Abstract
Generation Z is expected to be a dominant demographic and economic group. Cyber-waviness, constant reliance on smart devices that allows them to be always connected are among some of their intrinsic characteristics. The combination of this reality with the ever-changing technological environment is compelling retailers to reshape their business strategies, to meet this group desires and expectations and to foster their engagement. Augmented reality (AR) is emerging as a technological solution that pleases both consumers and retailers. This paper aims to answer two main questions: (1) How does generation Z evaluate an AR experience? (2) Which attributes/benefits do they value or not during an AR experience? Drawing on a qualitative methodology – content analysis of 34 interviewees – we discuss six main dimensions the potential customer value of the relationship between them and AR experiences under retailer context.

Abstract

Abstract
This paper addresses a hierarchical framework for the energy resources and network expansion planning of an Energy Distribution Company (EDC) that supplies its downward Active Industrial MicroGrids (AIMGs) with hot water and/or steam and electricity through its district heating and electric grid, respectively. The main contribution of this paper is that the proposed model considers AIMGs' electricity transactions with each other and/ or other customers through the EDC's electric main grid and investigates the impacts of these transactions on the expansion planning problem. The solution methodology is another contribution of this paper that tries to trade-off between accuracy and computational burden. The proposed framework uses a three-stage iterative heuristic optimization algorithm that considers different uncertainties of the planning and operational parameters. At the first stage, the algorithm determines the characteristics of energy system facilities for different stochastic parameter scenarios. At the second stage, the feasibility and optimality of AIMGs' electric transactions are evaluated and the optimal scheduling energy resources in normal states are determined. Finally, at the third stage, different demand response alternatives, load shedding and the AIMGs' electric transaction interruptions for contingent conditions are decided. The proposed method is applied to 9-bus, 33-bus and 123-bus IEEE test systems. Further, a full search algorithm is used to evaluate the quality of solutions of the proposed algorithm. The introduced algorithm reduced the total costs for the 9-bus, 33-bus and 123-bus system about 18.645%, 9.658%, and 4.849% with respect to the costs of custom expansion planning exercises, respectively.

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Abstract
Over the past decade, online crowdsourcing has established itself as an emerging paradigm that industry and government have been using to harness the cognitive abilities of a multitude of users distributed around the world. In this context, microtask crowdsourcing has become the method of choice for addressing a wide range of diverse problems. Microtasks typically require a minimum of time and cognitive effort, but combined individual efforts have made it possible to accomplish great achievements. The goal of this paper is to contribute to the ongoing effort of understanding whether the same success that microtask crowdsourcing has achieved in other domains can be obtained in the field of social news curation. In particular, we ask whether it is possible to turn online news curation, typically a social and collaborative activity on the Web, into a model in which curatorial activities are mapped into microtasks to be performed by a crowd of online users.

Abstract
This paper presents a model for supporting the investment planning decision-making from the perspective of an independent energy provider that wants to integrate Battery Energy Storage Systems (BESS) in distribution networks. For supporting the decision, a conditional set of economically viable optimal solutions for the business model of buying and selling energy is identified in order to allow other decision criteria (e.g. loss reduction, reliability, ancillary services, etc.) to be evaluated to enhance the economic benefits as the result of the synergy between different applications of BESS. For this, a novel approach optimization model based on the metaheuristic Differential Evolutionary Particle Swarm Optimization (DEEPSO) and the Group Method Data Handling (GMDH) neural network is proposed for sizing, location, and BESS operation schedule. Experimental results indicate that after identifying the breakeven cost of the business model, a good conditional decision set can be obtained for assessing then other business alternatives.