Abstract
This paper proposes a camera-to-3D Light Detection And Ranging calibration framework through the optimization of atomic transformations. The system is able to simultaneously calibrate multiple cameras with Light Detection And Ranging sensors, solving the problem of Bundle. In comparison with the state-of-the-art, this work presents several novelties: the ability to simultaneously calibrate multiple cameras and LiDARs; the support for multiple sensor modalities; the calibration through the optimization of atomic transformations, without changing the topology of the input transformation tree; and the integration of the calibration framework within the Robot Operating System (ROS) framework. The software pipeline allows the user to interactively position the sensors for providing an initial estimate, to label and collect data, and visualize the calibration procedure. To test this framework, an agricultural robot with a stereo camera and a 3D Light Detection And Ranging sensor was used. Pairwise calibrations and a single calibration of the three sensors were tested and evaluated. Results show that the proposed approach produces accurate calibrations when compared to the state-of-the-art, and is robust to harsh conditions such as inaccurate initial guesses or small amount of data used in calibration. Experiments have shown that our optimization process can handle an angular error of approximately 20 degrees and a translation error of 0.5 meters, for each sensor. Moreover, the proposed approach is able to achieve state-of-the-art results even when calibrating the entire system simultaneously.

Abstract
Purpose The COVID-19 pandemic has significantly impacted the European Union (EU) through heavy pressure on health services, business activity and people's life. To mitigate these effects, government agencies, civil society and the private sector are working together in proposing innovative initiatives. In this sense, this study aims to characterize and explore the relevance of these projects to mitigate the effects of COVID-19. Design/methodology/approach The Observatory of Public Sector Innovation provided by the Organization for Economic Co-operation and Development was considered to enable the identification and exploration of innovative projects to combat COVID-19. A methodology based on mixed methods is adopted to initially identify quantitatively the distribution of these projects, followed by a qualitative approach based on thematic analysis that allows exploring their relevance. Findings A total of 206 initiatives in the EU have been identified. The distribution of these projects is quite asymmetric, with Portugal and Austria totaling 33.52% of these projects. Most of these projects focus on the areas of public health, infection detection and control, virtual education, local commerce, digital services literacy, volunteering and solidarity and hackathons. Originality/value This work is relevant to identifying and understanding the various areas in which COVID-19 initiatives have been developed. This information is of great relevance for the actors involved in this process to be able to replicate these initiatives in their national, regional and local contexts.

Abstract
Deep Learning (DL) training requires efficient access to large collections of data, leading DL frameworks to implement individual I/O optimizations to take full advantage of storage performance. However, these optimizations are intrinsic to each framework, limiting their applicability and portability across DL solutions, while making them inefficient for scenarios where multiple applications compete for shared storage resources. We argue that storage optimizations should be decoupled from DL frameworks and moved to a dedicated storage layer. To achieve this, we propose a new Software-Defined Storage architecture for accelerating DL training performance. The data plane implements self-contained, generally applicable I/O optimizations, while the control plane dynamically adapts them to cope with workload variations and multi-tenant environments. We validate the applicability and portability of our approach by developing and integrating an early prototype with the TensorFlow and PyTorch frameworks. Results show that our I/O optimizations significantly reduce DL training time by up to 54% and 63% for TensorFlow and PyTorch baseline configurations, while providing similar performance benefits to framework-intrinsic I/O mechanisms provided by TensorFlow.

Abstract
The importance of energy storage continues to grow, whether in power generation, consumer electronics, aviation, or other systems. Therefore, energy management in batteries is becoming an increasingly crucial aspect of optimizing the overall system and must be done properly. Very few works have been found in the literature proposing the implementation of algorithms such as Extended Kalman Filter (EKF) to predict the State of Charge (SOC) in small systems such as mobile robots, where in some applications the computational power is severely lacking. To this end, this work proposes an implementation of the two algorithms mainly reported in the literature for SOC estimation, in an ATMEGA328P microcontroller-based BMS. This embedded system is designed taking into consideration the criteria already defined for such a system and adding the aspect of flexibility and ease of implementation with an average error of 5% and an energy efficiency of 94%. One of the implemented algorithms performs the prediction while the other will be responsible for the monitoring.

Abstract
The present paper seeks to address a gap in the literature regarding green marketing and examines the relationship between ecological concern, inward and outward environmental attitudes, purchasing behavior and environmental behavior as antecedents of green purchasing behavior. The data was gathered through an online survey carried out in Portugal with 530 valid answers. Structural Equation Modelling Partial Least Squares (SEM-PLS) was used to evaluate the model. A t-test was applied to identify differences between men and women. The results show that ecological concern, environmental attitude, environmental behavior and purchase intention are good predictors of green purchase behavior. Women scored higher than men on all variables, meaning that they are indeed superior environmentalists than men. Green purchase behavior is strongly influenced by both purchase intention and environmental behavior, so green brands should focus on targeting individuals that already take some actions in what concerns the environment, or to those who intend to do so.

Abstract
Field-grown grapevines are often exposed to multiple environmental stresses, which challenges wine-growers to develop sustainable measures to sustain vine growth, yield, and quality. Under field conditions this task is demanding, due to differences in the magnitudes of stresses and associated plant responses. In this study we explored the hypothesis that kaolin-particle film application improves grapevine photoprotection through the regulation of xanthophyll cycle genes, limiting the thermal dissipation of excess energy under harsh environmental conditions. Hence, we selected two grapevine varieties, Touriga-Nacional (TN) and Touriga-Franca (TF), grown in the Douro Demarcated Region, and evaluated changes in light dissipation mechanisms, xanthophyll cycle components, and the expression of xanthophyll cycle genes during the 2017 summer season. The results showed that, from veraison to ripening, kaolin triggered the up-regulation of violaxanthin de-epoxidase (VvVDE1) and zeaxanthin epoxidase (VvZEP1) genes, indicating optimised regulation of the xanthophyll cycle. Kaolin treatment also decreased chlorophyll (Chla, Chlb, Chl(a+b)) and carotenoid (Car) accumulation under increasing summer stress conditions in both varieties and lowered the non-photochemical quenching (NPQ) of grapevines on ripening, suggesting a long-term response to summer stress. In addition, kaolin-treated grapevines showed increased Chla/Chlb and lower Chl(a+b)/Car ratios, displaying some features of high light adapted leaves. Overall, this study suggests that kaolin application enabled grapevines to benefit from fluctuating periods of summer stress by managing chlorophyll and carotenoid content and limiting down-regulation of both photochemistry and photoinhibition processes. Under Mediterranean field conditions, kaolin application can be considered an efficient method of minimising summer stress impact on grapevines.