Search results for: real-time data acquisition and reporting

Authors: M. Strémy, P. Závacký, P. Cuninka, M. Juhás

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Main goal of this paper is to introduce our design of private cloud for storing large amount of data, especially pictures, and to provide good technological backend for data analysis based on parallel processing and business intelligence. We have tested hypervisors, cloud management tools, storage for storing all data and Hadoop to provide data analysis on unstructured data. Providing high availability, virtual network management, logical separation of projects and also rapid deployment of physical servers to our environment was also needed.

Keywords: cloud, glusterfs, hadoop, juju, kvm, maas, openstack, virtualization

Procedia PDF Downloads 352

Authors: Joseph Bentil

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Successful acquisition of 21st-century competencies needed by students to navigate through the ever-changing world requires that they are taught and molded by 21st-century teachers with the needed professional competencies. Accordingly, this study sought to understand the readiness and how efficacious pre-service Social Studies specialism students are towards the implementation of the Common Core Social Studies Curriculum in the Junior High Schools in Ghana. Theory of Experience served as the theoretical lens for the study. Working within the pragmatist paradigm, this study utilized the cross-sectional descriptive survey design with a mixed method approach where, through census sampling technique, all the 120 pre-service Social Studies specialism students were sampled for the study. A structured questionnaire and an interview guide were the instruments employed for data collection. Descriptive statistics (mean, standard deviation and inferential statistics like independent samples t-test, one-way between groups ANOVA and Pearson Product Moment Correlation) were employed in the analysis the research questions and hypotheses with the aid of version 28 of SPSS while the qualitative data was analyzed using thematic analysis. The findings discovered that pre-service Social Studies teachers were highly ready and efficacious towards implementing the Common Core Junior High School Social Studies curriculum. However, male pre-service teachers were highly efficacious and ready than their female counterparts. Besides, it was disclosed that pre-service teachers within the 31-40 years age bracket were found to be highly efficacious and ready than their colleagues with 20-30 and below 20 years age bracket respectively. The findings further revealed that there was a moderate and statistically significant positive relationship between pre-service teachers’ readiness and efficacy in implementing the Common Core Social Studies curriculum. Therefore, the study recommended that interventional programmes aimed at raising the readiness and efficacy beliefs of pre-service teachers should be targeted towards female preservice teachers and those below 20 years age bracket for successful implementation and realization of the competencies enshrined in the common core social Studies curriculum.

Keywords: pre-service, readiness, social studies, teachers

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Authors: Amitha Puranik, V. S. Binu, Seena Biju

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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

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Authors: Marjila Burhanzoi, Kenta Onohara, Tomoaki Ikegami

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Faults in photovoltaic (PV) modules should be detected to the greatest extent as early as possible. For that conventional fault detection methods such as electrical characterization, visual inspection, infrared (IR) imaging, ultraviolet fluorescence and electroluminescence (EL) imaging are used, but they either fail to detect the location or category of fault, or they require expensive equipment and are not convenient for onsite application. Hence, these methods are not convenient to use for monitoring small-scale PV systems. Therefore, low cost and efficient inspection techniques with the ability of onsite application are indispensable for PV modules. In this study in order to establish efficient inspection technique, correlation between faults and magnetic flux density on the surface is of crystalline PV modules are investigated. Magnetic flux on the surface of normal and faulted PV modules is measured under the short circuit and illuminated conditions using two different sensor devices. One device is made of small integrated sensors namely 9-axis motion tracking sensor with a 3-axis electronic compass embedded, an IR temperature sensor, an optical laser position sensor and a microcontroller. This device measures the X, Y and Z components of the magnetic flux density (Bx, By and Bz) few mm above the surface of a PV module and outputs the data as line graphs in LabVIEW program. The second device is made of a laser optical sensor and two magnetic line sensor modules consisting 16 pieces of magnetic sensors. This device scans the magnetic field on the surface of PV module and outputs the data as a 3D surface plot of the magnetic flux intensity in a LabVIEW program. A PC equipped with LabVIEW software is used for data acquisition and analysis for both devices. To show the effectiveness of this method, measured results are compared to those of a normal reference module and their EL images. Through the experiments it was confirmed that the magnetic field in the faulted areas have different profiles which can be clearly identified in the measured plots. Measurement results showed a perfect correlation with the EL images and using position sensors it identified the exact location of faults. This method was applied on different modules and various faults were detected using it. The proposed method owns the ability of on-site measurement and real-time diagnosis. Since simple sensors are used to make the device, it is low cost and convenient to be sued by small-scale or residential PV system owners.

Keywords: fault diagnosis, fault location, integrated sensors, PV modules

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Authors: Pui Hong Chung, Wing Chi Ho, Jun Li, Cyrus Leung, Ek Yeoh

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Background: Cigarette smoking is associated with poor tuberculosis (TB) outcomes in terms of progression of active TB, relapse of TB and TB-related mortality, but the association with latent tuberculosis infection (LTBI) is unclear. The systematic review conducted aimed at studying the association between active smoking and LTBI, and likelihood of dose-response relationship. Methods: Two independent reviewers searched three electronic databases comprising PudMed, Medline by EBSCOHOST, ExcerptaMedica Database (EMBASE), from inception up to 31st Dec 2015 for studies reporting data on current smoking and the LTBI with tuberculin skin test (TST) or interferon-γ release assays (IGRAs) results, comparing the odds ratios (ORs) of outcome measure of TST or IGRAs among current smokers with 95% confidence intervals (CI). Results: Seven studies were identified, including six cross-sectional studies and one longitudinal cohort study. The outcome measures from three studies were in TST, three studies in IGRAs and one for both tests. For TST, OR ranging from 1.39 to 3.40 (95% CI) with all studies shown positive association between cigarette smoking and LTBI. For IGRAs, OR ranging from 0.47 to 1.89 (95% CI) with one study shown the negative association that might be related to impaired interferon-gamma production in immunosuppressive persons. One identified study demonstrated positive dose-response relationship in TST result. Conclusions: Cigarette smoking is likely to be a risk factor of LTBI. There is the important implication for TB and tobacco control program to halt TB by empowering public health policy. Further study is also needed to provide more evidence of the dose-response model/relationship.

Keywords: latent tuberculosis infection, systematic review, active smoking, model

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Authors: Chelali Mohammed

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In the context of the application of the competency-based approach in the system educational Algeria, the price of physical education and sport must privilege the acquisition of learning approaches and especially the approach science, which from problem situations, research and develops him information processing and application of knowledge and know-how in new situations in the words of ‘JOHN DEWEY’ ‘learning by practice’. And to achieve these goals and make teaching more EPS motivating, consistent and concrete, it is appropriate to perform a pedagogical approach freed from the constraints and open to creativity and student-centered in the light of the competency approach adopted in the formal curriculum. This approach is not unusual, but we think it is a highly professional nature requires the competence of the teacher.

Keywords: approach competencies, physical, education, teachers

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Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

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Authors: Du T. Tran, Quyen T. L. Hoang

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The role of language learning strategies in second language acquisition has received increased attention across several disciplines in recent years. Language learning strategies have been shown to occur in many studies over the passing years with the aim of improving the efficiency of language learning. Following previous studies, this study endeavors to scrutinize language learning strategies employed by the students at Vo Minh Duc high school and the effect of motivation on students’ learning strategy choices. The responses are examined quantitatively and qualitatively to enhance their validity and reliability. Data are collected from 342 students’ responses to the questionnaire, interviews with ten teachers and fifteen students, and classroom observations. The findings reveal that students’ motivation has an enormous impact on the choice of language learning strategies. The results simultaneously show that students use many language learning strategies to enhance their communicative competence, but the most frequently used ones are cognitive and affective ones. Significant correlations among types of learning strategies and the influence of motivation on the choices of language learning strategies were consistent with previous studies. The study’s results are expected to be beneficial to teachers of English and students in terms of narrowing the gap between the students' language learning strategies and their teaching methodologies preferences and sketching out the best strategies to enhance students’ speaking skills. The implications of these findings and the importance of viewing learners holistically are discussed, and recommendations are made for ongoing research.

Keywords: learning strategies, speaking skills, memorization strategies, cognitive strategies, affective strategies

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Maria Paula Santos, Ana Lucas

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Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Keywords: critical success factors, data quality, data quality management, Delphi, Q-Sort

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Authors: Erietta-Christina Arnaoutaki, Stelios-Elion Bousi, Marinos Zachiotis, Simoni Zarkada, Alexandra Chrysagi, Mamdouh Fahad Alenazi, Dimitri Aristotle Raptis

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Objective: This review aimed to evaluate the mental health status of patients with esophageal cancer following surgical treatment, as well as the role of psychological distress screening tests in this patient population. Methods: Studies reporting psychometric screening tools used in esophageal cancer patients after esophagectomy, published before January 2024 on PubMed, Scopus, and CENTRAL databases, were searched and analyzed. Results: Six non-randomized control trials were selected for inclusion in this scoping review, which involved 1059 patients undergoing esophagectomy for esophageal cancer. Among the included studies, five employed the Hospital Anxiety and Depression Scale (HADS) for anxiety and/or depression screening, while one used the MD Anderson Symptom Inventory for gastrointestinal cancer (MDASI-GI) for sadness screening. A range of time points was used to evaluate these patients: 102 patients were evaluated at 1 month, 230 patients at 3 months, 218 patients at 6 months, 653 patients at 12 months, and 154 patients at 24 months postoperatively. Analysis of data pooled from three studies employing the HADS revealed a prevalence of 19.45% for anxiety and 17.92% for depression at the 12-month follow-up and mean scores of 3.91 (3.12) and 3.56 (3.12) for the HADS anxiety (HADS-A) and depression (HADS-D) subscales respectively, at any time postoperatively. Conclusion: The findings show a neglected concern regarding the mental health of esophageal cancer survivors following surgical treatment. The use of psychometric screening tools is essential to address psychological distress and improve the quality of life of these patients.

Keywords: esophageal cancer, esophagectomy, psychological distress, anxiety, depression, psychometric tests, HADS, MDASI-GI

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Authors: Sukriti Sabharwal, Sonali Bhattar, Shikhar Saxena

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Introduction: COVID-19 molecular diagnostic laboratory is the cornerstone of the COVID-19 disease diagnosis as the patient’s treatment and management protocol depend on the molecular results. For this purpose, it is extremely important that the laboratory conducting these results adheres to the quality management processes to increase the accuracy and validity of the reports generated. We started our own molecular diagnostic setup at the onset of the pandemic. Therefore, we conducted this study to generate our quality management data to help us in improving on our weak points. Materials and Methods: A total of 14561 samples were evaluated by the retrospective observational method. The quality variables analysed were classified into pre-analytical, analytical, and post-analytical variables, and the results were presented in percentages. Results: Among the pre-analytical variables, sample leaking was the most common cause of the rejection of samples (134/14561, 0.92%), followed by non-generation of SRF ID (76/14561, 0.52%) and non-compliance to triple packaging (44/14561, 0.3%). The other pre-analytical aspects assessed were incomplete patient identification (17/14561, 0.11%), insufficient quantity of samples (12/14561, 0.08%), missing forms/samples (7/14561, 0.04%), samples in the wrong vials/empty VTM tubes (5/14561, 0.03%) and LIMS entry not done (2/14561, 0.01%). We are unable to obtain internal quality control in 0.37% of samples (55/14561). We also experienced two incidences of cross-contamination among the samples resulting in false-positive results. Among the post-analytical factors, a total of 0.07% of samples (11/14561) could not be dispatched within the stipulated time frame. Conclusion: Adherence to quality control processes is foremost for the smooth running of any diagnostic laboratory, especially the ones involved in critical reporting. Not only do the indicators help in keeping in check the laboratory parameters but they also allow comparison with other laboratories.

Keywords: laboratory quality management, COVID-19, molecular diagnostics, healthcare

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Authors: Ryan B. Simpson, Margaret A. Waskow, Aishwarya Venkat, Elena N. Naumova

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The Centers for Disease Control and Prevention (CDC) estimate that 31 known foodborne pathogens cause 9.4 million cases of these illnesses annually in US. Over 90% of these illnesses are associated with exposure to Campylobacter, Cryptosporidium, Cyclospora, Listeria, Salmonella, Shigella, Shiga-Toxin Producing E.Coli (STEC), Vibrio, and Yersinia. Contaminated products contain parasites typically causing an intestinal illness manifested by diarrhea, stomach cramping, nausea, weight loss, fatigue and may result in deaths in fragile populations. Since 1998, the National Outbreak Reporting System (NORS) has allowed for routine collection of suspected and laboratory-confirmed cases of food poisoning. While retrospective analyses have revealed common pathogen-specific seasonal patterns, little is known concerning the stability of those patterns over time and whether they can be used for preventative forecasting. The objective of this study is to construct a calendar of foodborne outbreaks of nine infections based on the peak timing of outbreak incidence in the US from 1996 to 2017. Reported cases were abstracted from FoodNet for Salmonella (135115), Campylobacter (121099), Shigella (48520), Cryptosporidium (21701), STEC (18022), Yersinia (3602), Vibrio (3000), Listeria (2543), and Cyclospora (758). Monthly counts were compiled for each agent, seasonal peak timing and peak intensity were estimated, and the stability of seasonal peaks and synchronization of infections was examined. Negative Binomial harmonic regression models with the delta-method were applied to derive confidence intervals for the peak timing for each year and overall study period estimates. Preliminary results indicate that five infections continue to lead as major causes of outbreaks, exhibiting steady upward trends with annual increases in cases ranging from 2.71% (95%CI: [2.38, 3.05]) in Campylobacter, 4.78% (95%CI: [4.14, 5.41]) in Salmonella, 7.09% (95%CI: [6.38, 7.82]) in E.Coli, 7.71% (95%CI: [6.94, 8.49]) in Cryptosporidium, and 8.67% (95%CI: [7.55, 9.80]) in Vibrio. Strong synchronization of summer outbreaks were observed, caused by Campylobacter, Vibrio, E.Coli and Salmonella, peaking at 7.57 ± 0.33, 7.84 ± 0.47, 7.85 ± 0.37, and 7.82 ± 0.14 calendar months, respectively, with the serial cross-correlation ranging 0.81-0.88 (p < 0.001). Over 21 years, Listeria and Cryptosporidium peaks (8.43 ± 0.77 and 8.52 ± 0.45 months, respectively) have a tendency to arrive 1-2 weeks earlier, while Vibrio peaks (7.8 ± 0.47) delay by 2-3 weeks. These findings will be incorporated in the forecast models to predict common paths of the spread, long-term trends, and the synchronization of outbreaks across etiological agents. The predictive modeling of foodborne outbreaks should consider long-term changes in seasonal timing, spatiotemporal trends, and sources of contamination.

Keywords: foodborne outbreak, national outbreak reporting system, predictive modeling, seasonality

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Authors: Lilian Noronha Nassif

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Cybercrime investigation demands an appropriated evidence collection mechanism. If the investigator does not acquire digital proofs in a forensic sound, some important information can be lost, and judges can discard case evidence because the acquisition was inadequate. The correct digital forensic seizing involves preparation of professionals from fields of law, police, and computer science. This paper presents important challenges faced during evidence collection in different perspectives of places. The crime scene can be virtual or real, and technical obstacles and privacy concerns must be considered. All pointed challenges here highlight the precautions to be taken in the digital evidence collection and the suggested procedures contribute to the best practices in the digital forensics field.

Keywords: digital evidence, digital forensics process and procedures, mobile forensics, cloud forensics

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Authors: Pornpong Porpraphant

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This paper aimed to demonstrate how work performance quality development activity carried out for employees in an organization could lead to the organizational success and competency as a whole. The case studies selected for this research were the Thai huge corporate including Siam Cement Group or SCG, PTT Public Company Limited, and Electricity Generating Authority of Thailand or EGAT. The in- depth interview was applied with the three main groups that included the facilitator group, the managerial group, and the operational officer group. The Plan- Do- Check- Act approach was also utilized as to build up a conceptual model in corporate management that fostered employees’ knowledge acquisition, resulting in an improved work performance.

Keywords: high performance organization, quality, work performance quality development

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Authors: Avigail Moor

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Rape by an acquaintance is a prevalent type of sexual assault that is often misperceived and downplayed. To date, there has been no empirical investigation of the prevailing social attitudes towards this type of rape as compared to stranger rape. The present study seeks to address this issue by evaluating widely held attitudes towards these different types of rape. The mediating role of gender and rape myths acceptance is assessed as well. Three hundred and twenty participants, equally divided by gender, completed self-report questionnaires. The results indicate that sexual coercion by strangers is perceived as rape to a significantly greater degree than forced sex by an acquaintance, which in turn is believed to be more harmful than coercion within a steady relationship, particularly by men who view rape in accordance with prevailing rape-supportive attitudes. The same pattern of differentiation emerged in the participants' attitudes toward the psychological harm expected following each, as well as the advisability of reporting the incidents to the police. Implications for preventive efforts are discussed.

Keywords: rape supportive attitudes, acquaintance rape, sexual assault, gender differences

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Authors: Vinay Kumar, M. Kishor, Sathyanarayana Rao Ts

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Background: Growth of elderly people in the general population in recent years is termed as ‘greying of the world’ where there is a shift from high mortality & fertility to low mortality and fertility, resulting in an increased proportion of older people as seen in India. Improved health care promises longevity but socio-economic factors like poverty, joint families and poor services pose a psychological threat. Epidemiological data regarding the prevalence of mental disorders in geriatric population with physical illness is required for proper health planning. Methods: Sixty consecutive elderly patients aged 60 years or above of both sexes, reporting with physical illness to general outpatient registration counter of JSS Medical College and Hospital, Mysore, India, were considered for the Study. With informed consent, they were screened with General Health Questionnaire (GHQ-12) and were further evaluated for diagnosing mental disorders according to WHO International Classification of Diseases (ICD-10) criteria. Results: Mental disorders were detected in 48.3%, predominantly depressive disorders, nicotine dependence, generalized anxiety disorder, alcohol dependence and least was dementia. Most common physical illness was cardiovascular disease followed by metabolic, respiratory and other diseases. Depressive disorders, substance dependence and dementia were more associated with cardiovascular disease compared to metabolic disease and respiratory diseases were more associated with nicotine dependence. Conclusions: Depression and Substance use disorders among elderly population is of concern, which needs to be further studied with larger population. Psychiatric morbidity will adversely have an impact on physical illness which needs proper assessment and management. This will enhance our understanding and prioritize our planning for future.

Keywords: Geriatric, mental disorders, physical illness, psychiatry

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: Ioannis Souris

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In this paper, we would like to present how we taught German through playwriting and analyze the usefulness of this method for teaching languages and cultivating a sense of political agency in students and teachers alike. Last academic year, we worked at the German Saturday School in Greenwich, London. This school offers Saturday German lessons to children whose parents are German, living in London. The lessons are two hours long, and the children’s level of German varies according to how often or how much German is spoken at home or how often the families visit Germany (as well as other factors which will be discussed in more detail in the paper). The directors of the school provide teachers with learning material and course books, but they strongly encourage individual input on lesson structure and methods of teaching German. The class we taught consisted of six eight-to-nine-year-olds. Midway into the academic year, we ran out of teaching material, and we, therefore, decided to write a play. In the paper, we would like to explore the process we followed in creating or writing this play and how this encouraged the children to collaborate and exercise their skills in writing, storytelling, speaking, and opinion-sharing. We want to examine the impact this project had on the children who wrote and performed the play, the wider community of the Saturday school, and the development of our language teaching practice. We found, for instance, that some students, who were quiet or shy, became very open and outspoken in the process of writing and performing the play. They took the initiative and led the process, putting us, their teachers, in the role of simple observers or facilitators. When we showed the play in front of the school, the other children and teachers, as audience members, also became part of the process as they commented on the plot, language, and characters and gave feedback on further development. In the paper, we will discuss how this teaching project fits into recent developments in the research of creativity and the teaching of languages and how engagement with creative approaches to teaching has the potential to question and subvert traditional notions of ‘lesson’, ‘teacher’, and ‘student’. From the moment a questioning of norms takes place, we inadvertently raise questions about politics, agency, and resistance. We will conclude the paper with a definition of what we mean by ‘political agency’ within the context of our teaching project and education, in general, and why inspiring creativity and imagination within teaching can be considered a political act. Finally, our aim in this paper will be to propose the possibility of analyzing teaching languages through creativity and political agency theories.

Keywords: innovation in language teaching and learning, language acquisition and learning, language curriculum development, language education

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Authors: Usama Ahmed

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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

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Authors: Deepak Jugran, Rajesh Gill

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Purpose: To study the demographic, sociological, gender and clinical profile of the children presented for childhood cataract at a public sector tertiary eye care centre in India. Methodology: The design of the study is retrospective, and hospital-based data is available with the Central Registration Department of the PGIMER, Chandigarh. The majority of the childhood cataract cases are being reported in this hospital, yet not each and every case of childhood cataract approaches PGI, Chandigarh. Nevertheless, this study is going to be pioneering research in India, covering five-year data of the childhood cataract patients who visited the Advanced Eye Centre, PGIMER, Chandigarh, from 1.1.2015 to 31.12.2019. The SPSS version 23 was used for all statistical calculations. Results: A Total of 354 children were presented for childhood cataract from 1.1.2015 to 31.12.2019. Out of 354 children, 248 (70%) were male, and 106 (30%) were female. In-spite of 2 flagship programmes, namely the National Programme for Control of Blindness (NPCB) and Aayushman Bharat (PM JAY) for eradication of cataract, no children received any financial assistance from these two programmes. A whopping 99% of these children belong to the poor families. In most of these families, the mothers were house-wives and did not employ anywhere. These interim results will soon be conveyed to the Govt. of India so that a suitable mechanism can be evolved to address this pertinent issue. Further, the disproportionate ratio of male and female children in this study is an area of concern as we don’t know whether the prevalence of childhood cataract is lower in female children or they are not being presented on time in the hospital by the families. Conclusion: The World Health Organization (WHO) has categorized Childhood blindness resulting from cataract as a priority area and urged all member countries to develop institutionalized mechanisms for its early detection, diagnosis and management. The childhood cataract is an emerging and major cause of preventable and avoidable childhood blindness, especially in low and middle-income countries. In the formative years, the children require a sound physical, mental and emotional state, and in the absence of either one of them, it can severely dent their future growth. The recent estimate suggests that India could suffer an economic loss of US$12 billion (Rs. 88,000 Crores) due to blindness, and almost 35% of cases of blindness are preventable and avoidable if detected at an early age. Besides reporting these results to the policy makers, synchronized efforts are needed for early detection and management of avoidable causes of childhood blindness such as childhood cataract.

Keywords: childhood blindness, cataract, Who, Npcb

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Authors: Nhac Thanh Huong

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New teaching methods have led to the changes in the teachers’ roles in an English class, in which teachers’ error correction is an integral part. Language error and corrective feedback have been the interest of many researchers in foreign language teaching. However, the techniques and the effectiveness of teachers’ feedback have been a question of much controversy. This present case study has been carried out with a view to finding out the patterns of teachers’ corrective feedback and their impact on students’ uptake in English speaking lessons of legal English major students at Hanoi Law University. In order to achieve those aims, the study makes use of classroom observations as the main method of data collection to seeks answers to the two following questions: 1. What patterns of corrective feedback occur in English speaking lessons for second- year legal English major students in Hanoi Law University?; 2. To what extent does that corrective feedback lead to students’ uptake? The study provided some important findings, among which was a close relationship between corrective feedback and uptake. In particular, recast was the most commonly used feedback type, yet it was the least effective in terms of students’ uptake and repair, while the most successful feedback, namely meta-linguistic feedback, clarification requests and elicitation, which led to students’ generated repair, was used at a much lower rate by teachers. Furthermore, it revealed that different types of errors needed different types of feedback. Also, the use of feedback depended on the students’ English proficiency level. In the light of findings, a number of pedagogical implications have been drawn in the hope of enhancing the effectiveness of teachers’ corrective feedback to students’ uptake in foreign language acquisition process.

Keywords: corrective feedback, error, uptake, speaking English lesson

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Authors: Kuldeep Rawat, Michael O'Shea, Maureen McGough

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The paper will discuss a National Institute of Justice (NIJ) funded project to provide cost-effective aviation technologies and research to support counter-poaching operations related to endangered, protected, and/or regulated wildlife. The goal of this project is to provide cost-effective aviation technology and research support to Kenya Wildlife Service (KWS) in their counter-poaching efforts. In pursuit of this goal, Elizabeth City State University (ECSU) is assisting the National Institute of Justice (NIJ) in enhancing the Kenya Wildlife Service’s aviation technology and related capacity to meet its counter-poaching mission. Poaching, at its core, is systemic as poachers go to the most extreme lengths to kill high target species such as elephant and rhino. These high target wildlife species live in underdeveloped or impoverished nations, where poachers find fewer barriers to their operations. In Kenya, with fifty-nine (59) parks and reserves, spread over an area of 225,830 square miles (584,897 square kilometers) adequate surveillance on the ground is next to impossible. Cost-effective aviation surveillance technologies, based on a comprehensive needs assessment and operational evaluation, are needed to curb poaching and effectively prevent wildlife trafficking. As one of the premier law enforcement Air Wings in East Africa, KWS plays a crucial role in Kenya, not only in counter-poaching and wildlife conservation efforts, but in aerial surveillance, counterterrorism and national security efforts as well. While the Air Wing has done, a remarkable job conducting aerial patrols with limited resources, additional aircraft and upgraded technology should significantly advance the Air Wing’s ability to achieve its wildlife protection mission. The project includes: (i) Needs Assessment of the KWS Air Wing, to include the identification of resources, current and prospective capacity, operational challenges and priority goals for expansion, (ii) Acquisition of Low-Cost Aviation Technology to meet priority needs, and (iii) Operational Evaluation of technology performance, with a focus on implementation and effectiveness. The Needs Assessment reflects the priorities identified through two site visits to the KWS Air Wing in Nairobi, Kenya, as well as field visits to multiple national parks receiving aerial support and interviewing/surveying KWS Air wing pilots and leadership. Needs Assessment identified some immediate technology needs that includes, GPS with upgrades, including weather application, Night flying capabilities, to include runway lights and night vision technology, Cameras and surveillance equipment, Flight tracking system and/or Emergency Position Indicating Radio Beacon, Lightweight ballistic-resistant body armor, and medical equipment, to include a customized stretcher and standard medical evacuation equipment. Results of this assessment, along with significant input from the KWS Air Wing, will guide the second phase of this project: technology acquisition. Acquired technology will then be evaluated in the field, with a focus on implementation and effectiveness. Results will ultimately be translated for any rural or tribal law enforcement agencies with comparable aerial surveillance missions and operational environments, and jurisdictional challenges, seeking to implement low-cost aviation technology. Results from Needs Assessment phase, including survey results and our ongoing technology acquisition and baseline operational evaluation will be discussed in the paper.

Keywords: aerial surveillance mission, aviation technology, counter-poaching, wildlife protection

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Authors: Diego Saldo Alves, Marcelo Paveck Ayub

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The quality of accounting information is very important for the decision-making of managers, investors government and other information users. The opinion of the independent audit has a significant influence on the decision-making, especially the investors. Therefore, the aim of this study is to analyze the reasons that companies listed on Brazilian Stock Exchange B3, if they received qualified opinion and disclaimer of opinion of the independent auditors. We analyzed the reports of the independent auditors of 23 Brazilian companies listed in B3 that received qualified opinion and disclaimer of opinion between the years 2012 and 2017. The findings show that the companies do not comply the International Financial Reporting Standard, IFRS, also they did not provide documentation to prove the operations performed, did not account expenses, problems in corporate governance and internal controls.

Keywords: audit, disclaimer of opinion, independent auditors, qualified opinion

Procedia PDF Downloads 193

Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

Procedia PDF Downloads 82

Authors: Adrian Florin Busu

Abstract:

The communicative approach, or communicative language teaching, used for enhancing communicative skills in students in automatics is a modern teaching approach based on the concept of learning a language through having to communicate real meaning. In the communicative approach, real communication is both the objective of learning and the means through which it takes place. This approach was initiated during the 1970’s and quickly became prominent, as it proposed an alternative to the previous systems-oriented approaches. In other words, instead of focusing on the acquisition of grammar and vocabulary, the communicative approach aims at developing students’ competence to communicate in the target language with an enhanced focus on real-life situations. To put it in an nutshell, CLT considers using the language to be just as important as actually learning the language.

Keywords: communication, approach, objective, learning

Procedia PDF Downloads 160

Authors: Firdaousse Laincer, Rahima Laribi, Abderazak Tamendjari, Rovellini Venturini

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Phenolic compounds present in olive oil have received much attention in recent years due to their beneficial functional and nutritional effects. Phenolic composition, antibacterial activity of phenolic extracts of olive oil varieties from Algeria were investigated. The analysis of polyphenols was performed by Folin-Ciocalteu and HPLC. As a result, many phenolic compounds were identified and quantified by using HPLC; derivatives of oleuropein and ligstroside, hydroxytyrosol, tyrosol, flavonoids, and lignans reporting unique and characteristic phenolic profile. These phenolic fractions also differentiate the total antibacterial activity. Among the bacteria tested, S. aureus and, to a lesser extent, B. subtilis showed the highest sensitivity; the MIC varied from 0.6 to 1.6 mg•mL-1 and 1.2 to 1.8 mg•mL-1, respectively. The results obtained denote that Algerian olive oils may constitute a good source of healthy compounds, phenolics compounds, in the diet, suggesting that their consumption could be useful in the prevention of diseases.

Keywords: antibacterial activity, olive oil, phenols, HPLC

Procedia PDF Downloads 452

Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

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In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

Procedia PDF Downloads 103

Authors: Hani Albogami

Abstract:

The internal audit function is considered as one of the internal corporate governance mechanisms that may have an impact on improving earnings quality by constraining earnings management. The internal audit function is also a unique corporate governance mechanism because internal auditors have more involvement with the day-to-day operations comparing to the audit committee, and also internal auditors audit their companies the whole year compared to the external auditor who audits only a certain time of the year. The relationships between internal audit function and earnings management can be understood by some theories. Therefore, this paper provides a theoretical background of the influence of the quality of internal audit function on earnings management. In particular, the agency theory, institutional theory, singling theory, and resource dependency theory are adapted by this paper to provide some understanding and analyses that can be a basis for future research to contribute to the corporate governance academic studies.

Keywords: internal audit, corporate governance, earnings management, accounting

Procedia PDF Downloads 200

Authors: M. N. Yahaya, K. A. Salako, U. Z. Magawata

Abstract:

Vertical electrical sounding (VES) method was used to investigate the groundwater potential at the southern part of Gulumbe district, Kebbi State, north-western part of Nigeria. The study was carried out with the aim of determining the subsurface layer’s parameters (resistivity and thickness) and uses the same to characterize the groundwater potential of the study area. The Schlumberger configuration was used for data acquisition. A total number of thirty-three (33) sounding points (VES) were surveyed over six profiles. The software IPI2WIN was used to obtain n-layered geo-electric sections. The geo-electric section drawn from the results of the interpretation revealed that three subsurface layers could be delineated, which comprise of top soil, sand, sandstone, coarse sand, limestone, and gravelly sand. The results of the resistivity sounding were correlated with the lithological logs of nearby boreholes that expose cross-section geologic units around the study area. We found out that the area is dominated by three subsurface layers. The coarse sand layers constituted the aquifer zones in the majority of sounding stations. Thus, this present study concluded that the depth of any borehole in the study area should be located between the depth of 18.5 to 39 m. The study further classified the VES points penetrated based on their conductivity content as highly suitable, suitable, moderately suitably, and poor zones for groundwater exploration. Hence, from this research, we recommended that boreholes can be sited in high conductivity zones across VES 2, 11, 13, 16, 20, 21, 27, and 33, respectively.

Keywords: vertical electrical sounding, resistivity, geo-electric, resistivity, aquifer and groundwater

Procedia PDF Downloads 164