Search results for: healthcare data security

Authors: Sergio Paez Moncaleano, Alvaro Mauricio Montenegro

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This paper presents a novel statistical methodology for measuring and founding constructs in Latent Regression Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations on Item Response Theory (IRT). In addition, based on the fundamentals of submodel theory and with a convergence of many ideas of IRT, we propose an algorithm not just to solve the dimensionality problem (nowadays an open discussion) but a new research field that promises more fear and realistic qualifications for examiners and a revolution on IRT and educational research. In the end, the methodology is applied to a set of real data set presenting impressive results for the coherence, speed and precision. Acknowledgments: This research was financed by Colciencias through the project: 'Multidimensional Item Response Theory Models for Practical Application in Large Test Designed to Measure Multiple Constructs' and both authors belong to SICS Research Group from Universidad Nacional de Colombia.

Keywords: item response theory, dimensionality, submodel theory, factorial analysis

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Authors: Anthony Oppong Kyekyeku, Sussana Antwi-Boasiako, Emmanuel De-Graft Johnson Owusu Ansah

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In this observational study, the performance of a known conventional storage system was tested and evaluated for fitness for its intended purpose. The system has a scope extended for the storage of dry cocoa beans. System sensitivity, reproducibility and uncertainties are not known in details. This study discusses the system performance in the context of existing literature on factors that influence the quality of cocoa beans during storage. Controlled conditions were defined precisely for the system to give reliable base line within specific established procedures. Minitab® 17 and R statistical software (R-3.3.1) were used for the statistical analyses. The approach to the storage system testing was to observe and compare through laboratory test methods the quality of the cocoa beans samples before and after storage. The samples were kept in Kilner jars and the temperature of the storage environment controlled and monitored over a period of 408 days. Standard test methods use in international trade of cocoa such as the cut test analysis, moisture determination with Aqua boy KAM III model and bean count determination were used for quality assessment. The data analysis assumed the entire population as a sample in order to establish a reliable baseline to the data collected. The study concluded a statistically significant mean value at 95% Confidence Interval (CI) for the performance data analysed before and after storage for all variables observed. Correlational graphs showed a strong positive correlation for all variables investigated with the exception of All Other Defect (AOD). The weak relationship between the before and after data for AOD had an explained variability of 51.8% with the unexplained variability attributable to the uncontrolled condition of hidden infestation before storage. The current study concluded with a high-performance criterion for the storage system.

Keywords: benchmarking performance data, cocoa beans, hidden infestation, storage system validation

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Authors: Gurjeet Singh, Rabindra K. Panda

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Soil moisture is a key hydrologic state variable and is intrinsically linked to the Earth's water, climate and carbon cycles. On ecological point of view, the soil moisture is a fundamental natural resource providing the transpirable water for plants. Soil moisture varies both temporally and spatially due to spatiotemporal variation in rainfall, vegetation cover, soil properties and topography. Satellite derived soil moisture provides spatio-temporal extensive data. However, the spatial resolution of a typical satellite (L-band radiometry) is of the order of tens of kilometers, which is not good enough for developing efficient agricultural water management schemes at the field scale. In the present study, the soil moisture from radiometer data has been disaggregated using blending approach to achieve higher resolution soil moisture data. The radiometer estimates of soil moisture at a 40 km resolution have been disaggregated to 10 km, 5 km and 1 km resolutions. The disaggregated soil moisture was compared with the observed data, consisting of continuous sensor based soil moisture profile measurements, at three monitoring sites and extensive spatial near-surface soil moisture measurements, concurrent with satellite monitoring in the 500 km2 study watershed in the Eastern India. The estimated soil moisture status at different spatial scales can help in developing efficient agricultural water management schemes to increase the crop production and water use efficiency.

Keywords: disaggregation, eastern India, radiometers, soil moisture, water use efficiency

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Authors: Will Brown

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Martin Heidegger is considered to be one of the leading philosophical lights of the 20th century with his lecture/essay 'The Question Concerning Technology' proving to be an invaluable text in the study of technology and the understanding of how technology influences the world it is set upon. However, this text has not as of yet been applied to the rapid rise and proliferation of ‘smart’ cities. This article is premised upon the application of the aforementioned text and the smart city in order to provide a fresh, if not critical analysis and interpretation of this phenomena. The first section below provides a brief literature review of smart urbanism in order to lay the groundwork necessary to apply Heidegger’s work to the smart city, from which a framework is developed to interpret the infusion of digital sensing technologies and the urban milieu. This framework is comprised of four concepts put forward in Heidegger’s text: circumscribing, bringing-forth, challenging, and standing-reserve. A concluding chapter is based upon the notion of enframement, arguing that once the rubric of data collection is placed within the urban system, future systems will require the capability to harvest data, resulting in an ever-renewing smart city.

Keywords: air quality sensing, big data, Martin Heidegger, smart city

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Authors: Thozama Kwinana-Mandindi

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The contribution made by wild edible plants to the livelihoods, food baskets and diets of the indigenous people, particularly among the rural dwellers is invaluable. These wild vegetables are among the non-conventional crops which are widely distributed throughout the wild regions in South Africa, indigenous communities have always exploited for micro-nutrient supply. They also supply significant complex, recently discovered compounds, naturally occurring phytonutrients. In order to protect and promote sustainable use of these plants for household food security, there is a need to better understand them through studies and innovations. Assessment of the wild edible plants’ safety is very key to the promotion as an agricultural product which can be utilised during dry seasons and periods of food scarcity to alleviate nutrient insecurity. Through the use of Scanning Electron Microscope (SEM) and energy dispersive X-ray spectroscopy (EDXS), the study is seen as the vital step in taking a holistic view of the value of the four most consumed wild vegetables in the Eastern Cape Province of South Africa as they were analysed for safety and appraised for components that can influence utilisation. Results indicate that they can be relied upon and cultivation be promoted.

Keywords: nature’s resource, wild vegetables, appraisal for safety, SEM

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Authors: Bekim Avdiaj

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The transition period of Kosovo society brought fundamental changes in all the spheres of organizing life. This was the period when also in the cultural tradition the biggest movement and an emerging from ‘isolation’ or from the ‘shell’ occurred. Transformation of the traditional and embracing of the modern began here. The same was experienced and is currently being experienced also by Gjakova and its surrounding which is historically renowned for its great tradition and culture. The population of this region is actually facing a transition from the traditional system into the modern one and quite often with huge leaps. These ‘movements’ or ‘evolutions’ of the society of this region, besides the numerous positive things it ‘harvested’, also brought things that do not at all correspond with their tradition as well as new criminal occurrences which in the past were not present in this area. Furthermore, some of the ‘new’ behaviours that are embraced from other ‘cultures’ and ‘civilizations’, and which are often exceeded, are quite perturbing. The security situation is also worrying, particularly following the appearance of some new criminal occurrences. Therefore, with this research paper we will strive to analyse the new cultural “intersections” as well as the nature of the origin of some new very worrying criminal occurrences. We will present there also some factors inciting into these occurrences, which were confessed by the persons involved in these criminal occurrences and who come from this very region.

Keywords: crime, occurrence, culture, Gjakova Region

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Authors: María José López Rey, Mar Chaves Carrillo, Manuela Caballero Guisado

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The data presented here are part of a broader investigation on active population aging. The work was carried out in November 2020 in Extremadura, a region of southern Spain. This R + D + I project, called "Active aging scenarios in Extremadura: intervention proposals," was carried out by a team of professors, researchers from the University of Extremadura. The project has been financed by the European Regional Development Funds and the Government of Extremadura. Here, we focus on aspects that have to do with the experience of health, especially during the COVID-19 pandemic, and how this has affected the population related to the main sociodemographic variables. In an exercise of methodological triangulation, thus providing robustness to the analysis, primary data, obtained from the survey designed ad hoc, are combined with other secondary data from various sources and studies carried out in Spain (Sociological Research Centre, and National Institute of Statistics). The survey was carried out on a representative sample of the population over 55 years old, coming from Extremadura. Among the findings, we must highlight the practical invariability of perceptions based on the main sociodemographic variables, as well as some differences indicated by the variables sex and age.

Keywords: aging, health, COVID-19, perceptions

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Giacomo Barbieri

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In soilless culture, the management of the nutrient solution is the most important aspect for crop growing. Fertigation dose, frequency and nutrient concentration must be planned with the objective of reaching an optimal crop growth by limiting the utilized resources and the associated costs. The definition of efficient fertigation strategies is a complex problem since fertigation requirements vary on the basis of different factors, and crops are sensitive to small variations on fertigation parameters. To the best of author knowledge, a small-scale test bench that is flexible for both nutrient solution preparation and precise irrigation is currently missing, limiting the investigations in standard practices for soilless culture. Starting from the analysis of the state of the art, this paper proposes a small-scale system that is potentially able to concurrently test different fertigation strategies. The system will be designed and implemented throughout a three year project started on August 2018. However, due to the importance of the topic within current challenges as food security and climate change, this work is spread considering that may inspire other universities and organizations.

Keywords: soilless culture, fertigation, test bench, small-scale, automation

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Authors: Kika Ioannou Kazamia

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This paper examines the action research cycle of the second phase of longitudinal research on sustainable interior design practices, between two groups of stakeholders, designers and clients. During this phase of the action research, the second step - the change stage - of Lewin’s change management model has been utilized to change values, approaches, and attitudes toward sustainable design practices among the participants. Affective domain learning theory is utilized to attach new values. Learning with the use of information technology, collaborative learning, and problem-based learning are the learning methods implemented toward the acquisition of the objectives. Learning methods, and aims, require the design of interventions with participants' involvement in activities that would lead to the acknowledgment of the benefits of sustainable practices. Interventions are steered to measure participants’ decisions for the worth and relevance of ideas, and experiences; accept or commit to a particular stance or action. The data collection methods used in this action research are observers’ reports, participants' questionnaires, and interviews. The data analyses use both quantitative and qualitative methods. The main beneficial aspect of the quantitative method was to provide the means to separate many factors that obscured the main qualitative findings. The qualitative method allowed data to be categorized, to adapt the deductive approach, and then examine for commonalities that could reflect relevant categories or themes. The results from the data indicate that during the second phase, designers and clients' participants altered their behaviours.

Keywords: design, change, sustainability, learning, practices

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Authors: Bahadir Aydin

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Today it is difficult to reach accurate knowledge because of mass data. This huge data makes the environment more and more caotic. Data is a main piller of intelligence. There is a close tie between knowledge and intelligence. Information gathered from different sources can be modified, interpreted and classified by using knowledge development process. This process is applied in order to attain intelligence. Within this process the effect of knowledge is crucial. Knowledge is classified as explicit and tacit knowledge. Tacit knowledge can be seen as "only the tip of the iceberg”. This tacit knowledge accounts for much more than we guess in all intelligence cycle. If the concept of intelligence scrutinized, it can be seen that it contains risks, threats as well as success. The main purpose for all organization is to be succesful by eliminating risks and threats. Therefore, there is a need to connect or fuse existing information and the processes which can be used to develop it. By the help of process the decision-maker can be presented with a clear holistic understanding, as early as possible in the decision making process. Planning, execution and assessments are the key functions that connects to information to knowledge. Altering from the current traditional reactive approach to a proactive knowledge development approach would reduce extensive duplication of work in the organization. By new approach to this process, knowledge can be used more effectively.

Keywords: knowledge, intelligence cycle, tacit knowledge, KIDW

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: Malraj Bharatha Kiriella

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The purpose of this paper is to investigate the determinants of tourism foreign direct investment (TFDI) to selected developing countries during 1978-2017. The study used pooled panel data to estimate an econometric model. The findings show that market size and institutional barriers are determining factors for TFDI in countries, while other variables of positive country conditions, FDI-related government policy, tourism-related infrastructure and labor conditions are insignificant. The result shows that institutional effects are positive, while market size negatively affects TFDI inflows. The research is limited to eight developing countries. The results can be used to support government policy on TFDI. The paper makes the following contributions: First, it provides important insight and understanding into the TFDI decision-making process in developing countries. Second, both TFDI theory and evidence are minimal, and an econometric model developed on the basis of available literature has been empirically tested.

Keywords: determinants, developing countries, FDI in tourism, panel data

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Authors: Arafat A. A. Shabaneh

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Harsh environments demand a developed detection of an optical communication system to ensure a high level of security and safety. Fiber Bragg gratings (FBG) are emerging sensing instruments that respond to variations in strain and temperature via varying wavelengths. In this paper, cascaded uniform FBG as a strain sensor for 6 km length at 1550 nm wavelength with 30 oC is designed with analyzing of dynamic strain and wavelength shifts. FBG is placed in a small segment of optical fiber, which reflects light of a specific wavelength and passes the remaining wavelengths. This makes a periodic alteration in the refractive index within the fiber core. The alteration in the modal index of fiber produced due to strain consequences in a Bragg wavelength. When the developed sensor exposure to a strain of cascaded uniform FBG by 0.01, the wavelength is shifted to 0.0000144383 μm. The sensing accuracy of the developed sensor is 0.0012. Simulation results show reliable and effective strain monitoring sensors for remote sensing applications.

Keywords: Cascaded fiber Bragg gratings, Strain sensor, Remote sensing, Wavelength shift

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Authors: Eid Alharbi, Yaser Miaji, Saeed Alzahrani

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The importance of fruit quality and freshness is potential in today’s life. Most recent studies show and automatic online sorting system according to the internal disorder for fresh apple fruit has developed by using near infrared (NIR) spectroscopic technology. The automatic convener belts system along with sorting mechanism was constructed. To check the internal quality of the apple fruit, apple was exposed to the NIR radiations in the range 650-1300 nm and the data were collected in form of absorption spectra. The collected data were compared to the reference (data of known sample) analyzed and an electronic signal was pass to the sorting system. The sorting system was separate the apple fruit samples according to electronic signal passed to the system. It is found that absorption of NIR radiation in the range 930-950 nm was higher in the internally defected samples as compared to healthy samples. On the base of this high absorption of NIR radiation in 930-950 nm region the online sorting system was constructed.

Keywords: mechatronics design, NIR, fruit quality, spectroscopic technology

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Authors: Lavanya Madhuri Bollipo, K. V. Kadambari

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Objective: To discuss key issues in the diagnosis of Parkinson disease (PD), To discuss features influencing PD progression, To discuss importance of brain SPECT data in PD diagnosis, and To discuss the essentiality of machine learning techniques in early diagnosis of PD. An accurate and early diagnosis of PD is nowadays a challenge as clinical symptoms in PD arise only when there is more than 60% loss of dopaminergic neurons. So far there are no laboratory tests for the diagnosis of PD, causing a high rate of misdiagnosis especially when the disease is in the early stages. Recent neuroimaging studies with brain SPECT using 123I-Ioflupane (DaTSCAN) as radiotracer shown to be widely used to assist the diagnosis of PD even in its early stages. Machine learning techniques can be used in combination with image analysis procedures to develop computer-aided diagnosis (CAD) systems for PD. This paper addressed recent studies involving diagnosis of PD in its early stages using brain SPECT data with Machine Learning Techniques.

Keywords: Parkinson disease (PD), dopamine transporter, single-photon emission computed tomography (SPECT), support vector machine (SVM)

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Authors: Nick Papanikolaou

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In modern world politics, International relations are very complex not only in their construction but also in their interpretation the ex-Yugoslavian(western Balkans) countries, due to the establishment of independent states, have also risen pending geopolitical and territorial issues such as the Kosovo dispute widely known as an active frozen conflict. Science of anthropology and its subfield of anthropology of conflict can suggest a sustainable plan of communities coexistence and abolishment of fondamentalism. The 1244 Security Council Resolution provides a framework of implementation of a transitional international joint international armed presence for ensuring control and stability in the territory. The changing international relations landscape and the rise of the integration of the Western Balkans in the European Union have brought the question of Kosovo and all the till now internationally controlled system of governance to a dead end. A new solution that will ensure a sustainable future needs to be applied in order to solve this case in a way that rights of both albanians and Serbians will be equally respected and both populations will coexist peacefully. What this presentation aims for is to present a plan for the peaceful coexistence and sovreignty of habitants of Kosovo in a whole new way of governance.

Keywords: sovereignty, Kosovo, Western Balkans, anthropology of conflict

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Authors: Thanduxolo Elford Fana

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Rationale and Purpose: Performance management and development system are central to effective and efficient service delivery, especially in highly labour intensive sectors such as South African public health. Performance management and development systems seek to ensure that good employee performance is rewarded accordingly, while those who underperform are developed so that they can reach their full potential. An effective and efficiently implemented performance management system motivates and improves employee engagement. The purpose of this study is to examine the implementation of the performance management and development system and the challenges that are encountered during its implementation in the Eastern Cape Provincial Department of Health. Methods: A qualitative research approach and a case study design was adopted in this study. The primary data were collected through observations, focus group discussions with employees, a group interview with shop stewards, and in-depth interviews with supervisors and managers, from April 2019 to September 2019. There were 45 study participants. In-depth interviews were held with 10 managers at facility level, which included chief executive officer, chief medical officer, assistant director’s in human resources management, patient admin, operations, finance, and two area manager and two operation managers nursing. A group interview was conducted with five shop stewards and an in-depth interview with one shop steward from the group. Five focus group discussions were conducted with clinical and non-clinical staff. The focus group discussions were supplemented with an in-depth interview with one person from each group in order to counter the group effect. Observations included moderation committee, contracting, and assessment meetings. Findings: The study shows that the performance management and development system was not properly implemented. There was non-compliance to performance management and development system policy guidelines in terms of time lines for contracting, evaluation, payment of incentives to good performers, and management of poor performance. The study revealed that the system is ineffective in raising the performance of employees and unable to assist employees to grow. The performance bonuses were no longer paid to qualifying employees. The study also revealed that lack of capacity and commitment, poor communication, constant policy changes, financial constraints, weak and highly bureaucratic management structures, union interference were challenges that were encountered during the implementation of the performance management and development system. Lastly, employees and supervisors were rating themselves three irrespective of how well or bad they performed. Conclusion: Performance management is regarded as vital to improved performance of the health workforce and healthcare service delivery among populations. Effective implementation of performance management and development system depends on well-capacitated and unbiased management at facility levels. Therefore, there is an urgent need to improve communication, link performance management to rewards, and capacitate staff on performance management and development system, as it is key to improved public health sector outcomes or performance.

Keywords: challenges, implementation, performance management and development system, public hospital

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Authors: Tao Feng, Xiaomei Ma, Xian Guo, Jing Wang

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Named Data Network (NDN) is one of the future Internet architecture, all nodes (i.e., hosts, routers) are allowed to have a local cache, used to satisfy incoming requests for content. However, depending on caching allows an adversary to perform attacks that are very effective and relatively easy to implement, such as content pollution attack. In this paper, we use a method of secure network coding based on homomorphic signature system to solve this problem. Firstly ,we use a dynamic public key technique, our scheme for each generation authentication without updating the initial secret key used. Secondly, employing the homomorphism of hash function, intermediate node and destination node verify the signature of the received message. In addition, when the network topology of NDN is simple and fixed, the code coefficients in our scheme are generated in a pseudorandom number generator in each node, so the distribution of the coefficients is also avoided. In short, our scheme not only can efficiently prevent against Intra/Inter-GPAs, but also can against the content poisoning attack in NDN.

Keywords: named data networking, content polloution attack, network coding signature, internet architecture

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Authors: Hantian Wu, Bo Huang, Yuan Zeng

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Divisions between wealthy and poor, private and public landscapes are propagated by the increasing economic inequality of cities. While these are the spatial reflections of larger social issues and problems, urban design can at least employ spatial techniques that promote more inclusive rather than exclusive, overlapping rather than segregated, interlinked rather than disconnected landscapes. Indeed, the type of edge or border between urban landscapes plays a critical role in the way the environment is perceived. China experiences rapid urbanization, which poses unpredictable environmental challenges. The urban green cover and water body are under changes, which highly relevant to resident wealth and happiness. However, very limited knowledge and data on their rapid changes are available. In this regard, enhancing the monitoring of urban landscape with high-frequency method, evaluating and estimating the impacts of the urban landscape changes, and understating the driving forces of urban landscape changes can be a significant contribution for urban planning and studying. High-resolution remote sensing data has been widely applied to urban management in China. The map of urban land use map for the entire China of 2018 with 10 meters resolution has been published. However, this research focuses on the large-scale and high-resolution remote sensing land use but does not precisely focus on the seasonal change of urban covers. High-resolution remote sensing data has a long-operation cycle (e.g., Landsat 8 required 16 days for the same location), which is unable to satisfy the requirement of monitoring urban-landscape changes. On the other hand, aerial-remote or unmanned aerial vehicle (UAV) sensing are limited by the aviation-regulation and cost was hardly widely applied in the mega-cities. Moreover, those data are limited by the climate and weather conditions (e.g., cloud, fog), and those problems make capturing spatial and temporal dynamics is always a challenge for the remote sensing community. Particularly, during the rainy season, no data are available even for Sentinel Satellite data with 5 days interval. Many natural events and/or human activities drive the changes of urban covers. In this case, enhancing the monitoring of urban landscape with high-frequency method, evaluating and estimating the impacts of the urban landscape changes, and understanding the mechanism of urban landscape changes can be a significant contribution for urban planning and studying. This project aims to use the high spatiotemporal fusion of remote sensing data to create short-cycle, high-resolution remote sensing data sets for exploring the high-frequently urban cover changes. This research will enhance the long-term monitoring applicability of high spatiotemporal fusion of remote sensing data for the urban landscape for optimizing the urban management of landscape border to promoting the inclusive of the urban landscape to all communities.

Keywords: urban land cover changes, remote sensing, high spatiotemporal fusion, urban management

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Y. Sunecher, N. Mamode Khan, V. Jowaheer

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This paper considers the modelling of a non-stationary bivariate integer-valued autoregressive moving average of order one (BINARMA(1,1)) with correlated Poisson innovations. The BINARMA(1,1) model is specified using the binomial thinning operator and by assuming that the cross-correlation between the two series is induced by the innovation terms only. Based on these assumptions, the non-stationary marginal and joint moments of the BINARMA(1,1) are derived iteratively by using some initial stationary moments. As regards to the estimation of parameters of the proposed model, the conditional maximum likelihood (CML) estimation method is derived based on thinning and convolution properties. The forecasting equations of the BINARMA(1,1) model are also derived. A simulation study is also proposed where BINARMA(1,1) count data are generated using a multivariate Poisson R code for the innovation terms. The performance of the BINARMA(1,1) model is then assessed through a simulation experiment and the mean estimates of the model parameters obtained are all efficient, based on their standard errors. The proposed model is then used to analyse a real-life accident data on the motorway in Mauritius, based on some covariates: policemen, daily patrol, speed cameras, traffic lights and roundabouts. The BINARMA(1,1) model is applied on the accident data and the CML estimates clearly indicate a significant impact of the covariates on the number of accidents on the motorway in Mauritius. The forecasting equations also provide reliable one-step ahead forecasts.

Keywords: non-stationary, BINARMA(1, 1) model, Poisson innovations, conditional maximum likelihood, CML

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Authors: Tayo P. Ogundunmade, Adedayo A. Adepoju

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Terrorist attacks in liberal democracies bring about a few pessimistic results, for example, sabotaged public support in the governments they target, disturbing the peace of a protected environment underwritten by the state, and a limitation of individuals from adding to the advancement of the country, among others. Hence, seeking for techniques to understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities is the topmost priority of the government in every country. This research aim is to develop an efficient deep learning-based predictive model for the prediction of future terrorist activities in Nigeria, addressing low-quality prediction accuracy problems associated with the existing solution methods. The proposed predictive AI-based model as a counterterrorism tool will be useful by governments and law enforcement agencies to protect the lives of individuals in society and to improve the quality of life in general. A Heterogeneous Bayesian Neural Network (HETBNN) model was derived with Gaussian error normal distribution. Three primary transfer functions (HOTTFs), as well as two derived transfer functions (HETTFs) arising from the convolution of the HOTTFs, are namely; Symmetric Saturated Linear transfer function (SATLINS ), Hyperbolic Tangent transfer function (TANH), Hyperbolic Tangent sigmoid transfer function (TANSIG), Symmetric Saturated Linear and Hyperbolic Tangent transfer function (SATLINS-TANH) and Symmetric Saturated Linear and Hyperbolic Tangent Sigmoid transfer function (SATLINS-TANSIG). Data on the Terrorist activities in Nigeria gathered through questionnaires for the purpose of this study were used. Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error are the forecast prediction criteria. The results showed that the HETFs performed better in terms of prediction and factors associated with terrorist activities in Nigeria were determined. The proposed predictive deep learning-based model will be useful to governments and law enforcement agencies as an effective counterterrorism mechanism to understand the parameters of terrorism and to design strategies to deal with terrorism before an incident actually happens and potentially causes the loss of precious lives. The proposed predictive AI-based model will reduce the chances of terrorist activities and is particularly helpful for security agencies to predict future terrorist activities.

Keywords: activation functions, Bayesian neural network, mean square error, test error, terrorism

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Authors: Salima R. Rajwani, Tazeen Ali, Rubina Barolia, Yasmin Parpio, Nasreen Alwani, Salima B. Virani

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Introduction: Nurses and physicians have a critical role in initiating lifesaving interventions during cardiac arrest. It is important that timely delivery of high quality Cardio Pulmonary Resuscitation (CPR) with advanced resuscitation skills and management of cardiac arrhythmias is a key dimension of code during cardiac arrest. It will decrease the chances of patient survival if the healthcare professionals are unable to initiate CPR timely. Moreover, traditional training will not prepare physicians and nurses at a competent level and their knowledge level declines over a period of time. In this regard, simulation training has been proven to be effective in promoting resuscitation skills. Simulation teaching learning strategy improves knowledge level, and skills performance during resuscitation through experiential learning without compromising patient safety in real clinical situations. The purpose of the study is to evaluate the effectiveness of simulation training in Advanced Cardiac Life Support Courses by using the selfefficacy tool. Methods: The study design is a quantitative research design and non-randomized quasi-experimental study design. The study examined the effectiveness of simulation through self-efficacy in two instructional methods; one is Medium Fidelity Simulation (MFS) and second is Traditional Training Method (TTM). The sample size was 220. Data was compiled by using the SPSS tool. The standardized simulation based training increases self-efficacy, knowledge, and skills and improves the management of patients in actual resuscitation. Results: 153 students participated in study; CG: n = 77 and EG: n = 77. The comparison was done between arms in pre and post-test. (F value was 1.69, p value is <0.195 and df was 1). There was no significant difference between arms in the pre and post-test. The interaction between arms was observed and there was no significant difference in interaction between arms in the pre and post-test. (F value was 0.298, p value is <0.586 and df is 1. However, the results showed self-efficacy scores were significantly higher within experimental group in post-test in advanced cardiac life support resuscitation courses as compared to Traditional Training Method (TTM) and had overall (p <0.0001) and F value was 143.316 (mean score was 45.01 and SD was 9.29) verses pre-test result showed (mean score was 31.15 and SD was 12.76) as compared to TTM in post-test (mean score was 29.68 and SD was 14.12) verses pre-test result showed (mean score was 42.33 and SD was 11.39). Conclusion: The standardized simulation-based training was conducted in the safe learning environment in Advanced Cardiac Life Suport Courses and physicians and nurses benefited from self-confidence, early identification of life-threatening scenarios, early initiation of CPR, and provides high-quality CPR, timely administration of medication and defibrillation, appropriate airway management, rhythm analysis and interpretation, and Return of Spontaneous Circulation (ROSC), team dynamics, debriefing, and teaching and learning strategies that will improve the patient survival in actual resuscitation.

Keywords: advanced cardiac life support, cardio pulmonary resuscitation, return of spontaneous circulation, simulation

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Authors: J. Bahrawi, M. Elhag

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Estimation of evapotranspiration is always a major component in water resources management. Traditional techniques of calculating daily evapotranspiration based on field measurements are valid only for local scales. Earth observation satellite sensors are thus used to overcome difficulties in obtaining daily evapotranspiration measurements on regional scale. The Surface Energy Balance System (SEBS) model was adopted to estimate daily evapotranspiration and relative evaporation along with other land surface energy fluxes. The model requires agro-climatic data that improve the model outputs. Advance Along Track Scanning Radiometer (AATSR) and Medium Spectral Resolution Imaging Spectrometer (MERIS) imageries were used to estimate the daily evapotranspiration and relative evaporation over the entire Nile Delta region in Egypt supported by meteorological data collected from six different weather stations located within the study area. Daily evapotranspiration maps derived from SEBS model show a strong agreement with actual ground-truth data taken from 92 points uniformly distributed all over the study area. Moreover, daily evapotranspiration and relative evaporation are strongly correlated. The reliable estimation of daily evapotranspiration supports the decision makers to review the current land use practices in terms of water management, while enabling them to propose proper land use changes.

Keywords: daily evapotranspiration, relative evaporation, SEBS, AATSR, MERIS, Nile Delta

Procedia PDF Downloads 257

Authors: Khalid A. Al-Afandy, El-Sayyed El-Rabaie, Osama Salah, Ahmed El-Mhalaway

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This paper presents a high secure data hiding technique using image cropping and Least Significant Bit (LSB) steganography. The predefined certain secret coordinate crops will be extracted from the cover image. The secret text message will be divided into sections. These sections quantity is equal the image crops quantity. Each section from the secret text message will embed into an image crop with a secret sequence using LSB technique. The embedding is done using the cover image color channels. Stego image is given by reassembling the image and the stego crops. The results of the technique will be compared to the other state of art techniques. Evaluation is based on visualization to detect any degradation of stego image, the difficulty of extracting the embedded data by any unauthorized viewer, Peak Signal-to-Noise Ratio of stego image (PSNR), and the embedding algorithm CPU time. Experimental results ensure that the proposed technique is more secure compared with the other traditional techniques.

Keywords: steganography, stego, LSB, crop

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Authors: Bulelani Ngamntwini, Liezel Cilliers

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South Africa has one of the highest prevalence of obese people on the African continent. Forty-six percent of the adults in South Africa are physically inactive. Fitness applications can be used to increase physical inactivity. However, the uptake of mobile fitness applications in South Africa has been found to be poor due to usability challenges with the technology. The study developed a usability framework to influence the intention to use mobile fitness applications in South Africa. The study made use of a positivistic approach to collect data. A questionnaire was used to collect quantitative data from 377 respondents that have used mobile fitness applications in the past. A response rate of 80.90% was recorded. To analyse the data, the Pearson correlation was used to determine the relationships between the various hypotheses. There are four usability factors, efficiency, effectiveness, satisfaction, and learnability, which contribute to the intention of users to make use of mobile fitness applications. The study, therefore, recommends that for a mobile fitness application to be successful, these four factors must be considered and incorporated by developers when designing the applications.

Keywords: obese, overweight, physical inactivity, mobile fitness application, usability factors

Procedia PDF Downloads 157

Authors: Yiding Qu, Svetlana V. Komarova

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Chemokines acting through their cognate chemokine receptors guide the directional migration of the cell along the chemokine gradient. Several chemokine receptors were recently identified as non-signaling (decoy), based on their ability to bind the chemokine but produce no measurable signal in the cell. The function of these decoy receptors is not well understood. We examined the expression of a decoy receptor CCRL1 and a signaling receptor that binds to the same ligands, CCR7, in prostate cancer using publically available microarray data (www.oncomine.org). The expression of both CCRL1 and CCR7 increased in an approximately half of prostate carcinoma samples and the majority of metastatic cancer samples compared to normal prostate. Moreover, the expression of CCRL1 positively correlated with the expression of CCR7. These data suggest that CCR7 and CCRL1 can be used as clinical markers for the early detection of transformation from carcinoma to metastatic cancer. In addition, these data support our hypothesis that the non-signaling chemokine receptors actively stimulate cell migration.

Keywords: bioinformatics, cell migration, decoy receptor, meta-analysis, prostate cancer

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Authors: Aikaterini Tsalampouni

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The European Union has long been the most developed model of economic and political integration that has brought a common market, a common currency and a standardization of national policies in certain areas in consistent with EU values and principles. To this direction, there is a parallel process of social integration that effect public policy decisions of member states. Even though social policy, i.e. social protection and moreover healthcare policy, still remains in state's responsibility to develop, EU applies different mechanisms in order to influence health policy systems, since from a more federalist point of view, EU ought to expand its regulatory and legislative roles in as many policy areas as possible. Recently, the pandemic has become a turning point for health care provision and at the same time has also highlighted the need to strengthen the EU’s role in coordinating health care. This paper analyses the EU health policy in general, as well as the response to COVID-19 pandemic with an attempt to identify indications of interaction between EU policies and the promotion of sustainable and resilient health systems. More analytically, the paper investigates the EU binding legal instruments, non-binding legal instruments, monitoring and assessment instruments and instruments for co-financing concerning health care provision in member states and records the evolution of health policies before and during the COVID-19 pandemic. The paper concludes by articulating some remarks regarding the improvement of health policy in EU. Since the ability to deal with a pandemic depends on continuous and increased investment in health systems, the involvement of the EU can lead to a policy convergence, necessary for the resilience of the systems, maintaining at the same time, a strong health policy framework in Europe.

Keywords: EU health policy, EU response to COVID-19, European Health Union, health systems in Europe

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Authors: Jaechun No

Abstract:

This paper focuses on I/O optimizations of N-hybrid (New-Form of hybrid), which provides a hybrid file system space constructed on SSD and HDD. Although the promising potentials of SSD, such as the absence of mechanical moving overhead and high random I/O throughput, have drawn a lot of attentions from IT enterprises, its high ratio of cost/capacity makes it less desirable to build a large-scale data storage subsystem composed of only SSDs. In this paper, we present N-hybrid that attempts to integrate the strengths of SSD and HDD, to offer a single, large hybrid file system space. Several experiments were conducted to verify the performance of N-hybrid.

Keywords: SSD, data section, I/O optimizations, hybrid system

Procedia PDF Downloads 413