Abstract
The truth is that competitivity has gained a strong growth in business, and it is important that companies pay attention to the practice of their relational marketing strategies. Technology, innovation, and digital have been transforming the way society operates in the market. Organizations must look for current opportunities in order to add value of their business and negotiation process and of course in the way they act and interact with the target. This way, the current research demonstrates the importance of CRM in relational marketing practices, particularly in non-profit organizations. Regarding the methodology, a case study was developed using a qualitative methodology through semi-structured interviews in a convenience sample, with the aim of retaining the opinion fundraising and marketing responsible department between the different organizations under study. The main result of this exploratory study appears to prove the importance of using CRM for the good practice of relational marketing strategies in order to attract, retain, and build trust with their stakeholders.

Abstract
Tide refers to a phenomenon that causes the change of water level in oceans. Tidal level forecasting plays an important role in many real-world applications especially those related to oceanic and coastal areas. For instance, accurate forecasting of tidal level can significantly increase the vessels' safety as an excessive level of tidal makes serious problems in the movement of vessels. In this work, we propose a deep learning-based prediction interval framework in order to model the forecasting uncertainties of tidal current datasets. The proposed model develops optimum prediction intervals (PIs) focused on the deep learning-based CNN-LSTM model (CLSTM), and nonparametric approach termed as the lower upper bound estimation (LUBE) model. Moreover, we develop a novel deep neuroevolution algorithm based on a two-stage modification of the gaining-sharing knowledge optimization algorithm to optimize the architecture of the CLSTM automatically without the procedure of trial and error. This leads to a decline in the complexity raises in designing manually the deep learning architectures, as well as an enhancement in the performance of the PIs. We also utilize coverage width criterion to establish an excellent correlation appropriately between both the PI coverage probability and PI normalized average width. We indicate the searching efficiency and high accuracy of our proposed framework named as MGSK-CLSTM-LUBE by examining over the practical collected tidal current datasets from the Bay of Fundy, NS, Canada.

Abstract
Modern large-scale I/O applications that run on HPC infrastructures are increasingly becoming metadata-intensive. Unfortunately, having multiple concurrent applications submitting massive amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to unresponsiveness of the storage backend and overall performance degradation. To address these challenges, we present PADLL, a storage middleware that enables system administrators to proactively control and ensure QoS over metadata workflows in HPC storage systems. We demonstrate its performance and feasibility by controlling the rate of both synthetic and realistic I/O workloads. Results show that PADLL can dynamically control metadata-aggressive workloads, prevent I/O burstiness, and ensure I/O fairness and prioritization.

Abstract
Color comparison is a key aspect in many areas of application, including industrial applications, and different metrics have been proposed. In many applications, this comparison is required to be closely related to human perception of color differences, thus adding complexity to the process. To tackle this, different approaches were proposed through the years, culminating in the CIEDE2000 formulation. In our previous work, we showed that simple color properties could be used to reduce the computational time of a color similarity decision process that employed this metric, which is recognized as having high computational complexity. In this paper, we show mathematically and experimentally that these findings can be adapted and extended to the recently proposed CIEDE2000 PF metric, which has been recommended by the CIE for industrial applications. Moreover, we propose new efficient models that not only achieve lower error rates, but also outperform the results obtained for the CIEDE2000 metric.

Abstract

Abstract
Serious Games use game strategies to encourage participants to make decisions and face challenges in a training environment; the more interactive the game, the more engaged the participants are with the content. Moreover, the best way to train is to simulate and identify scenarios for decision making, recreating situations, and strategies for learning. The Serious Games for training have this purpose. A Serious Game for Training can be refined with a game narrative, a methodology centered on the player to present independent and straightforward scenarios, giving solutions through the game story. The challenge is to rethink a unique narrative according to the individual player's experience. The present systematic literature review aims to answer which are the benefits of using Design Thinking for serious game narratives; the benefits of learning theories; the Design Thinking benefits for innovative solutions; and how game design elements can create an engaging Serious Game experience.