Search results for: correlated data aggregation

Authors: Y. Sunecher, N. Mamode Khan, V. Jowaheer

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This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts.

Keywords: non-stationary, BINARMA(1, 1) model, Poisson innovations, conditional maximum likelihood, CML

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Authors: J. Bahrawi, M. Elhag

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Estimation of evapotranspiration is always a major component in water resources management. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. Earth observation satellite sensors are thus used to overcome difficulties in obtaining daily evapotranspiration measurements on regional scale. The Surface Energy Balance System (SEBS) model was adopted to estimate daily evapotranspiration and relative evaporation along with other land surface energy fluxes. The model requires agro-climatic data that improve the model outputs. Advance Along Track Scanning Radiometer (AATSR) and Medium Spectral Resolution Imaging Spectrometer (MERIS) imageries were used to estimate the daily evapotranspiration and relative evaporation over the entire Nile Delta region in Egypt supported by meteorological data collected from six different weather stations located within the study area. Daily evapotranspiration maps derived from SEBS model show a strong agreement with actual ground-truth data taken from 92 points uniformly distributed all over the study area. Moreover, daily evapotranspiration and relative evaporation are strongly correlated. The reliable estimation of daily evapotranspiration supports the decision makers to review the current land use practices in terms of water management, while enabling them to propose proper land use changes.

Keywords: daily evapotranspiration, relative evaporation, SEBS, AATSR, MERIS, Nile Delta

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Authors: R. Periyasamy, Deepak Joshi, Sneh Anand

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Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution.

Keywords: arch index, computational tool, static foot pressure intensity image, foot pressure distribution, linear discriminant analysis

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Authors: Arnold M. Brener, Lesbek Tashimov, Ablakim S. Muratov

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The paper deals with the special model for coagulation kernels which represents new control parameters in the Smoluchowski equation for binary aggregation. On the base of the model the new approach to evaluating aggregative stability of disperse systems has been submitted. With the help of this approach the simple estimates for aggregative stability of various types of hydrophilic nano-suspensions have been obtained.

Keywords: aggregative stability, coagulation kernels, disperse systems, mathematical model

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Authors: Caroline Nnokwe, Iheanyi Eneremadu

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Teachers and principals are vital and integral parts of the educational system. For educational objectives to be met, the role of teachers and the functions of the principals are not to be overlooked. However, the inability of teachers and principals to carry out their roles effectively has impacted the outcome of the students’ performance. The study, therefore, examined teachers’ roles and principal’s administrative functions as correlates of effective academic performance of public secondary school students in Imo state, Nigeria. Four research questions and two hypotheses guided the study. The study adopted a correlation research design. The sample size was 5,438 respondents via the Yaro-Yamane technique, which consists of 175 teachers, 13 principals and 5,250 students using the proportional stratified random sampling technique. The instruments for data collection were a researcher-made questionnaire titled Teachers’ Role/Principals’ Administrative Functions Questionnaire (TRPAFQ) with a Cronbach Alpha coefficient of .82 and student's internal results obtained from the school authorities. Data collected were analyzed using the Pearson product-moment correlation coefficient and simple linear regression. Research questions were answered using Pearson Product Moment Correlation statistics, while the hypotheses were tested at 0.05 level of significance using regression analysis. The findings of the study showed that the educational qualification of teachers, organizing, and planning correlated student’s academic performance to a great extent, while availability and proper use of instructional materials by teachers correlated the academic performance of students to a very high extent. The findings also revealed that there is a significant relationship between teachers’ role, principals’ administrative functions and student’s academic performance of public secondary schools in Imo State, The study recommended among others that there is the need for government, through the ministry of education, and education authorities to adequately staff their supervisory department in order to carry out proper supervision of secondary school teachers, and also provide adequate instructional materials to ensure greater academic performance among secondary school students of Imo state, Nigeria.

Keywords: instructional materials, principals’ administrative functions, students’ academic performance, teacher role

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Authors: Asma Ben Yaghlane, Mohamed Naceur Azaiez

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In this paper, we briefly introduce the concept of Poisson attacks in the case of defense/attack strategies where attacks are assumed to be continuous. We suggest a game model in which the attacker will combine both criteria of a sufficient confidence level of a successful attack and a reasonably small size of the estimation error in order to launch an attack. Here, estimation error arises from assessing the system failure upon attack using aggregate data at the system level. The corresponding error is referred to as aggregation error. On the other hand, the defender will attempt to deter attack by making one or both criteria inapplicable. The defender will build his/her strategy by both strengthening the targeted system and increasing the size of error. We will formulate the defender problem based on appropriate optimization models. The attacker will opt for a Bayesian updating in assessing the impact on the improvement made by the defender. Then, the attacker will evaluate the feasibility of the attack before making the decision of whether or not to launch it. We will provide illustrations to better explain the process.

Keywords: attacker, defender, game theory, information

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Authors: Julien Caudeville, Muriel Ismert

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Analyzing the relationship between environment and health has become a major preoccupation for public health as evidenced by the emergence of the French national plans for health and environment. These plans have identified the following two priorities: (1) to identify and manage geographic areas, where hotspot exposures are suspected to generate a potential hazard to human health; (2) to reduce exposure inequalities. At a regional scale and fine resolution of exposure outcome prerequisite, environmental monitoring networks are not sufficient to characterize the multidimensionality of the exposure concept. In an attempt to increase representativeness of spatial exposure assessment approaches, risk composite indicators could be built using additional available databases and theoretical framework approaches to combine factor risks. To achieve those objectives, combining data process and transfer modeling with a spatial approach is a fundamental prerequisite that implies the need to first overcome different scientific limitations: to define interest variables and indicators that could be built to associate and describe the global source-effect chain; to link and process data from different sources and different spatial supports; to develop adapted methods in order to improve spatial data representativeness and resolution. A GIS-based modeling platform for quantifying human exposure to chemical substances (PLAINE: environmental inequalities analysis platform) was used to build health risk indicators within the Lorraine region (France). Those indicators combined chemical substances (in soil, air and water) and noise risk factors. Tools have been developed using modeling, spatial analysis and geostatistic methods to build and discretize interest variables from different supports and resolutions on a 1 km2 regular grid within the Lorraine region. By example, surface soil concentrations have been estimated by developing a Kriging method able to integrate surface and point spatial supports. Then, an exposure model developed by INERIS was used to assess the transfer from soil to individual exposure through ingestion pathways. We used distance from polluted soil site to build a proxy for contaminated site. Air indicator combined modeled concentrations and estimated emissions to take in account 30 polluants in the analysis. For water, drinking water concentrations were compared to drinking water standards to build a score spatialized using a distribution unit serve map. The Lden (day-evening-night) indicator was used to map noise around road infrastructures. Aggregation of the different factor risks was made using different methodologies to discuss weighting and aggregation procedures impact on the effectiveness of risk maps to take decisions for safeguarding citizen health. Results permit to identify pollutant sources, determinants of exposure, and potential hotspots areas. A diagnostic tool was developed for stakeholders to visualize and analyze the composite indicators in an operational and accurate manner. The designed support system will be used in many applications and contexts: (1) mapping environmental disparities throughout the Lorraine region; (2) identifying vulnerable population and determinants of exposure to set priorities and target for pollution prevention, regulation and remediation; (3) providing exposure database to quantify relationships between environmental indicators and cancer mortality data provided by French Regional Health Observatories.

Keywords: health risk, environment, composite indicator, hotspot areas

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Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: Mustafa M. Donma, Orkide Donma

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Obesity is a clinical state associated with low-grade inflammation. It is also a major risk factor for insulin resistance (IR). In its advanced stages, metabolic syndrome (MetS), a much more complicated disease which may lead to life-threatening problems, may develop. Obesity-mediated IR seems to correlate with the inflammation. Human studies performed particularly on pediatric population are scarce. The aim of this study is to detect possible associations between inflammation and IR in terms of some related ratios. 549 children were grouped according to their age- and sex-based body mass index (BMI) percentile tables of WHO. MetS components were determined. Informed consent and approval from the Ethics Committee for Clinical Investigations were obtained. The principles of the Declaration of Helsinki were followed. The exclusion criteria were infection, inflammation, chronic diseases and those under drug treatment. Anthropometric measurements were obtained. Complete blood cell, fasting blood glucose, insulin, and C-reactive protein (CRP) analyses were performed. Homeostasis model assessment of insulin resistance (HOMA-IR), systemic immune inflammation (SII) index, tense index, alanine aminotransferase to aspartate aminotransferase ratio (ALT/AST), neutrophils to lymphocyte (NLR), platelet to lymphocyte, and lymphocyte to monocyte ratios were calculated. Data were evaluated by statistical analyses. The degree for statistical significance was 0.05. Statistically significant differences were found among the BMI values of the groups (p < 0.001). Strong correlations were detected between the BMI and waist circumference (WC) values in all groups. Tense index values were also correlated with both BMI and WC values in all groups except overweight (OW) children. SII index values of children with normal BMI were significantly different from the values obtained in OW, obese, morbid obese and MetS groups. Among all the other lymphocyte ratios, NLR exhibited a similar profile. Both HOMA-IR and ALT/AST values displayed an increasing profile from N towards MetS3 group. BMI and WC values were correlated with HOMA-IR and ALT/AST. Both in morbid obese and MetS groups, significant correlations between CRP versus SII index as well as HOMA-IR versus ALT/AST were found. ALT/AST and HOMA-IR values were correlated with NLR in morbid obese group and with SII index in MetS group, (p < 0.05), respectively. In conclusion, these findings showed that some parameters may exhibit informative differences between the early and late stages of obesity. Important associations among HOMA-IR, ALT/AST, NLR and SII index have come to light in the morbid obese and MetS groups. This study introduced the SII index and NLR as important inflammatory markers for the discrimination of normal and obese children. Interesting links were observed between inflammation and IR in morbid obese children and those with MetS, both being late stages of obesity.

Keywords: children, inflammation, insulin resistance, metabolic syndrome, obesity

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Authors: Faisal Kalota

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Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: big data, learning analytics, analytics, big data in education, Hadoop

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Authors: Dong Tang, Yongli Zhao

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The study of the microstructure of asphalt is of great importance for the analysis of its macroscopic properties. However, the peculiarities of the chemical composition of the asphalt itself and the limitations of existing direct imaging techniques have caused researchers to face many obstacles in studying the microstructure of asphalt. The advantage of small-angle X-ray scattering (SAXS) is that it allows quantitative determination of the internal structure of opaque materials and is suitable for analyzing the microstructure of materials. Therefore, the SAXS technique was used to study the evolution of microstructures on the nanoscale during asphalt aging. And the reasons for the change in scattering contrast during asphalt aging were also explained with the help of Fourier transform infrared spectroscopy (FTIR). SAXS experimental results show that the SAXS curves of asphalt are similar to the scattering curves of scattering objects with two-level structures. The Porod curve for asphalt shows that there is no obvious interface between the micelles and the surrounding mediums, and there is only a fluctuation of the hot electron density between the two. The Beaucage model fit SAXS patterns shows that the scattering coefficient P of the asphaltene clusters as well as the size of the micelles, gradually increase with the aging of the asphalt. Furthermore, aggregation exists between the micelles of asphalt and becomes more pronounced with increasing aging. During asphalt aging, the electron density difference between the micelles and the surrounding mediums gradually increases, leading to an increase in the scattering contrast of the asphalt. Under long-term aging conditions due to the gradual transition from maltenes to asphaltenes, the electron density difference between the micelles and the surrounding mediums decreases, resulting in a decrease in the scattering contrast of asphalt SAXS. Finally, this paper correlates the macroscopic properties of asphalt with microstructural parameters, and the results show that the high-temperature rutting resistance of asphalt is enhanced and the low-temperature cracking resistance decreases due to the aggregation of micelles and the generation of new micelles. These results are useful for understanding the relationship between changes in microstructure and changes in properties during asphalt aging and provide theoretical guidance for the regeneration of aged asphalt.

Keywords: asphalt, Beaucage model, microstructure, SAXS

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Authors: Han Xue, Zhang Lanyue

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In order to seek more effective feature extraction technology, feature extraction method based on MFCC combined with vector hydrophone is exposed in the paper. The sound pressure signal and particle velocity signal of two kinds of ships are extracted by using MFCC and its evolution form, and the extracted features are fused by using fisher-ratio and correlated distance criterion. The features are then identified by BP neural network. The results showed that MFCC, First-Order Differential MFCC and Second-Order Differential MFCC features can be used as effective features for recognition of underwater targets, and the fusion feature can improve the recognition rate. Moreover, the results also showed that the recognition rate of the particle velocity signal is higher than that of the sound pressure signal, and it reflects the superiority of vector signal processing.

Keywords: vector information, MFCC, differential MFCC, fusion feature, BP neural network

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Authors: Sarah Nasser, Anne Moreau, Alain Hedoux, Romain Jeantet, Guillaume Delaplace

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Background: Spray dryed powders containing some caseins are commonly produced in dairy industry. It is widely admitted that the structure of casein evolves during powder storage, inducing a loss of solubility. However few studies evaluate accurately the destabilization mechanisms at molecular and mesoscopic level, in particular for Native Phospho Casein powder (NPC). Consequently, at the state of the art, it is very difficult to assess which secondary structure change or crosslinks initiate insolubility during storage. To address this issue, controlled ageing conditions have been applied to a NPC powder (which was obtained by spray drying a concentrate containing a higher content of casein (90%), whey protein (8%) and lactose (few %)). Evolution of structure and loss of solubility, with the effects of temperature and time of storage were systematically reported. Methods: FTIR spectroscopy, Raman and Circular Dichroism were used to monitor changes of secondary structure in dry powder and in solution after rehydration. Besides, proteomic tools and electrophoresis have been performed after varying storage conditions for evaluating aggregation and post translational modifications, like lactosylation or phosphorylation. Finally, Tof Sims and MEB were used to follow in parallel evolution of structure in surface and skin formation due to storage. Results + conclusion: These results highlight the important role of storage temperature in the stability of NPC. It is shown that this is not lactosylation at the heart of formation of aggregates, as advanced in others publications This is almost the rise of multitude post translational modifications (chemical cross link), added to disulphide bridges (physical cross link) wich contribute to the destabilisation of structure and aggregation of casein. A relative quantification of each kind of cross link, source of aggregates, is proposed. In addition, it has been proved that migration of lipids and formation of skin in surface during the ageing also explains the evolution of structure casein and thus the alterations of functional properties of NPC powder.

Keywords: casein, cross link, powder, storage

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Authors: Gidado Lawal Likko

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The study examined the relationship of achievement goals, metacognitive awareness and academic success among students of colleges of education in North Western Nigeria. The study was guided by three objectives. The first two were to find out whether students’ achievement goals and metacognitive awareness correlate with their academic success. 358 students comprising 242 males (67.6%) and 116 females (32.4%) were studied. Correlation survey was employed in the conduct of the study. The instruments used to collect data were students’ bio data form, achievement goals inventory (Roedel, Schraw and Plake, 1994), metacognitive awareness inventory (Schraw & Dennison, 1994) and students’ CGPA (NCCE minimum standard, 2013) was used as the index of academic success. Pearson Product Moment and regression analysis were the statistical techniques used to analyze the data. Results of the analysis indicated that students’ achievement goals (r=0.554, p=0.004) and metacognitive awareness (r= 0.67, p=0.001) positively correlated with their academic success. Similarly, significant relationship exists between achievement goals and metacognitive awareness (r=0.77, p=0.000). Part of the recommendations is the need for the management of all colleges of education to have educational interventions aimed at developing students’ metacognitive awareness which will foster purposeful self-regulation of their learning. This could be achieved by periodic assessment of students’ metacognitive awareness which will serve as feedback as they move from one educational level to another.

Keywords: academic success, goal orientation, metacognitive awareness, prospective teachers

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Authors: Mojdeh Mardani, Robert Stupnisky

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Microaggressions are discriminatory and degrading slights manifested from negative and often unconscious beliefs about marginalised groups, including women and people of colour. This quantitative research analyses survey data collected from 10 USA Universities. This research presents the impacts of microaggressions on productivity and motivation of Underrepresented Minority (URM) faculty, especially women and those with intersecting marginalized identities, such as women who identify with a race other than white. Results of this study revealed that on average, URM women were 50% more susceptible to gender microaggressions, which correlated negatively with autonomy and competence, and positively with a motivation.

Keywords: gender microaggressions, gender discrimination, underrepresented minority, female faculty, URM faculty, motivation, productivity, STEM

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Authors: Orkide Donma, Mustafa M. Donma

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Obesity, a disease associated with a low-grade inflammation, is a risk factor for the development of metabolic syndrome (MetS). So far, MetS risk factors such as parameters related to glucose and lipid metabolisms as well as blood pressure were considered for the evaluation of this disease. There are still some ambiguities related to the characteristic features of MetS observed particularly in pediatric population. Hormonal imbalance is also important, and quite a lot information exists about the behaviour of some hormones in adults. However, the hormonal profiles in pediatric metabolism have not been cleared yet. The aim of this study is to investigate the profiles of cortisol, insulin, and thyroid hormones in children with MetS. The study population was composed of morbid obese (MO) children without (Group 1) and with (Group 2) MetS components. WHO BMI-for age and sex percentiles were used for the classification of obesity. The values above 99 percentile were defined as morbid obesity. Components of MetS (central obesity, glucose intolerance, high blood pressure, high triacylglycerol levels, low levels of high density lipoprotein cholesterol) were determined. Anthropometric measurements were performed. Ratios as well as obesity indices were calculated. Insulin, cortisol, thyroid stimulating hormone (TSH), free T₃ and free T₄ analyses were performed by electrochemiluminescence immunoassay. Data were evaluated by statistical package for social sciences program. p<0.05 was accepted as the degree for statistical significance. The mean ages±SD values of Group 1 and Group 2 were 9.9±3.1 years and 10.8±3.2 years, respectively. Body mass index (BMI) values were calculated as 27.4±5.9 kg/m² and 30.6±8.1 kg/m², successively. There were no statistically significant differences between the ages and BMI values of the groups. Insulin levels were statistically significantly increased in MetS in comparison with the levels measured in MO children. There was not any difference between MO children and those with MetS in terms of cortisol, T₃, T₄ and TSH. However, T₄ levels were positively correlated with cortisol and negatively correlated with insulin. None of these correlations were observed in MO children. Cortisol levels in both MO as well as MetS group were significantly correlated. Cortisol, insulin, and thyroid hormones are essential for life. Cortisol, called the control system for hormones, orchestrates the performance of other key hormones. It seems to establish a connection between hormone imbalance and inflammation. During an inflammatory state, more cortisol is produced to fight inflammation. High cortisol levels prevent the conversion of the inactive form of the thyroid hormone T₄ into active form T₃. Insulin is reduced due to low thyroid hormone. T₃, which is essential for blood sugar control- requires cortisol levels within the normal range. Positive association of T₄ with cortisol and negative association of it with insulin are the indicators of such a delicate balance among these hormones also in children with MetS.

Keywords: children, cortisol, insulin, metabolic syndrome, thyroid hormones

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Authors: Visith Siriphuwanun, Yodying Punjasawadwong, Suwinai Saengyo, Kittipan Rerkasem

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Objective: To determine incidences and factors associated with perioperative cardiac arrest in trauma patients who received anesthesia for emergency surgery. Design and setting: Retrospective cohort study in trauma patients during anesthesia for emergency surgery at a university hospital in northern Thailand country. Patients and methods: This study was permitted by the medical ethical committee, Faculty of Medicine at Maharaj Nakorn Chiang Mai Hospital, Thailand. We clarified data of 19,683 trauma patients receiving anesthesia within a decade between January 2007 to March 2016. The data analyzed patient characteristics, traumas surgery procedures, anesthesia information such as ASA physical status classification, anesthesia techniques, anesthetic drugs, location of anesthesia performed, and cardiac arrest outcomes. This study excluded the data of trauma patients who had received local anesthesia by surgeons or monitoring anesthesia care (MAC) and the patient which missing more information. The factor associated with perioperative cardiac arrest was identified with univariate analyses. Multiple regressions model for risk ratio (RR) and 95% confidence intervals (CI) were used to conduct factors correlated with perioperative cardiac arrest. The multicollinearity of all variables was examined by bivariate correlation matrix. A stepwise algorithm was chosen at a p-value less than 0.02 was selected to further multivariate analysis. A P-value of less than 0.05 was concluded as statistically significant. Measurements and results: The occurrence of perioperative cardiac arrest in trauma patients receiving anesthesia for emergency surgery was 170.04 per 10,000 cases. Factors associated with perioperative cardiac arrest in trauma patients were age being more than 65 years (RR=1.41, CI=1.02–1.96, p=0.039), ASA physical status 3 or higher (RR=4.19–21.58, p < 0.001), sites of surgery (intracranial, intrathoracic, upper intra-abdominal, and major vascular, each p < 0.001), cardiopulmonary comorbidities (RR=1.55, CI=1.10–2.17, p < 0.012), hemodynamic instability with shock prior to receiving anesthesia (RR=1.60, CI=1.21–2.11, p < 0.001) , special techniques for surgery such as cardiopulmonary bypass (CPB) and hypotensive techniques (RR=5.55, CI=2.01–15.36, p=0.001; RR=6.24, CI=2.21–17.58, p=0.001, respectively), and patients who had a history of being alcoholic (RR=5.27, CI=4.09–6.79, p < 0.001). Conclusion: Incidence of perioperative cardiac arrest in trauma patients receiving anesthesia for emergency surgery was very high and correlated with many factors, especially age of patient and cardiopulmonary comorbidities, patient having a history of alcoholic addiction, increasing ASA physical status, preoperative shock, special techniques for surgery, and sites of surgery including brain, thorax, abdomen, and major vascular region. Anesthesiologists and multidisciplinary teams in pre- and perioperative periods should remain alert for warning signs of pre-cardiac arrest and be quick to manage the high-risk group of surgical trauma patients. Furthermore, a healthcare policy should be promoted for protecting against accidents in high-risk groups of the population as well.

Keywords: perioperative cardiac arrest, trauma patients, emergency surgery, anesthesia, factors risk, incidence

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Authors: Olubukayo-Opeyemi Oyetayo, Oscar Mendez-Lucio, Andreas Bender, Hans Kiefer

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The thermal melting curve of a protein provides information on its conformational stability and could provide cues on its aggregation behavior. Naturally-occurring osmolytes have been shown to improve the thermal stability of most proteins in a concentration-dependent manner. They are therefore commonly employed as additives in therapeutic protein purification and formulation. A number of intertwined and seemingly conflicting mechanisms have been put forward to explain the observed stabilizing effects, the most prominent being the preferential exclusion mechanism. We attempted to probe and summarize molecular mechanisms for thermal stabilization of a monoclonal antibody (mAb) by developing quantitative structure-activity relationships using a rationally-selected library of 120 osmolyte-like compounds in the polyhydric alcohols, amino acids and methylamines classes. Thermal stabilization potencies were experimentally determined by thermal shift assays based on differential scanning fluorimetry. The cross-validated QSAR model was developed by partial least squares regression using descriptors generated from Molecular Operating Environment software. Careful evaluation of the results with the use of variable importance in projection parameter (VIP) and regression coefficients guided the selection of the most relevant descriptors influencing mAb thermal stability. For the mAb studied and at pH 7, the thermal stabilization effects of tested compounds correlated positively with their fractional polar surface area and inversely with their fractional hydrophobic surface area. We cannot claim that the observed trends are universal for osmolyte-protein interactions because of protein-specific effects, however this approach should guide the quick selection of (de)stabilizing compounds for a protein from a chemical library. Further work with a large variety of proteins and at different pH values would help the derivation of a solid explanation as to the nature of favorable osmolyte-protein interactions for improved thermal stability. This approach may be beneficial in the design of novel protein stabilizers with optimal property values, especially when the influence of solution conditions like the pH and buffer species and the protein properties are factored in.

Keywords: thermal stability, monoclonal antibodies, quantitative structure-activity relationships, osmolytes

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Authors: Sandeep Sharma, Sarabjit Singh

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As per the user demand and growth trends of large free data the storage solutions are now becoming more challenge-able to protect, store and to retrieve data. The days are not so far when the storage companies and organizations are start saying 'no' to store our valuable data or they will start charging a huge amount for its storage and protection. On the other hand as per the environmental conditions it becomes challenge-able to maintain and establish new data warehouses and data centers to protect global warming threats. A challenge of small data is over now, the challenges are big that how to manage the exponential growth of data. In this paper we have analyzed the growth trend of big data and its future implications. We have also focused on the impact of the unstructured data on various concerns and we have also suggested some possible remedies to streamline big data.

Keywords: big data, unstructured data, volume, variety, velocity

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Authors: Yi-Tung Lin

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Researchers commonly assess implicit self-esteem with the Implicit Association Test (IAT). The IAT’s measure, often referred to as the IAT effect, indicates the strengths of automatic preferences for the self relative to others, which is often considered an index of implicit self-esteem. However, based on the Dual-process theory, the IAT does not rely entirely on the automatic process; it is also influenced by a controlled process. The present study, therefore, analyzed the IAT data with the Quad model, separating four processes on the IAT performance: the likelihood that automatic association is activated by the stimulus in the trial (AC); that a correct response is discriminated in the trial (D); that the automatic bias is overcome in favor of a deliberate response (OB); and that when the association is not activated, and the individual fails to discriminate a correct answer, there is a guessing or response bias drives the response (G). The AC and G processes are automatic, while the D and OB processes are controlled. The AC parameter is considered as the strength of the association activated by the stimulus, which reflects what implicit measures of social cognition aim to assess. The stronger the automatic association between self and positive valence, the more likely it will be activated by a relevant stimulus. Therefore, the AC parameter was used as the index of implicit self-esteem in the present study. Meanwhile, the relationship between implicit self-esteem and depression is not fully investigated. In the cognitive theory of depression, it is assumed that the negative self-schema is crucial in depression. Based on this point of view, implicit self-esteem would be negatively associated with depression. However, the results among empirical studies are inconsistent. The aims of the present study were to examine the psychometric properties of the AC (i.e., test-retest reliability and its correlations with explicit self-esteem and depression) and compare it with that of the IAT effect. The present study had 105 patients with depressive disorders completing the Rosenberg Self-Esteem Scale, Beck Depression Inventory-II and the IAT on the pretest. After at least 3 weeks, the participants completed the second IAT. The data were analyzed by the latent-trait multinomial processing tree model (latent-trait MPT) with the TreeBUGS package in R. The result showed that the latent-trait MPT had a satisfactory model fit. The effect size of test-retest reliability of the AC and the IAT effect were medium (r = .43, p < .0001) and small (r = .29, p < .01) respectively. Only the AC showed a significant correlation with explicit self-esteem (r = .19, p < .05). Neither of the two indexes was correlated with depression. Collectively, the AC parameter was a satisfactory index of implicit self-esteem compared with the IAT effect. Also, the present study supported the results that implicit self-esteem was not correlated with depression.

Keywords: cognitive modeling, implicit association test, implicit self-esteem, quad model

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Authors: Jalal Haghighat Monfared, Zahra Akbari

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Today, business executives need to have useful information to make better decisions. Banks have also been using information tools so that they can direct the decision-making process in order to achieve their desired goals by rapidly extracting information from sources with the help of business intelligence. The research seeks to investigate whether there is a relationship between the quality of decision making and the business intelligence capabilities of Mellat Bank. Each of the factors studied is divided into several components, and these and their relationships are measured by a questionnaire. The statistical population of this study consists of all managers and experts of Mellat Bank's General Departments (including 190 people) who use commercial intelligence reports. The sample size of this study was 123 randomly determined by statistical method. In this research, relevant statistical inference has been used for data analysis and hypothesis testing. In the first stage, using the Kolmogorov-Smirnov test, the normalization of the data was investigated and in the next stage, the construct validity of both variables and their resulting indexes were verified using confirmatory factor analysis. Finally, using the structural equation modeling and Pearson's correlation coefficient, the research hypotheses were tested. The results confirmed the existence of a positive relationship between decision quality and business intelligence capabilities in Mellat Bank. Among the various capabilities, including data quality, correlation with other systems, user access, flexibility and risk management support, the flexibility of the business intelligence system was the most correlated with the dependent variable of the present research. This shows that it is necessary for Mellat Bank to pay more attention to choose the required business intelligence systems with high flexibility in terms of the ability to submit custom formatted reports. Subsequently, the quality of data on business intelligence systems showed the strongest relationship with quality of decision making. Therefore, improving the quality of data, including the source of data internally or externally, the type of data in quantitative or qualitative terms, the credibility of the data and perceptions of who uses the business intelligence system, improves the quality of decision making in Mellat Bank.

Keywords: business intelligence, business intelligence capability, decision making, decision quality

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Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

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Authors: Jainendra Singh, Zaheeruddin

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A typical wireless sensor networks (WSNs) consists of several tiny and low-power sensors which use radio frequency to perform distributed sensing tasks. The longevity of wireless sensor networks (WSNs) is a major issue that impacts the application of such networks. While routing protocols are striving to save energy by acting on sensor nodes, recent studies show that network lifetime can be enhanced by further involving sink mobility. A common approach for energy efficiency is partitioning the network into clusters with correlated data, where the representative nodes simply transmit or average measurements inside the cluster. In this paper, we propose an energy- efficient homogenous clustering (EHC) technique. In this technique, the decision of each sensor is based on their residual energy and an estimate of how many of its neighboring cluster heads (CHs) will benefit from it being a CH. We, also explore the routing algorithm in clustered WSNs. We show that the proposed schemes significantly outperform current approaches in terms of packet delay, hop count and energy consumption of WSNs.

Keywords: wireless sensor network, energy efficiency, clustering, routing

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Authors: Preeti Pal, Anjali Pal

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Chitosan is an effective sorbent for removal of contaminants from wastewater. However, the ability of pure chitosan is specific because of its cationic charge. It causes repulsion in the removal process of various cationic charged molecules. The present study has been carried out for the successful removal of Pb²⁺ ions from aqueous solution by modified chitosan beads. Surface modification of chitosan (CS) beads was performed by using the anionic surfactant (AS), sodium dodecyl sulfate (SDS). Micelle aggregation property of SDS has been utilized for the formation of bilayer over the CS beads to produce surfactant modified chitosan (SMCS) beads. Prepared adsorbents were characterized by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) in order to find out their composition and surface morphology. SMCS beads, when compared to the pure CS beads, showed three times higher adsorption. This higher adsorption is believed to be due to the adsolubilization of Pb²⁺ ions on SDS bilayer. This bilayer provides more adsorption sites for quick and effective removal of Pb²⁺ ions from the aqueous phase. Moreover, the kinetic and adsorption isotherm models were employed to the obtained data for the description of the lead adsorption processes. It was found that the removal kinetics follows pseudo-second order model. Adsorption isotherm data fits well to the Langmuir model. The maximum adsorption capacity obtained is 100 mg/g at the dosage of 0.675 g/L for 50 mg/L of Pb²⁺. The adsorption capacity is subject to increase with increasing the Pb²⁺ ions concentration in the solution. The results indicated that the prepared hydrogel beads are efficient adsorbent for removal of Pb²⁺ ions from the aqueous medium.

Keywords: adsolubilisation, anionic surfactant, bilayer, chitosan, Pb²⁺

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Authors: Jessica Torres, James W. Sturges

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This correlational study explored the potential relationship between social media use and social connectedness. College students (n = 190) were surveyed using the revised Social Connectedness Scale (SCS-R) and were asked about the number of hours they used social media platforms such as Instagram, TikTok, Twitter, Snapchat, and Facebook. We also developed and administered a 14-item Social Media Use Scale (SMUS) to measure potentially maladaptive social media use, such as use that likely interfered with other activities. The SMUS was found to have good inter-item consistency (Cronbach’s alpha = .92) and was significantly correlated with hours of use, r(182) = .622, p < .001. As expected, we found that the SCS-R scores were inversely related to total hours of social media use, r(182) = -.188 (p < .005). This suggested that lots of time allocated to online interactions is negatively associated with social connectedness in general. Interestingly, however, higher social connectedness scores were associated specifically with Snapchat use, r(28) = .210, p = .004. This may have to do with the specific nature of the Snapchat experience and perhaps its original use for one-to-one communication. The use of other social media platforms (Tiktok, Instagram, Twitter) was not related to better social connectedness scores. Although we failed to find that scores on our measure of problem use (the SMUS) were correlated with social connectedness, we are hopeful that the SMUS will be of use in identifying patterns of maladaptive social media use that may have an impact on other important outcome measures of adaptive functioning and well-being.

Keywords: adaptive functioning, college students, social connectedness, social media use

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Authors: Yang Bao, Shi Wei Deng, WangQun Lin

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This paper introduces the concept and principle of data cleaning, analyzes the types and causes of dirty data, and proposes several key steps of typical cleaning process, puts forward a well scalability and versatility data cleaning framework, in view of data with attribute dependency relation, designs several of violation data discovery algorithms by formal formula, which can obtain inconsistent data to all target columns with condition attribute dependent no matter data is structured (SQL) or unstructured (NoSQL), and gives 6 data cleaning methods based on these algorithms.

Keywords: data cleaning, dependency rules, violation data discovery, data repair

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Authors: Xia Huo

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The composition and metabolites of the gut microbiota can be altered by environmental pollutants. However, the effect of co-exposure to multiple pollutants on the human gut microbiota has not been sufficiently studied. In this study, gut microorganisms and their metabolites were compared between 33 children from Guiyu and 34 children from Haojiang. The exposure level was assessed by estimating the daily intake (EDI) of polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), 6PPD-quinone (6PPDQ), and metal(loid)s in dust. Significant correlations were found between the EDIs of 6PPDQ, BDE28, PCB52, Ni, Cu, and both the alpha diversity index and specific metabolites in single-element models. The study found that the Bayesian kernel machine regression (BKMR) model showed a negative correlation between the EDIs of five pollutants (6PPDQ, BDE28, PCB52, Ni, and Cu) and the Chao 1 index, particularly beyond the 55th percentile. Furthermore, the EDIs of these five pollutants were positively correlated with the levels of the metabolite 2,4-diaminobutyric acid while negatively correlated with the levels of d-erythro-sphingosine and d-threitol. Our research suggests that exposure to 6PPDQ, BDE28, PCB52, Ni, and Cu in kindergarten dust is associated with alterations in the gut microbiota and its metabolites. These alterations may be associated with neurodevelopmental abnormalities in children.

Keywords: gut microbiota, 6PPDQ, PBDEs, PCBs, metal(loid)s, BKMR

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Authors: Benaissa Fatima, Alkama Rezak, Annesi-Maesano Isabella

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To assess the health impact of the air pollution on the population of Béjaia, we carried out a descriptive epidemiologic inquiry near the medical establishments of three areas. From the registers of hospital admissions, we collected data on the hospital mortality and admissions relating to the various cardiorespiratory pathologies generated by this type of pollution. In parallel, data on the automobile fleet of Bejaia and other measurements were exploited to show that the concentrations of the pollutants are strongly correlated with the concentration the urban traffic. This study revealed that the whole of the population is touched, but the sensitivity to pollution can show variations according to the age, the sex and the place of residence. So the under population of the town of Bejaia marked the most raised death and morbidity rates, followed that of Kherrata. Weak rates are recorded for under rural population of Feraoun. This approach enables us to conclude that the population of Béjaia could not escape the urban pollution generated by her old automobile fleet. To install a monitoring and measuring site of the air pollution in this city could provide a beneficial tool to protect its inhabitants by them informing on quality from the air that they breathe and measurements to follow to minimize the impacts on their health and by alerting the authorities during the critical situations.

Keywords: air, urban pollution, health, impacts

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Authors: Abdelkader Mendas

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The integration of MultiCriteria Decision Making (MCDM) approaches in a Geographical Information System (GIS) provides a powerful spatial decision support system which offers the opportunity to efficiently produce the land suitability maps for agriculture. Indeed, GIS is a powerful tool for analyzing spatial data and establishing a process for decision support. Because of their spatial aggregation functions, MCDM methods can facilitate decision making in situations where several solutions are available, various criteria have to be taken into account and decision-makers are in conflict. The parameters and the classification system used in this work are inspired from the FAO (Food and Agriculture Organization) approach dedicated to a sustainable agriculture. A spatial decision support system has been developed for establishing the land suitability map for agriculture. It incorporates the multicriteria analysis method ELECTRE Tri (ELimitation Et Choix Traduisant la REalité) in a GIS within the GIS program package environment. The main purpose of this research is to propose a conceptual and methodological framework for the combination of GIS and multicriteria methods in a single coherent system that takes into account the whole process from the acquisition of spatially referenced data to decision-making. In this context, a spatial decision support system for developing land suitability maps for agriculture has been developed. The algorithm of ELECTRE Tri is incorporated into a GIS environment and added to the other analysis functions of GIS. This approach has been tested on an area in Algeria. A land suitability map for durum wheat has been produced. Through the obtained results, it appears that ELECTRE Tri method, integrated into a GIS, is better suited to the problem of land suitability for agriculture. The coherence of the obtained maps confirms the system effectiveness.

Keywords: multicriteria decision analysis, decision support system, geographical information system, land suitability for agriculture

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Authors: Yulia V. Krasavtseva, Tatiana V. Kornilova

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Risk-taking behaviors are not only dictated by cognitive components but also involve emotional aspects. Anticipatory emotions, involving both cognitive and affective mechanisms, are involved in decision-making in general, and risk-taking in particular. Affective reactions are prompted when an expectation or prediction is either validated or invalidated in the achieved result. This study aimed to combine predictions, anticipatory emotions, affective reactions, and personality traits in the context of risk-taking behaviors. An experimental online method Emotion and Prediction In a Casino (EPIC) was used, based on a casino-like roulette game. In a series of choices, the participant is presented with progressively riskier roulette combinations, where the potential sums of wins and losses increase with each choice and the participant is given a choice: to 'walk away' with the current sum of money or to 'play' the displayed roulette, thus accepting the implicit risk. Before and after the result is displayed, participants also rate their emotions, using the Self-Assessment Mannequin [Bradley, Lang, 1994], picking a picture, representing the intensity of pleasure, arousal, and dominance. The following personality measures were used: 1) Personal Decision-Making Factors [Kornilova, 2003] assessing risk and rationality; 2) I7 – Impulsivity Questionnaire [Kornilova, 1995] assessing impulsiveness, risk readiness, and empathy and 3) Subjective Risk Intelligence Scale [Craparo et al., 2018] assessing negative attitude toward uncertainty, emotional stress vulnerability, imaginative capability, and problem-solving self-efficacy. Two groups of participants took part in the study: 1) 98 university students (Mage=19.71, SD=3.25; 72% female) and 2) 94 online participants (Mage=28.25, SD=8.25; 89% female). Online participants were recruited via social media. Students with high rationality rated their pleasure and dominance before and after choices as lower (ρ from -2.6 to -2.7, p < 0.05). Those with high levels of impulsivity rated their arousal lower before finding out their result (ρ from 2.5 - 3.7, p < 0.05), while also rating their dominance as low (ρ from -3 to -3.7, p < 0.05). Students prone to risk-rated their pleasure and arousal before and after higher (ρ from 2.5 - 3.6, p < 0.05). High empathy was positively correlated with arousal after learning the result. High emotional stress vulnerability positively correlates with arousal and pleasure after the choice (ρ from 3.9 - 5.7, p < 0.05). Negative attitude to uncertainty is correlated with high anticipatory and reactive arousal (ρ from 2.7 - 5.7, p < 0.05). High imaginative capability correlates negatively with anticipatory and reactive dominance (ρ from - 3.4 to - 4.3, p < 0.05). Pleasure (.492), arousal (.590), and dominance (.551) before and after the result were positively correlated. Higher predictions positively correlated with reactive pleasure and arousal. In a riskier scenario (6/8 chances to win), anticipatory arousal was negatively correlated with the pleasure emotion (-.326) and vice versa (-.265). Correlations occur regardless of the roulette outcome. In conclusion, risk-taking behaviors are linked not only to personality traits but also to anticipatory emotions and affect in a modeled casino setting. Acknowledgment: The study was supported by the Russian Foundation for Basic Research, project 19-29-07069.

Keywords: anticipatory emotions, casino game, risk taking, impulsiveness

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