Abstract
What causes ineffective external idea evaluation in open innovation (OI) still remains an unsolved puzzle, with most such studies focused on creative idea generation or using samples of untrained evaluators. To help better understand the microfoundations of OI, this article examines the effects of mood on external idea evaluation using a practitioner sample. Drawing on "mood-as-an-input" theory, in two behavioral experiments using music induction, cognitive tasks, and idea framing, we test how one's mood affects the innovativeness rating of an externally developed idea, and examine whether this effect is stable within a mood state regardless of the level of creativity (high and low) of an idea. We found that people in happy and sad mood conditions differ in their evaluation of the same external idea, which is explained by differences in assessment of creativity of an idea and not the perceived certainty of its success. Moreover, a given mood state does not affect how ideas low in creativity are rated in their innovativeness, compared to ideas high in creativity. This article by investigating effects of mood within an OI process augments individual level OI literature, while informing the ways external idea evaluation can be managed toward enhancing OI potential.

Abstract
Long-term storage expansion planning has usually employed representative days and intra-annual time series aggregation methodologies to reduce the computation complexity. This paper proposes a shift on the approach to the economic evaluation of these systems by implementing an intra-annual time series cost evaluation that considers different uncertainty trajectories. This methodology aims to determine the best possible investment strategies for the available computational budget using strategy game-based decision-making models, as Monte Carlo tree search. The proof of concept is illustrated by a single-bus equivalent test system and compared to a deterministic evaluation for a limited uncertainty model.

Abstract
In the GRAVITY+ project, GRAVITY is presently undergoing a series of upgrades to enhance its performance, add wide field capability and thereby expand its sky coverage. Some aspects of these improvements have already been implemented and commissioned by the end of 2021, making them accessible to the community. The augmentation of sky coverage involves increasing the maximum angular separation between the celestial science object and the fringe tracking object from the previous 2 arcseconds (limited by the field of view of the VLTI) to 20 - 30 arcseconds (constrained by atmospheric conditions during observation). Phase 1 of GRAVITY+ Wide utilizes the earlier PRIMA Differential Delay Lines to compensate for the optical path length variation between the science and fringe tracking beams throughout an observation. In phase 2, we are upgrading the existing beam compressors (BC) to integrate optical path length difference compensation directly into the BC. This modification eliminates five optical reflections per beam, thereby enhancing the optical throughput of the VLTI-GRAVITY [GRAPHICS] system and the bandwidth of the vibrational control. We will present the implementation of phase 2 and share preliminary results from our testing activities for GRAVITY+ Wide.

Abstract

Abstract

Abstract
One of the main challenges of one-way carsharing systems is to maximize profit by attracting potential customers and utilizing the fleet efficiently. Pricing plans are mid or long-term decisions that affect customers' decision to join a carsharing system and may also be used to influence their travel behavior to increase fleet utilization e.g., favoring rentals on off-peak hours. These plans contain different attributes, such as registration fee, travel distance fee, and rental time fee, to attract various customer segments, considering their travel habits. This paper aims to bridge a gap between business practice and state of the art, moving from unique single-tariff plan assumptions to a realistic market offer of multi-attribute plans. To fill this gap, we develop a mixed-integer linear programming model and a solving method to optimize the value of plans' attributes that maximize carsharing operators' profit. Customer preferences are incorporated into the model through a discrete choice model, and the Brooklyn taxi trip dataset is used to identify specific customer segments, validate the model's results, and deliver relevant managerial insights. The results show that developing customized plans with time- and location-dependent rates allows the operators to increase profit compared to fixed-rate plans. Sensitivity analysis reveals how key parameters impact customer choices, pricing plans, and overall profit.