Abstract
The amount of data produced by distributed devices, such as smart devices and the IoT, is increasing continuously. The cost of transmitting data and also distributed computing power raise interest in distributed data mining (DDM). However, in a pure DDM scenario, data availability may not be enough to generate reliable models in a distributed environment. So, the ability to exchange data efficiently and effectively will become a crucial component of DDM. In this paper, we propose the concept of the Machine Learning Data Market (MLDM), a framework for the exchange of data among autonomous agents. We consider a set of learning agents in a cooperative distributed ML, where agents negotiate data to improve the models they use locally. In the proposed data market, the system's predictive accuracy is investigated, as well as the economic value of data. The question addressed in this paper is: How data exchange among the agents will improve the accuracy of the learning model. Agent budget is defined as a limitation of negotiation. We defined a multi-agent system with negotiation and assessed it against the multi-agent system baseline and the single-agent system. The proposed framework is analyzed based on the different sizes of batch data collected over time to find out how this changes the effect of the negotiation on the accuracy of the model. The results indicate that even simple negotiation among agents increases their learning accuracy.

Abstract
Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces (APIs), compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system.

Abstract
This paper intends to give an effective hybrid planning of distributed generation and distribution automation in distribution networks aiming to improve the reliability and operation indices. The distribution automation platform consists of automatic voltage and VAR control and automatic fault management systems. The objective function minimizes the sum of the expected daily investment, operation, energy loss and reliability costs. The scheme is constrained by linearized AC optimal power flow equations and planning model of sources and distribution automation. A stochastic programming approach is also implemented in this paper based on a hybrid method of Monte Carlo simulation and simultaneous backward method to model uncertainty parameters of the understudy model including load, energy price and availability of network equipment. Finally, the proposed strategy is implemented on an IEEE 69-bus radial distribution network and different case studies are presented to demonstrate the economic and technical benefits of the investigated model. By allocating the optimal places for sources and distribution automation across the distribution network and extracting the optimal performance, the proposed scheme can simultaneously enhance economic, operation, and reliability indices in the distribution system compared to power flow studies.

Abstract
The combined adoption of Agile and DevOps enables organizations to cope with the increasing complexity of managing customer requirements and requests. It fosters the emergence of a more collaborative and Agile framework to replace the waterfall models applied to software development flow and the separation of development teams from operations. This study aims to explore the benefits of the combined adoption of both models. A qualitative methodology is adopted by including twelve case studies from international software engineering companies. Thematic analysis is employed in identifying the benefits of the combined adoption of both paradigms. The findings reveal the existence of twelve benefits, highlighting the automation of processes, improved communication between teams, and reduction in time to market through process integration and shorter software delivery cycles. Although they address different goals and challenges, the Agile and DevOps paradigms when properly combined and aligned can offer relevant benefits to organizations. The novelty of this study lies in the systematization of the benefits of the combined adoption of Agile and DevOps considering multiple perspectives of the software engineering business environment.

Abstract
The last decade has witnessed a growing tendency to promote deeper exploitation of power systems infrastructure, postponing investments in networks reinforcement. In particular, the literature on smart grids research often emphasizes their potential to defer investments. The study reported in this paper analyses the impact of reinforcement decisions, comparing the long-term costs associated with different network conditions and economic analysis parameters. The results support the conclusion that network reinforcement deferral is not a panacea, as it often generates costly situations in the long-term. The challenge is not to find new ways to postpone investments, but to find the most beneficial criterion to trigger the grid reinforcements actions. Another contribution of the present work is a decision support system to identify the most economical network reinforcement criterion in terms of the peak to capacity ratio.

Abstract
Demand response (DR) programs can offer various benefits especially in microgrid environments with renewable energy systems (RESs) and energy storage technologies when effectively planned and managed. Accordingly, this study proposes an energy management approach for a neighborhood including residential end-users with photovoltaic (PV) systems, a shared energy storage system (ESS) and an electric vehicle (EV) fleet. The proposed approach presents a novel energy credit mechanism (ECM) for the EV fleet and households separately to exploit the EV batteries and store the excess PV energy in the neighborhood through the shared ESS for later use. End-users gain energy credits before a DR event and use these credits during the peak periods to minimize their total energy cost (TEC), resulted in a decrease in the peak demand. Also, the energy credits gained by the EV fleet are used through the vehicle-to-home (V2H) and vehicle-to-grid (V2G) services with the same objective. In order to conduct a more realistic analysis, a battery degradation cost estimation model is employed and the uncertain behavior of the EV users is considered. The case studies show that the proposed optimization strategy has the capability of considerably reducing the energy costs and peak demand.