A Framework for Accurate Drought Forecasting System Using Semantics-Based Data Integration Middleware

Published Conference ProceedingsTechnological advancement in Wireless Sensor Networks (WSN) has made it become an invaluable component of a reliable environmental monitoring system; they form the ‘digital skin’ through which to ‘sense’ and collect the context of the surroundings and provides information on the process leading to complex events such as drought. However, these environmental properties are measured by various heterogeneous sensors of different modalities in distributed locations making up the WSN, using different abstruse terms and vocabulary in most cases to denote the same observed property, causing data heterogeneity. Adding semantics and understanding the relationships that exist between the observed properties, and augmenting it with local indigenous knowledge is necessary for an accurate drought forecasting system. In this paper, we propose the framework for the semantic representation of sensor data and integration with indigenous knowledge on drought using a middleware for an efficient drought forecasting system

Development of Semantics-Based Distributed Middleware for Heterogeneous Data Integration and its Application for Drought

ThesisDrought is a complex environmental phenomenon that affects millions of people and communities all over the globe and is too elusive to be accurately predicted. This is mostly due to the scalability and variability of the web of environmental parameters that directly/indirectly causes the onset of different categories of drought. Since the dawn of man, efforts have been made to uniquely understand the natural indicators that provide signs of likely environmental events. These indicators/signs in the form of indigenous knowledge system have been used for generations. Also, since the dawn of modern science, different drought prediction and forecasting models/indices have been developed which usually incorporate data from sparsely located weather stations in their computation, producing less accurate results – due to lack of the desired scalability in the input datasets. The intricate complexity of drought has, however, always been a major stumbling block for accurate drought prediction and forecasting systems. Recently, scientists in the field of ethnoecology, agriculture and environmental monitoring have been discussing the integration of indigenous knowledge and scientific knowledge for a more accurate environmental forecasting system in order to incorporate diverse environmental information for a reliable drought forecast. Hence, in this research, the core objective is the development of a semantics-based data integration middleware that encompasses and integrates heterogeneous data models of local indigenous knowledge and sensor data towards an accurate drought forecasting system for the study areas of the KwaZulu-Natal province of South Africa and Mbeere District of Kenya. For the study areas, the local indigenous knowledge on drought gathered from the domain experts and local elderly farmers, is transformed into rules to be used for performing deductive inference in conjunction with sensors data for determining the onset of drought through an automated inference generation module of the middleware. The semantic middleware incorporates, inter alia, a distributed architecture that consists of a streaming data processing engine based on Apache Kafka for real-time stream processing; a rule-based reasoning module; an ontology module for semantic representation of the knowledge bases. The plethora of sub-systems in the semantic middleware produce a service(s) as a combined output – in the form of drought forecast advisory information (DFAI). The DFAI as an output of the semantic middleware is disseminated across multiple channels for utilisation by policy-makers to develop mitigation strategies to combat the effect of drought and their drought-related decision-making processes

A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring

ArticleIn recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework

Perception and use of open access electronic thesis and dissertations by the undergraduate students of University of Ilorin, Nigeria

The study examined the used and perception of open access electronic thesis and dissertation by undergraduate students of the University of Ilorin, Nigeria. A total of 375 students drawn from 15 faculties that made up the university represent the sample for the study. Survey research was adopted for the study while a questionnaire titled ‘Use and Perception of Open Access Electronic Thesis and Dissertation Questionnaire’ was used for the collection of data. Five research questions were developed and answered. The results revealed that the use of open access electronic thesis and dissertation is very low and most of the respondents demonstrate limited awareness of the availability of the thesis and dissertation for research which negatively its use. Some of the challenges identified with the use of ETDS include lack of awareness of software and hardware for using ETDs, difficulties of access to computer and internet where ETDs can be retrieved, printed and downloaded, discomfort reading text on a computer screen and inadequate skills in using search engine to browse for ETDs online. The study recommends among others that students should acquire more skills in the use of open electronic thesis and dissertations as it is vital source of information research.Keywords: Electronic Thesis and Dissertations, ETDs, Open Access, Perception, Research, Institutional Repository, Undergraduate, University of Ilorin, Nigeri

TRENDS AND PROSPECTS OF DIGITAL TWIN TECHNOLOGIES: A REVIEW

© Quantum Journal of Engineering, Science and Technology (QJOEST). This is an open access article under the CC BY-NC-ND licence, https://creativecommons.org/licenses/by-nc-nd/4.0/The plethora of technologically developed software and digital types of machinery are widely applied for industrial production and the digitalization of building technologies. The fourth industrial revolution and the underlying digital transformation, known as Industry 4.0 is reshaping the way individuals live and work fundamentally. However, the advent of Industry 5.0 remodels the representation of industrial data for digitalization. As a result, massive data of different types are being produced. However, these data are hysteretic and isolated from each other, leading to low efficiency and low utilization of these valuable data. Simulation based on the theoretical and static model has been a conventional and powerful tool for the verification, validation, and optimization of a system in its early planning stage, but no attention is paid to the simulation application during system run-time. Dynamic simulation of various systems and the digitalization of the same is made possible using the framework available with Digital Twin. After a complete search of several databases and careful selection according to the proposed criteria, 63 academic publications about digital twin are identified and classified. This paper conducts a comprehensive and in-depth review of this literature to analyze the digital twin from the perspective of concepts, technologies, and industrial applicationsPeer reviewe

Physically challenged undergraduates‟ satisfaction with library and information services in Kwara State higher institutions

Despite the fact that academic libraries put their effort to satisfy the needs of their users, they however still neglect some group of users (physically challenged) in the information and service provision. This study was therefore undertaken to find out the physically challenged undergraduates satisfaction with library and information services in Kwara State higher education institutions. Survey research design was adopted to give in-depth information about the study while data was collected through questionnaire. Five research questions were answered by study. The results revealed that information needs of the physically challenged undergraduates in various higher institutions are the same. The level of availability and accessibility of information materials and services to these groups of users was also revealed in the study. Limitations such as non-inclusion of the physically challenged students in the decision making process of the library as well as unavailability of specific information materials that suits the disability of the physically challenged students in the library was also identified. Based on these findings, the study recommends training of library staff to meet the needs of physically challenged users and inclusion of these special group

Prevalence, trends, outcomes, and disparities in hospitalizations for nonalcoholic fatty liver disease in the United States

Background: As the frequency of nonalcoholic fatty liver disease (NAFLD) continues to rise in the United States (US) community, more patients are hospitalized with NAFLD. However, data on the prevalence and outcomes of hospitalizations with NAFLD are lacking. We investigated the prevalence, trends and outcomes of NAFLD hospitalizations in the US. Methods: Hospitalizations with NAFLD were identified in the National Inpatient Sample (2007-2014) by their ICD-9-CM codes, and the prevalence and trends over an 8-year period were calculated among different demographic groups. After excluding other causes of liver disease among the NAFLD cohorts (n=210,660), the impact of sex, race and region on outcomes (mortality, discharge disposition, length of stay [LOS], and cost) were computed using generalized estimating equations (SAS 9.4). Results: Admissions with NAFLD tripled from 2007-2014 at an average rate of 79/100,000 hospitalizations/year (P<0.0001), with a larger rate of increase among males vs. females (83/100,000 vs. 75/100,000), Hispanics vs. Whites vs. Blacks (107/100,000 vs. 80/100,000 vs. 48/100,000), and government-insured or uninsured patients vs. privately-insured (94/100,000 vs. 74/100,000). Males had higher mortality, LOS, and cost than females. Blacks had longer LOS and poorer discharge destination than Whites; while Hispanics and Asians incurred higher cost than Whites. Uninsured patients had higher mortality, longer LOS, and poorer discharge disposition than the privately-insured. Conclusions: Hospitalizations with NAFLD are rapidly increasing in the US, with a disproportionately higher burden among certain demographic groups. Measures are required to arrest this ominous trend and to eliminate the disparities in outcome among patients hospitalized with NAFLD