As the Coronavirus (COVID-19) expands its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. Humans are increasingly confronted with numerous forms of man-made and natural emergency situations. Emergency situations cannot be prevented, but they can be better managed. Successful management of emergency situations requires proper planning, guided response and well-coordinated efforts throughout the life cycle of emergency management. Literature suggests that emergency management efforts benefit from a well-integrated data-based emergency management information system. the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. This perspective paper, proposing networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same. This study can be adapted to help organiztions facing with the new normal in their organization.

The objective of this research is to propose a new algorithm based on the existing Grain - 128 stream cipher algorithm. The comparison of Grain - 128 and Modified Grain - 128 will be evaluated by using NIST Statistical Test Suite. The NIST Statistical Test Suite is conducted to determine the randomness of both algorithms. Conclusively, the Modified Grain - 128 is random based on 1% of significance level compared to the Grain - 128 which is not random at the same significance level.

This paper introduces a new approach in countermeasuring XML signature wrapping attack called the Spatial Signature Algorithm (SSA). The motivation for proposing the SSA approach is due to the limitation of the SOAP (Simple Object Access Protocol) in handling the XML signature wrapping attacks. A different strategy is to be planned in order to deter such attack without extensive computational expense. Spatial Signature Algorithm builds upon the notion of ratio signature that is recommended by a research in biotechnology. The research suggests the possibility of diagnosing a specific disease based on the idea of ratios, specifically on the comparative relationship between elements to detect the emergence of certain threats. Bridging this notion to security, the principle of using space and ratio to detect abnormality is extended to the application of spatial information and digital signature to detect and combat the XML wrapping signature attack.

Image-based data integration in eHealth and life sciences is typically concerned with the method used for anatomical space mapping, needed to retrieve, compare and analyse large volumes of biomedical data. In mapping one image onto another image, a mechanism is used to match and find the corresponding spatial regions which have the same meaning between the source and the matching image. Image-based data integration is useful for integrating data of various information structures. Here we discuss a broad range of issues related to data integration of various information structures, review exemplary work on image representation and mapping, and discuss the challenges that these techniques may bring.

As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010–2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.