A detailed review of the education sector in Australia as in the data provided by the 2006 edition of the OECD's annual publication, 'Education at a Glance' is presented. While the data has shown that in almost all OECD countries educational attainment levels are on the rise, with countries showing impressive gains in university qualifications, it also reveals that a large of share of young people still do not complete secondary school, which remains a baseline for successful entry into the labour market.

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Education at a Glance

Recursive algorithms where random observations enter are studied in a fairly general framework. An important feature is that the observations may depend on previous ?outputs? of the algorithm. The considered class of algorithms contains, e.g., stochastic approximation algorithms, recursive identification algorithms, and algorithms for adaptive control of linear systems. It is shown how a deterministic differential equation can be associated with the algorithm. Problems like convergence with probality one, possible convergence points and asymptotic behavior of the algorithm can all be studied in terms of this differential equation. Theorems stating the precise relationships between the differential equation and the algorithm are given as well as examples of applications of the results to problems in identification and adaptive control.

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Analysis of Recursive Stochastic Algorithms

As rates of new coronavirus disease 2019 (COVID-19) cases decline across Europe owing to nonpharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. We use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we find that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe means that half as many lockdown periods would be required to end continent-wide community transmission.

https://science.sciencemag.org/content/sci/369/6510/1465.full.pdf

Assessing the impact of coordinated COVID-19 exit strategies across Europe

The article aims at describing processes of building Business Intelligence (BI) systems. Taking the BI systems specifics into consideration, the authors present a suggested methodology of the systems creation and implementation in organisations. The considerations are focused on objectives and functional areas of the BI in organisations. Hence, in this context the approach to be used while building and implementing the BI involves two major stages that are of interactive nature, i.e. BI creation and BI "consumption". A large part of the article is devoted to presenting objectives and tasks that are realised while building and implementing BI.

Approach to Building and Implementing Business Intelligence Systems

The relation between length of lockdown, numbers of infected people and deaths of Covid-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to Covid-19 and to constrain the deterioration of economic system

Interrogation system for optical sensor using filter bank and artificial neural network

Optical image sensors are the most common remote sensing data acquisition devices present in Unmanned Aerial Systems (UAS). In this context, assigning a location in a geographic frame of reference to the acquired image is a necessary task in the majority of the applications. This process is denominated direct georeferencing when ground control points are not used. Despite it applies simple mathematical fundamentals, the complete direct georeferencing process involves much information, such as camera sensor characteristics, mounting measurements, attitude and position of the UAS, among others. In addition, there are many rotations and translations between the different reference frames, among many other details, which makes the whole process a considerable complex operation. Another problem is that manufacturers and software tools may use different reference frames posing additional difficulty when implementing the direct georeferencing. As this information is spread among many sources, researchers may face difficulties on having a complete vision of the method. In fact, there is absolutely no paper in the literature that explain this process in a comprehensive way. In order to supply this implicit demand, this paper presents a comprehensive method for direct georeferencing of aerial images acquired by cameras mounted on UAS, where all required information, mathematical operations and implementation steps are explained in detail. Finally, in order to show the practical use of the method and to prove its accuracy, both simulated and real flights were performed, where objects of the acquired images were georeferenced.

/pdf/comprehensive-direct-georeferencing-of-aerial-images-for-1uys68pz.pdf

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications

Compared to standard solutions, soft robotics presents enhanced adaptability to unpredictable and unstructured environments, encompassing advances in fabrication, modeling, and control. The absence of a general theory for the latter is one of the biggest challenges in the field, which constrains these robots’ employment in real-world applications. This research proposes the application of Scheduling by Multiple Edge Reversal (SMER) in the activation of soft legs to be applied in multi-legged robots. A soft device was developed to be tested as a robot’s leg to evaluate the proposed application. A logic controller for this device was designed using the SMER technique. Image processing techniques were used to assess the functionality of the proposed strategy, which demands limited resources. The vision tracking system is composed of a set of infrared-reflective patches, an infrared illuminator, and a pair of cameras with no infrared filters. Results revealed that it is possible to use SMER techniques to activate soft robotics systems and that the methods employed to develop and test the device were appropriate.

A Rhythmic Activation Mechanism for Soft Multi-legged Robots

Dye-sensitized solar cells, termed as DSSC’s have been gaining interest from researchers and industry in the last few years. Such an interest derives from their low-cost manufacturing and easy processing to replace conventional silicon cells. A DSSC operating system consists of three steps: a photoelectrode (anode), an electrolyte solution and a counter electrode (cathode). The dyes aims to expand the absorption band of the device. The most popular dyes are those containing anthocyanins that exist in leaves, fruits, roots, among others. Two photosensitizing natural dyes are proposed in this paper: a passion fruit (Passi-flora eduris) and green coconut (Cocos n ucifera). Measurements such as structural and morphological, optical characterization and electrochemical analyzes of the photoelectrode coated with TiO 2 and platinum were performed. The photo-voltaic efficient performance were measured and compared with the literature. The electrolytic solution made in laboratory fulfilled its function, regenerating the dye with ions. The results showed that Passiflora eduris dye showed the best efficiency 0.52% compared to other types of fruits in the literature. The results reveal that both present good performance for DSSC’s manufacturing.

Passiflora edulis and Cocos nucifera extracts as light-harvesters for efficient dye-sensitized solar cells

In the last decades, current trends in autonomous navigation have demonstrated an increased use of computational vision over traditional techniques. This relies on the fact that most of the spaces are designed for human navigation. As a result, they are filled with visual cues. In this sense, visual recognition is an essential ability to avoid obstacles when an autonomous vehicle interacts with the real world. Data collection using Unmanned Aerial Vehicles (UAVs) navigating in a real-world scenario is a high-cost and time-expensive activity. For this reason, one of the most valuable assets of technology companies is a database containing locations and interactions. One solution to this problem is the adoption of a photo-realistic 3D simulator as a data source. Using this resource, it is possible to gather a significant amount of data. Therefore, this research creates a dataset for instance segmentation using images from a frontal UAV camera navigating in a 3D simulator. This work applies a state-of-the-art deep learning technique, the Mask-RCNN. The architecture takes an image input and predicts per-pixel instance segmentation. Experimental results showed that Mask RCNN has superior performance in our dataset when refining a model trained using COCO dataset. Besides, the proposed methodology presents a good generalization capability due to the promising results in real-world data.

Diego B. Haddad

Papers

Interrogation system for optical sensor using filter bank and artificial neural network

Comprehensive Direct Georeferencing of Aerial Images for Unmanned Aerial Systems Applications

A Rhythmic Activation Mechanism for Soft Multi-legged Robots

Passiflora edulis and Cocos nucifera extracts as light-harvesters for efficient dye-sensitized solar cells

Aerial Image Instance Segmentation Through Synthetic Data Using Deep Learning