Abstract
We present PAID, a framework that allows developers to implement portable I/O policies and optimizations for different applications with minor modifications to their original code base. The chief insight behind PALO is that if we are able to intercept and differentiate requests as they flow through different layers of the I/O stack, we can enforce complex storage policies without significantly changing the layers themselves. PAIO adopts ideas from the Software-Defined Storage community, building data plane stages that mediate and optimize I/O requests across layers and a control plane that coordinates and fine-tunes stages according to different storage policies. We demonstrate the performance and applicability of PALO with two use cases. The first improves 99th percentile latency by 4 x in industry-standard LSM-based key-value stores. The second ensures dynamic per-application bandwidth guarantees under shared storage environments.

Abstract
In recent years, there has been an increase of Renewable Energy Sources (RES) in energy markets that has to lead their agents to become more proactive. In this scenario, a market structure based on Peer-to-Peer (P2P) transactions is very promising but presents challenges for the network operation. A critical challenge is to ensure that network constraints are not violated due to energy trades between peers and neither due to the use of reserve capacity. In this paper, it is proposed a new iterative sequential approach for energy and reserve P2P market that ensures the feasibility of both energy and reserve transactions under network constraints. The methodology considers the interaction between the prosumers and the Distribution System Operator (DSO) in making the final market/operation decision and can be integrated into the existing distribution system. The proposed approach includes the estimation of reserve requirements based on the RES uncertain behavior from historical generation data, which allows identifying RES patterns. The proposed model is assessed through a case study that uses a 14-bus system, under the technical and economic criteria. The results show that the approach can ensure a feasible network operation encompassing energy and reserve markets.

Abstract
Chatbots are website artificial intelligence-based and automated customer support tools to improve the customer experience, to reduce costs, and to improve service quality. This study aims to understand and analyze the user-technology interaction and technology-engagement success measures to assess online customer engagement with chatbots and the impact on repurchase intention, within e-commerce websites. The sample data consists of 227 online consumer responses collected through an electronic survey. Only 165 respondents, which have used a chatbot to assist the online purchase process, are included in the effective sample. This research contributes to the digital marketing literature by complementing existing research exploring human-technology interactions, assessing how consumers interact with chatbot technology and how it affects customer engagement and behavioral outcomes within e-retail contexts. The study findings provide several challenges for managers. Finally, it discusses emerging trends in the digital marketing field, offering insights for future research avenues. © 2023, IGI Global. All rights reserved.

Abstract
Data mining approaches are increasingly important to enable dealing with the constantly rising challenges in power and energy systems. Classification models, in particular, are suitable for predicting classes of new observations based on previous cases. This chapter illustrates the advantages of the use of classification models, namely artificial neural networks and support vector machines, to predict the behavior profiles of electricity market negotiation players. A clustering model is used to identify similarities in the behavior of players, resulting in a set of negotiation profiles. The negotiation behavior of new players is then classified as belonging to one of these profiles, allowing for an automated adaptation of the negotiation process according to the expected reactions of the opponent. © 2023 The Institute of Electrical and Electronics Engineers, Inc.

Abstract
Negative organizations, where powerful people manage to keep a negative strategy in place, one which does not benefit the firm but perpetuates their power, is a reality discussed herein. Positive organizations, led by positive leaders who do not feel threatened by brilliant employees who have brilliant ideas, may be less prominent than we think and should not be taken for granted. Following thirty years of working in organizations, both large and small, the author has come to realize that the status quo tends to be very strong, and that innovating and disrupting that balance is not only dangerous but seldom succeeds. More research is necessary in this field to prove this theory right. This article aims to point readers and researchers in the right direction and to challenge one to think just how negative organizations may be. The article is based on the experience of the author; on a look at the case of Nokia (the former handheld mobile phone division), seen to be a negative organization; as well as on in-depth personal interviews with three experts (a purposive sample) on the topic of positive versus negative organizations; and, finally, the results of two surveys (n = 116—millennials; and n = 115—Generation Z) are shared. A total of 94.8% of the Generation Z respondents (109 respondents in total) believe negative organizations to exist (where the status quo may prevail over innovative individuals and innovation to the detriment of the global organizational strategy), which is seen to be very encouraging for this research study.

Abstract
Climate change and global warming interconnected with the new contexts created by the COVID-19 pandemic and the Russia-Ukraine conflict have brought serious challenges to national and international organizations, especially in terms of food security and agricultural planning. These circumstances are of particular concern due to the impacts on food chains and the resulting disruptions in supply and price changes. The digital agricultural transition in Era 4.0 can play a decisive role in dealing with these new agendas, where drones and sensors, big data, the internet of things and machine learning all have their inputs. In this context, the main objective of this study is to highlight insights from the literature on the relationships between machine learning and food security and their contributions to agricultural planning in the context of Agriculture 4.0. For this, a systematic review was carried out based on information from text and bibliographic data. The proposed objectives and methodologies represent an innovative approach, namely, the consideration of bibliometric evaluation as a support for a focused literature review related to the topics addressed here. The results of this research show the importance of the digital transition in agriculture to support better policy and planning design and address imbalances in food chains and agricultural markets. New technologies in Era 4.0 and their application through Climate-Smart Agriculture approaches are crucial for sustainable businesses (economically, socially and environmentally) and the food supply. Furthermore, for the interrelationships between machine learning and food security, the literature highlights the relevance of platforms and methods, such as, for example, Google Earth Engine and Random Forest. These and other approaches have been considered to predict crop yield (wheat, barley, rice, maize and soybean), abiotic stress, field biomass and crop mapping with high accuracy (R2 approximate to 0.99 and RMSE approximate to 1%).