Search results for: data infrastructure

Authors: Azadeh Bashiri

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Introduction: In order to better designing of electronic health record system in Iran, integration of health information systems based on a common language must be done to interpret and exchange this information with this system is required. Background: This study, provides a conceptual model of radiology reporting system using unified modeling language. The proposed model can solve the problem of integration this information system with electronic health record system. By using this model and design its service based, easily connect to electronic health record in Iran and facilitate transfer radiology report data. Methods: This is a cross-sectional study that was conducted in 2013. The student community was 22 experts that working at the Imaging Center in Imam Khomeini Hospital in Tehran and the sample was accorded with the community. Research tool was a questionnaire that prepared by the researcher to determine the information requirements. Content validity and test-retest method was used to measure validity and reliability of questioner respectively. Data analyzed with average index, using SPSS. Also, Visual Paradigm software was used to design a conceptual model. Result: Based on the requirements assessment of experts and related texts, administrative, demographic and clinical data and radiological examination results and if the anesthesia procedure performed, anesthesia data suggested as minimum data set for radiology report and based it class diagram designed. Also by identifying radiology reporting system process, use case was drawn. Conclusion: According to the application of radiology reports in electronic health record system for diagnosing and managing of clinical problem of the patient, provide the conceptual Model for radiology reporting system; in order to systematically design it, the problem of data sharing between these systems and electronic health records system would eliminate.

Keywords: structured radiology report, information needs, minimum data set, electronic health record system in Iran

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Authors: Praniil Nagaraj

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This paper proposes a method to collect homeless data using crowdsourcing and presents an approach to analyze the data, demonstrating its potential to strengthen existing and future policies aimed at promoting socio-economic equilibrium. The 2022 Annual Homeless Assessment Report (AHAR) to Congress highlighted alarming statistics, emphasizing the need for effective decision-making and budget allocation within local planning bodies known as Continuums of Care (CoC). This paper's contributions can be categorized into three main areas. Firstly, a unique method for collecting homeless data is introduced, utilizing a user-friendly smartphone app (currently available for Android). The app enables the general public to quickly record information about homeless individuals, including the number of people and details about their living conditions. The collected data, including date, time, and location, is anonymized and securely transmitted to the cloud. It is anticipated that an increasing number of users motivated to contribute to society will adopt the app, thus expanding the data collection efforts. Duplicate data is addressed through simple classification methods, and historical data is utilized to fill in missing information. The second contribution of this paper is the description of data analysis techniques applied to the collected data. By combining this new data with existing information, statistical regression analysis is employed to gain insights into various aspects, such as distinguishing between unsheltered and sheltered homeless populations, as well as examining their correlation with factors like unemployment rates, housing affordability, and labor demand. Initial data is collected in San Francisco, while pre-existing information is drawn from three cities: San Francisco, New York City, and Washington D.C., facilitating the conduction of simulations. The third contribution focuses on demonstrating the practical implications of the data processing results. The challenges faced by key stakeholders, including charitable organizations and local city governments, are taken into consideration. Two case studies are presented as examples. The first case study explores improving the efficiency of food and necessities distribution, as well as medical assistance, driven by charitable organizations. The second case study examines the correlation between micro-geographic budget expenditure by local city governments and homeless information to justify budget allocation and expenditures. The ultimate objective of this endeavor is to enable the continuous enhancement of the quality of life for the underprivileged. It is hoped that through increased crowdsourcing of data from the public, the Generosity Curve and the Need Curve will intersect, leading to a better world for all.

Keywords: crowdsourcing, homelessness, socio-economic policies, statistical regression

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Authors: Jisun Lee, Jay Hyoun Kwon

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As an alternative way to compensate the INS (inertial navigation system) error in non-GNSS (Global Navigation Satellite System) environment, geophysical database referenced navigation is being studied. In this study, both gravity gradient and terrain data were combined to complement the weakness of sole geophysical data as well as to improve the stability of the positioning. The main process to compensate the INS error using geophysical database was constructed on the basis of the EKF (Extended Kalman Filter). In detail, two type of combination method, centralized and decentralized filter, were applied to check the pros and cons of its algorithm and to find more robust results. The performance of each navigation algorithm was evaluated based on the simulation by supposing that the aircraft flies with precise geophysical DB and sensors above nine different trajectories. Especially, the results were compared to the ones from sole geophysical database referenced navigation to check the improvement due to a combination of the heterogeneous geophysical database. It was found that the overall navigation performance was improved, but not all trajectories generated better navigation result by the combination of gravity gradient with terrain data. Also, it was found that the centralized filter generally showed more stable results. It is because that the way to allocate the weight for the decentralized filter could not be optimized due to the local inconsistency of geophysical data. In the future, switching of geophysical data or combining different navigation algorithm are necessary to obtain more robust navigation results.

Keywords: Extended Kalman Filter, geophysical database referenced navigation, gravity gradient, terrain

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Authors: Khan R. Rahaman, Quazi K. Hassan

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Global changes in climate, environment, economies, populations, governments, institutions, and cultures converge in localities. Changes at a local scale, in turn, contribute to global changes as well as being affected by them. Our hypothesis is built on a consideration that temperature does vary at local level (i.e., termed as local warming) in comparison to the predicted models at the regional and/or global scale. To date, the bulk of the research relating local places to global climate change has been top-down, from the global toward the local, concentrating on methods of impact analysis that use as a starting point climate change scenarios derived from global models, even though these have little regional or local specificity. Thus, our focus is to understand such trends over the southern Alberta, which will enable decision makers, scientists, researcher community, and local people to adapt their policies based on local level temperature variations and to act accordingly. Specific objectives in this study are: (i) to understand the local warming (temperature in particular) trend in context of temperature normal during the period 1961-2010 at point locations using meteorological data; (ii) to validate the data by using specific yearly data, and (iii) to delineate the spatial extent of the local warming trends and understanding influential factors to adopt situation by local governments. Existing data has brought the evidence of such changes and future research emphasis will be given to validate this hypothesis based on remotely sensed data (i.e. MODIS product by NASA).

Keywords: local warming, climate change, urban area, Alberta, Canada

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Authors: Stephanie Boue, Nicolas Sierro, Julia Hoeng, Manuel C. Peitsch

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Systems biology relies on large numbers of data points and sophisticated methods to extract biologically meaningful signal and mechanistic understanding. For example, analyses of transcriptomics and proteomics data enable to gain insights into the molecular differences in tissues exposed to diverse stimuli or test items. Whereas the interpretation of endpoints specifically measuring a mechanism is relatively straightforward, the interpretation of big data is more complex and would benefit from comparing results obtained with diverse analysis methods. The sbv IMPROVER project was created to implement solutions to verify systems biology data, methods, and conclusions. Computational challenges leveraging the wisdom of the crowd allow benchmarking methods for specific tasks, such as signature extraction and/or samples classification. Four challenges have already been successfully conducted and confirmed that the aggregation of predictions often leads to better results than individual predictions and that methods perform best in specific contexts. Whenever the scientific question of interest does not have a gold standard, but may greatly benefit from the scientific community to come together and discuss their approaches and results, datathons are set up. The inaugural sbv IMPROVER datathon was held in Singapore on 23-24 September 2016. It allowed bioinformaticians and data scientists to consolidate their ideas and work on the most promising methods as teams, after having initially reflected on the problem on their own. The outcome is a set of visualization and analysis methods that will be shared with the scientific community via the Garuda platform, an open connectivity platform that provides a framework to navigate through different applications, databases and services in biology and medicine. We will present the results we obtained when analyzing data with our network-based method, and introduce a datathon that will take place in Japan to encourage the analysis of the same datasets with other methods to allow for the consolidation of conclusions.

Keywords: big data interpretation, datathon, systems toxicology, verification

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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: Nihad Chakri, Btissam El Amrani, Faouzi Berrada, Fouad Amraoui

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In Morocco, water scarcity requires the exploitation of non-conventional resources. Rural areas are under-equipped with sanitation infrastructure, unlike urban areas. Decentralized and low-cost solutions could improve the quality of life of the population and the environment. In this context, the Faculty of Sciences Ain Chock "FSAC" has undertaken an integrated project to treat part of its wastewater using a decentralized compact system. The project will propose alternative solutions that are inexpensive and adapted to the context of peri-urban and rural areas in order to treat the wastewater generated and use it for irrigation, watering, and cleaning. For this purpose, several tests were carried out in the laboratory in order to develop a liquid waste treatment system optimized for local conditions. Based on the results obtained at the laboratory scale of the different proposed scenarios, we designed and implemented a prototype of a mini wastewater treatment plant for the Faculty. In this article, we will outline the steps of dimensioning, construction, and monitoring of the mini-station in our Faculty.

Keywords: wastewater, purification, optimization, vertical filter, MBBR process, sizing, decentralized pilot, reuse, irrigation, sustainable development

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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: Wang Liguang, Zhang Xueqing

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The build-operate-transfer (BOT) project delivery system has provided effective routes to mobilize private sector funds, innovative technologies, management skills and operational efficiencies for public infrastructure development and have been widely used in China during the last 20 years. Many BOT projects in China will be smoothly transferred to the government soon and the transfer stage, which is considered as the last stage, must be studied carefully and handled well to achieve the overall success of BOT projects. There will be many issues faced by both the public sector and private sector in the transfer stage of BOT projects, including project post-assessment, technology and documents transfer, personal training and staff transition, etc. and sometimes additional legislation is needed for future operation and management of facilities. However, most previous studies focused on the bidding, financing, and building and operation stages instead of transfer stage. This research identifies nine key issues in the transfer stage of BOT projects through a comprehensive study on three cases in China, and the expert interview and expert discussion meetings are held to validate the key issues and give detail analysis. A proposed framework of transfer management is prepared based on the experiences derived and lessons drawn from the case studies and expert interview and discussions, which is expected to improve the transfer management of BOT projects in practice.

Keywords: BOT project, key issues, transfer management, transfer stage

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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

Procedia PDF Downloads 149

Authors: Kabuga Daniel

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Road accidents resulting to deaths and injuries have become a new public health challenge especially in developing countries including Tanzania. Reports from Tanzania Traffic Police Force shows that last year 2016 accidents increased compare to previous year 2015, accident happened from 3710 up to 5219, accidents and safety data indicate that children are the most vulnerable to road crashes where 78 pupils died and 182 others were seriously injured in separate roads accident last year. A survey done by Amend indicates that Pupil mode of transport in Dar es salaam schools are by walk 87%, bus 9.21%, car 1.32%, motorcycle 0.88%, 3-wheeler 0.24%, train 0.14%, bicycle 0.10%, ferry 0.07%, and combined mode 0.44%. According to this study, majority of school children’s uses walking mode, most of school children’s agreed to continue using walking mode and request to have signs for traffic control during crossing road like STOP sign and CHILD CROSSING sign for safe crossing. Because children not only sit inside this buses (Daladala) but also they walk in a group to/from school, and few (33.2%) parents or adults are willing to supervise their children’s during working to school while 50% of parents agree to let their children walking alone to school if the public transport started from nearby street. The study used both qualitative and quantitative methods of research by conducting physical surveying on sample districts. The main objectives of this research are to carries out all factors affecting school children’s when they use public road, to promote and encourage the safe use of public road by all classes especially pupil or student through the circulation of advice, information and knowledge gain from research and to recommends future direction for the developments for road design or plan to vulnerable users. The research also critically analyze the problems causing death and injuries to school children’s in Dar es Salaam Region. This study determines the relationship between road traffic accidents and factors, such as socio-economic, status, and distance from school, number of sibling, behavioral problems, knowledge and attitudes of public and their parents towards road safety and parent educational study traffic. The study comes up with some of recommendations including Infrastructure Improvements like, safe footpaths, Safe crossings, Speed humps, Speed limits, Road signs. However, Planners and policymakers wishing to increase walking and cycling among children need to consider options that address distance constraints, the land use planners and transport professionals use better understanding of the various factors that affect children’s choices of school travel mode, results suggest that all school travel attributes should be considered during school location.

Keywords: accidents, childrens, school, Tanzania

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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: Victor Emery David Jr, Md S. Hossain

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Sewage sludge management has been a problem faced by most developing cities as in the case of Monrovia. The management of sewage sludge in Monrovia is still in its infant stage. The city is still struggling with poor sanitation, clogged pipes, shortage of septic tanks, lack of resources/human capacity, inadequate treatment facilities, open defecation, the absence of clear guidelines, etc. The rapid urban population growth of Monrovia has severely stressed Monrovia’s marginally functional urban WSS system caused by the civil conflict which led to break down in many sectors as well as infrastructure. The sewerage system which originally covered 17% of the population of Monrovia was down to serving about 7% because of bursts and blockages causing backflows in other areas. Prior to the Civil War, the average water production for Monrovia was about 68,000 m3/day but has now dropped to about 10,000 m3/day. Only small parts of Monrovia currently have direct access to the piped water supply while most areas depend on trucked water delivered to community collection points or household tanks, and/or on water from unprotected dug wells or hand pumps. There are only two functional treatment plants; The Fiamah Treatment plant and the White Plains Treatment Plant.

Keywords: Fiamah Treatment plant, management, Monrovia/Montserrado County, sewage, sludge

Procedia PDF Downloads 274

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: Anna Crivellari, Valerio Cozzani

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Even though most of the electricity produced in the entire world still comes from fossil fuels, new policies are being implemented in order to promote a more sustainable use of energy sources. Offshore renewable resources have become increasingly attractive thanks to the huge entity of power potentially obtained. However, the intermittent nature of renewables often limits the capacity of the systems and creates mismatches between supply and demand. Hydrogen is foreseen to be a promising vector to store and transport large amounts of excess renewable power by using existing oil and gas infrastructure. In this work, an offshore hybrid energy system integrating wind energy conversion with hydrogen production was conceptually defined and applied to offshore gas platforms. A techno-economic analysis was performed by considering two different locations for the installation of the innovative power system, i.e., the North Sea and the Adriatic Sea. The water depth, the distance of the platform from the onshore gas grid, the hydrogen selling price and the green financial incentive were some of the main factors taken into account in the comparison. The results indicated that the use of well-defined indicators allows to capture specifically different cost and revenue features of the analyzed systems, as well as to evaluate their competitiveness in the actual and future energy market.

Keywords: cost analysis, energy efficiency assessment, hydrogen production, offshore wind energy

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Authors: Debasis Sarkar

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Development of a Smart City would depend upon the development of its infrastructure in a smart way. Primarily based on the ideology of the fourth industrial revolution a Smart City project should have Smart governance, smart health care, smart building, smart transportation, smart mobility, smart energy, smart technology and smart citizen. Considering the Indian scenario of current state of cities in India, it has become very essential to decide the specific parameters which would govern the development of a Smart City project. It has been observed that there are significant parameters beyond Information and Communication Technology (ICT), which govern the development of a Smart City project. This paper is an attempt to identify the Critical Success Factors (CSF) which are significantly responsible for the development of a Smart City project in Western India. Responses to questionnaire survey were analyzed on basis of Likert scale. They were further critically evaluated with help of Factor Comparison Method (FCM) and Analytical Hierarchy Process (AHP). The project authorities need to incorporate Building Information Modeling (BIM) to make the smart city project more collaborative. To make the project more sustainable, use of flyash in the concrete used, reduced usage of cement and steel, use of alternate fuels like biodiesel is recommended.

Keywords: analytical hierarchical process, building information modeling, critical success factors, factor comparison method

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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: 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: Mohd Asrul Affedi, Nyi Nyi Naing

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Overdispersion is present in count data, and basically when a phenomenon happened, a Negative Binomial (NB) is commonly used to replace a standard Poisson model. Analysis of count data event, such as mortality cases basically Poisson regression model is appropriate. Hence, the model is not appropriate when existing a zero values. The zero-inflated negative binomial model is appropriate. In this article, we modelled the mortality cases as a dependent variable by age categorical. The objective of this study to determine existing overdispersion in mortality data of AIDS co-infection patients in Kelantan.

Keywords: negative binomial death rate, overdispersion, zero-inflation negative binomial death rate, AIDS

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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

Abstract:

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

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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

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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

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Authors: Todor Bačkalić, Marinko Maslarić, Milosav Georgijević, Sanja Bojić

Abstract:

Water transport could be the backbone of the future European combined transport system. The future transport policy in landlocked countries from the Danube Region has to be based on inland waterway transport (IWT). The development of the container transport on inland waterways depends directly on technical-exploitative characteristics of the network of inland waterways. Research of navigational abilities of inland waterways is the basic step in transport planning. The size of the vessel’s draught (T) is the limiting value in project tasks and it depends on the depth of the waterway. Navigation characteristics of rivers have to be determined as precise as possible, especially from the aspect of determination of the possible draught of vessels. This article outlines a rationale, why it is necessary to develop competence about infrastructure risk in water transport. Climate changes are evident and require special attention and global monitoring. Current risk assessment methods for Inland waterway transport just consider some dramatic events. We present a new method for the assessment of risk and vulnerability of inland waterway transport where river depth represents a crucial part. The analysis of water level changes in the lower Danube was done for two significant periods (1965-1979 and 1998-2012).

Keywords: container vessel, draught, probability, the Danube

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Authors: Thanduxolo Nomngcoyiya, Simon M. Kang’ethe

Abstract:

The paper aimed to critically analyse gaps pertaining to the cultural policy environments governing traditional male circumcision in the Eastern Cape as exemplified by an empirical case study. The original study which this paper is derived from utilized qualitative paradigm; and encompassed 28 participants. It used in-depth one-on-one interviews complemented by focus group discussions and key informants as a method of data collection. It also adopted interview guide as a data collection instrument. The original study was cross-sectional in nature, and the data was audio recorded and transcribed later during the data analysis and coding process. The study data analysis was content thematic analysis and identified the following key major findings on the culture of male circumcision policy: Lack of clarity on culture of male circumcision policy operations; Myths surrounding procedures on culture of male circumcision; Divergent views on cultural policies between government and male circumcision custodians; Unclear cultural policies on selection criteria of practitioners; and Lack of policy enforcement and implementation on transgressors of culture of male circumcision. It recommended: a stringent selection criteria of practitioners; a need to carry out death-free male circumcision; a need for male circumcision stakeholders to work with other culture and tradition-friendly stakeholders.

Keywords: human rights, policy enforcement, traditional male circumcision, traditional surgeons and nurses

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