A novel hybrid cybersecurity scheme against false data injection attacks in automated power systems

The conventional power systems are evolving as smart grids. In recent times cyberattacks on smart grids have been increasing. Among diferent attacks, False Data Injection (FDI) is considered as an emerging threat that has signifcant impact. By exploiting the vulnerabilities of IEC 61850 Generic Object-Oriented Substation Events (GOOSE) and Sam-pled Values (SV) attackers can launch diferent FDI attacks. In this paper, a real-time set up capable of simulating FDI on GOOSE and SV protocols is developed to evaluate the impact of such attacks on power grid. IEC 62351 stipulates cybersecurity guidelines for GOOSE and SV, but only at communication or Information Technology (IT) level. Hence there is a need to develop a holistic security both at IT and Operation Technology (OT) level. In this regard, a novel sequence content resolver-based hybrid security scheme suitable to tackle FDI attacks on GOOSE and SV is proposed. Furthermore, the computational performance of the proposed hybrid security scheme is presented to demonstrate its applicability to the time critical GOOSE and SV protocols.     

Applications of association rule mining in health informatics: a survey

Association rule mining is an effective data mining technique which has been used widely in health informatics research right from its introduction. Since health informatics has received a lot of attention from researchers in last decade, and it has developed various sub-domains, so it is interesting as well as essential to review state of the art health informatics research. As knowledge discovery researchers and practitioners have applied an array of data mining techniques for knowledge extraction from health data, so the application of association rule mining techniques to health informatics domain has been focused and studied in detail in this survey. Through critical analysis of applications of association rule mining literature for health informatics from 2005 to 2014, it has been explored that, instead of the more efficient alternative approaches, the Apriori algorithm is still a widely used frequent itemset generation technique for application of association rule mining for health informatics. Moreover, other limitations related to applications of association rule mining for health informatics have also been identified and recommendations have been made to mitigate those limitations. Furthermore, the algorithms and tools utilized for application of association rule mining have also been identified, conclusions have been drawn from the literature surveyed, and future research directions have been presented.     

GPS‐level accurate camera localization with HorizonNet

This paper investigates the problem of position estimation of unmanned surface vessels (USVs) operating in coastal areas or in the archipelago. We propose a position estimation method where the horizon line is extracted in a 360° panoramic image around the USV. We design a convolutional neural network (CNN) architecture to determine an approximate horizon line in the image and implicitly determine the camera orientation (the pitch and roll angles). The panoramic image is warped to compensate for the camera orientation and to generate an image from an approximately level camera. A second CNN architecture is designed to extract the pixelwise horizon line in the warped image. The extracted horizon line is correlated with digital elevation model data in the Fourier domain using a minimum output sum of squared error correlation filter. Finally, we determine the location of the maximum correlation score over the search area to estimate the position of the USV. Comprehensive experiments are performed in field trials conducted over 3 days in the archipelago. Our approach provides excellent results by achieving robust position estimates with global positioning system (GPS)‐level accuracy in previously unvisited test areas.     

Analysing complex behaviour of hydrological systems through a system dynamics approach

The interaction among various water cycle components consists of complex, non-linear, and bidirectional (interdependent) biophysical processes which can be interpreted using feedback loops in a system dynamics (SD) environment. This paper demonstrates application of an SD approach with two case studies using a specialised software tool, Vensim. The first case study simulates water balance in a rice field system on a daily basis under aerobic conditions with provision of supplemental irrigation on demand. A physically based conceptual water balance model was developed and then implemented using Vensim to simulate the processes that occur in the field water balance system including percolation, surface runoff, actual evapotranspiration, and capillary rise. The second case study simulates surface–groundwater dynamic interactions in an irrigation area where river water and groundwater are two key sources of irrigation. The modelled system encompasses dynamically linked processes including seepage from the river, evaporation from a shallow watertable, groundwater storage, and lateral flow from upland to lowland areas. The model can be applied to simulate responses of different irrigation management scenarios, to develop strategies to improve water use efficiency and control watertable, to prevent salinization in upland, and to reduce the cost of groundwater abstraction in lowland areas. The discussed applications of the SD approach conclude that it helps to conceptualize and simulate complex and dynamic water system processes deterministically which are otherwise partly simulated by conventional hydrologic and stochastic modelling approaches. It is recognised that conceptualization and implementation phases of this approach are challenging, however, the latter is greatly assisted by modern computer softwares.     

Design,closed-form modeling and analysis of SU-8 based electrothermal microgripper for biomedical applications

Microsystem Technologies - This paper presents design analysis of an SU-8 based 2-DoF electrothermal microgripper by developing a closed-form analytical model. The analytical model considers both...     

Automatic Eyewitness Identification During Disasters by Forming a Feature-Word Dictionary

Social media provide digitally interactional technologies to facilitate information sharing and exchanging individuals. Precisely, in case of disasters, a massive corpus is placed on platforms such as Twitter. Eyewitness accounts can benefit humanitarian organizations and agencies, but identifying the eyewitness Tweets related to the disaster from millions of Tweets is difficult. Different approaches have been developed to address this kind of problem. The recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic rules. However, these approaches suffer from limitations as they are dataset-dependent and not scalable. In this paper, we proposed a method to identify eyewitnesses from Twitter. To experiment, we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the eyewitness. Considering each feature, a dictionary of words was created with the Word Dictionary Maker algorithm, which is the crucial contribution of this research. This algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words dictionary. Further, keyword matching for each feature in tweets is performed. If a feature exists in a tweet, it is termed as 1; otherwise, 0. Similarly, for 13 features, we created a file that reflects features in each tweet. To classify the tweets based on features, Naïve Bayes, Random Forest, and Neural Network were utilized. The approach was implemented on different disasters like earthquakes, floods, hurricanes, and Forest fires. The results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure values. At the same time, the proposed approach gained a 0.88 value of F-measure. The results were comparable as the proposed approach is not dataset-dependent. Therefore, it can be used for the identification of eyewitness accounts.     

MobiByte: An Application Development Model for Mobile Cloud Computing

Mobile cloud computing presents an effective solution to overcome smartphone constraints, such as limited computational power, storage, and energy. As the traditional mobile application development models do not support computation offloading, mobile cloud computing requires novel application development models that can facilitate the development of cloud enabled mobile applications. This paper presents a mobile cloud application development model, named MobiByte, to enhance mobile device applications’ performance, energy efficiency, and execution support. MobiByte is a context-aware application model that uses multiple data offloading techniques to support a wide range of applications. The proposed model is validated using prototype applications and detailed results are presented. Moreover, MobiByte is compared with the most recent application models with a conclusion that it outperforms the existing application models in many aspects like energy efficiency, performance, generality, context awareness, and privacy.     

A proteomic investigation of isogenic radiation resistant prostate cancer cell lines

To model the problem of radiation resistance in prostate cancer, cell lines mimicking a clinical course of conventionally fractionated or hypofractionated radiotherapy have been generated. Proteomic analysis of radiation resistant and radiosensitive DU145 prostate cancer cells detected 4410 proteins. Over 400 proteins were differentially expressed across both radiation resistant cell lines and pathway analysis revealed enrichment in epithelial to mesenchymal transition, glycolysis and hypoxia. From the radiation resistant protein candidates, the cell surface protein CD44 was identified in the glycolysis and epithelial to mesenchymal transition pathways and may serve as a potential therapeutic target.