Abstract
Climate change, limited natural resources, and the increase in the world's population impose society to produce food more sustainably, with lower energy and water consumption. The use of robots in agriculture is one of the most promising solutions to change the paradigm of agricultural practices. Agricultural robots should be seen as a way to make jobs easier and lighter, and also a way for people who do not have agricultural skills to produce their food. The PixelCropRobot is a low-cost, open-source robot that can perform the processes of monitoring and watering plants in small gardens. This work proposes a mission supervisor for PixelCropRobot, and general agricultural robots, and presents a prototype of user interface to this mission supervision. The communication between the mission supervisor and the other components of the system is done using ROS2 and MQTT, and mission file standardized. The mission supervisor receives a prescription map, with information about the respective mission, and decomposes them into simple tasks. An A* algorithm then defines the priority of each mission that depends on factors like water requirements, and distance travelled. This concept of mission supervisor was deployed into the PixelCropRobot and was validated in real conditions, showing a enormous potential to be extended to other agricultural robots.

Abstract
This paper aims to study battery response under two operation strategies to analyze the annual cycles and operation costs (revenues) via sensitivity analysis. A battery model that considers performance parameters (AC-AC RTE, DOD, and C-rates) for different technologies is approached to identify how these parameters influence battery behavior and revenue. Strategies refer to (A) energy arbitrage, EA, and (B) EA and the provision of tertiary reserve. Simulations conducted for real data from Portuguese electricity and regulation markets showed regardless of the strategy used, the annual cycles and revenue are dominated by the performance parameters, instead of price volatility. In addition, for batteries with higher C-rates, as the AC-AC RTE is reduced up to 80%, the annual cycles and revenues are significantly reduced to 50% and 45% respectively, regarding its ideal model (100% AC-AC RTE). For lower C-rates, the annual cycles and revenues are slightly reduced with AC-AC RTE reductions. Specifically, strategy B revealed that annual cycles and revenue could also be influenced by the capacity requirements and the control area where batteries are providing services. © 2024 IEEE.

Abstract
Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage.

Abstract

Abstract
Methylation is considered one of the proteins' most important post-translational modifications (PTM). Plasticity and cellular dynamics are among the many traits that are regulated by methylation. Currently, methylation sites are identified using experimental approaches. However, these methods are time-consuming and expensive. With the use of computer modelling, methylation sites can be identified quickly and accurately, providing valuable information for further trial and investigation. In this study, we propose a new machine-learning model called MeSEP to predict methylation sites that incorporates both evolutionary and structural-based information. To build this model, we first extract evolutionary and structural features from the PSSM and SPD2 profiles, respectively. We then employ Extreme Gradient Boosting (XGBoost) as the classification model to predict methylation sites. To address the issue of imbalanced data and bias towards negative samples, we use the SMOTETomek-based hybrid sampling method. The MeSEP was validated on an independent test set (ITS) and 10-fold cross-validation (TCV) using lysine methylation sites. The method achieved: an accuracy of 82.9% in ITS and 84.6% in TCV; precision of 0.92 in ITS and 0.94 in TCV; area under the curve values of 0.90 in ITS and 0.92 in TCV; F1 score of 0.81 in ITS and 0.83 in TCV; and MCC of 0.67 in ITS and 0.70 in TCV. MeSEP significantly outperformed previous studies found in the literature. MeSEP as a standalone toolkit and all its source codes are publicly available at https://github.com/arafatro/MeSEP.

Abstract
Citizen engagement with Sustainable Energy Solutions (SES) is considered essential for the current energy transition, since decarbonization requires individuals to shift from passive consumers to citizens actively involved with the energy system. However, citizen engagement research has remained peripheral and scattered, particularly in what regards the drivers of engagement behaviors. To address this challenge, this study examines how different forms of perceived value of SES (utilitarian, social, and environmental) influence different types of citizen engagement behaviors (information seeking, proactive managing, sharing feedback, helping other users, and advocating). To this end, we developed a quantitative study in the context of a H2020 EU project, with a sample of 456 citizens from the city of Alkmaar (the Netherlands). Our findings show that the utilitarian value of SES has a significant effect on all the engagement behaviors, except for sharing feedback. Social value has a significant influence on the more socially related engagement behaviors, such as sharing feedback, helping other users, and advocating. Finally, environmental value has an indirect effect on information seeking, proactive managing, and advocating, but only when mediated through awareness of consequences. The implications of this study should allow SES providers to design more relevant offerings and policymakers to develop better citizen engagement strategies.