Search results for: resolution digital data

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: Angel Liduvino Vara-Vela, Dirceu Luis Herdies, Debora Souza Alvim, Eder Paulo Vendrasco, Silvio Nilo Figueroa, Jayant Pendharkar, Julio Pablo Reyes Fernandez

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Biomass-burning episodes are quite common in the central Amazon rainforest and represent a dominant source of aerosols during the dry season, between August and October. The increase in the occurrence of fires in 2019 in the world’s largest biomes has captured the attention of the international community. In particular, a rare and extreme smoke-related event occurred in the afternoon of Monday, August 19, 2019, in the most populous city in the Western Hemisphere, the São Paulo Metropolitan Area (SPMA), located in southeastern Brazil. The sky over the SPMA suddenly blackened, with the day turning into night, as reported by several news media around the world. In order to clarify whether or not the smoke that plunged the SPMA into sudden darkness was related to wildfires in the Amazon rainforest region, a set of 48-hour simulations over South America were performed using the Weather Research and Forecasting with Chemistry (WRF-Chem) model at 20 km horizontal resolution, on a daily basis, during the period from August 16 to August 19, 2019. The model results were satisfactorily compared against satellite-based data products and in situ measurements collected from air quality monitoring sites. Although a very strong smoke transport coming from the Amazon rainforest was observed in the middle of the afternoon on August 19, its impact on air quality over the SPMA took place in upper levels far above the surface, where, conversely, low air pollutant concentrations were observed.

Keywords: Amazon rainforest, biomass burning aerosols, São Paulo metropolitan area, WRF-Chem model

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Authors: Meron Okbandrias

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The South African formal economy is rule-based economy, unlike most African and Asian markets. It has a highly developed financial market. In such a market, foreign migrants have dominated the small or spaza shops that service the poor. They are highly competitive and capture significant market share in South Africa. This paper analyses the factors that assisted the foreign migrants in having a competitive age. It does that by interviewing Somali, Bangladesh, and Ethiopian shop owners in Cape Town analysing the data through a narrative analysis. The paper also analyses the 2019 South African consumer report. The three migrant nationalities mentioned above dominate the spaza shop business and have significant distribution networks. The findings of the paper indicate that family, ethnic, and nationality based network, in that order of importance, form bases for a relationship-based business network that has trust as its mainstay. Therefore, this network ensures the pooling of resources and abiding by certain principles outside the South African rule-based system. The research identified practises like bulk buying within a community of traders, sharing information, buying from a within community distribution business, community based transportation system and providing seed capital for people from the community to start a business is all based on that relationship-based system. The consequences of not abiding by the rules of these networks are social and economic exclusion. In addition, these networks have their own commercial and social conflict resolution mechanisms aside from the South African justice system. Network theory and relationship based systems theory form the theoretical foundations of this paper.

Keywords: migrant, spaza shops, relationship-based system, South Africa

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Authors: Andrea Miconi

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The research will focus on the results of the Tempus European Project eMEDia dedicated to Cross-Media Journalism. The project is founded by the European Commission as it involves four European partners - IULM University, Tampere University, University of Barcelona, and the Mediterranean network Unimed - and three Tunisian Universities – IPSI La Manouba, Sfax and Sousse – along with the Tunisian Ministry for Higher Education and the National Syndicate of Journalists. The focus on Tunisian condition is basically due to the role played by digital activists in its recent history. The research is dedicated to the relationship between political participation, news-making practices and the spread of social media, as it is affecting Tunisian society. As we know, Tunisia during the Arab Spring had been widely considered as a laboratory for the analysis the use of new technologies for political participation. Nonetheless, the literature about the Arab Spring actually fell short in explaining the genesis of the phenomenon, on the one hand by isolating technologies as a casual factor in the spread of demonstrations, and on the other by analyzing North-African condition through a biased perspective. Nowadays, it is interesting to focus on the consolidation of the information environment three years after the uprisings. And what is relevant, only a close, in-depth analysis of Tunisian society is able to provide an explanation of its history, and namely of the part of digital media in the overall evolution of political system. That is why the research is based on different methodologies: desk stage, interviews, and in-depth analysis of communication practices. Networked journalism is the condition determined by the technological innovation on news-making activities: a condition upon which professional journalist can no longer be considered the only player in the information arena, and a new skill must be developed. Along with democratization, nonetheless, the so-called citizen journalism is also likely to produce some ambiguous effects, such as the lack of professional standards and the spread of information cascades, which may prove to be particularly dangerous in an evolving media market as the Tunisian one. This is why, according to the project, a new profile must be defined, which is able to manage this new condition, and which can be hardly reduced to the parameters of traditional journalistic work. Rather than simply using new devices for news visualization, communication professionals must also be able to dialogue with all new players and to accept the decentralized nature of digital environments. This networked nature of news-making seemed to emerge during the Tunisian revolution, when bloggers, journalists, and activists used to retweet each other. Nonetheless, this intensification of communication exchange was inspired by the political climax of the uprising, while all media, by definition, are also supposed to bring some effects on people’s state of mind, culture and daily life routines. That is why it is worth analyzing the consolidation of these practices in a normal, post-revolutionary situation.

Keywords: cross-media, education, Mediterranean, networked journalism, social media, Tunisia

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Authors: J. Shiue, I. H. Chen, S. W. Y. Chiu, Y. L. Wang

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In this work, we present a nondestructive method to characterize bacteria in a TEM system. Unlike the conventional TEM specimen preparation method, which needs to thin the specimen in a destructive way, or spread the samples on a tiny millimeter sized carbon grid, our method is easy to operate without the need of sample pretreatment. With a specially designed transparent chip that allows the electron beam to pass through, and a custom made chip holder to fit into a standard TEM sample holder, the bacteria specimen can be easily prepared on the chip without any pretreatment, and then be observed under TEM. The centimeter-sized chip is covered with Au nanoparticles in the surface as the markers which allow the bacteria to be observed easily on the chip. We demonstrate the success of our method by using E. coli as an example, and show that high-resolution TEM images of E. coli can be obtained with the method presented. Some E. coli morphology characteristics imaged using this method are also presented.

Keywords: bacteria, chip, nanoparticles, TEM

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Authors: Yusuf Emirov, Abdullatif Hakami, Prasanta K Biswas, Elias K Stefanakos, Sesha S Srinivasan

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This study is aimed to develop microencapsulated thermochromic materials and the analysis of core-shell formation using high resolution electron microscopy. The candidate metal oxides (e.g., titanium oxide and silicon oxide) used for the microencapsulation of thermochromic materials are based on the microemulsion route that involves the micelle formation using different surfactants. The effectiveness of the core-shell microstructure formationrevealed the influence of surfactants and the metal oxide precursor concentrations. Additionally, a detailed thermal and color chromic behavior of these core-shell microcapsules are evaluated with the pristine thermochromic dye particles.

Keywords: core-shell thermochromic materials, core-shell microstructure formation, thermal and color chromic behavior of core-shell microcapsules, development micro-capsulated thermochromic materials

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Authors: Kadam Bhambri, Neena Gupta

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All optical wavelength conversion is essential in present day optical networks for transparent interoperability, contention resolution, and wavelength routing. The incorporation of all optical wavelength convertors leads to better utilization of the network resources and hence improves the efficiency of optical networks. Wavelength convertors that can work with Dispersion Managed (DM) solitons are attractive due to their superior transmission capabilities. In this paper, wavelength conversion for dispersion managed soliton signals was demonstrated at 100 Gbps through semiconductor optical amplifier and an optical filter. The wavelength conversion was achieved for a 1550 nm input signal to1555nm output signal. The output signal was measured in terms of BER, Q factor and system margin.    

Keywords: all optical wavelength conversion, dispersion managed solitons, semiconductor optical amplifier, cross gain modultation

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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: Levent Basayigit, Sinan Demir, Yusuf Ucar, Burhan Kara

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Many research projects require accurate delineation of the different land cover type of the agricultural area. Especially it is critically important for the definition of specific plants like cannabis. However, the complexity of vegetation stands structure, abundant vegetation species, and the smooth transition between different seconder section stages make vegetation classification difficult when using traditional approaches such as the maximum likelihood classifier. Most of the time, classification distinguishes only between trees/annual or grain. It has been difficult to accurately determine the cannabis mixed with other plants. In this paper, a mixed distribution models approach is applied to classify pure and mix cannabis parcels using Worldview-2 imagery in the Lakes region of Turkey. Five different land use types (i.e. sunflower, maize, bare soil, and cannabis) were identified in the image. A constrained Gaussian mixture discriminant analysis (GMDA) was used to unmix the image. In the study, 255 reflectance ratios derived from spectral signatures of seven bands (Blue-Green-Yellow-Red-Rededge-NIR1-NIR2) were randomly arranged as 80% for training and 20% for test data. Gaussian mixed distribution model approach is proved to be an effective and convenient way to combine very high spatial resolution imagery for distinguishing cannabis vegetation. Based on the overall accuracies of the classification, the Gaussian mixed distribution model was found to be very successful to achieve image classification tasks. This approach is sensitive to capture the illegal cannabis planting areas in the large plain. This approach can also be used for monitoring and determination with spectral reflections in illegal cannabis planting areas.

Keywords: Gaussian mixture discriminant analysis, spectral mixture model, Worldview-2, land parcels

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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: 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: Chris Preston

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As we devise ever more sophisticated methods of on-line marketing, devising systems that are able to reach into the everyday lives of consumers, we are confronted by a generation of children who face unprecedented intervention by commercial organisations into young minds, via electronic devices, and whether by computer, tablet or phone, such children have been somehow reduced to the status of their devices, with little regard for their well being as individuals. This discussion paper seeks to draw attention to such practice and questions the ethics of digital marketing methods.

Keywords: online marketing to children, online research of children, online targeting of children, consumer rights, ethics

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Authors: Nikolina Ribarić

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E-learning is most commonly defined as a set of applications and processes, such as Web-based learning, computer-based learning, virtual classrooms, and digital collaboration, that enable access to instructional content through a variety of electronic media. The main goal of an e-learning system is learning, and the way to evaluate the impact of an e-learning system is by examining whether students learn effectively with the help of that system. Testmoz is a program for online preparation of knowledge evaluation assignments. The program provides teachers with computer support during the design of assignments and evaluating them. Students can review and solve assignments and also check the correctness of their solutions. Research into the increase of motivation by the practice of providing teaching content by applying online tests prepared in the Testmoz program was carried out with students of the 8th grade of Ljubo Babić Primary School in Jastrebarsko. The students took the tests in their free time, from home, for an unlimited number of times. SPSS was used to process the data obtained by the research instruments. The results of the research showed that students preferred to practice teaching content and achieved better educational results in chemistry when they had access to online tests for repetition and practicing in relation to subject content which was checked after repetition and practicing in "the classical way" -i.e., solving assignments in a workbook or writing assignments in worksheets.

Keywords: chemistry class, e-learning, motivation, Testmoz

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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: Amrin Moger, Martin Mathew, Sagar Bhalerao

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Mass media may be categorized into print and digital, former being the traditional form of reaching the masses to inform and educate on various issues. The Indian print media is more than two centuries old. Its strengths have largely been shaped by its historical experience and, in particular, by its association with the freedom struggle as well as movements for social emancipation, reform, and amelioration. Therefore, it is highly regarded in the Indian society. Persons with disability are part of Indian Society. Persons with Disability have always been looked down upon and not considered as part of the society. People with disabilities were commonly feared, pitied, and neglected. Much of the literature on disability in India has pointed to the importance of the concept of karma in attitudes to disability, with disability perceived either as punishment for misdeeds in the past lives of the PWD, or the wrongdoings of their parents. Some Indian authors consider the passage of the PWD Act as a landmark step in the history of rehabilitation services in India have put it, ‘At a profoundly serious and spiritual level, disability represents divine justice’. The newspaper has to play a role where it changes this attitude of the people. A short comparative content analysis of two English newspapers of Mumbai edition was selected, to analyze the language that is used for reporting disability issues. Software Package for Social Science (SPSS) was used to gather and analyze data.

Keywords: content analysis, disability, newspaper dailies, language

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Authors: Qinghua Xing, Noha M. Mesbah, Haisheng Wang, Jun Li, Baisuo Zhao

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Droplet digital PCR (ddPCR) is being increasingly adopted for gene detection and quantification because of its higher sensitivity and specificity. According to previous observations and our lab data, it is essential to use endogenous reference genes (RGs) when investigating gene expression at the mRNA level under salt stress. This study aimed to select and validate suitable RGs for gene expression under salt stress using ddPCR. Six candidate RGs were selected based on the tandem mass tag (TMT)-labeled quantitative proteomics of Alkalicoccus halolimnae at four salinities. The expression stability of these candidate genes was evaluated using statistical algorithms (geNorm, NormFinder, BestKeeper and RefFinder). There was a small fluctuation in cycle threshold (Ct) value and copy number of the pdp gene. Its expression stability was ranked in the vanguard of all algorithms, and was the most suitable RG for quantification of expression by both qPCR and ddPCR of A. halolimnae under salt stress. Single RG pdp and RG combinations were used to normalize the expression of ectA, ectB, ectC, and ectD under four salinities. The present study constitutes the first systematic analysis of endogenous RG selection for halophiles responding to salt stress. This work provides a valuable theory and an approach reference of internal control identification for ddPCR-based stress response models.

Keywords: endogenous reference gene, salt stress, ddPCR, RT-qPCR, Alkalicoccus halolimnae

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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: Jessica A. Soto-Bear, Virginia González-Vélez, Norma Castañeda-Villa, Amparo Gil

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Glutamate release of the cochlear inner hair cell (IHC) ribbon synapse is a fundamental step in transferring sound information in the auditory pathway. Otoferlin is the calcium sensor in the IHC and its activity has been related to many auditory disorders. In order to simulate secretion dynamics occurring in the IHC in a few milliseconds timescale and with high spatial resolution, we proposed an active-zone model solved with Monte Carlo algorithms. We included models for calcium buffered diffusion, calcium-binding schemes for vesicle fusion, and L-type voltage-gated calcium channels. Our results indicate that calcium influx and calcium binding is managing IHC secretion as a function of voltage depolarization, which in turn mean that IHC response depends on sound intensity.

Keywords: inner hair cells, Monte Carlo algorithm, Otoferlin, secretion

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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: 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: Anastasia K. Eleftheriadou, Athanasios Sfetsos, Nikolaos Gounaris

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The present work examines the suitability of a seasonal forecasting model downscaled with a very high spatial resolution in order to assess the energy performance and requirements of buildings. The application of the developed model is applied on Greece for a period and with a forecast horizon of 5 months in the future. Greece, as a country in the middle of a financial crisis and facing serious societal challenges, is also very sensitive to climate changes. The commonly used method for the correlation of climate change with the buildings energy consumption is the concept of Degree Days (DD). This method can be applied to heating and cooling systems for a better management of environmental, economic and energy crisis, and can be used as medium (3-6 months) planning tools in order to predict the building needs and country’s requirements for residential energy use.

Keywords: downscaled seasonal models, degree days, energy performance

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Authors: Stephen Kyakulumbye, Vivian Kobusingye

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Generally, World over, COVID-19 has had adverse effects on all sectors but with more debilitating effects on the education sector. After reactive lockdowns, education institutions that could continue teaching and learning had to go a distance mediated by digital technological tools. In Uganda, the Ministry of Education thereby issued COVID-19 Online Distance E-learning (ODeL) emergent guidelines. Despite such guidelines, academic institutions in Uganda and similar developing contexts with academically constrained resource environments were caught off-guard and ill-prepared to transform from face-to-face learning to online distance learning mode. Most academic institutions that migrated spontaneously did so with no deliberate tools, systems, strategies, or software to cause active, meaningful, and engaging learning for students. By experience, most of these academic institutions shifted to Zoom and WhatsApp and instead conducted online teaching in real-time than blended synchronous and asynchronous tools. This paper provides students’ experiences while blending synchronous and asynchronous content-creating and learning tools within a technological resource-constrained environment to navigate in such a challenging Uganda context. These conceptual case-based findings, using experience from Uganda Christian University (UCU), point at the design of learning activities with two certain characteristics, the enhancement of synchronous learning technologies with asynchronous ones to mitigate the challenge of system breakdown, passive learning to active learning, and enhances the types of presence (social, cognitive and facilitatory). The paper, both empirical and experiential in nature, uses online experiences from third-year students in Bachelor of Business Administration student lectured using asynchronous text, audio, and video created with Open Broadcaster Studio software and compressed with Handbrake, all open-source software to mitigate disk space and bandwidth usage challenges. The synchronous online engagements with students were a blend of zoom or BigBlueButton, to ensure that students had an alternative just in case one failed due to excessive real-time traffic. Generally, students report that compared to their previous face-to-face lectures, the pre-recorded lectures via Youtube provided them an opportunity to reflect on content in a self-paced manner, which later on enabled them to engage actively during the live zoom and/or BigBlueButton real-time discussions and presentations. The major recommendation is that lecturers and teachers in a resource-constrained environment with limited digital resources like the internet and digital devices should harness this approach to offer students access to learning content in a self-paced manner and thereby enabling reflective active learning through reflective and high-order thinking.

Keywords: synchronous learning, asynchronous learning, active learning, reflective learning, resource-constrained environment

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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: Deboraj Muchahary, Amlan Deep Borah Abir J. Mondal, Alak Majumder

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A discrete cosine transform (DCT) is described and a technique to compute it using fast Fourier transform (FFT) is developed. In this work, DCT of a finite length sequence is obtained by incorporating CORDIC methodology in radix-2 FFT algorithm. The proposed methodology is simple to comprehend and maintains a regular structure, thereby reducing computational complexity. DCTs are used extensively in the area of digital processing for the purpose of pattern recognition. So the efficient computation of DCT maintaining a transparent design flow is highly solicited.

Keywords: DCT, DFT, CORDIC, FFT

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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: Lynette Sheridan Burns, Benjamin J. Matthews

Abstract:

The context of post-industrial journalism is one in which the material circumstances of mechanical publication have been displaced by digital technologies, increasing the distance between the orthodoxy of the newsroom and the culture of journalistic writing. Content is, with growing frequency, created for delivery via the internet, publication on web-based ‘platforms’ and consumption on screen media. In this environment, the question is not ‘who is a journalist?’ but ‘what is journalism?’ today. The changes bring into sharp relief new distinctions between journalistic work and journalistic labor, providing a key insight into the current transition between the industrial journalism of the 20^th century, and the post-industrial journalism of the present. In the 20^th century, the work of journalists and journalistic labor went hand-in-hand as most journalists were employees of news organizations, whilst in the 21^st century evidence of a decoupling of ‘acts of journalism’ (work) and journalistic employment (labor) is beginning to appear. This 'decoupling' of the work and labor that underpins journalism practice is far reaching in its implications, not least for institutional structures. Under these conditions we are witnessing the emergence of expanded ‘entrepreneurial’ journalism, based on smaller, more independent and agile - if less stable - enterprise constructs that are a feature of creative industries. Entrepreneurial journalism is realized in a range of organizational forms from social enterprise, through to profit driven start-ups and hybrids of the two. In all instances, however, the primary motif of the organization is an ideological definition of journalism. An example is the Scoop Foundation for Public Interest Journalism in New Zealand, which owns and operates Scoop Publishing Limited, a not for profit company and social enterprise that publishes an independent news site that claims to have over 500,000 monthly users. Our paper demonstrates that this journalistic work meets the ideological definition of journalism; conducted within the creative industries using an innovative organizational structure that offers a new, viable post-industrial future for journalism.

Keywords: creative industries, digital communication, journalism, post industrial

Procedia PDF Downloads 280

Authors: Renata Kurpiewska-Korbut

Abstract:

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

Procedia PDF Downloads 92

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 136