Abstract
This paper proposes a centralized energy management system for low voltage (LV) distribution networks. The main contribution of this model is to manage the energy serving at the local energy communities in the presence of electric vehicle supply equipment (EVSE). Unlocking the demand response potential by the EVSE at the distribution network with the contribution of the active residential prosumers has been investigated in this study under different operational planning scenarios. The developed model is based on the multi-temporal optimal power flow (MTOPF) concept while the unbalanced nature of LV networks has been addressed using unbalanced power flow equations. The aggregator can effectively manage the optimal charging of electric vehicles (EVs) by home and public chargers available at the distribution network. Simulation results on a modified unbalanced LV network illustrate that the optimal operation of EVSE minimizes the electricity costs of end-users. The simulation results show that the operating costs and systems losses reduce by 9.22% and 43.45%, respectively. These results have been obtained considering the switching actions and 100% PV power generation index using the presented MV-LV coordinated operational model. Besides, the energy storage systems improve the peak-to-average (PAR) ratio by 9.87%.

Abstract
The cost of transportation of raw materials is a significant part of the procurement costs in the forestry industry. As a result, routing and scheduling techniques were introduced to the transportation of raw materials from extraction sites to transformation mills. However, little to no attention has been given to date to the material reception process at the mill. Another factor that motivated this study was the formation of large waiting queues at the mill gates and docks. Queues increase the reception time and associated costs. This work presents the development of a scheduling and reception system for deliveries at a mill. The scheduling system is based on Trucking Appointment Systems (TAS), commonly used at maritime ports, and on revenue management concepts. The developed system allocates each delivery to a timeslot and to an unloading dock using revenue management concepts. Each delivery is segmented according to its priority. Higher-segment deliveries have priority when there are multiple candidates to be allocated for one timeslot. The developed scheduling system was tested on a set of 120 daily deliveries at a Portuguese paper pulp mill and led to a reduction of 66% in the daily reception cost when compared to a first-in, first-out (FIFO) allocation approach. The average waiting time was also significantly reduced, especially in the case of high-priority trucks.

Abstract
The Prisoner Transportation Problem is an NP-hard combinatorial problem and a complex variant of the Dial-a- Ride Problem. Given a set of requests for pick-up and delivery and a homogeneous fleet, it consists of assigning requests to vehicles to serve all requests, respecting the problem constraints such as route duration, capacity, ride time, time windows, multi-compartment assignment of conflicting prisoners and simultaneous services in order to optimize a given objective function. In this paper, we present anew solution framework to address this problem that leads to an efficient heuristic. A comparison with computational results from previous papers shows that the heuristic is very competitive for some classes of benchmark instances from the literature and clearly superior in the remaining cases. Finally, suggestions for future studies are presented.

Abstract
This paper aims to characterise the relevance of sustainability practices in the context of software companies that adopt the Scrum methodology. In the first phase, a multidimensional framework for software sustainability was built, based on the individual, technical, environmental, and social dimensions. Subsequently, a quantitative study was carried out using a survey answered by 397 Scrum professionals working in software companies registered in Portugal. The results reveal significant asymmetries in the implementation of sustainable practices, in which micro companies experience the greatest difficulties in their implementation. The findings also indicate that the practices most adopted by organisations are in the technical and individual dimensions, where a proactive level of maturity is evident. On the other hand, environmental and social practices are still poorly implemented and appear mainly at a reactive level due to the needs of the projects or their teams. © 2025 Elsevier B.V., All rights reserved.

Abstract
Precision Agriculture (PA) has emerged as an approach to optimize production, comprise different technology and principles focusing on how to improve agricultural production. Currently, one of the main foundations of PA is the use of artificial intelligence, through deep learning (DL) algorithms. By processing large volumes of complex data, DL enhances decision-making and boosts farming efficiency. However, these methods are hungry for annotated data, which contrasts with the scarce availability of annotated agricultural data and the costs of annotation. Self-supervised learning (SSL) has emerged as a solution to tackle the lack of annotated agricultural data. This study presents a review of the application of SSL methods to computer vision tasks in the agricultural context. The aim is to create a starting point for professionals and scientists who intend to apply these methods using agricultural data. The results of 33 studies found in the literature are discussed, highlighting their pros and cons. In most of the studies, SSL outperformed its supervised counterpart, using datasets from 4000 to 60,000 samples. Potential directions for improving future research are suggested.

Abstract
This paper deals with cost-effective strategies for controlling indoor temperature using different technologies, including inverter-based and thermostatic control systems. In this regard, the indoor temperature control model incorporates instant heat loss coefficient, heat transfer capability, and heat energy conversion coefficient. The decision variable is the power setpoint of the energy conversion system, which can be operated in both cooling and heating modes. The thermal system coefficients have been estimated based on historical data for energy consumption, indoor, and outdoor temperatures of the case study presented, which are the minimal datasets required for the coefficient estimation. The inverter-based model benefits from the quasi-continuous power consumption model, while the thermostatic model has a hysteresis functionality resulting in discrete power consumption with several turn-on and turn-off modes, which can be controlled by changing the thresholds. The flexible thermal range resulted in 4.715% and 6.235% cost reductions for thermostat-based and inverter-driven heat pumps, respectively. © 2025 Elsevier B.V., All rights reserved.