Search results for: linked data

Authors: Mona Almansoori, Ahmed Mustafa, Ahmad Elshamy

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Wireless nodes in a sensor network gather and process critical information designed to process and communicate, information flooding through such network is critical for decision making and data processing, the integrity of such data is one of the most critical factors in wireless security without compromising the processing and transmission capability of the network. This paper presents mechanism to securely transmit data over a chain of sensor nodes without compromising the throughput of the network utilizing available battery resources available at the sensor node.

Keywords: hybrid protocol, data integrity, lightweight encryption, neighbor based key sharing, sensor node data processing, Z-MAC

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Authors: Fabrizio Ascione, Martina Borrelli, Rosa Francesca De Masi, Silvia Ruggiero, Giuseppe Peter Vanoli

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The Directive 2010/31/EC 'Directive of the European Parliament and of the Council of 19 may 2010 on the energy performance of buildings' moved the targets of the previous version toward more ambitious targets, for instance by establishing that, by 31 December 2020, all new buildings should demand nearly zero-energy. Moreover, the demonstrative role of public buildings is strongly affirmed so that also the target nearly zero-energy buildings is anticipated, in January 2019. On the other hand, given the very low turn-over rate of buildings (in Europe, it ranges between 1-3%/yearly), each policy that does not consider the renovation of the existing building stock cannot be effective in the short and medium periods. According to this proposal, the study provides a novel, holistic approach to design the refurbishment of educational buildings in colder cities of Mediterranean regions enabling stakeholders to understand the uncertainty to use numerical modelling and the real environmental and economic impacts of adopting some energy efficiency technologies. The case study is a university building of Molise region in the centre of Italy. The proposed approach is based on the application of the cost-optimal methodology as it is shown in the Delegate Regulation 244/2012 and Guidelines of the European Commission, for evaluating the cost-optimal level of energy performance with a macroeconomic approach. This means that the refurbishment scenario should correspond to the configuration that leads to lowest global cost during the estimated economic life-cycle, taking into account not only the investment cost but also the operational costs, linked to energy consumption and polluting emissions. The definition of the reference building has been supported by various in-situ surveys, investigations, evaluations of the indoor comfort. Data collection can be divided into five categories: 1) geometrical features; 2) building envelope audit; 3) technical system and equipment characterization; 4) building use and thermal zones definition; 5) energy building data. For each category, the required measures have been indicated with some suggestions for the identifications of spatial distribution and timing of the measurements. With reference to the case study, the collected data, together with a comparison with energy bills, allowed a proper calibration of a numerical model suitable for the hourly energy simulation by means of EnergyPlus. Around 30 measures/packages of energy, efficiency measure has been taken into account both on the envelope than regarding plant systems. Starting from results, two-point will be examined exhaustively: (i) the importance to use validated models to simulate the present performance of building under investigation; (ii) the environmental benefits and the economic implications of a deep energy refurbishment of the educational building in cold climates.

Keywords: energy simulation, modelling calibration, cost-optimal retrofit, university building

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Authors: M. Sai Veerendra

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Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight investment fraud activities. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project on developing a new data mining solution for AML Units in an international investment bank in Ireland, we survey recent data mining approaches for AML. In this paper, we present not only these approaches but also give an overview on the important factors in building data mining solutions for AML activities.

Keywords: data mining, clustering, money laundering, anti-money laundering solutions

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Authors: Lourenco Camnahas, Eileen Willis, Greg Fisher, Jessie Gunson, Pascale Dettwiller, Charlene Thornton

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Stock-out of pharmaceuticals is a common issue at all level of health services in Timor-Leste, a small post-conflict country. This lead to the research questions: what are the current methods used to quantify pharmaceutical supplies; what factors contribute to the on-going pharmaceutical stock-out? The study examined factors that influence the pharmaceutical supply chain system. Methodology: Privett and Goncalvez dependency model has been adopted for the design of the qualitative interviews. The model examines pharmaceutical supply chain management at three management levels: management of individual pharmaceutical items, health facilities, and health systems. The interviews were conducted in order to collect information on inventory management, logistics management information system (LMIS) and the provision of pharmaceuticals. Andersen' behavioural model for healthcare utilization also informed the interview schedule, specifically factors linked to environment (healthcare system and external environment) and the population (enabling factors). Forty health professionals (bureaucrats, clinicians) and six senior officers from a United Nations Agency, a global multilateral agency and a local non-governmental organization were interviewed on their perceptions of factors (healthcare system/supply chain and wider environment) impacting on stock out. Additionally, policy documents for the entire healthcare system, along with population data were collected. Findings: An analysis using Pozzebon’s critical interpretation identified a range of difficulties within the system from poor coordination to failure to adhere to policy guidelines along with major difficulties with inventory management, quantification, forecasting, and budgetary constraints. Weak logistics management information system, lack of capacity in inventory management, monitoring and supervision are additional organizational factors that also contributed to the issue. There were various methods of quantification of pharmaceuticals applied in the government sector, and non-governmental organizations. Lack of reliable data is one of the major problems in the pharmaceutical provision. Global Fund has the best quantification methods fed by consumption data and malaria cases. There are other issues that worsen stock-out: political intervention, work ethic and basic infrastructure such as unreliable internet connectivity. Major issues impacting on pharmaceutical quantification have been identified. However, current data collection identified limitations within the Andersen model; specifically, a failure to take account of predictors in the healthcare system and the environment (culture/politics/social. The next step is to (a) compare models used by three non-governmental agencies with the government model; (b) to run the Andersen explanatory model for pharmaceutical expenditure for 2 to 5 drug items used by these three development partners in order to see how it correlates with the present model in terms of quantification and forecasting the needs; (c) to repeat objectives (a) and (b) using the government model; (d) to draw a conclusion about the strength.

Keywords: inventory management, pharmaceutical forecasting and quantification, pharmaceutical stock-out, pharmaceutical supply chain management

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Authors: Kisik Song, Kyuwoong Kim, Sungjoo Lee

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Many global firms and corporations derive new technology and opportunity by identifying vacant technology from patent analysis. However, previous studies failed to focus on technologies that promised continuous growth in industrial fields. Most studies that derive new technology opportunities do not test practical effectiveness. Since previous studies depended on expert judgment, it became costly and time-consuming to evaluate new technologies based on patent analysis. Therefore, research suggests a quantitative and systematic approach to technology evaluation indicators by using patent data to and from customer communities. The first step involves collecting two types of data. The data is used to construct evaluation indicators and apply these indicators to the evaluation of new technologies. This type of data mining allows a new method of technology evaluation and better predictor of how new technologies are adopted.

Keywords: data mining, evaluating new technology, technology opportunity, patent analysis

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Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

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Authors: Eszter Hamza

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The competitiveness of economic operators is based on interoperability, which is relatively low in Hungary. The development of co-operation is high priority in Common Agricultural Policy 2014-2020. The aim of the paper to assess co-operations in Hungarian agriculture, estimate the economic outputs and benefits of co-operations, based on statistical data processing and literature. Further objective is to explore the potential of agricultural co-operation with the help of interviews and questionnaire survey. The research seeks to answer questions as to what fundamental factors play role in the development of co-operation, and what are the motivations of the actors and the key success factors and pitfalls. The results were analysed using econometric methods. In Hungarian agriculture we can find several forms of co-operation: cooperatives, producer groups (PG) and producer organizations (PO), machinery cooperatives, integrator companies, product boards and interbranch organisations. Despite the several appearance of the agricultural co-operation, their economic weight is significantly lower in Hungary than in western European countries. Considering the agricultural importance, the integrator companies represent the most weight among the co-operations forms. Hungarian farmers linked to co-operations or organizations mostly in relation to procurement and sales. Less than 30 percent of surveyed farmers are members of a producer organization or cooperative. The trust level is low among farmers. The main obstacle to the development of formalized co-operation, is producers' risk aversion and the black economy in agriculture. Producers often prefer informal co-operation instead of long-term contractual relationships. The Hungarian agricultural co-operations are characterized by non-dynamic development, but slow qualitative change. For the future, one breakout point could be the association of producer groups and organizations, which in addition to the benefits of market concentration, in the dissemination of knowledge, advisory network operation and innovation can act more effectively.

Keywords: agriculture, co-operation, producer organisation, trust level

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Authors: Haifa Ben Saber, Mourad Elloumi

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In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called, Enumerative tree (EnumTree) for biclustering of binary microarray data. is an algorithm adopting the approach of enumerating biclusters. This algorithm extracts all biclusters consistent good quality. The main idea of ​​EnumLat is the construction of a new tree structure to represent adequately different biclusters discovered during the process of enumeration. This algorithm adopts the strategy of all biclusters at a time. The performance of the proposed algorithm is assessed using both synthetic and real DNA micryarray data, our algorithm outperforms other biclustering algorithms for binary microarray data. Biclusters with different numbers of rows. Moreover, we test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevent biclusters.

Keywords: DNA microarray, biclustering, gene expression data, tree, datamining.

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Authors: Lynn Ruggieri

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The worldwide pandemic has only increased sustainability awareness. The public is demanding that businesses be held accountable for their impact on the environment. While financial data enjoys uniformity in reporting requirements, there are no uniform reporting requirements for non-financial data. Europe is leading the way with some standards being implemented for reporting non-financial sustainability data; however, there is no uniformity globally. And without uniformity, there is not a clear understanding of what information to include and how to disclose it. Sustainability reporting will provide important information to stakeholders and will enable businesses to understand their impact on the environment. Therefore, there is a crucial need for this data. This paper looks at the history of sustainability reporting in the countries of the European Union and throughout the world and makes a case for worldwide reporting requirements for sustainability.

Keywords: financial reporting, non-financial data, sustainability, global financial reporting

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Authors: Richard Kajjura, Joyce Kikafunda, Roger Whitehead

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Introduction: Indigenous complementary recipes for children (6-23 months) are bulky and inextricably linked. The potential contribution of indigenous complementary foods to infant’s vitamin A and iron needs is not well investigated in Uganda. Less is known whether children in Uganda are living with or without adequate supply of vitamin A and iron nutrients. In this study, vitamin A and iron contents were assessed in the complementary foods fed to infants aged 6-11 months in a Peri-urban setting in Kampala District in Central Uganda. Objective: Assessment of vitamin A and iron contents of indigenous complementary foods of children as fed and associated demographic factor. Method: In a cross sectional study design, one hundred and three (153) households with children aged 6-11 months were randomly selected to participate in the assessment. Complementary food samples were collected from the children’s mothers/caretakers at the time of feeding the child. The mothers’ socio-demographic characteristics of age, education, marital status, occupation and sex collected a semi-qualitative questionnaire. The Vitamin A and iron contents in the complementary foods were analyzed using a UV/VIS spectrophotometer for vitamin A and Atomic Absorption spectrophotometer for iron samples. The data was analyzed using Gene-stat software program. Results: The mean vitamin A content was 97.0± 72.5 µg while that of iron was 1.5 ± 0.4 mg per 100g of food sample as fed. The contribution of indigenous complementary foods found was 32% for vitamin A and 15% iron of the recommended dietary allowance. Age of children was found to be significantly associated Vitamin A and Iron supply potential. Conclusion: The contribution of indigenous complementary foods to infant’s vitamin A and iron needs was low. Complementary foods in Uganda are more likely to be deficient in vitamin A and iron content. Nutrient dense dietary supplementation should be intervened in to make possible for Ugandan children attain full growth potential.

Keywords: indigenous complementary food, infant, iron, vitamin A

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Authors: Omar Farshad Jeelani, Makaire Njie, Viktoriia M. Korzhuk

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Recently, the application of AI-powered algorithms in healthcare continues to flourish. Particularly, access to healthcare information, including patient health history, diagnostic data, and PII (Personally Identifiable Information) is paramount in the delivery of efficient patient outcomes. However, as the exchange of healthcare information between patients and healthcare providers through AI-powered solutions increases, protecting a person’s information and their privacy has become even more important. Arguably, the increased adoption of healthcare AI has resulted in a significant concentration on the security risks and protection measures to the security and privacy of healthcare data, leading to escalated analyses and enforcement. Since these challenges are brought by the use of AI-based healthcare solutions to manage healthcare data, AI-based data protection measures are used to resolve the underlying problems. Consequently, this project proposes AI-powered safeguards and policies/laws to protect the privacy of healthcare data. The project presents the best-in-school techniques used to preserve the data privacy of AI-powered healthcare applications. Popular privacy-protecting methods like Federated learning, cryptographic techniques, differential privacy methods, and hybrid methods are discussed together with potential cyber threats, data security concerns, and prospects. Also, the project discusses some of the relevant data security acts/laws that govern the collection, storage, and processing of healthcare data to guarantee owners’ privacy is preserved. This inquiry discusses various gaps and uncertainties associated with healthcare AI data collection procedures and identifies potential correction/mitigation measures.

Keywords: data privacy, artificial intelligence (AI), healthcare AI, data sharing, healthcare organizations (HCOs)

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Authors: Sahil Imtiyaz

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One of the biggest challenges has emerged from the ever-expanding, dynamic, and instantaneously changing space-Big Data; and to find a data point and inherit wisdom to this space is a hard task. In this paper, we reduce the space of big data in Hamiltonian formalism that is in concordance with Ising Model. For this formulation, we simulate the system using dye laser in FORTRAN and analyse the dynamics of the data point in energy well of rhodium atom. After mapping the photon intensity and pulse width with energy and potential we concluded that as we increase the energy there is also increase in probability of tunnelling up to some point and then it starts decreasing and then shows a randomizing behaviour. It is due to decoherence with the environment and hence there is a loss of ‘quantumness’. This interprets the efficiency parameter and the extent of quantum evolution. The results are strongly encouraging in favour of the use of ‘Topological Property’ as a source of information instead of the qubit.

Keywords: big data, optimization, quantum evolution, hamiltonian, dye laser, fermionic computations

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Authors: Muhammad Muhammad Suleiman

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Cloud Computing is a versatile concept that refers to a service that allows users to outsource their data without having to worry about local storage issues. However, the most pressing issues to be addressed are maintaining a secure and reliable data repository rather than relying on untrustworthy service providers. In this study, we look at how stenography approaches and collaboration with Digital Watermarking can greatly improve the system's effectiveness and data security when used for Cloud Computing. The main requirement of such frameworks, where data is transferred or exchanged between servers and users, is safe data management in cloud environments. Steganography is the cloud is among the most effective methods for safe communication. Steganography is a method of writing coded messages in such a way that only the sender and recipient can safely interpret and display the information hidden in the communication channel. This study presents a new text steganography method for hiding a loaded hidden English text file in a cover English text file to ensure data protection in cloud computing. Data protection, data hiding capability, and time were all improved using the proposed technique.

Keywords: cloud computing, steganography, information hiding, cloud storage, security

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: time series, fluctuation in statistical characteristics, optimal learning, change-point algorithm

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Authors: Lizette Grobler

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Although the notion of environmental citizenship has become an established concept linked to scholarship on sustainability internationally, it is not the case in South Africa. This literature review aims to investigate whether the concept is a viable construct in the South African household waste management context. This literature review firstly examines different approaches to environmental citizenship and the normative notions of environmental values, attitudes, and behaviour advocated by proponents of each tradition. Secondly, this paper deals with the application of environmental citizenship as a measure to address household waste. Thirdly, this paper interrogates the utilization of the concept in South African scholarly literature on waste management. The paper argues for the introduction of the concept as a potential approach to behavioural change in the household waste management context.

Keywords: environmental citizenship, environmental responsibility, household waste, ownership of waste

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Authors: İbrahi̇m Kara, Seher Arslankaya

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Frequently preferred in the field of engineering in particular, data mining has now begun to be used in the field of health as well since the data in the health sector have reached great dimensions. With data mining, it is aimed to reveal models from the great amounts of raw data in agreement with the purpose and to search for the rules and relationships which will enable one to make predictions about the future from the large amount of data set. It helps the decision-maker to find the relationships among the data which form at the stage of decision-making. In this study, it is aimed to determine the risk of heart attack at the first stage, to control it, and to make its resource planning with the method of data mining. Through the early and correct diagnosis of heart attacks, it is aimed to reveal the factors which affect the diseases, to protect health and choose the right treatment methods, to reduce the costs in health expenditures, and to shorten the durations of patients’ stay at hospitals. In this way, the diagnosis and treatment costs of a heart attack will be scrutinized, which will be useful to determine the risk of the disease at the first stage, to control it, and to make its resource planning.

Keywords: data mining, decision support systems, heart attack, health sector

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Authors: Akalu Banbeta, Emmanuel Lesaffre, Reynaldo Martina, Joost Van Rosmalen

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Including data from previous studies (historical data) in the analysis of the current study may reduce the sample size requirement and/or increase the power of analysis. The most common example is incorporating historical control data in the analysis of a current clinical trial. However, this only applies when the historical control dataare similar enough to the current control data. Recently, several Bayesian approaches for incorporating historical data have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP) both for single control as well as for multiple historical control arms. Here, we examine the performance of the MAP and the MPP approaches for the analysis of (over-dispersed) count data. To this end, we propose a computational method for the MPP approach for the Poisson and the negative binomial models. We conducted an extensive simulation study to assess the performance of Bayesian approaches. Additionally, we illustrate our approaches on an overactive bladder data set. For similar data across the control arms, the MPP approach outperformed the MAP approach with respect to thestatistical power. When the means across the control arms are different, the MPP yielded a slightly inflated type I error (TIE) rate, whereas the MAP did not. In contrast, when the dispersion parameters are different, the MAP gave an inflated TIE rate, whereas the MPP did not.We conclude that the MPP approach is more promising than the MAP approach for incorporating historical count data.

Keywords: count data, meta-analytic prior, negative binomial, poisson

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Authors: John Gaber

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Planners regularly use citizen input as empirical data to help them better understand community issues they know very little about. This type of community data is based on the lived experiences of local residents and is known as "emic" data. What is becoming more common practice for planners is their use of data from local experts and stakeholders (known as "etic" data or the outsider perspective) to help them fill in the gaps in their analysis of applied planning research projects. Utilizing international Health Impact Assessment (HIA) data, I look at who planners invite to their citizen input investigations. Research presented in this paper shows that planners access a wide range of emic and etic community perspectives in their search for the “community’s view.” The paper concludes with how planners can chart out a new empirical path in their execution of emic/etic citizen participation strategies in their applied planning research projects.

Keywords: citizen participation, emic data, etic data, Health Impact Assessment (HIA)

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Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

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As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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Authors: S. Ravindran, Y.Liu, I. Gratchev, D.Jeng

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Rainfall-induced shallow landslides are more common in the northern New South Wales (NSW), Australia. From 2009 to 2017, around 105 rainfall-induced landslides occurred along the road corridors and caused temporary road closures in the northern NSW. Rainfall causing shallow landslides has different distributions of rainfall varying from uniform, normal, decreasing to increasing rainfall intensity. The duration of rainfall varied from one day to 18 days according to historical data. The objective of this research is to analyse slope instability of some of the sites in the northern NSW by varying cumulative rainfall using SLOPE/W and SEEP/W and compare with field data of rainfall causing shallow landslides. The rainfall data and topographical data from public authorities and soil data obtained from laboratory tests will be used for this modelling. There is a likelihood of shallow landslides if the cumulative rainfall is between 100 mm to 400 mm in accordance with field data.

Keywords: landslides, modelling, rainfall, suction

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

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Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

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Authors: Thedy Yogasara, Fredy Agus

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In designing a product, it is currently crucial to focus not only on the product’s usability based on performance measures, but also on user experience (UX) that includes pragmatic and hedonic aspects of product use. These aspects play a significant role in fulfillment of user needs, both functionally and psychologically. Pragmatic quality refers to as product’s perceived ability to support the fulfillment of behavioral goals. It is closely linked to functionality and usability of the product. In contrast, hedonic quality is product’s perceived ability to support the fulfillment of psychological needs. Hedonic quality relates to the pleasure of ownership and use of the product, including stimulation for personal development and communication of user’s identity to others through the product. This study evaluates the pragmatic and hedonic aspects of gaming mice G600 and Razer Krait using AttrakDiff tool to create an improved design that is able to generate positive UX. AttrakDiff is a method that measures pragmatic and hedonic scores of a product with a scale between -3 to +3 through four attributes (i.e. Pragmatic Quality, Hedonic Quality-Identification, Hedonic Quality-Stimulation, and Attractiveness), represented by 28 pairs of opposite words. Based on data gathered from 15 participants, it is identified that gaming mouse G600 needs to be redesigned because of its low grades (pragmatic score: -0.838, hedonic score: 1, attractiveness score: 0.771). The redesign process focuses on the attributes with poor scores and takes into account improvement suggestions collected from interview with the participants. The redesigned mouse G600 is evaluated using the previous method. The result shows higher scores in pragmatic quality (1.929), hedonic quality (1.703), and attractiveness (1.667), indicating that the redesigned mouse is more capable of creating pleasurable experience of product use.

Keywords: AttrakDiff, hedonic aspect, pragmatic aspect, product design, user experience

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Authors: M. A. I Perera, C. R. Wijesinghe, A. R. Weerasinghe

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his study was conducted to review and identify the unsupervised techniques that can be employed to analyze gene expression data in order to identify better subtypes of tumors. Identifying subtypes of cancer help in improving the efficacy and reducing the toxicity of the treatments by identifying clues to find target therapeutics. Process of gene expression data analysis described under three steps as preprocessing, clustering, and cluster validation. Feature selection is important since the genomic data are high dimensional with a large number of features compared to samples. Hierarchical clustering and K Means are often used in the analysis of gene expression data. There are several cluster validation techniques used in validating the clusters. Heatmaps are an effective external validation method that allows comparing the identified classes with clinical variables and visual analysis of the classes.

Keywords: cancer subtypes, gene expression data analysis, clustering, cluster validation

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Authors: Matthew Montebello

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Student engagement within a virtual learning environment generates masses of data points that can significantly contribute to the learning analytics that lead to decision support. Ideally, similar data is collected during student interaction with a physical learning space, and as a consequence, data is present at a large scale, even in relatively small classes. In this paper, we report of such an occurrence during classes held in a HiFlex modality as we investigate the advantages of adopting such a methodology. We plan to take full advantage of the learner-generated data in an attempt to further enhance the effectiveness of the adopted learning environment. This could shed crucial light on operating modalities that higher education institutions around the world will switch to in a post-COVID era.

Keywords: HiFlex, big data in higher education, learning analytics, virtual learning environment

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Authors: Shahzad Hassan, Kamran Saeed

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People are always in search of Wi-Fi hotspots because Internet is a major demand nowadays. But like all other technologies, there is still room for improvement in the Wi-Fi technology with regards to the speed and quality of connectivity. In order to address these aspects, Harald Haas, a professor at the University of Edinburgh, proposed what we know as the Li-Fi (Light Fidelity). Li-Fi is a new technology in the field of wireless communication to provide connectivity within a network environment. It is a two-way mode of wireless communication using light. Basically, the data is transmitted through Light Emitting Diodes which can vary the intensity of light very fast, even faster than the blink of an eye. From the research and experiments conducted so far, it can be said that Li-Fi can increase the speed and reliability of the transfer of data. This paper pays particular attention on the assessment of the performance of this technology. In other words, it is a 5G technology which uses LED as the medium of data transfer. For coverage within the buildings, Wi-Fi is good but Li-Fi can be considered favorable in situations where large amounts of data are to be transferred in areas with electromagnetic interferences. It brings a lot of data related qualities such as efficiency, security as well as large throughputs to the table of wireless communication. All in all, it can be said that Li-Fi is going to be a future phenomenon where the presence of light will mean access to the Internet as well as speedy data transfer.

Keywords: communication, LED, Li-Fi, Wi-Fi

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Authors: Renata Kurpiewska-Korbut

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Making an assumption that the use and sharing of data generated in humanitarian action constitute a core function of humanitarian organizations, the paper analyzes the position of the largest Polish humanitarian non-governmental organizations in the humanitarian data ecosystem in Ukraine and their approach to non-personal and personal data management since February of 2022. Both expert interviews and document analysis of non-profit organizations providing a direct response in the Ukrainian crisis context, i.e., the Polish Humanitarian Action, Caritas, Polish Medical Mission, Polish Red Cross, and the Polish Center for International Aid and the applicability of theoretical perspective of contingency theory – with its central point that the context or specific set of conditions determining the way of behavior and the choice of methods of action – help to examine the significance of data complexity and adaptive approach to data management by relief organizations in the humanitarian supply chain network. The purpose of this study is to determine how the existence of well-established and accurate internal procedures and good practices of using and sharing data (including safeguards for sensitive data) by the surveyed organizations with comparable human and technological capabilities are implemented and adjusted to Ukrainian humanitarian settings and data infrastructure. The study also poses a fundamental question of whether this crisis experience will have a determining effect on their future performance. The obtained finding indicate that Polish humanitarian organizations in Ukraine, which have their own unique code of conduct and effective managerial data practices determined by contingencies, have limited influence on improving the situational awareness of other assistance providers in the data ecosystem despite their attempts to undertake interagency work in the area of data sharing.

Keywords: humanitarian data ecosystem, humanitarian data management, polish NGOs, Ukraine

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Authors: C. O’Brien, Chloe Reiss

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While South Africa has been the chosen host country for over 1,2 million asylum seekers/refugees it has at the same time, been struggling to address the needs of its own people who are still trapped in poverty with little prospects of employment. This limited exploratory, qualitative study was undertaken in Cape Town with a purposive sample of 21 key personnel from various NPOs providing a service to asylum seekers/refugees. Individual in-depth face to face interviews were carried out and the main findings were: Some of the officials at the Department of Home Affairs, health personnel, landlords, school principals, employers, bank officials and police officers were prejudicial in their practices towards asylum seekers/ refugees. The major constraints experienced by NPOs in this study were linked to a lack of funding and minimal government support, strained relationship with the Department of Home Affairs and difficulties in accessing refugees. And finally, the strategies adopted by these NPOs included networking with other service providers, engaging in advocacy, raising community awareness and liaising with government. Thus, more focused intervention strategies are needed to build social cohesion, address prejudices which fuels xenophobic attacks and raise awareness/educate various sectors about refugee rights. Given this burgeoning global problem, social work education and training should include curriculum content on migrant issues. Furthermore, larger studies using mixed methodology approaches would yield more nuanced data and provide for more strategic interventions.

Keywords: refugees and asylum seekers, constraints of service delivery, non-profit organisations, refugee challenges

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Authors: Daniel C. Bonzo

Abstract:

Practical considerations lead to the use of unit of analysis within subjects, e.g., bleeding episodes or treatment-related adverse events, in rare disease settings. This is coupled with data augmentation techniques such as extrapolation to enlarge the subject base. In general, one can think about extrapolation of data as extending information and conclusions from one estimand to another estimand. This approach induces hierarchichal clustered data with varying cluster sizes. Extrapolation of clinical trial data is being accepted increasingly by regulatory agencies as a means of generating data in diverse situations during drug development process. Under certain circumstances, data can be extrapolated to a different population, a different but related indication, and different but similar product. We consider here the problem of estimation (point and interval) using a mixed-models approach under an extrapolation. It is proposed that estimators (point and interval) be constructed using weighting schemes for the clusters, e.g., equally weighted and with weights proportional to cluster size. Simulated data generated under varying scenarios are then used to evaluate the performance of this approach. In conclusion, the evaluation result showed that the approach is a useful means for improving statistical inference in rare disease settings and thus aids not only signal detection but risk-benefit evaluation as well.

Keywords: clustered data, estimand, extrapolation, mixed model

Procedia PDF Downloads 129

Authors: Young-Kwan Choi, Gang-Wook Shin, Sung-Taek Hong

Abstract:

Water supply system consumes large amount of power load during water treatment and transportation of purified water. Many energy conserving high efficiency materials such as DC motor and LED light have recently been introduced to water supply system for energy conservation. This paper performed empirical analysis on BLDC, AC motors, and comparatively analyzed the change in power according to DC power supply ratio in order to conserve energy of a next-generation water treatment system called vertical water treatment system. In addition, a DC distribution system linked with photovoltaic generation was simulated to analyze the energy conserving effect of DC load.

Keywords: vertical water treatment system, DC power supply, energy efficiency, BLDC

Procedia PDF Downloads 497

Authors: Dali Nikolaishvili, Nino Khareba, Mariam Tsitsagi

Abstract:

Tbilisi has been a capital of Georgia since the 5ᵗʰ century. City area was covered by forest in historical past. Nowadays the situation has been changing dramatically. Dozens of problems are caused by damages/destruction of green cover and solution, at one glance, seems to be uncomplicated (planting trees and creating green quarters), but on the other hand, according to the increasing tendency, the built up of areas still remains unsolved. Finding out the ways to overcome such obstacles is important even for protecting the health of society. Making of Retrospective cartography of the forest area of Tbilisi with use of GIS technology and remote sensing was the main aim of the research. Research about the dynamic of forest-cover in Tbilisi and its surroundings included the following steps: assessment of the dynamic of forest in Tbilisi and its surroundings. The survey was mainly based on the retrospective mapping method. Using of GIS technology, studying, comparing and identifying the narrative sources was the next step. And the last one was analyzed of the changes from the 80s to the present days on the basis of decryption of remotely sensed images. After creating a unified cartographic basis, the mapping and plans of different periods have been linked to this geodatabase. Data about green parks, individual old plants existing in the private yards and respondents' Information (according to a questionnaire created in advance) was added to the basic database, the general plan of Tbilisi and Scientific works as well. On the basis of analysis of historic, including cartographic sources, forest-cover maps for different periods of time were made. In addition, was made the catalog of individual green parks (location, area, typical composition, name and so on), which was the basis of creating several thematic maps. Areas with a high rate of green area degradation were identified. Several maps depicting the dynamics of forest cover of Tbilisi were created and analyzed. The methods of linking the data of the old cartographic sources to the modern basis were developed too, the result of which may be used in Urban Planning of Tbilisi. Understanding, perceiving and analyzing the real condition of green cover in Tbilisi and its problems, in turn, will help to take appropriate measures for the maintenance of ancient plants, to develop forests and to plan properly parks, squares, and recreational sites. Because the healthy environment is the main condition of human health and implies to the rational development of the city.

Keywords: catalogue of green area, GIS, historical cartography, cartography, remote sensing, Tbilisi

Procedia PDF Downloads 131