Alternative sweetener from curculigo fruits

This study gives an overview on the advantages of Curculigo Latifolia as an alternative sweetener and a health product. The purpose of this research is to provide another option to the people who suffer from diabetes. In this research, Curculigo Latifolia was chosen, due to its unique properties and widely known species in Malaysia. In order to obtain the sweet protein from the fruit, it must go through a couple of procedures. First we harvested the fruits from the Curculigo trees that grow wildly in the garden. Next, the Curculigo fruits were dried in the oven at 50 0C for 3 days. Finally, the dried fruits were blended in order to get a fine powder. Curculin is a sweet protein with a taste-modifying activity of converting sourness to sweetness. The curculin content from the sample shown are directly proportional to the mass of the Curculigo fine powder. While the FTIR result shows that the sample spectrum at peak 1634 cm–1 contains secondary amines. At peak 3307 cm–1 contains alkynes

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems

Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.Comment: 32 pages, 12 figure

Qualitative and Quantitative Approaches for Evaluation of Safety Risks in Coal Mines

The safety in underground coal mines continues to be a major problem in the Indian mining industry. Despite significant measures taken by the Directorate General of Mines Safety (DGMS) to reduce the number of mining accidents in underground coal mines, the number remains high. To improve the safety conditions, it has become a prerequisite to performing risk assessment for various operations in Indian mines. It is noted that many research studies conducted in the past are limited to either statistical analysis of accidents or study of single equipment or operation using qualitative and quantitative techniques. Limited work has been done to identify, analyse, and evaluate the safety risks of a complete underground coal mine in India. The present study attempts to determine the appropriate qualitative and quantitative risk assessment approaches for the evaluation of safety risks in Indian underground coal mines. This thesis addresses several important objectives as (i) to identify the type of safety risk analysis techniques suitable for evaluating various mining scenarios (ii) to identify and analyse the hazard factors and hazardous events that affects the safety in underground coal using the qualitative and quantitative approaches (iii) to evaluate the risk level (RL) of the hazardous factors/groups, hazardous events, and the overall mine using the proposed methodology. In this research work, the qualitative techniques, i.e. Failure Mode and Effects Analysis (FMEA), Workplace Risk Assessment and Control (WRAC), and the quantitative techniques, i.e. Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) were applied in an underground coal mine to identify and analyse the hazard factors and hazard events. The analysis of FMEA and WRAC results concluded that the qualitative risk assessment is easy to execute and practical as they are not dependent on the historical data; rather they need experience and close examination. On the other hand, they may yield subjective results due to instinctive human assessment. The analysis of the FTA and ETA results concluded that the quantitative risk assessment could not be performed in Indian underground coal mines due to lack of probability, exposure, and consequence data. To overcome the mentioned problems in qualitative and quantitative techniques, a methodology was proposed for evaluation of the safety risks of hazard events, hazard groups, and overall mine. The proposed methodology is the unification of fuzzy logic, VIKOR (In Serbia: VIseKriterijumska Optimizacija I Kompromisno Resenje, that means: Multi-criteria Optimization and Compromise Solution), and Analytic Hierarchy Process (AHP) techniques. Because of the imprecise nature of the information available in the mining industry, fuzzy logic was employed to evaluate the risk of each hazardous event in terms of consequence, exposure, and probability. VIKOR as was used to rank the evaluated risk of hazardous events. AHP technique helps to determine the relative importance of the risk factors. Therefore, AHP technique was integrated into the risk model so that the risk evaluation can progress from hazardous event level to hazard factor level and finally to overall mine level. To reduce the calculation time significantly and to increase the speed of the proposed risk assessment process, a user-friendly Graphical User Interface (TRAM) was developed using the C# language through Microsoft Visual Studio 2015 and .Net libraries. The proposed methodology developed in this thesis was applied to six underground coal mines. The results presented the risk level of hazard events, hazards groups and overall mine of six mines. The mine-5 has the highest risk level among the evaluated mines. The ranking order of the mines observed based on the overall risk level is mine-5> mine-1 > mine-2 > mine-3 > mine-6 > mine-4. The results of the proposed methodology were compared with DGMS proposed rapid ranking method. This is observed that the proposed methodology presents better evaluation than other approaches. This study could help the mine management to prepare safety measures based on the risk rankings obtained. It may also aid to evaluate accurate risk levels with identified hazards while preparing risk management plans

FUZZY BASED SECURITY ALGORITHM FOR WIRELESS SENSOR NETWORKS IN THE INTERNET OF THINGS PARADIGM

Published ThesisThe world is embracing the idea of Internet of Things and Industrial Revolution 4.0. However, this acceptance of computerised evolution is met with a myriad of challenges, where consumers of this technology are also growing ever so anxious about the security of their personal data as well as reliability of data collected by the millions and even billions of sensors surrounding them. Wireless sensor networks are the main baseline technology driving Internet of things; by their very inherent nature, these networks are too vulnerable to attacks and yet the network security tools designed for conventional computer networks are not effective in countering these attacks. Wireless sensors have low computational resources, may be highly mobile and in most cases, these networks do not have a central point which can be marked as an authentication point for the sensors, any node can join or leave whenever they want. This leaves the sensors and the internet of things applications depending on them highly susceptible to attacks, which may compromise consumer information and leave security breaches in situation that need absolute security such as homes or even the cars they drive. There are many possibilities of things that could go wrong when hackers gain control of sensors in a car or a house. There have been many solutions offered to address security of Wireless Sensor Networks; however, most of those solutions are often not customised for African context. Given that most African countries have not kept pace with the development of these underlying technologies, blanket adoption of the solutions developed for consumption in the developed world has not yielded optimal results. The focus of this research was the development of an Intrusion Detection System that works in a hierarchical network structured Wireless Sensor Network, where cluster heads oversee groups of nodes and relay their data packets all the way to the sink node. This is a reactive Intrusion Detection System (IDS) that makes use of a fuzzy logic based algorithm for verification of intrusion detections. This system borrows characteristics of traditional Wireless Sensor Networks in that it is hosted external to the nodes; that is, on a computer or server connected to the sink node. The rational for this is the premise that developing the system in this manner optimises the power and processing resource of nodes because no part of the IDS is found in the nodes and they are left to focus purely on sensing. The Intrusion Detection System makes use of remote Over The Air programming to communicate with compromised nodes, to either shut down or reboot and is designed with the ZigBee protocol in mind. Additionally, this Intrusion Detection System is intended to being part of a larger Internet of Things integration framework being proposed at the Central University of Technology. This framework is aimed at developing an Internet of Things adoption strategy customised for African needs and regionally local consumers. To evaluate the effectiveness of the solution, the rate of false detections being picked out by the security algorithm were reduced through the use of fuzzy logic systems; this resulted in an accuracies of above 90 %. The algorithm is also very light when asymptotic notation is applied, making it ideal for Wireless Sensors. Lastly, we also put forward the Xbee version of the Triple Modular Redundancy architecture, customised for Wireless sensor networks in order to beef-up on the security solution presented in this dissertation

An ontological framework for the formal representation and management of human stress knowledge

There is a great deal of information on the topic of human stress which is embedded within numerous papers across various databases. However, this information is stored, retrieved, and used often discretely and dispersedly. As a result, discovery and identification of the links and interrelatedness between different aspects of knowledge on stress is difficult. This restricts the effective search and retrieval of desired information. There is a need to organize this knowledge under a unifying framework, linking and analysing it in mutual combinations so that we can obtain an inclusive view of the related phenomena and new knowledge can emerge. Furthermore, there is a need to establish evidence-based and evolving relationships between the ontology concepts.Previous efforts to classify and organize stress-related phenomena have not been sufficiently inclusive and none of them has considered the use of ontology as an effective facilitating tool for the abovementioned issues.There have also been some research works on the evolution and refinement of ontology concepts and relationships. However, these fail to provide any proposals for an automatic and systematic methodology with the capacity to establish evidence-based/evolving ontology relationships.In response to these needs, we have developed the Human Stress Ontology (HSO), a formal framework which specifies, organizes, and represents the domain knowledge of human stress. This machine-readable knowledge model is likely to help researchers and clinicians find theoretical relationships between different concepts, resulting in a better understanding of the human stress domain and its related areas. The HSO is formalized using OWL language and Protégé tool.With respect to the evolution and evidentiality of ontology relationships in the HSO and other scientific ontologies, we have proposed the Evidence-Based Evolving Ontology (EBEO), a methodology for the refinement and evolution of ontology relationships based on the evidence gleaned from scientific literature. The EBEO is based on the implementation of a Fuzzy Inference System (FIS).Our evaluation results showed that almost all stress-related concepts of the sample articles can be placed under one or more category of the HSO. Nevertheless, there were a number of limitations in this work which need to be addressed in future undertakings.The developed ontology has the potential to be used for different data integration and interoperation purposes in the domain of human stress. It can also be regarded as a foundation for the future development of semantic search engines in the stress domain

Psychosocial factors and safety in high-risk industries: a systematic literature review

Most large-scale industrial catastrophes (like the Deepwater Horizon oil spill, or Fukushima-Daiichi nuclear disaster) result from a combination of faults in technical arrangements and neglected social structures featuring a workplace. Whereas it has been acknowledged that human-factor causes can be attributed to accidents in high-risk industries, research in this domain remains scattered and in need of integration. Considered from a psychological perspective, the primary objective of this study is therefore to systematically review existing associations between psychosocial work characteristics and safety in high-risk industries. While grounded in the Job Demands-Resources (JD-R) theoretical model, this study adopts a systematic literature methodology and synthesizes identified empirical evidence through a framework synthesis approach. Results indicate that there is preliminary evidence of a link between the exposure to workplace psychosocial factors and safety in high-risk industries. Studies of the linkages between psychosocial factors and safety behavior are more prevalent and do more often find significant associations between the variables than studies that investigate associations between psychosocial factors and safety outputs. Moreover, results indicate that job demand factors are likely to trigger employees’ health-impairing mental/physical conditions that can constitute a precursor of unsafe behavior. Results imply as well the existence of a link between work-induced psychosocial states (typically in a form of stress or exhaustion) and safety. Limitations in the existing evidence base are recognized, thoroughly discussed with several suggestions for further development of the research field being offered. Practical and theoretical implications of the results are presented.publishedVersio