Abstract
We provide further evidence that implementing software countermeasures against timing attacks is a non-trivial task and requires domain-specific software development processes: we report an implementation bug in the s2n library, recently released by AWS Labs. This bug ( now fixed) allowed bypassing the balancing countermeasures against timing attacks deployed in the implementation of the MAC-then-Encode-then-CBC-Encrypt (MEE-CBC) component, creating a timing side-channel similar to that exploited by Lucky 13. Although such an attack could only be launched when the MEE-CBC component is used in isolation - Albrecht and Paterson recently confirmed in independent work that s2n's second line of defence, once reinforced, provides adequate mitigation against current adversary capabilities - its existence serves as further evidence to the fact that conventional software validation processes are not effective in the study and validation of security properties. To solve this problem, we define a methodology for proving security of implementations in the presence of timing attackers: first, prove black-box security of an algorithmic description of a cryptographic construction; then, establish functional correctness of an implementation with respect to the algorithmic description; and finally, prove that the implementation is leakage secure. We present a proof-of-concept application of our methodology to MEE-CBC, bringing together three different formal verification tools to produce an assembly implementation of this construction that is verifiably secure against adversaries with access to some timing leakage. Our methodology subsumes previous work connecting provable security and side-channel analysis at the implementation level, and supports the verification of a much larger case study. Our case study itself provides the first provable security validation of complex timing countermeasures deployed, for example, in OpenSSL.

Abstract
Relational learning algorithms mine complex databases for interesting patterns. Usually, the search space of patterns grows very quickly with the increase in data size, making it impractical to solve important problems. In this work we present the design of a relational learning system, that takes advantage of graphics processing units (GPUs) to perform the most time consuming function of the learner, rule coverage. To evaluate performance, we use four applications: a widely used relational learning benchmark for predicting carcinogenesis in rodents, an application in chemo-informatics, an application in opinion mining, and an application in mining health record data. We compare results using a single and multiple CPUs in a multicore host and using the GPU version. Results show that the GPU version of the learner is up to eight times faster than the best CPU version.

Abstract
Nowadays, there is a global consensus that integrating renewable energy sources (RES) is highly needed to meet an increasing demand for electricity and reduce the overall carbon footprint of power production. Framed in this context, the coordination of RES integration with distributed energy storage systems (DESS), along with the network's switching capability and/or network reinforcement, is expected to significantly improve system flexibility, thereby increasing chances of accommodating large-scale RES power. This paper presents an innovative method to quantify the impacts of network switching and/or reinforcement as well as installing DESSs on the level of renewable power integrated in the system. To carry out this analysis, a dynamic and multi-objective stochastic mixed integer linear programming (S-MILP) model is developed, which jointly takes into account the optimal RES-based DGs and DESS integration in coordination with distribution network reinforcement and/or switching. A standard distribution network system is used as a case study. Numerical results show the capability of DESSs integration in dramatically increasing the level of renewable DGs integrated in the system. Although case-dependent, the impact of network switching on RES power integration is not significant.

Abstract
Background/Aim: In this study, we evaluated the dimensions and volume of rat mammary tumors and the association of these variables with tumor invasiveness. Materials and Methods: Tumors were measured by caliper and ultrasonography. Volume was determined by water displacement and by application of four formulas using tumor length (L), width (W) and depth (D) or tumor weight. Results: Results confirmed the data obtained in our previous work, where we verified that mammary tumors grow as oblate spheroids. Conclusion: The determination of mammary tumor volume by applying the formula V=(4/3) x pi x(L/2) x(L/2) x(D/2) is the best way to evaluate tumor volume in vivo. Beyond volume evaluation by water displacement, the determination on the basis of tumor weight is the most accurate way to evaluate tumor volume after animal sacrifice or tumor excision. According to our results, it is not possible to predict if a tumor is invasive or non-invasive by its dimensions, volume or weight. Future work in chemically-induced mammary cancer should use ultrasonography and water displacement or tumor weight to determine tumor volume in vivo and after animal sacrifice or tumor excision, respectively.

Abstract
The reuse of treated wastewater could be a promising measure to attenuate the water scarcity burden. In agriculture, irrigation with wastewater may contribute to improve production yields, reduce the ecological footprint and promote socioeconomic benefits. However, it cannot be considered exempt of adverse consequences in environmental and human health. Apart from the introduction of some biological and chemical hazardous agents, the disturbance of the indigenous soil microbial communities and, thus, of vital soil functions impacting soil fertility may occur. The consequences of these disturbances are still poorly understood. This chapter summarises the physicochemical and microbiological alterations in soil resultant from irrigation with treated wastewater that are described in scientific literature. These alterations, which involve a high complexity of variables (soil, wastewater, climate, vegetal cover), may have impacts on soil quality and productivity. In addition, possible health risks may arise, in particular through the direct or indirect contamination of the food chain with micropollutants, pathogens or antibiotic resistance determinants. The current state of the art suggests that irrigation with treated wastewater may have a multitude of long-term implications on soil productivity and public health. Although further research is needed, it seems evident that the analysis of risks associated with irrigation with treated wastewater must take into account not only the quality of water, but other aspects as diverse as soil microbiota, soil type or the cultivated plant species. © 2016 Springer International Publishing Switzerland.

Abstract