesana

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Oxidative stress is known to depreciate normal functioning of osteoblast cells. Present study reports oxidative/inflammatory signatures in fluoride exposed human osteosarcoma (HOS) cells and its possible association with the genes involved in bone developmental pathway. Microarray analysis was performed to understand the possible molecular mechanisms of stress-mediated bone lose in HOS cells. Cells were chronically exposed with sub-lethal concentration of fluoride. Global gene expression is profiling revealed 34 up regulated and 2598 down-regulated genes, which were associated with several biological processes including bone development, osteoblast differentiation, stress response, inflammatory response, apoptosis, regulation of cell proliferation. Microarray data were further validated through qRT-PCR and western blot analyses using key representative genes. Based on these findings, it can be proposed that chronic exposure of fluoride may impair bone development via oxidative and inflammatory stress. The present finding also provides important biological clues, which will be helpful for the development of therapeutic targets against diseases related bone.

Keywords: bone, HOS cells, microarray, stress

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Authors: Sandhya Baskaran, Hari Kumar Nagabushanam

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

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

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Authors: Budoor Al Abid

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

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

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Authors: Pavel Shcherban, Vlad Golovanov

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

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

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Authors: Fazel Zinat Motlagh, Reza Chaman, Rashid Ghafari, Zahra Behzad, Ahmad Ali Eslami

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Background: Chronic disease management requires the individual to perform several self-care behaviors. Self-efficacy, a widely used psychosocial concept, is associated with the ability to manage chronic disease. In this study, we examine the association between self-efficacy and self-care behaviors related to hypertension. Methods: In this cross-sectional study, conducted in Kohgiluye Boyer Ahmad province, the south of Iran, a total of 1836 hypertension patients, were randomly selected and participated in the study. Self-care behavior was measured with using H-SCALE (Hypertension Self-Care Activity Level Effects). Logistic regression conducted to detect correlation between self-efficacy and adherence to hypertension self-care behaviors. Results: Less than half (40.8%) of the participants reported that they have good self-efficacy to manage hypertension. Good self-efficacy was significantly associated with improve in adherence to medication (95% CI: 1.68, 1.83), eating a low-salt diet (95% CI: 1.44–1.73), physical activity (95% CI: 1.39–1.55), quit smoking (95% CI: 0.38–0.47), and weight management techniques (95% CI: 0.66–0.82). Conclusion: Hypertension self-efficacy was associated with adherence to self-care behaviors among adult with hypertension. According to our finding hypertension is a manageable condition. Self-efficacy is important factor in adherence with self-care behaviors related with hypertension.

Keywords: self-efficacy, hypertension, self-care, Iran

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Authors: Thomas F. Ducey, Kristin M. Trippe, James A. Ippolito, Jeffrey M. Novak, Mark G. Johnson, Gilbert C. Sigua

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The Oronogo-Duenweg mining belt –approximately 20 square miles around the Joplin, Missouri area– is a designated United States Environmental Protection Agency Superfund site due to lead-contaminated soil and groundwater by former mining and smelting operations. Over almost a century of mining (from 1848 to the late 1960’s), an estimated ten million tons of cadmium, lead, and zinc containing material have been deposited on approximately 9,000 acres. Sites that have undergone remediation, in which the O, A, and B horizons have been removed along with the lead contamination, the exposed C horizon remains incalcitrant to revegetation efforts. These sites also suffer from poor soil microbial activity, as measured by soil extracellular enzymatic assays, though 16S ribosomal ribonucleic acid (rRNA) indicates that microbial diversity is equal to sites that have avoided mine-related contamination. Soil analysis reveals low soil organic carbon, along with high levels of bio-available zinc, that reflect the poor soil fertility conditions and low microbial activity. Our study looked at the use of several materials to restore and remediate these sites, with the goal of improving soil health. The following materials, and their purposes for incorporation into the study, were as follows: manure-based biochar for the binding of zinc and other heavy metals responsible for phytotoxicity, locally sourced biosolids and compost to incorporate organic carbon into the depleted soils, effective microorganisms harvested from nearby pristine sites to provide a stable community for nutrient cycling in the newly composited 'soil material'. Our results indicate that all four materials used in conjunction result in the greatest benefit to these mine-impacted soils, based on above ground biomass, microbial biomass, and soil enzymatic activities.

Keywords: locally effective microorganisms, biochar, remediation, reclamation

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Authors: Daniela Prado, Lexi Frankel, Amalia Ardeljan, Lokesh Manjani, Matthew Cardeiro, Omar Rashid

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Background: Clostridium difficile (C. diff) is a gram-positive bacterium that is known to cause life-threatening diarrhea and severe inflammation of the colon. It originates as an alteration of the gut microbiome and can be transmitted through spores. Recent studies have shown a high association between the development of C. diff in cancer patients due to extensive hospitalization. However, research is lacking regarding C. diff’s association in the causation or prevention of cancer. The objective of this study was to therefore assess the correlation between Clostridium difficile infection (CDI) and the incidence of bone cancer. Methods: This retrospective analysis used data provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to evaluate the patients infected versus patients not infected with C. diff using ICD-10 and ICD-9 codes. Access to the database was granted by the Holy Cross Health, Fort Lauderdale, for the purpose of academic research. Standard statistical methods were used. Results: Between January 2010 and December 2019, the query was analyzed and resulted in 78863 patients in both the infected and control group, respectively. The two groups were matched by age range and CCI score. The incidence of bone cancer was 659 patients (0.835%) in the C. diff group compared to 1941 patients (2.461%) in the control group. The difference was statistically significant by a P-value < 2.2x10^-16 with an odds ratio (OR)= 0.33 (0.31-0.37) with a 95% confidence interval (CI). Treatment for CDI was analyzed for both C. diff infected and noninfected populations. 91 out of 16,676 (0.55%) patients with a prior C. diff infection and treated with antibiotics were compared to the control group were 275 out of 16,676 (1.65%) patients with no history of CDI and received antibiotic treatment. Results remained statistically significant by P-value <2.2x10-16 with an OR= 0.42 (0.37, 0.48). and a 95% CI. Conclusion: The study shows a statistically significant correlation between C. diff and a reduced incidence of bone cancer. Further evaluation is recommended to assess the potential of C. difficile in reducing bone cancer incidence.

Keywords: bone cancer, colitis, clostridium difficile, microbiome

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Authors: Agnieszka H. Ludwig-Slomczynska, Michal T. Seweryn, Przemyslaw Kapusta, Ewelina Pitera, Katarzyna Cyganek, Urszula Mantaj, Lucja Dobrucka, Ewa Wender-Ozegowska, Maciej T. Malecki, Pawel Wolkow

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Obesity results from an imbalance between energy intake and its expenditure. Genome-Wide Association Study (GWAS) analyses have led to discovery of only about 100 variants influencing body mass index (BMI), which explain only a small portion of genetic variability. Analysis of gene epistasis gives a chance to discover another part. Since it was shown that interaction and communication between nuclear and mitochondrial genome are indispensable for normal cell function, we have looked for epistatic interactions between the two genomes to find their correlation with BMI. Methods: The analysis was performed on 366 T1DM patients using Illumina Infinium OmniExpressExome-8 chip and followed by imputation on Michigan Imputation Server. Only genes which influence mitochondrial functioning (listed in Human MitoCarta 2.0) were included in the analysis – variants of nuclear origin (MAF > 5%) in 1140 genes and 42 mitochondrial variants (MAF > 1%). Gene expression analysis was performed on GTex data. Association analysis between genetic variants and BMI was performed with the use of Linear Mixed Models as implemented in the package 'GENESIS' in R. Analysis of association between mRNA expression and BMI was performed with the use of linear models and standard significance tests in R. Results: Among variants involved in epistasis between mitochondria and nucleus we have identified one in mitochondrial transcription factor, TFB2M (rs6701836). It interacted with mitochondrial variants localized to MT-RNR1 (p=0.0004, MAF=15%), MT-ND2 (p=0.07, MAF=5%) and MT-ND4 (p=0.01, MAF=1.1%). Analysis of the interaction between nuclear variant rs6701836 (nuc) and rs3021088 localized to MT-ND2 mitochondrial gene (mito) has shown that the combination of the two led to BMI decrease (p=0.024). Each of the variants on its own does not correlate with higher BMI [p(nuc)=0.856, p(mito)=0.116)]. Although rs6701836 is intronic, it influences gene expression in the thyroid (p=0.000037). rs3021088 is a missense variant that leads to alanine to threonine substitution in the MT-ND2 gene which belongs to complex I of the electron transport chain. The analysis of the influence of genetic variants on gene expression has confirmed the trend explained above – the interaction of the two genes leads to BMI decrease (p=0.0308). Each of the mRNAs on its own is associated with higher BMI (p(mito)=0.0244 and p(nuc)=0.0269). Conclusıons: Our results show that nuclear-mitochondrial epistasis can influence BMI in T1DM patients. The correlation between transcription factor expression and mitochondrial genetic variants will be subject to further analysis.

Keywords: body mass index, epistasis, mitochondria, type 1 diabetes

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Authors: Mahadev Bhise, Nabanita Majumder

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Recent studies have found association between air pollutants and mortality, particularly how concentration of air pollutant explains under-five mortality across the countries. Thus, the present study evaluates the relationship between Carbon dioxide (CO2) emission and under-five mortality, while controlling other well-being determinant of Under-five mortality in 100 countries using panel unbalanced cross sectional data. We have used PCSE and GMM model for the period 1990-2011 to meet our objectives. Our findings suggest that, the positive relationship between lagged periods of carbon dioxide and under-five mortality; the percentage of rural population with access of improved water is negatively associated with under-five mortality, while in case of urban population with access of improved water, is positively related to under-five mortality. Access of sanitation facility, food production index, GDP per capita, and concentration of urban population have significant negative impact on under-five mortality. Further, total fertility rate is significantly associated (positive) with under-five mortality which indicates relative change in fertility is related to relative change in under-five mortality.

Keywords: arbon dioxide (CO2), under-five mortality (0q5), gross domestic product (GDP), urban population, food production, panel corrected standard errors (PCSE), generalized method of moments (GMM)

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Authors: Mehrdad Sheikhvatan, Mohammad Ali Boroumand, Mehrdad Behmanesh, Shayan Ziaee

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Contradictory results have been obtained regarding the role of integrin, beta 3 (ITGB3) gene polymorphisms in occurrence of acute myocardial infarction (MI) in patients with coronary artery disease (CAD). Hence, we aimed to assess the association between 1565C/T polymorphism of ITGB3 gene and increased risk for acute MI in patients who suffered premature CAD in Iranian population. Our prospective study included 1000 patients (492 men and 508 women aged 21 to 55 years) referred to Tehran Heart center during a period of four years from 2008 to 2011 with the final diagnosis of premature CAD and classified into two groups with history of MI (n = 461) and without of MI (n = 539). The polymorphism variants were determined by PCR-RFLP technique by entering 10% of randomized samples and then genotyping of the polymorphism was also conducted by High Resolution Melting (HRM) method. Among study samples, 640 were followed with a median follow-up time 45.74 months for determining association of long-term major adverse cardiac events (MACE) and genotypes of polymorphisms. There was no significant difference in the frequency of 1565C/T polymorphism between the MI and non-MI groups. The frequency of wild genotype was 69.2% and 72.2%, the frequency of homozygous genotype was 21.3% and 18.4%, and the frequency of mutant genotype was 9.5% and 9.5%, respectively (p=0.505). Results were also similar when adjusted for covariates in a multivariate logistic regression model. No significant difference was also found in total-MACE free survival rate between the patients with different genotypes of 1565C/T polymorphism in both MI and non-MI group. The carriage of the 1565C/T polymorphism of ITGB3 gene seems unlikely to be a significant risk factor for the development of MI in Iranian patients with premature CAD. The presence of this ITGB3 gene polymorphism may not also predict long-term cardiac events.

Keywords: coronary artery disease, myocardial infarction, gene, integrin, beta 3, polymorphism

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Authors: Ubaid Ullah Kamgar, Ajaz Ahmed Suhaff, Mohammad Maqbool Dar

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Objective: The aim is to study the association of MRI findings of T₂/FLAIR white matter hyperintensities among patients with psychiatric disorders. Background and Rationale: MRI findings in psychiatric disorders can vary widely depending on specific disorders and individual differences. However, some general patterns have been observed, such as, in Depression - reduced volume in areas such as the prefrontal cortex and hippocampus; in Schizophrenia - enlarged ventricles, abnormalities in frontal and temporal lobes, as well as hippocampus and thalamus; in Bipolar Disorder – reduced volume in the prefrontal cortex and hippocampus and abnormalities in the amygdala; in OCD – abnormalities in the orbitofrontal cortex, anterior cingulate cortex and striatum. However, many patients show findings of white-matter hyper-intensities, which are usually considered non-specific in psychiatry. These hyperintensities are low attenuation in the deep and white matter. The pathogenic mechanisms of white matter hyperintensities are not well-understood and have been attributed to cerebral small vessel disease. The aim of the study is to study the association of the above MRI findings in patients with psychiatric disorders after ruling out neurological disorders (if any are found). Methodology: Patients admitted to psychiatric hospitals or presenting to OPDs with underlying psychiatric disorders, having undergone MRI Brain as part of investigations, and having T₂/FLAIR white-matter hyperintensities on MRI were taken to study the association of the above MRI findings with different psychiatric disorders. Results: Out of the 22 patients having MRI findings of T₂/FLAIR white-matter hyper-intensities, the underlying psychiatric comorbidities were: Major Depressive Disorder in 7 pts; Obsessive Compulsive Disorder in 5 pts; Bipolar Disorder in 5 pts; Dementia (vascular type) in 5pts. Discussion and conclusion: In our study, the white matter hyper-intensities were found mostly in MDD (32%), OCD (22.7%), Bipolar Disorder (22.7%) and Dementia in 22.7% of patients. In conclusion, the presence of white-matter hyperintensities in psychiatric disorders underscores the complex interplay between vascular, neurobiological and psychosocial factors. Further research with a large sample size is needed to fully elucidate their clinical significance.

Keywords: white-matter hyperintensities, OCD, MDD, dementia, bipolar disorder.

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Authors: Riffat Iqbal, Sidra Ihsan, Andleeb Batool, Maryam Mukhtar

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Glaucoma is a gathering of optic neuropathies described by dynamic degeneration of retinal ganglionic cells. It is clinically and innately heterogenous illness containing a couple of particular forms each with various causes and severities. Primary open-angle glaucoma (POAG) is the most generally perceived kind of glaucoma. This study investigated the genetic association of single nucleotide polymorphisms (SNPs; rs10483727 and rs33912345) at the SIX1/SIX6 locus with primary open-angle glaucoma (POAG) in the Pakistani population. The SIX6 gene plays an important role in ocular development and has been associated with morphology of the optic nerve. A total of 100 patients clinically diagnosed with glaucoma and 100 control individuals of age over 40 were enrolled in the study. Genomic DNA was extracted by organic extraction method. The SNP genotyping was done by (i) PCR based restriction fragment length polymorphism (RFLP) and sequencing method. Significant genetic associations were observed for rs10483727 (risk allele T) and rs33912345 (risk allele C) with POAG. Hence, it was concluded that Six6 gene is genetically associated with pathogenesis of Glaucoma in Pakistan.

Keywords: genotyping, Pakistani population, primary open-angle glaucoma, SIX6 gene

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Authors: Ryan Chalk, G. M. Shafiullah

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Climate change is a pertinent issue facing governments and societies around the world. The industrial revolution has resulted in a steady increase in the average global temperature. The mining and energy production industries have been significant contributors to this change prompting government to intervene by promoting low emission technology within these sectors. This paper initially reviews the energy problem in Australia and the mining sector with a focus on the energy requirements and production methods utilised in Western Australia (WA). Renewable energy in the form of utility-scale solar photovoltaics (PV) provides a solution to these problems by providing emission-free energy which can be used to supplement the existing natural gas turbines in operation at the proposed site. This research presents a custom renewable solution for the mining site considering the specific township network, local weather conditions, and seasonal load profiles. A summary of the required PV output is presented to supply slightly over 50% of the towns power requirements during the peak (summer) period, resulting in close to full coverage in the trench (winter) period. Dig Silent Power Factory Software has been used to simulate the characteristics of the existing infrastructure and produces results of integrating PV. Large scale PV penetration in the network introduce technical challenges, that includes; voltage deviation, increased harmonic distortion, increased available fault current and power factor. Results also show that cloud cover has a dramatic and unpredictable effect on the output of a PV system. The preliminary analyses conclude that mitigation strategies are needed to overcome voltage deviations, unacceptable levels of harmonics, excessive fault current and low power factor. Mitigation strategies are proposed to control these issues predominantly through the use of high quality, made for purpose inverters. Results show that use of inverters with harmonic filtering reduces the level of harmonic injections to an acceptable level according to Australian standards. Furthermore, the configuration of inverters to supply active and reactive power assist in mitigating low power factor problems. Use of FACTS devices; SVC and STATCOM also reduces the harmonics and improve the power factor of the network, and finally, energy storage helps to smooth the power supply.

Keywords: climate change, mitigation strategies, photovoltaic (PV), power quality

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Authors: Mehran Asadi, Afrand Agah

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

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

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