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This literature review explores the existing work and practices in applying thematic analysis natural language processing techniques to financial data in cloud environments. This work aims to improve two of the five Vs of the big data system. We used the PRISMA approach (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for the review. We analyzed the research papers published over the last 10 years about the topic in question using a keyword-based search and bibliometric analysis. The systematic literature review was conducted in multiple phases, and filters were applied to exclude papers based on the title and abstract initially, then based on the methodology/conclusion, and, finally, after reading the full text. The remaining papers were then considered and are discussed here. We found that automated data discovery methods can be augmented by applying an NLP-based thematic analysis on the financial data in cloud environments. This can help identify the correct classification/categorization and measure data quality for a sentiment analysis.

Given they are two critical infrastructure areas, the security of electricity and gas networks is highly important due to potential multifaceted social and economic impacts. Unexpected errors or sabotage can lead to blackouts, causing a significant loss for the public, businesses, and governments. Climate change and an increasing number of consequent natural disasters (e.g., bushfires and floods) are other emerging network resilience challenges. In this paper, we used network science to examine the topological resilience of national energy networks with two case studies of Australian gas and electricity networks. To measure the fragility and resilience of these energy networks, we assessed various topological features and theories of percolation. We found that both networks follow the degree distribution of power-law and the characteristics of a scale-free network. Then, using these models, we conducted node and edge removal experiments. The analysis identified the most critical nodes that can trigger cascading failure within the network upon a fault. The analysis results can be used by the network operators to improve network resilience through various mitigation strategies implemented on the identified critical nodes.

The goals of climate change mitigation or energy security policies might lead to societal transformations due to large-scale deployment of new technologies or changes in relations about generation, distribution and transmission of electricity. The goals are frequently developed at the national governance levels; however, their implementation takes place at the local governance level. The process of energy transition gives crucial importance to the participation of local communities and their positive attitudes. Such a process should include measures for active engagement of inhabitants and local government while providing a chance to participate in decision-making processes that affect the lives of these people. Currently, participatory governance of energy transition takes place mainly within the discussion about details of the projects and criteria for selection of possible alternatives. It does not include discussion about the need for energy transition or preferences for technologies for decarbonization of the electricity sector. This study investigated social factors linked to the transition of the Austrian energy sector towards a higher share of renewable energy sources. The empirical data show factors which influence the level of awareness about climate change mitigation and energy transition as well as differences in patterns of support for renewable energy sources from various social groups.

We have engineered biomaterials that display nanoclusters of ligands that bind both integrin and syndecan-4 cell receptors. These surfaces regulate cell behaviors under static conditions including adhesion, spreading, actin stress fiber formation, and migration. The syndecan-4 receptors are also critical mediators of cellular mechanotransduction. In this contribution we assess whether this novel class of materials can regulate the response of cells to applied mechanical stimulation, using the shear stress imparted by laminar fluid flow as a model stimulus. Specifically, we assess endothelial cell detachment due to flow, cell alignment due to flow, and cell adhesion from the flowing fluid. A high degree of cell retention was observed on surfaces containing integrin-binding ligands or a mixed population of integrin- and syndecan-binding ligands. However, the presence of both ligand types was necessary for the cells to align in the direction of flow. These results imply that integrin engagement is necessary for adhesion strength, but engagement of both receptor types aids in appropriate mechanotransduction. Additionally, it was found that surfaces functionalized with both ligand types were able to scavenge a larger number of cells from flow, and to do so at a faster rate, compared to surfaces functionalized with only integrin- or syndecan-binding ligands. These results show that interfaces functionalized with both integrin- and syndecan-binding ligands regulate a significant range of biophysical cell behaviors in response to shear stress.

Meniscal tear is one of the prevalent knee disorders among young athletes and the aging population, and requires correct diagnosis and surgical intervention, if necessary. Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need for automatic detection techniques. This paper presents a type-2 fuzzy expert system for meniscal tear diagnosis using PD magnetic resonance images (MRI). The scheme of the proposed type-2 fuzzy image processing model is composed of three distinct modules: Pre-processing, Segmentation, and Classification. lambda-nhancement algorithm is used to perform the pre-processing step. For the segmentation step, first, Interval Type-2 Fuzzy C-Means (IT2FCM) is applied to the images, outputs of which are then employed by Interval Type-2 Possibilistic C-Means (IT2PCM) to perform post-processes. Second stage concludes with re-estimation of "eta" value to enhance IT2PCM. Finally, a Perceptron neural network with two hidden layers is used for Classification stage. The results of the proposed type-2 expert system have been compared with a well-known segmentation algorithm, approving the superiority of the proposed system in meniscal tear recognition.

Meniscal tear is one of the prevalent knee disorders among young athletes and the aging population, and requires correct diagnosis and surgical intervention, if necessary. Not only the errors followed by human intervention but also the obstacles of manual meniscal tear detection highlight the need for automatic detection techniques. This paper presents a type-2 fuzzy expert system for meniscal tear diagnosis using PD magnetic resonance images (MRI). The scheme of the proposed type-2 fuzzy image processing model is composed of three distinct modules: Pre-processing, Segmentation, and Classification. lambda-nhancement algorithm is used to perform the pre-processing step. For the segmentation step, first, Interval Type-2 Fuzzy C-Means (IT2FCM) is applied to the images, outputs of which are then employed by Interval Type-2 Possibilistic C-Means (IT2PCM) to perform post-processes. Second stage concludes with re-estimation of "eta" value to enhance IT2PCM. Finally, a Perceptron neural network with two hidden layers is used for Classification stage. The results of the proposed type-2 expert system have been compared with a well-known segmentation algorithm, approving the superiority of the proposed system in meniscal tear recognition.

This study explores the role of renewable energy (RE) penetration in Malaysia's energy security (ES) and its implications for the country's target of 20% capacity in the energy mix by 2025. Renewable energy (RE) is a critical driver of long-term energy security. In 2018, the share of renewable energy in Malaysia's energy mix was 9%, falling far short of the national target of 20% penetration by 2025. This study employs a system dynamics approach to investigate the relationship between RE penetration and correlated indicators from energy security (ES) dimensions: energy availability, environmental sustainability, and socio-economics. The causal relationships between the three-dimensional indicators of ES have been established using causal and stock and flow logic. Simulated results show that energy consumption has increased sharply, while energy efficiency and economic growth have only increased by a small margin with an increase in RE from 2015 to 2020. The energy intensity is expected to rise slightly by the end of the fifth year. As a result, the overall impact is positive for Malaysia's environmental sustainability while reducing its reliance on energy imports and meeting national economic growth demands.

This study explores the evolving structure of the rising field of “network of networks” (NoN). Reviewing publications dating back to 1931, we describe the evolution of major NoN research themes in different scientific disciplines and the gradual emergence of an integrated field. We analyse the co-occurrence networks of keywords used in all 7818 scientific publications in Scopus database that mention NoN and other related terms (i.e., “interconnected networks”, “multilayer networks”, “multiplex networks”, “interdependent networks”, “multinetworks”, “multilevel networks”, and “multidimensional networks”). The results show that the NoN began to form as a field mainly in the 1990s around research on neural networks. Diverse aspects of NoN research, indicated by dominant keywords such as “interconnection”, “multilayer”, and “interdependence”, gradually spread to computer and physical sciences. As of 2018, network interdependence – with its application in network resilience and prevention of cascading failure – seems to be one of the key topics attracting broad academic attention. Another noteworthy observation is the emergence of a distinct cluster of terms relevant to nanoscience and nanotechnology. It is envisaged from the analysis that NoN concepts will develop stronger ties with nanoscience with increasing understanding and data acquisition from the molecular, atomic, and subatomic levels.

Background: Natamycin is the only topical ophthalmic antifungal drug approved by the Food and Drug Administration (FDA) of the United States, but has unsatisfactory factors such as high dosing frequency.Methods: We report the synthesis and preparation of self-assembled poly(ethylene glycol)-block-poly(glycidyl methacrylate) (PEG-b-PGMA) micelles. These nanoparticles exhibit sustained delivery of a hydrophobic natamycin by topical administration on eye due to the hydrolysable properties of PGMA segments of micelle. Hydrolysis of glycidyl groups within a physiologically relevant environment provides an additional driving force for drug release by generation of hydrophilic hydroxyl groups to 'push' the encapsulated hydrophobic drug away from the resultant hydrophilic domains and into surrounding environment.Results: In vitro and in vivo results revealed that the self-assembled micelles and the encapsulated natamycin were not cytotoxic and the released drug have strong antifungal ability to Candida albicans. Importantly, sustained natamycin release from micelles leads to the reduced administration frequency of natamycin from 8 times per day to 3 times per day in rabbits suffering from fungal keratitis (FK).Conclusion: This study demonstrates a facile method that can greatly reduce dosing frequency of natamycin administration and thus improve long-term patient compliance.