Abstract
The Agri-Food production requirements needs a more efficient and autonomous processes, and robotics will play a significant role in this process. Deploying agricultural robots on the farm is still a challenging task. Particularly in slope terrains, where it is crucial to avoid obstacles and dangerous steep slope zones. Path planning solutions may fail under several circumstances, as the appearance of a new obstacle. This work proposes a novel open-source solution called AgRobPP-CA to autonomously perform obstacle avoidance during robot navigation. AgRobPP-CA works in real-time for local obstacle avoidance, allowing small deviations, avoiding unexpected obstacles or dangerous steep slope zones, which could impose a fall of the robot. Our results demonstrated that AgRobPP-CA is capable of avoiding obstacles and high slopes in different vineyard scenarios, with low computation requirements. For example, in the last trial, AgRobPP-CA avoided a steep ramp that could impose a fall to the robot.

Abstract
This study applies a data-driven anomaly detection frame-work based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed frame-work efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network.

Abstract
Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

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.