Search results for: negative data

Authors: Dimitris Balios, Stefanos Tantos

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Economic growth and social evolution are connected to trust relationships in a society. The quality of the accounting information, the tax information system and the tax audit mechanism evolve multiple benefits in an economy. Tax evasion, the illegal practice where people and companies do not pay taxes, is a crime because of the negative effect in economy and society. In this paper, we describe a theoretical framework on the characteristics of a fair and efficient tax auditing information system which could be a tool against tax evasion, a tool for an economy to grow, especially in countries that face fluctuations in economic activity. We conclude that a fair and efficient tax auditing information system increases the reliability of tax administration, improves taxpayers’ tax compliance and causes a developmental trajectory for the economy.

Keywords: auditing information system, auditing mechanism, tax evasion, taxation

Procedia PDF Downloads 148

Authors: Fares Hedayatit, Arnauld Joly, Panagiotis Papadimitriou

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Many machine learning tasks such as text annotation usually require training over very big datasets, e.g., millions of web documents, that can be represented in a sparse input space. State-of the-art tree-based ensemble algorithms cannot scale to such datasets, since they include operations whose running time is a function of the input space size rather than a function of the non-zero input elements. In this paper, we propose an efficient splitting algorithm to leverage input sparsity within decision tree methods. Our algorithm improves training time over sparse datasets by more than two orders of magnitude and it has been incorporated in the current version of scikit-learn.org, the most popular open source Python machine learning library.

Keywords: big data, sparsely representable data, tree-based models, scalable learning

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Authors: Mohammadali Mokhtari

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Developing countries increasingly recognize the need to reduce energy subsidies. Cash transfers are proposed instead of subsidies to mitigate the negative effects of energy subsidy reforms. However, there is little evidence about the redistribution effect of these subsidy-to-cash reforms. We study the redistribution impact of the largest subsidy-to-cash reform in the history of developing countries in Iran in 2010. We find a strong pro-poor impact of the reform using five inequality measures, including the Gini index and the ratio of rich to poor expenditures. Finally, we show this pro-poor impact is on average and discuss other possible mechanisms by which low-income groups objected to these pro-poor reforms in the next round, which took place in November 2019 and led to wide and large protests.

Keywords: energy economics, subsidy reform, inequality, Middle East, Iran

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Authors: Mohammad Aldubyan, Fateh Belaid, Anwar Gasim

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This paper aims at linking to link residential energy efficiency to the rebound effect concept, a well-known behavioral phenomenon in which service consumption increases when consumers notice a reduction in monetary spending on energy due to improvements in energy efficiency. It provides insights on into how and why the rebound effect happens when energy efficiency improves and whether this phenomenon is positive or negative. It also shows one technique to estimate the rebound effect on the national residential level. The paper starts with a bird’s eye view of the rebound effect and then dives in in-depth into measuring the rebound effect and evaluating its impact. Finally, the paper estimates the rebound effect in the Saudi residential sector through by linking pre-estimated price elasticities of demand to the Saudi residential building stock.

Keywords: energy efficiency, rebound effect, energy consumption, residential electricity demand

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Authors: Ezrah Kipkirui Tonui, George Otieno Orwa

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A survey of 138 farmers was conducted in Rift valley, Kenya, in November and December 2013 in three counties (Uasin-gishu, Elgeyo-marakwet, and Tranzoia) to determine public knowledge and attitude towards genetically modified (GM) maize. Above two third (70%) of the respondents had knowledge of GM maize, mostly those educated and male. Female was found to be having low knowledge on GM maize. Public acknowledged the technology’s potential positive impacts, with more than 90% willing to adopt and more than 98% willing to buy GM seedlings at any given price. A small percentage less than 3% were of a negative opinion about willing to buy and adopt GM seeds. We conclude that GM technology has a role to play in food security in Kenya. However, the public needs more information about the technology, which can be provided through established sources of information and training. Finally, public knowledge and attitude on GM maize should be studied on a regular basis, and the survey population broadened to 47 counties.

Keywords: public, knowledge, attitudes, GM maize, Kenya

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Authors: Isabel Mousinho de Figueiredo

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Pure economic loss can be brought into the 21st century and become a useful tool to keep the tort of negligence within reasonable limits, provided the concept is minutely reexamined. The term came about when wealth was physical, and Law wanted to be a modern science. As a tool to draw the line, it leads to satisfactory decisions in most cases, but needlessly creates distressing conundrums in others, and these are the ones parties bother to litigate about. Economic loss is deemed to be pure based on a blind negative criterion of physical harm, that inadvertently smelts vastly disparate problems into an indiscernible mass, with arbitrary outcomes. These shortcomings are usually dismissed as minor byproducts, for the lack of a better formula. Law could instead stick to the sound paradigms of the intended rule, and be more specific in identifying the losses deserving of compensation. This would provide a better service to Bench and Bar, and effectively assist everyone navigating the many challenges of Accident Law.

Keywords: accident law, comparative tort law, negligence, pure economic loss

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Authors: Juan F. Muñoz-Rosas, Rosa M. García-Fernández, Encarnación Álvarez-Verdejo, Pablo J. Moya-Fernández

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Poverty measurement is a very important topic in many studies in social sciences. One of the most important indicators when measuring poverty is the low income proportion. This indicator gives the proportion of people of a population classified as poor. This indicator is generally unknown, and for this reason, it is estimated by using survey data, which are obtained by official surveys carried out by many statistical agencies such as Eurostat. The main feature of the mentioned survey data is the fact that they contain several variables. The variable used to estimate the low income proportion is called as the variable of interest. The survey data may contain several additional variables, also named as the auxiliary variables, related to the variable of interest, and if this is the situation, they could be used to improve the estimation of the low income proportion. In this paper, we use Monte Carlo simulation studies to analyze numerically the performance of estimators based on several auxiliary variables. In this simulation study, we considered real data sets obtained from the 2011 European Union Survey on Income and Living Condition. Results derived from this study indicate that the estimators based on auxiliary variables are more accurate than the naive estimator.

Keywords: inclusion probability, poverty, poverty line, survey sampling

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Authors: Jonas Hamann, Siavash Saki, Tobias Hagen

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Discretization of urban areas is a crucial aspect in many spatial analyses. The process of discretization of space into subspaces without overlaps and gaps is called tessellation. It helps understanding spatial space and provides a framework for analyzing geospatial data. Tessellation methods can be divided into two groups: regular tessellations and irregular tessellations. While regular tessellation methods, like squares-grids or hexagons-grids, are suitable for addressing pure geometry problems, they cannot take the unique characteristics of different subareas into account. However, irregular tessellation methods allow the border between the subareas to be defined more realistically based on urban features like a road network or Points of Interest (POI). Even though Python is one of the most used programming languages when it comes to spatial analysis, there is currently no library that combines different tessellation methods to enable users and researchers to compare different techniques. To close this gap, we are proposing TessPy, an open-source Python package, which combines all above-mentioned tessellation methods and makes them easily accessible to everyone. The core functions of TessPy represent the five different tessellation methods: squares, hexagons, adaptive squares, Voronoi polygons, and city blocks. By using regular methods, users can set the resolution of the tessellation which defines the finesse of the discretization and the desired number of tiles. Irregular tessellation methods allow users to define which spatial data to consider (e.g., amenity, building, office) and how fine the tessellation should be. The spatial data used is open-source and provided by OpenStreetMap. This data can be easily extracted and used for further analyses. Besides the methodology of the different techniques, the state-of-the-art, including examples and future work, will be discussed. All dependencies can be installed using conda or pip; however, the former is more recommended.

Keywords: geospatial data science, geospatial data analysis, tessellations, urban studies

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Authors: Dmitry V. Fomichev, Vladimir V. Solonin

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This paper presents the findings from a numerical simulation of the flow in 37-rod fuel assembly models spaced by a double-wire trapezoidal wrapping as applied to the BREST-OD-300 experimental nuclear reactor. Data on a high static pressure distribution within the models, and equations for determining the fuel bundle flow friction factors have been obtained. Recommendations are provided on using the closing turbulence models available in the ANSYS Fluent. A comparative analysis has been performed against the existing empirical equations for determining the flow friction factors. The calculated and experimental data fit has been shown. An analysis into the experimental data and results of the numerical simulation of the BREST-OD-300 fuel rod assembly hydrodynamic performance are presented.

Keywords: BREST-OD-300, ware-spaces, fuel assembly, computation fluid dynamics

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Authors: Abdel-Aziz M. Mohamed, Nermeen Coutry

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Lead time is an important measure of supply chain performance. It impacts both customer satisfactions as well as the total cost of inventory. This paper presents the result of a study on the analysis of the customer order lead-time for a multinational company. In the study, the lead time was divided into three stages: order entry, order fulfillment, and order delivery. A sample of size 2,425 order lines from the company records were considered for this study. The sample data includes information regarding customer orders from the time of order entry until order delivery. Data regarding the lead time of each sage for different orders were also provided. Summary statistics on lead time data reveals that about 30% of the orders were delivered after the scheduled due date. The result of the multiple linear regression analysis technique revealed that component type, logistics parameter, order size and the customer type have significant impact on lead time. Data analysis on the stages of lead time indicates that stage 2 consumes over 50% of the lead time. Pareto analysis was made to study the reasons for the customer order delay in each of the 3 stages. Recommendation was given to resolve the problem.

Keywords: lead time reduction, customer satisfaction, service quality, statistical analysis

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Authors: Ali Alshumrani, Nathan Clarke, Bogdan Ghite, Stavros Shiaeles

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Digital forensics has become an essential tool in the investigation of cyber and computer-assisted crime. Arguably, given the prevalence of technology and the subsequent digital footprints that exist, it could have a significant role across almost all crimes. However, the variety of technology platforms (such as computers, mobiles, Closed-Circuit Television (CCTV), Internet of Things (IoT), databases, drones, cloud computing services), heterogeneity and volume of data, forensic tool capability, and the investigative cost make investigations both technically challenging and prohibitively expensive. Forensic tools also tend to be siloed into specific technologies, e.g., File System Forensic Analysis Tools (FS-FAT) and Network Forensic Analysis Tools (N-FAT), and a good deal of data sources has little to no specialist forensic tools. Increasingly it also becomes essential to compare and correlate evidence across data sources and to do so in an efficient and effective manner enabling an investigator to answer high-level questions of the data in a timely manner without having to trawl through data and perform the correlation manually. This paper proposes a Unified Forensic Analysis Tool (U-FAT), which aims to establish a common language for electronic information and permit multi-source forensic analysis. Core to this approach is the identification and development of forensic analyses that automate complex data correlations, enabling investigators to investigate cases more efficiently. The paper presents a systematic analysis of major crime categories and identifies what forensic analyses could be used. For example, in a child abduction, an investigation team might have evidence from a range of sources including computing devices (mobile phone, PC), CCTV (potentially a large number), ISP records, and mobile network cell tower data, in addition to third party databases such as the National Sex Offender registry and tax records, with the desire to auto-correlate and across sources and visualize in a cognitively effective manner. U-FAT provides a holistic, flexible, and extensible approach to providing digital forensics in technology, application, and data-agnostic manner, providing powerful and automated forensic analysis.

Keywords: digital forensics, evidence correlation, heterogeneous data, forensics tool

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Authors: Mohit Kumar, Mayur Betharia

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Healthcare domain, with the emergence of Electronic Medical Record (EMR), collects a lot of data which have been attracting Data Mining expert’s interest. In the past, doctors have relied on their intuition while making critical clinical decisions. This paper presents the means to analyze the Medical workflows to get business insights out of huge dumped medical databases. Market Basket Analysis (MBA) which is a special data mining technique, has been widely used in marketing and e-commerce field to discover the association between products bought together by customers. It helps businesses in increasing their sales by analyzing the purchasing behavior of customers and pitching the right customer with the right product. This paper is an attempt to demonstrate Market Basket Analysis applications in healthcare. In particular, it discusses the Market Basket Analysis Algorithm ‘Apriori’ applications within healthcare in major areas such as analyzing the workflow of diagnostic procedures, Up-selling and Cross-selling of Healthcare Systems, designing healthcare systems more user-friendly. In the paper, we have demonstrated the MBA applications using Angiography Systems, but can be extrapolated to other modalities as well.

Keywords: data mining, market basket analysis, healthcare applications, knowledge discovery in healthcare databases, customer relationship management, healthcare systems

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Authors: Jonardan Koner, Basabi Bhattacharya, Avinash Purandare

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The study is focused to find out the impact of infrastructural investment on economic development in Indian states. The study uses panel data analysis to measure the impact of infrastructural investment on Real Gross Domestic Product in Indian States. Panel data analysis incorporates Unit Root Test, Cointegration Teat, Pooled Ordinary Least Squares, Fixed Effect Approach, Random Effect Approach, Hausman Test. The study analyzes panel data (annual in frequency) ranging from 1991 to 2012 and concludes that infrastructural investment has a desirable impact on economic development in Indian. Finally, the study reveals that the infrastructural investment significantly explains the variation of economic indicator.

Keywords: infrastructural investment, real GDP, unit root test, cointegration teat, pooled ordinary least squares, fixed effect approach, random effect approach, Hausman test

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Authors: Anna M. N. Shifotoka, Richard Walker, Katie Haighton, Richard McNally

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An analysis of trends in Case Notification Rates (CNR) can be used to monitor the impact of Tuberculosis (TB) control interventions over time in order to inform the implementation of current and future TB interventions. A retrospective analysis of trends in TB CNR for two urban regions in Namibia, namely Khomas and Erongo regions, was conducted. TB case notification data were obtained from annual TB reports of the national TB programme, Ministry of Health and Social Services, covering the period from 1997 to 2015. Joinpoint regression was used to analyse trends in CNR for different types of TB groups. A trend was considered to be statistically significant when a p-value was less than 0.05. During the period under review, the crude CNR for all forms of TB declined from 808 to 400 per 100 000 population in Khomas, and from 1051 to 611 per 100 000 population in Erongo. In both regions, significant change points in trends were observed for all types of TB groups examined. In Khomas region, the trend for new smear positive pulmonary TB increased significantly by an annual rate of 4.1% (95% Confidence Interval (CI): 0.3% to 8.2%) during the period 1997 to 2004, and thereafter declined significantly by -6.2% (95%CI: -7.7% to -4.3%) per year until 2015. Similarly, the trend for smear negative pulmonary TB increased significantly by 23.7% (95%CI: 9.7 to 39.5) per year from 1997 to 2004 and thereafter declined significantly by an annual change of -26.4% (95%CI: -33.1% to -19.8%). The trend for all forms of TB CNR in Khomas region increased significantly by 8.1% (95%CI: 3.7 to 12.7) per year from 1997 to 2004 and thereafter declined significantly a rate of -8.7% (95%CI: -10.6 to -6.8). In Erongo region, the trend for smear positive pulmonary TB increased at a rate of 1.2% (95%CI: -1.2% to 3.6%) annually during the earlier years (1997 to 2008), and thereafter declined significantly by -9.3% (95%CI: -13.3% to -5.0%) per year from 2008 to 2015. Also in Erongo, the trend for all forms of TB CNR increased significantly by an annual rate of 4.0% (95%CI: 1.4% to 6.6%) during the years between 1997 to 2006 and thereafter declined significantly by -10.4% (95%CI: -12.7% to -8.0%) per year during 2006 to 2015. The trend for extra-pulmonary TB CNR declined but did not reach statistical significance in both regions. In conclusion, CNRs declined for all types of TB examined in both regions. Further research is needed to study trends for other TB dimensions such as treatment outcomes and notification of drug resistant TB cases.

Keywords: epidemiology, Namibia, temporal trends, tuberculosis

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Authors: Migael van Zyl, Stefanie Visser, Awelani Phaswana

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The electricity landscape in South Africa is characterized by frequent occurrences of loadshedding, a measure implemented by Eskom to manage electricity generation shortages by curtailing demand. Loadshedding, classified into stages ranging from 1 to 8 based on severity, involves the systematic rotation of power cuts across municipalities according to predefined schedules. However, this practice introduces distortions in recorded electricity demand, posing challenges to accurate forecasting essential for budgeting, network planning, and generation scheduling. Addressing this challenge requires the development of a methodology to quantify the impact of loadshedding and integrate it back into metered electricity demand data. Fortunately, comprehensive records of loadshedding impacts are maintained in a database, enabling the alignment of Loadshedding effects with hourly demand data. This adjustment ensures that forecasts accurately reflect true demand patterns, independent of loadshedding's influence, thereby enhancing the reliability of electricity supply management in South Africa. This paper presents a methodology for determining the hourly impact of load scheduling and subsequently adjusting historical demand data to account for it. Furthermore, two forecasting models are developed: one utilizing the original dataset and the other using the adjusted data. A comparative analysis is conducted to evaluate forecast accuracy improvements resulting from the adjustment process. By implementing this methodology, stakeholders can make more informed decisions regarding electricity infrastructure investments, resource allocation, and operational planning, contributing to the overall stability and efficiency of South Africa's electricity supply system.

Keywords: electricity demand forecasting, load shedding, demand side management, data science

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Authors: Barbra Bhebhe, Melvyn Quan

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A serological survey was done to detect antibodies against Shuni virus (SHUV) from cattle in Western Kenya. In Kenya the disease status of SHUV in cattle has never been established. It is a zoonotic virus and even though studies have been carried out as early as the 1960s, little research has been published and SHUV is still not a well-recognised Orthobunyavirus. One hundred serum samples were collected from healthy cattle in Kenya and tested for antibodies against SHUV by a serum neutralization assay. All antibody titre values were greater than 1:160, with most of the samples greater than 1:320. Of the samples tested, 87 % had titres greater than 1:320, 12% had a titre of 1:320 and 2% had a titre of 1:160. Samples were classified as positive if the antibody titre was ≥ 1:10 and negative if < 1:10. This study suggests that cattle are exposed commonly to SHUV, which may be endemic in Kenya.

Keywords: Shuni virus, Orthobunyavuruses, serum neutralization test, cell-culture

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Authors: Raif Parlakkaya, Ahmet Diken, Erkan Kara

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This paper examines the relationship between corporate governance rating and stock prices of 26 Turkish firms listed in Turkish stock exchange (Borsa Istanbul) by using panel data analysis over five-year period. The paper also investigates the stock performance of firms with governance rating with regards to the market portfolio (i.e. BIST 100 Index) both prior and after governance scoring began. The empirical results show that there is no relation between corporate governance rating and stock prices when using panel data for annual variation in both rating score and stock prices. Further analysis indicates surprising results that while the selected firms outperform the market significantly prior to rating, the same performance does not continue afterwards.

Keywords: corporate governance, stock price, performance, panel data analysis

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Authors: Emni Omar Daw Hussin, Pathmawathi Subramanian, Wong Li Ping

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Background: To provide quality care at the end of life, nurses should possess knowledge and skills to provide effective end-of-life care, as well as develop the attitudes and interpersonal competence to provide compassionate care. Aim: This study aimed to assess nurses’ knowledge and attitude towards end of life care and caring for terminal ill patients and to examine relationships among demographic variables and nurse’s knowledge and attitudes toward end of life care and caring for terminal ill patients. Method: a cross-sectional study was conducted at 1 tertiary hospital located in Kuala Lumpur, Malaysia. Self-administrative questionnaire was used to collect data from 553 nurses from over all departments except emergency department, operation theater and outpatient clinic. Two tools were used in this study, the Frommelt’s Attitude Toward Care of the Dying (FATCOD) Scale to assess the nurses’ attitude and End of Life Knowledge Assessment to assess the nurses’ knowledge. Result: the result of this study yielded that, the majority of participants (54.8%) and (54.4%) have less positive attitude and knowledge towards end of life care and caring for terminal ill patients respectively. As well as there is no significant relationship were found between nurses’ ethnicity, religion, and the total score of FATCOD scale; End of Life Knowledge Assessment score. On other hand there is significant relationship among nurses’ age, working experience, level of education, attending any post basic courses and the total score of both FATCOD scale and End of Life Knowledge Assessment. Conclusion: A lack of education and experience and post basic course about end of life care and palliative care may contribute to the negative attitudes and poor knowledge regarding end of life care. Providing sufficient courses about end of life care could enhance the nurses’ knowledge towards end of life care, as well as providing a reflective narrative environment in which nurses can express their personal feelings about death and dying could be a potentially effective approach. Implication for Practice: This study elaborates the need for further research to develop an effective educational programs to enhance nurses’ knowledge and to promote positive attitude towards death and dying, as well as enhance communication skills, and coping strategies.

Keywords: knowledge, attitude, nurse, end of life care

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Authors: Kenneth Kapalu Muzata, Dikeledi Mahlo, Pinkie Mabunda Mabunda

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This paper presents results of a study conducted to establish special education teachers’ knowledge and application of curriculum adaptation of the 2013 revised curriculum in Zambia. From a sample of 134 respondents (120 special education teachers, 12 education officers, and 2 curriculum specialists), the study collected both quantitative and qualitative data to establish whether teachers understood and applied the concept of curriculum adaptation in teaching learners with special education needs. To obtain data validity and reliability, the researchers collected data by use of mixed methods. Semi-structured questionnaires and interviews were administered. Lesson Observations and post-lesson discussions were conducted on 12 selected teachers from the 120 sample that answered the questionnaires. Frequencies, percentages, and significant differences were derived through the statistical package for social sciences. Qualitative data were analyzed with the help of NVIVO qualitative software to create themes and obtain coding density to help with conclusions. Both quantitative and qualitative data were concurrently compared and related. The results revealed that special education teachers lacked a thorough understanding of the concept of curriculum adaptation, thus denying learners with special education needs the opportunity to benefit from the revised curriculum. The teachers were not oriented on the revised curriculum and hence facing numerous challenges trying to adapt the curriculum. The study recommended training of special education teachers in curriculum adaptation.

Keywords: curriculum adaptation, special education, learners with special education needs, special education teachers

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Authors: Thi Huong Vu, Vijay Jayasena, Zhongxiang Fang, Gary Dykes

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D-pinitol (3-O-methyl ether of D-chiro-inosito) has been known to have health benefits for diabetic patients. Carob kibble has received attention due to the presence of high value D-pinitol and polyphenol antioxidants. D-pinitol was concentrated from carob kibble using submerged fermentation with Saccharomyces cerevisiae. Total carbohydrates and D-pinitol were determined by the phenol-sulphuric acid method and HPLC, respectively. The content of D-pinitol increased from approximately 43 to 70 mg/g dry weight after fermentation. The yeast consumed over 70% of total carbohydrates in carob kibble without any negative effect on D-pinitol content. A range of substrate medium pH’s from 5.0 – 7.0 had no significant effect on the removal of carbohydrates and D-pinitol. This method may provide a practical solution for production of D-pinitol from carob in a cost effective manner.

Keywords: carob kibble, d-pinitol, saccharomyces cerevisiae, submerged fermentation, total carbohydrates

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Authors: Marwa A. A. Ragab, Hadir M. Maher, Eman I. El-Kimary

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Co-administration of methotrexate (MTX) and aspirin (ASP) can cause a pharmacokinetic interaction and a subsequent increase in blood MTX concentrations which may increase the risk of MTX toxicity. Therefore, it is important to develop a sensitive, selective, accurate and precise method for their simultaneous determination in urine. A new hybrid chemometric method has been applied to the emission response data of the two drugs. Spectrofluorimetric method for determination of MTX through measurement of its acid-degradation product, 4-amino-4-deoxy-10-methylpteroic acid (4-AMP), was developed. Moreover, the acid-catalyzed degradation reaction enables the spectrofluorimetric determination of ASP through the formation of its active metabolite salicylic acid (SA). The proposed chemometric method deals with convolution of emission data using 8-points sin xi polynomials (discrete Fourier functions) after the derivative treatment of these emission data. The first and second derivative curves (D1 & D2) were obtained first then convolution of these curves was done to obtain first and second derivative under Fourier functions curves (D1/FF) and (D2/FF). This new application was used for the resolution of the overlapped emission bands of the degradation products of both drugs to allow their simultaneous indirect determination in human urine. Not only this chemometric approach was applied to the emission data but also the obtained data were subjected to non-parametric linear regression analysis (Theil’s method). The proposed method was fully validated according to the ICH guidelines and it yielded linearity ranges as follows: 0.05-0.75 and 0.5-2.5 µg mL-1 for MTX and ASP respectively. It was found that the non-parametric method was superior over the parametric one in the simultaneous determination of MTX and ASP after the chemometric treatment of the emission spectra of their degradation products. The work combines the advantages of derivative and convolution using discrete Fourier function together with the reliability and efficacy of the non-parametric analysis of data. The achieved sensitivity along with the low values of LOD (0.01 and 0.06 µg mL-1) and LOQ (0.04 and 0.2 µg mL-1) for MTX and ASP respectively, by the second derivative under Fourier functions (D2/FF) were promising and guarantee its application for monitoring the two drugs in patients’ urine samples.

Keywords: chemometrics, emission curves, derivative, convolution, Fourier transform, human urine, non-parametric regression, Theil’s method

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Authors: Clare Cunningham

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Whilst there is a strong global research base on the benefits of structured writing retreats and similar provisions, such as Shut Up and Write events, for academic staff and postgraduate researchers, very little has been published about the worth of such events for undergraduate students. This is despite the fact that, internationally, undergraduate student researchers experience similar pressures, distractions and feelings towards writing as those who are at more senior levels within the academy. This paper reports on a mixed-methods study with cohorts of third-year undergraduate students over the course of four academic years. This involved a range of research instruments adopted over the four years of the study. They include the administration of four questionnaires across three academic years, a collection of ethnographic recordings in the second year, and the collation of reflective journal entries and evaluations from all four years. The final two years of data collection took place during the period of Covid-19 restrictions when writing retreats moved to the virtual space which adds an additional dimension of interest to the analysis. The analysis involved the collation of quantitative questionnaire data to observe patterns in expressions of attitudes towards writing. Qualitative data were analysed thematically and used to corroborate and support the quantitative data when appropriate. The resulting data confirmed that one of the biggest challenges for undergraduate students mirrors those reported in the findings of studies focused on more experienced researchers. This is not surprising, especially given the number of undergraduate students who now work alongside their studies, as well as the increasing number who have caring responsibilities, but it has, as yet, been under-reported. The data showed that the groups of writing retreat participants all had very positive experiences, with accountability, a sense of community and procrastination avoidance some of the key aspects. The analysis revealed the sometimes transformative power of these events for a number of these students in terms of changing the way they viewed writing and themselves as writers. The data presented in this talk will support the proposal that retreats should much more widely be offered to undergraduate students across the world.

Keywords: academic writing, students, undergraduates, writing retreat

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Authors: S. M. Abd-Altwab, M. R. Noor El-Din

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This paper aims to the synthesis of new ethoxylated amide as bactericides to prevent the growth of Gram +ve and –ve bacteria of water-in-diesel fuel nanoemulsions over a long period of time as three months. To realize it, eight kinetically stable water-in-diesel fuel nanoemulsions differing in surfactant concentrations and water contents ranging from 4 to 8 and 5 to 8 wt.,wt.,% of total weight of the nanoemulsions, respectively were formed at a temperature of 20 °C. The performance of this ethoxylated amide as bactericides agents against two strains of Gram-negative bacteria, namely, Pseudomonas aeruginosa and Escherichia coli, and two strains of Gram-positive bacteria namely, Staphylococcus aureus and Bacillus subtilis, were evaluated as antimicrobial agents. The maximum and minimum antimicrobial activities were 85 and 71 % against S. aureus and E. coli, respectively, at a concentration of 5 mg/l, pH 7, and 37 °C.

Keywords: nanoemulsion, bacteriocide, diesel fuel, emulsifier

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Authors: Jade Fersterer, Tristan Snell, Mark Rickinson

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This paper is designed to provide a review of the literature concerning the impact of natural environments on student learning and wellbeing in Australia. Specific areas of interest include how child-led and teacher-led pedagogies differ in outdoor learning settings, and the impact of each approach on children’s well-being, behavior, relationships with others as well as educational outcomes. The review will include links to possibilities for future research, including a Ph.D. currently being undertaken in Australia, which aims to fulfill a considerable gap in psychological, educational and outdoor learning research, regarding how natural environments are being used by teachers to improve learning and wellbeing among primary school students. The proposed study aims to understand if children’s experience of learning, 1. in a natural environment, and 2. in a child-led way, can support and strengthen their skills across several areas of development, including those required for positive educational outcomes. Data will be collected from a sample of primary school students and teachers via both quantitative and qualitative methods, including a pre- and post-questionnaire, direct observation, and semi-structured interviews. The study will have valuable implications for the provision of quality education as well as the promotion of good health and wellbeing. The implications of the research will be useful not only for teachers and parents but also for Psychologists working with children and young people in both a school and clinical setting. Understanding the impacts and implications of child-led learning and exposure to natural environments provides the opportunity to build on the current school curriculum. The inclusion of child-led experiences in nature may provide a simple way to build enthusiasm for school and learning, cultivating skills for life and relationships as well as meeting current curriculum requirements and building capacity for ongoing academic pursuits. In addition, understanding the impact of learning in a natural environment on wellbeing will assist in the development and dissemination of an educational model that could help mitigate the negative health outcomes associated with reduced physical activity and decreasing contact with nature among children.

Keywords: child-led learning, educational outcomes, natural environments, wellbeing

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Authors: Mace Bentley, Zhuojun Duan, Tobias Gerken, Dudley Bonsal, Henry Way, Endre Szakal, Mia Pham, Hunter Donaldson, Chelsea Lang, Hayden Abbott, Leah Wilcynzski

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Air pollution has the potential to modify the lifespan and intensity of thunderstorms and the properties of lightning. Using data mining and geovisualization, we investigate how background climate and weather conditions shape variability in urban air pollution and how this, in turn, shapes thunderstorms as measured by the intensity, distribution, and frequency of cloud-to-ground lightning. A spatiotemporal analysis was conducted in order to identify thunderstorms using high-resolution lightning detection network data. Over seven million lightning flashes were used to identify more than 196,000 thunderstorms that occurred between 2006 - 2020 in the Washington, DC Metropolitan Region. Each lightning flash in the dataset was grouped into thunderstorm events by means of a temporal and spatial clustering algorithm. Once the thunderstorm event database was constructed, hourly wind direction, wind speed, and atmospheric thermodynamic data were added to the initiation and dissipation times and locations for the 196,000 identified thunderstorms. Hourly aerosol and air quality data for the thunderstorm initiation times and locations were also incorporated into the dataset. Developing thunderstorm climatologies using a lightning tracking algorithm and lightning detection network data was found to be useful for visualizing the spatial and temporal distribution of urban augmented thunderstorms in the region.

Keywords: lightning, urbanization, thunderstorms, climatology

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

Abstract:

This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

Procedia PDF Downloads 100

Authors: Usiobaifo Agharese Rosemary, Osaseri Roseline Oghogho

Abstract:

Diabetes is a complex group of disease with a variety of causes; it is a disorder of the body metabolism in the digestion of carbohydrates food. The application of machine learning in the field of medical diagnosis has been the focus of many researchers and the use of recognition and classification model as a decision support tools has help the medical expert in diagnosis of diseases. Considering the large volume of medical data which require special techniques, experience, and high diagnostic skill in the diagnosis of diseases, the application of an artificial intelligent system to assist medical personnel in order to enhance their efficiency and accuracy in diagnosis will be an invaluable tool. In this study will propose a diabetes diagnosis model using rough set and K-nearest Neighbor classifier algorithm. The system consists of two modules: the feature extraction module and predictor module, rough data set is used to preprocess the attributes while K-nearest neighbor classifier is used to classify the given data. The dataset used for this model was taken for University of Benin Teaching Hospital (UBTH) database. Half of the data was used in the training while the other half was used in testing the system. The proposed model was able to achieve over 80% accuracy.

Keywords: classifier algorithm, diabetes, diagnostic model, machine learning

Procedia PDF Downloads 332

Authors: Budhvin T. Withana, Sulochana Rupasinghe

Abstract:

The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

Procedia PDF Downloads 101

Authors: Shreyas Srinivas Rangan, Jurgis Porins

Abstract:

The internet traffic has increased exponentially due to the high demand for data rates by the users, and the constantly increasing metro networks and access networks are focused on improving the maximum transmit distance of the long-reach optical networks. One of the common methods to improve the maximum transmit distance of the long-reach optical networks at the component level is to use broadband optical amplifiers. The Erbium Doped Fiber Amplifier (EDFA) provides high amplification with low noise figure but due to the characteristics of EDFA, its operation is limited to C-band and L-band. In contrast, the Raman amplifier exhibits a wide amplification spectrum, and negative noise figure values can be achieved. To obtain such results, high powered pumping sources are required. Operating Raman amplifiers with such high-powered optical sources may cause fire hazards and it may damage the optical system. In this paper, we implement a hybrid optical amplifier configuration. EDFA and Raman amplifiers are used in this hybrid setup to combine the advantages of both EDFA and Raman amplifiers to improve the reach of the system. Using this setup, we analyze the maximum transmit distance of the network by obtaining a correlation diagram between the length of the single-mode fiber (SMF) and the Bit Error Rate (BER). This hybrid amplifier configuration is implemented in a Wavelength Division Multiplexing (WDM) system with a BER of 10⁻⁹ by using NRZ modulation format, and the gain uniformity noise ratio (signal-to-noise ratio (SNR)), the efficiency of the pumping source, and the optical signal gain efficiency of the amplifier are studied experimentally in a mathematical modelling environment. Numerical simulations were implemented in RSoft OptSim simulation software based on the nonlinear Schrödinger equation using the Split-Step method, the Fourier transform, and the Monte Carlo method for estimating BER.

Keywords: Raman amplifier, erbium doped fibre amplifier, bit error rate, hybrid optical amplifiers

Procedia PDF Downloads 62

Authors: B. J. Sandesh, Saurabha Jirgi, S. Vidya, Prakash Eljer, Gowri Srinivasa

Abstract:

In this paper, we propose a framework to help users to search and retrieve the portions in the lecture video of their interest. This is achieved by temporally segmenting and indexing the lecture video using the topic keywords. We use transcribed text from the video and documents relevant to the video topic extracted from the web for this purpose. The keywords for indexing are found by applying the non-negative matrix factorization (NMF) topic modeling techniques on the web documents. Our proposed technique first creates indices on the transcribed documents using the topic keywords, and these are mapped to the video to find the start and end time of the portions of the video for a particular topic. This time information is stored in the index table along with the topic keyword which is used to retrieve the specific portions of the video for the query provided by the users.

Keywords: video indexing and retrieval, lecture videos, content based video search, multimodal indexing

Procedia PDF Downloads 246