Search results for: healthcare data

Authors: Filiz Celik, Rachel Harrad, Rob Keasley, Paul Bennett

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The COVID-19 pandemic has posed a significant challenge to many, with some groups with particular vulnerability to adverse psychological impacts. These include those disadvantaged by mental ill health, either pre-existing or occurring during pregnancy or post-partum. Using a qualitative approach, the research aimed to identify the challenges posed by COVID-19 to women, their infants and families during the perinatal period and to suggest what further support can help alleviate the adverse mental health impact of COVID-19. 21 expectant and new mothers who were currently receiving support via a peri-natal mental health service participated in semi-structured interviews. In these interviews, participants explored the impact of changes in social circumstances and healthcare providers as a result of COVID-19 restrictions, with the resultant audio recordings transcribed and analyzed using Reflexive Thematic Analysis (RTA). Based on these accounts, it was concluded that women, their partners and potentially their infants experienced heightened peri-natal distress, and their experience at this time increased their risk for future mental health problems. Women described emerging as more vulnerable, owing to their role as primary caregivers during the perinatal period and also explained how social isolation and limited access to services meant protective buffers against mental health deterioration were reduced and the resources they needed in order to develop resilience were weakened. Although partners were invited to take part in the research, a sizeable volume of data could not be generated to fully assess the impact of the pandemic on a partner’s mental well-being. However, women expressed concerns about the paternal mental health of partners and husbands which invites us to be further vigilant to paternal mental health and associated experiences. Overall, these interviews serve to highlight and provide a voice to these women and their families who describe experiencing disadvantage at an already vulnerable time in their lives, as well as illustrating the need for services to prioritize the needs of this population when acute events strike, be those future pandemics or other disasters.

Keywords: patient experience, perinatal mental health, covid-19 pandemic, heightened anxiety, birth trauma, post-natal well-being

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Authors: P. S. Surya Meghana, K. Lingeshwaran, C. Kannan, V. Raghavendran, C. Priya

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One of the most basic and powerful tools in all of science and medicine is the light microscope, the fundamental device for laboratory as well as research purposes. With the improving technology, the need for portable, economic and user-friendly instruments is in high demand. The conventional microscope fails to live up to the emerging trend. Also, adequate access to healthcare is not widely available, especially in developing countries. The most basic step towards the curing of a malady is the diagnosis of the disease itself. The main aim of this paper is to diagnose Malaria with the most common device, cell phones, which prove to be the immediate solution for most of the modern day needs with the development of wireless infrastructure allowing to compute and communicate on the move. This opened up the opportunity to develop novel imaging, sensing, and diagnostics platforms using mobile phones as an underlying platform to address the global demand for accurate, sensitive, cost-effective, and field-portable measurement devices for use in remote and resource-limited settings around the world.

Keywords: cellular, hand-held, health care, image processing, malarial parasites, microscope

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Authors: Imalka Wasana Rathnayaka, Rasheda Khanam, Mohammad Mafizur Rahman

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TheCOVID-19 pandemic has created unprecedented challenges to both the health and economic states in countries around the world. This study aims to evaluate the effectiveness of governments' decisions to mitigate the risks of COVID-19 through proposing policy directions to reduce its magnitude. The study is motivated by the ongoing coronavirus outbreaks and comprehensive policy responses taken by countries to mitigate the spread of COVID-19 and reduce death rates. This study contributes to filling the knowledge by exploiting the long-term efficacy of extensive plans of governments. This study employs a Panel autoregressive distributed lag (ARDL) framework. The panels incorporate both a significant number of variables and fortnightly observations from22 countries. The dependent variables adopted in this study are the fortnightly death rates and the rates of the spread of COVID-19. Mortality rate and the rate of infection data were computed based on the number of deaths and the number of new cases per 10000 people.The explanatory variables are fortnightly values of indexes taken to investigate the efficacy of government interventions to control COVID-19. Overall government response index, Stringency index, Containment and health index, and Economic support index were selected as explanatory variables. The study relies on the Oxford COVID-19 Government Measure Tracker (OxCGRT). According to the procedures of ARDL, the study employs (i) the unit root test to check stationarity, (ii) panel cointegration, and (iii) PMG and ARDL estimation techniques. The study shows that the COVID-19 pandemic forced immediate responses from policymakers across the world to mitigate the risks of COVID-19. Of the four types of government policy interventions: (i) Stringency and (ii) Economic Support have been most effective and reveal that facilitating Stringency and financial measures has resulted in a reduction in infection and fatality rates, while (iii) Government responses are positively associated with deaths but negatively with infected cases. Even though this positive relationship is unexpected to some extent in the long run, social distancing norms of the governments have been broken by the public in some countries, and population age demographics would be a possible reason for that result. (iv) Containment and healthcare improvements reduce death rates but increase the infection rates, although the effect has been lower (in absolute value). The model implies that implementation of containment health practices without association with tracing and individual-level quarantine does not work well. The policy implication based on containment health measures must be applied together with targeted, aggressive, and rapid containment to extensively reduce the number of people infected with COVID 19. Furthermore, the results demonstrate that economic support for income and debt relief has been the key to suppressing the rate of COVID-19 infections and fatality rates.

Keywords: COVID-19, infection rate, deaths rate, government response, panel data

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Authors: Ali Hosseini

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Paralleling is a mechanism to decrease the time necessary to execute the programs. Sorting is one of the important operations to be used in different systems in a way that the proper function of many algorithms and operations depend on sorted data. CRCW_SORT algorithm executes ‘N’ elements sorting in O(1) time on SIMD (Single Instruction Multiple Data) computers with n^2/2-n/2 number of processors. In this article having presented a mechanism by dividing the input string by the hinge element into two less strings the number of the processors to be used in sorting ‘N’ elements in O(1) time has decreased to n^2/8-n/4 in the best state; by this mechanism the best state is when the hinge element is the middle one and the worst state is when it is minimum. The findings from assessing the proposed algorithm by other methods on data collection and number of the processors indicate that the proposed algorithm uses less processors to sort during execution than other methods.

Keywords: CRCW, SIMD (Single Instruction Multiple Data) computers, parallel computers, number of the processors

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Authors: Chris Ewe, Naoum Jamous, Holger Schrödl

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Like most entrepreneurs, data center operators pursue goals such as profit-maximization, improvement of the company’s reputation or basically to exist on the market. Part of those aims is to guarantee a given quality of service. Quality characteristics are specified in a contract called the service level agreement. Central part of this agreement is non-functional properties of an IT service. The system availability is one of the most important properties as it will be shown in this paper. To comply with availability requirements, data center operators can use virtualization technologies. A clear model to assess the effect of virtualization functions on the parts of a data center in relation to the system availability is still missing. This paper aims to introduce a basic model that shows these connections, and consider if the identified effects are positive or negative. Thus, this work also points out possible disadvantages of the technology. In consequence, the paper shows opportunities as well as risks of data center virtualization in relation to system availability.

Keywords: availability, cloud computing IT service, quality of service, service level agreement, virtualization

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Authors: Mahmoud Ahmed Farrag, Ali Zain Elabdeen Heikal, Mohamed Shawky Ahmed, Ahmed Osama Amer

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Crowd-sourced data refers to data that is collected and shared by a large number of individuals or organizations, often through the use of digital technologies such as mobile devices and social media. The shortage in crash data collection in developing countries makes it difficult to fully understand and address road safety issues in these regions. In developing countries, crowd-sourced data can be a valuable tool for improving road safety, particularly in urban areas where the majority of road crashes occur. This study is -to our best knowledge- the first to develop safety performance functions using crowd-sourced data by adopting a negative binomial structure model and the Full Bayes model to investigate traffic safety for urban road networks and provide insights into the impact of roadway characteristics. Furthermore, as a part of the safety management process, network screening has been undergone through applying two different methods to rank the most hazardous road segments: PCR method (adopted in the Highway Capacity Manual HCM) as well as a graphical method using GIS tools to compare and validate. Lastly, recommendations were suggested for policymakers to ensure safer roads.

Keywords: crowdsourced data, road crashes, safety performance functions, Full Bayes models, network screening

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Rishabh Srivastav, Divyam Sharma

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We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic.

Keywords: categorical data, fuzzy logic, genetic algorithm, K modes clustering, rough sets

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Authors: Ahmad Awajan, Sadam Al Wadi

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In this study, a hybrid method based on Empirical Mode Decomposition and Holt-Winter (EMD-HW) is used to forecast Amman stock market data. First, the data are decomposed by EMD method into Intrinsic Mode Functions (IMFs) and residual components. Then, all components are forecasted by HW technique. Finally, forecasting values are aggregated together to get the forecasting value of stock market data. Empirical results showed that the EMD- HW outperform individual forecasting models. The strength of this EMD-HW lies in its ability to forecast non-stationary and non- linear time series without a need to use any transformation method. Moreover, EMD-HW has a relatively high accuracy comparing with eight existing forecasting methods based on the five forecast error measures.

Keywords: Holt-Winter method, empirical mode decomposition, forecasting, time series

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Authors: Tarek M. Alguhane, Ayman H. Khalil, Nour M. Fayed, Ayman M. Ismail

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Early, pre-code, reinforced concrete structures present undetermined resistance to earthquakes. This situation is particularly unacceptable in the case of essential structures, such as healthcare structures and pilgrims' houses. Among these, existing old RC building in Madinah is seismically evaluated with and without infill wall and their dynamic characteristics are compared with measured values in the field using ambient vibration measurements (AVM). After, updating the mathematical models for this building with the experimental results, three dimensional pushover analysis (Nonlinear static analysis) was carried out using SAP 2000 software incorporating inelastic material properties for concrete, infill and steel. The purpose of this analysis is to evaluate the expected performance of structural systems by estimating, strength and deformation demands in design, and comparing these demands to available capacities at the performance levels of interest. The results are summarized and discussed.

Keywords: seismic assessment, pushover analysis ambient vibration, modal update

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Authors: Sandra T. Matarneh, Mark Danso-Amoako, Salam Al-Bizri, Mark Gaterell

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Today’s facilities are ever more sophisticated and the need for available and reliable information for operation and maintenance activities is vital. The key challenge for facilities managers is to have real-time accurate and complete information to perform their day-to-day activities and to provide their senior management with accurate information for decision-making process. Currently, there are various technology platforms, data repositories, or database systems such as Computer-Aided Facility Management (CAFM) that are used for these purposes in different facilities. In most current practices, the data is extracted from paper construction documents and is re-entered manually in one of these computerized information systems. Construction Operations Building information exchange (COBie), is a non-proprietary data format that contains the asset non-geometric data which was captured and collected during the design and construction phases for owners and facility managers use. Recently software vendors developed add-in applications to generate COBie spreadsheet automatically. However, most of these add-in applications are capable of generating a limited amount of COBie data, in which considerable time is still required to enter the remaining data manually to complete the COBie spreadsheet. Some of the data which cannot be generated by these COBie add-ins is essential for facilities manager’s day-to-day activities such as job sheet which includes preventive maintenance schedules. To facilitate a seamless data transfer between BIM models and facilities management systems, we developed a framework that enables automated data generation using the data extracted directly from BIM models to external web database, and then enabling different stakeholders to access to the external web database to enter the required asset data directly to generate a rich COBie spreadsheet that contains most of the required asset data for efficient facilities management operations. The proposed framework is a part of ongoing research and will be demonstrated and validated on a typical university building. Moreover, the proposed framework supplements the existing body of knowledge in facilities management domain by providing a novel framework that facilitates seamless data transfer between BIM models and facilities management systems.

Keywords: building information modeling, BIM, facilities management systems, interoperability, information management

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Authors: Noha Badkook, Maryam Alsubaie, Samaher Dawood, Enas Khairallah

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Cloud computing has introduced transformative benefits in data storage and accessibility while posing unique forensic challenges. This paper explores cloud forensics, focusing on investigating and analyzing evidence from cloud environments to address issues such as unauthorized data access, manipulation, and breaches. The research highlights the practical use of opensource forensic tools like Autopsy and Bulk Extractor in realworld scenarios, including unauthorized data sharing via Google Drive and the misuse of personal cloud storage for sensitive information leaks. This work underscores the growing importance of robust forensic procedures and accessible tools in ensuring data security and accountability in cloud ecosystems.

Keywords: cloud forensic, tools, challenge, autopsy, bulk extractor

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Authors: Partha Pratim Kalita

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As sound can be converted to light, light can also be deduced to sound. There are technologies to convert light to sound, but there are not many technologies related to the field where sound can be converted to a distinct vibrational sequence of light. Like the laws under which the principles of sound work, there are principles for the light to become quantum in nature. Thus, as we move from sound to the subtler aspects of light, we are moving from 3D to 5D. Either we will be making technologies of 3D in today’s world, or we will be really interested in making technologies of the 5D, depends on our understanding of how quantum 5D works. Right now, the entire world is talking about quantum, which is about the nature and behavior of subatomic particles, which is 5D. In practice, they are using metals and machines based on atomic structures. If we talk of quantum without taking note of the technologies of 5D and beyond, we will only be reinterpreting relative theories in the name of quantum. This paper, therefore, will explore the possibilities of moving towards quantum in its real essence with the Shoonya ecosystem (zero-point energy). In this context, the author shall highlight certain working models developed by him, which are currently in discussion with the Indian government.

Keywords: quantum mechanics, quantum technologies, healthcare, shoonya ecosystem, energy, human consciousness

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Authors: Tsung Chin Wu, Yu Chen Tsai, Rhay Hung Weng, Weir Sen Lin

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In recent years, nursing staff’s lower job satisfaction has led to higher turnover rates, and high turnover rates not only cause medical institution costs to increase but also the quality of medical care to decrease. From the perspective of internal marketing, institution staffs are internal customers, and institutions should focus and meet the needs of staff, so that staff will strive to meet the needs of external customers and provide them with the required care. However, few previous studies have investigated the impact of internal staff satisfaction on external customers. Therefore, this study aimed to conduct job satisfaction surveys on internal staff to investigate the relationship between job satisfaction and quality of medical care through statistical analysis of the study results. The related study results may serve as a reference for healthcare managers. This study was conducted using a questionnaire and the subjects were nursing staff from four hospitals. A total of 600 questionnaires were distributed and 577 valid questionnaires were returned with a response rate of 96.1%. After collecting the data, the reliability and validity of the study variables were confirmed by confirmatory factor analysis. The impact of internal marketing and job satisfaction on turnover intention of nursing staff was analyzed using descriptive analysis, one-way ANOVA, Pearson correlation analysis and multiple regression analysis. The study results showed that there was a significant difference between nursing staff’s job title and ‘professional participation’ and ‘shifts’. There was a significant difference between salary and ‘shifts’ and ‘turnover intention’, as well as between marriage and ‘remuneration’ and ‘turnover intention’. A significant difference was found between professional advancement and ‘professional growth’ and ‘type of leave’, as well as between division of service and ‘shifts’ and ‘turnover intention’. Pearson correlation analysis revealed a significant negative correlation between turnover intention and ‘internal marketing’, ‘interaction’, ‘professional participation’, ‘grasp of environment’, ‘remuneration’ and ‘shifts’, meaning that the higher the satisfaction, the lower the turnover intention. It is recommended that hospitals establish a comprehensive internal marketing mechanism to enhance staff satisfaction and in turn, reduce intention to resign, and the key to increasing job satisfaction is by establishing effective methods of internal communication.

Keywords: internal marketing, job satisfaction, turnover intention, nursing staff

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Authors: Anne Waithira Dormal

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Climate change is threatening pastoral livelihoods in Kajiado County, Kenya, through water shortages, livestock deaths, and increasing poverty. This study examines how these impacts differ for men and women within these communities. Limited access to resources, limited land and livestock rights, and limited decision-making power increase women's vulnerability, which is further burdened by traditional gender roles in water procurement. The research recognizes the complexity of climate change and emphasizes that factors such as wealth, family dynamics, and socioeconomic status also influence resilience. Effective adaptation strategies must address all genders. While livestock farming provides a safety net, socioeconomic empowerment through access to credit, healthcare, and education strengthens entire communities. An intersectional perspective that takes ethnicity, social status, and other factors into account is also crucial. This research, therefore, aims to examine how gender-specific adaptation strategies interact with gender and socioeconomic factors to determine the resilience of these Kenyan pastoralist communities. Such strategies, which address the specific needs and vulnerabilities of men and women, are expected to lead to increased resilience to climate change. The aim of the study is to identify effective, gender-specific adaptation strategies that can be integrated into climate change planning and implementation. Additionally, research awaits a deeper understanding of how socioeconomic factors interact with gender to influence vulnerability and resilience within these communities. The study uses a gender-sensitive qualitative approach with focus group discussions in four different pastoral and agropastoral communities. Both qualitative and demographic data are used to capture sources of income, education level, and household size of focus group respondents to increase the power of the analysis. While the research acknowledges the limitations of specific focus sites and potential biases in self-reporting, it offers valuable insights into gender and climate change in pastoral contexts. This study contributes to understanding gender-based vulnerabilities and building resilience in these communities.

Keywords: climate adaptation strategies, climate change, climate resilience, gendered vulnerability, pastoralism

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Authors: Amir Rashid

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Cloud Computing frameworks empower organizations to cut expenses by outsourcing computation resources on-request. As of now, customers of Cloud service providers have no methods for confirming the privacy and ownership of their information and data. To address this issue we propose the platform of a trusted cloud computing program (TCCP). TCCP empowers Infrastructure as a Service (IaaS) suppliers, for example, Amazon EC2 to give a shout box execution condition that ensures secret execution of visitor virtual machines. Also, it permits clients to bear witness to the IaaS supplier and decide if the administration is secure before they dispatch their virtual machines. This paper proposes a Trusted Cloud Computing Platform (TCCP) for guaranteeing the privacy and trustworthiness of computed data that are outsourced to IaaS service providers. The TCCP gives the deliberation of a shut box execution condition for a client's VM, ensuring that no cloud supplier's authorized manager can examine or mess up with its data. Furthermore, before launching the VM, the TCCP permits a client to dependably and remotely acknowledge that the provider at backend is running a confided in TCCP. This capacity extends the verification of whole administration, and hence permits a client to confirm the data operation in secure mode.

Keywords: cloud security, IaaS, cloud data privacy and integrity, hybrid cloud

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Authors: Obinna Afamefuna Echi

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Background: The global increase in the elderly population is anticipated to reach significant levels by 2050, presenting extensive economic, social, and healthcare challenges. Age-related cognitive decline, alterations in brain anatomy, and systemic inflammation are profound concerns that diminish the quality of life and increase susceptibility to diseases like Alzheimer's and cardiovascular diseases. Resistance training is presently studied for its potential neuroprotective and anti-inflammatory benefits in older adults. Objectives: This study aimed to explore the effects of different resistance training modalities on neurotrophic factors, inflammatory markers, and cognitive functions in the elderly. Methods: A controlled trial was conducted with 60 male participants aged 60-75, assigned to either 12 weeks of high-intensity blood flow restriction training (BFRT), muscle damaging resistance training (MDRT), or a non-exercising control group. Cognitive function, neurotrophic factors such as BDNF, and inflammatory markers including IL-6 and TNF were measured before and after the intervention period. Setting: Participants were recruited from Kaunas, Lithuania, with sessions facilitated at the Lithuanian Sports University and health assessments conducted at the Lithuanian University of Health Sciences. Results: Preliminary data suggested did not show significant improvements in BDNF levels and cognitive functions in the BFRT and MDRT groups compared to controls. However, there was a notable reduction in inflammatory markers, indicating potential health benefits beyond cognitive enhancement. Conclusion: The incorporation of resistance training can be a strategic intervention to mitigate age-associated cognitive decline and systemic inflammation, thereby enhancing overall health and quality of life in older adults. The results advocate for wider adoption and further study of resistance training as a preventive measure in ageing populations. Funding: The Lithuanian Sports University, the Research Council of Lithuania and the Lithuanian University of Health Sciences.

Keywords: ageing, resistance training, BDNF, cognitive function

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Authors: Ronit Chakraborty, Sugata Banerji

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There has been a marked increase in the reported prevalence of Autism Spectrum Disorder (ASD) among children in the US over the past two decades. This research has analyzed the growth in state-level ASD prevalence against 45 different potentially explanatory factors, including socio-economic, demographic, healthcare, public policy, and political factors. The goal was to understand if these factors have adequate predictive power in modeling the differential growth in ASD prevalence across various states and if they do, which factors are the most influential. The key findings of this study include (1) the confirmation that the chosen feature set has considerable power in predicting the growth in ASD prevalence, (2) the identification of the most influential predictive factors, (3) given the nature of the most influential predictive variables, an indication that a considerable portion of the reported ASD prevalence differentials across states could be attributable to over and under diagnosis, and (4) identification of Florida as a key outlier state pointing to a potential under-diagnosis of ASD there.

Keywords: autism spectrum disorder, clustering, machine learning, predictive modeling

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Authors: Sajid Ali, Dietrich Schröder

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The merging of internet and Web has created many disciplines and Web GIS is one these disciplines which is effectively dealing with the geospatial data in a proficient way. Web GIS technologies have provided an easy accessing and sharing of geospatial data over the internet. However, there is a single platform for easy and multiple accesses of the data lacks for the European Caribbean Association (Europaische Karibische Gesselschaft - EKG) to assist their members and other research community. The technique presented in this paper deals with designing of a geodatabase using PostgreSQL/PostGIS as an object oriented relational database management system (ORDBMS) for competent dissemination and management of spatial data and Web GIS by using OpenGeo Suite for the fast sharing and distribution of the data over the internet. The characteristics of the required design for the geodatabase have been studied and a specific methodology is given for the purpose of designing the Web GIS. At the end, validation of this Web based geodatabase has been performed over two Desktop GIS software and a web map application and it is also discussed that the contribution has all the desired modules to expedite further research in the area as per the requirements.

Keywords: desktop GISSoftware, European Caribbean association, geodatabase, OpenGeo suite, postgreSQL/PostGIS, webGIS, web map application

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Authors: Bylhah Mugotitsa, Jim Todd, Agnes Kiragga, Jay Greenfield, Evans Omondi, Lukoye Atwoli, Reinpeter Momanyi

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The INSPIRE network aims to build an open, ethical, sustainable, and FAIR (Findable, Accessible, Interoperable, Reusable) data science platform, particularly for longitudinal mental health (MH) data. While studies have been done at the clinical and population level, there still exists limitations in data and research in LMICs, which pose a risk of underrepresentation of mental disorders. It is vital to examine the existing longitudinal MH data, focusing on how FAIR datasets are. This landscape analysis aimed to provide both overall level of evidence of availability of longitudinal datasets and degree of consistency in longitudinal studies conducted. Utilizing prompters proved instrumental in streamlining the analysis process, facilitating access, crafting code snippets, categorization, and analysis of extensive data repositories related to depression, anxiety, and psychosis in Africa. While leveraging artificial intelligence (AI), we filtered through over 18,000 scientific papers spanning from 1970 to 2023. This AI-driven approach enabled the identification of 228 longitudinal research papers meeting inclusion criteria. Quality assurance revealed 10% incorrectly identified articles and 2 duplicates, underscoring the prevalence of longitudinal MH research in South Africa, focusing on depression. From the analysis, evaluating data and metadata adherence to FAIR principles remains crucial for enhancing accessibility and quality of MH research in Africa. While AI has the potential to enhance research processes, challenges such as privacy concerns and data security risks must be addressed. Ethical and equity considerations in data sharing and reuse are also vital. There’s need for collaborative efforts across disciplinary and national boundaries to improve the Findability and Accessibility of data. Current efforts should also focus on creating integrated data resources and tools to improve Interoperability and Reusability of MH data. Practical steps for researchers include careful study planning, data preservation, machine-actionable metadata, and promoting data reuse to advance science and improve equity. Metrics and recognition should be established to incentivize adherence to FAIR principles in MH research

Keywords: longitudinal mental health research, data sharing, fair data principles, Africa, landscape analysis

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Authors: Gaurav Kumar Sinha

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In an era defined by the proliferation of data and the utilization of cloud computing environments, the efficient transfer and processing of big data workloads across multi-cloud platforms have emerged as critical challenges. This research paper embarks on a comprehensive exploration of the complexities associated with managing and optimizing big data in a multi-cloud ecosystem.The foundation of this study is rooted in the recognition that modern enterprises increasingly rely on multiple cloud providers to meet diverse business needs, enhance redundancy, and reduce vendor lock-in. As a consequence, managing data across these heterogeneous cloud environments has become intricate, necessitating innovative approaches to ensure data integrity, security, and performance.The primary objective of this research is to investigate strategies and techniques for enhancing the efficiency of data transfer and processing in multi-cloud scenarios. It recognizes that big data workloads are characterized by their sheer volume, variety, velocity, and complexity, making traditional data management solutions insufficient for harnessing the full potential of multi-cloud architectures.The study commences by elucidating the challenges posed by multi-cloud environments in the context of big data. These challenges encompass data fragmentation, latency, security concerns, and cost optimization. To address these challenges, the research explores a range of methodologies and solutions. One of the key areas of focus is data transfer optimization. The paper delves into techniques for minimizing data movement latency, optimizing bandwidth utilization, and ensuring secure data transmission between different cloud providers. It evaluates the applicability of dedicated data transfer protocols, intelligent data routing algorithms, and edge computing approaches in reducing transfer times.Furthermore, the study examines strategies for efficient data processing across multi-cloud environments. It acknowledges that big data processing requires distributed and parallel computing capabilities that span across cloud boundaries. The research investigates containerization and orchestration technologies, serverless computing models, and interoperability standards that facilitate seamless data processing workflows.Security and data governance are paramount concerns in multi-cloud environments. The paper explores methods for ensuring data security, access control, and compliance with regulatory frameworks. It considers encryption techniques, identity and access management, and auditing mechanisms as essential components of a robust multi-cloud data security strategy.The research also evaluates cost optimization strategies, recognizing that the dynamic nature of multi-cloud pricing models can impact the overall cost of data transfer and processing. It examines approaches for workload placement, resource allocation, and predictive cost modeling to minimize operational expenses while maximizing performance.Moreover, this study provides insights into real-world case studies and best practices adopted by organizations that have successfully navigated the challenges of multi-cloud big data management. It presents a comparative analysis of various multi-cloud management platforms and tools available in the market.

Keywords: multi-cloud environments, big data workloads, data transfer optimization, data processing strategies

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Authors: Alicia Regodon Puyalto, Alfonso Garcia-Santos

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This article presents a method to analyze the use of indoor spaces based on data analytics obtained from inbuilt digital devices. The study uses the data generated by the in-place devices, such as smart locks, Wi-Fi routers, and electrical sensors, to gain additional insights on space occupancy, user behaviour, and comfort. Those devices, originally installed to facilitate remote operations, report data through the internet that the research uses to analyze information on human real-time use of spaces. Using an in-place Internet of Things (IoT) network enables a faster, more affordable, seamless, and scalable solution to analyze building interior spaces without incorporating external data collection systems such as sensors. The methodology is applied to a real case study of coliving, a residential building of 3000m², 7 floors, and 80 users in the centre of Madrid. The case study applies the method to classify IoT devices, assess, clean, and analyze collected data based on the analysis framework. The information is collected remotely, through the different platforms devices' platforms; the first step is to curate the data, understand what insights can be provided from each device according to the objectives of the study, this generates an analysis framework to be escalated for future building assessment even beyond the residential sector. The method will adjust the parameters to be analyzed tailored to the dataset available in the IoT of each building. The research demonstrates how human-centered data analytics can improve the future spatial design of indoor spaces.

Keywords: in-place devices, IoT, human-centred data-analytics, spatial design

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Authors: Ram Sharan Mehta

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Continuing changes in health and social care policy and practice have affected and changed the way in which nursing is practiced. One of the greatest challenges facing nursing today is to build on the essence of nursing as a caring profession whilst incorporating new technologies, ideas and approaches to future healthcare. The objective of this study was to find out the socio-demographic characteristics of the M.Sc. Nursing students and calculate the association between specialty subjects, caste, age group, and residence with SLC division, BN/BSN division, entrance score, and total nursing experience. Descriptive cross-sectional study design was used to conduct the study among all the 25 M.Sc. Nursing students studying at BPKIHS in 2012. Most of the students (56%) were of age group of 25-30 years, completed his academic courses with first division and succeeded in entrance test in first attempt (96%). Based on the results, it can conclude that most of the subjects were of young age, having high score achievers in SLC, I.Sc., CN, BN/BSN and Entrance test. The demographic characteristics do not influence in the academic scores of the students.

Keywords: profile, postgraduate nursing students, Nepal, influence

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Authors: Aditi Viswanathan, Shree Ranjani, Aruna Govada

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With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research seeks to develop an algorithm that implements the Support Vector Machine over a multi-class data set and is efficient in a distributed environment. For this, we recursively choose the best binary split of a set of classes using a greedy technique. Much like the divide and conquer approach. Our algorithm has shown better computation time during the testing phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the data set grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.

Keywords: distributed algorithm, MapReduce, multi-class, support vector machine

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Authors: Dale Dzemydiene, Aurelija Burinskiene, Arunas Miliauskas, Kristina Ciziuniene

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Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.

Keywords: multi-objective, analysis, data flow, freight delivery, methodology

Procedia PDF Downloads 180

Authors: Amjad Khan

Abstract:

The security of Vehicular ad hoc networking is of great importance as it involves serious life threats. Thus to provide secure communication amongst Vehicles on road, the conventional security system is not enough. It is necessary to prevent the network resources from wastage and give them protection against malicious nodes so that to ensure the data bandwidth availability to the legitimate nodes of the network. This work is related to provide a non conventional security system by introducing some constraints to minimize the DoS (Denial of services) especially data and bandwidth. The data packets received by a node in the network will pass through a number of tests and if any of the test fails, the node will drop those data packets and will not forward it anymore. Also if a node claims to be the nearest node for forwarding emergency messages then the sender can effectively identify the true or false status of the claim by using these constraints. Consequently the DoS(Denial of Services) attack is minimized by the instant availability of data without wasting the network resources.

Keywords: black hole attack, grey hole attack, intransient traffic tempering, networking

Procedia PDF Downloads 284

Authors: Zhou Yang, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

Abstract:

Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction.

Keywords: traffic prediction, raw data utilization, context building, data reduction

Procedia PDF Downloads 128

Authors: Abdalla Abdelnabi, Yousf Abushalah

Abstract:

The G Formation is a major gas producing reservoir in the SM Field, eastern, Libya. It is called G limestone because it consists of shallow marine limestone. Well data and 3D-Seismic in conjunction with the results of a previous study were used to delineate the hydrocarbon reservoir of Middle Eocene G-Formation of SM Field area. The data include three-dimensional seismic data acquired in 2009. It covers approximately an area of 75 mi² and with more than 9 wells penetrating the reservoir. Seismic data are used to identify any stratigraphic and structural and features such as channels and faults and which may play a significant role in hydrocarbon traps. The well data are used to calculation petrophysical analysis of S field. The average porosity of the Middle Eocene G Formation is very good with porosity reaching 24% especially around well W 6. Average water saturation was calculated for each well from porosity and resistivity logs using Archie’s formula. The average water saturation for the whole well is 25%. Structural mapping of top and bottom of Middle Eocene G formation revealed the highest area in the SM field is at 4800 ft subsea around wells W4, W5, W6, and W7 and the deepest point is at 4950 ft subsea. Correlation between wells using well data and structural maps created from seismic data revealed that net thickness of G Formation range from 0 ft in the north part of the field to 235 ft in southwest and south part of the field. The gas water contact is found at 4860 ft using the resistivity log. The net isopach map using both the trapezoidal and pyramid rules are used to calculate the total bulk volume. The original gas in place and the recoverable gas were calculated volumetrically to be 890 Billion Standard Cubic Feet (BSCF) and 630 (BSCF) respectively.

Keywords: 3D seismic data, well logging, petrel, kingdom suite

Procedia PDF Downloads 150

Authors: Kapil Pandey, Vishnu Goyal

Abstract:

Spatial and temporal data analysis is very well known in the field of satellite image processing. When spatial data are correlated with time, series analysis it gives the significant results in change detection studies. In this paper the GIS and Remote sensing techniques has been used to find the change detection using time series satellite imagery of Uttarakhand state during the years of 1990-2010. Natural vegetation, urban area, forest cover etc. were chosen as main landuse classes to study. Landuse/ landcover classes within several years were prepared using satellite images. Maximum likelihood supervised classification technique was adopted in this work and finally landuse change index has been generated and graphical models were used to present the changes.

Keywords: GIS, landuse/landcover, spatial and temporal data, remote sensing

Procedia PDF Downloads 433

Authors: Hailye Tekleselassie

Abstract:

Edge computing is a spread computing outline that transports initiative applications closer to data sources such as IoT devices or local edge servers, and possible happenstances would skull the action of new technologies. However, this investigation was attained to investigation the consciousness of technology and communications organization workers and computer users who support the service cloud. Surveys were used to achieve these objectives. Surveys were intended to attain these aims, and it is the functional using survey. Enquiries about confidence are also a key question. Problems like data privacy, integrity, and availability are the factors affecting the company’s acceptance of the service cloud.

Keywords: IoT, data, security, edge computing

Procedia PDF Downloads 83