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Mapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, directly using the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.

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The Vehicle Routing Problem with Divisible Deliveries and Pickups (VRPDDP) is under-explored in literature, yet it has a wide application in practice in a reverse logistics context, where the collection returnable items must also be ensured along with the traditional delivery of products to customers. problem considers that each customer has both delivery and pickup demands and may be visited twice in the same or different routes (i.e., splitting customers' visits). In several reverse logistics problems, capacity restrictions are required to either allow the movement of the driver inside the vehicle to arrange the loads or to avoid cross-contamination between delivery and pickup loads. In this work, explore the economic and the environmental impacts of the VRPDDP, with and without restrictions the free capacity, and compare it with the traditional Vehicle Routing Problem with Simultaneous Deliveries and Pickups (VRPSDP), on savings achieved by splitting customers visits. An exact method, solved through Gurobi, and an ALNS metaheuristic are coded in Python and used to test well-known and newly generated instances. A multi-objective approach based on the augmented e-constraint method is applied to obtain and compare solutions minimizing costs and CO2 emissions. The results demonstrate that splitting customer visits reduces the CO2 emissions for load-constrained distribution problems. Moreover, savings percentage of the VRPDDP when compared to the VRPSDP is higher for instances with a random network than when a clustered network of customers is considered.

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This work presents a risk-averse stochastic programming model for the optimal planning of hybrid electrical energy systems (HEES), considering the regulatory policy applied to distribution systems in Brazil. Uncertainties associated with variables related to photovoltaic (PV) generation, load demand, fuel price for diesel generation and electricity tariff are considered, through the definition of scenarios. The conditional value-at-risk (CVaR) metric is used in the optimization problem to consider the consumer's risk propensity. The model determines the number and type of PV panels, diesel generation, and battery storage capacities, in which the objective is to minimize investment and operating costs over the planning horizon. Case studies involving a large commercial consumer are carried out to evaluate the proposed model. Results showed that under normal conditions only the PV system is viable. The PV/diesel system tends to be viable in adverse hydrological conditions for risk-averse consumers. Under this condition, the PV/battery system is viable for a reduction of 87% in the battery investment cost. An important conclusion is that the risk analysis tool is essential to assist consumers in the decision-making process of investing in HEES.

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<p>Gamma radiation measured over the ocean is mainly due to airborne radionuclides, as gamma emission by radon degassing from the ocean is negligible. Airborne gamma-emitting elements include radon progeny (Pb-2114, Bi-214, Pb-210) and cosmogenic radionuclides such as Be-7. Radon progeny attaches readily to aerosols, thus the fate of gamma-emitting radon progeny, after its formation by radioactive decay from radon, is expected to be closely linked to that of aerosols.</p> <p>Gamma radiation measurements over the Atlantic Ocean were made on board the ship-rigged sailing ship NRP Sagres in the framework of project SAIL (Space-Atmosphere-Ocean Interactions in the marine boundary Layer). The measurements were performed continuously with a NaI(Tl) scintillator counting all gamma rays from 475 keV to 3 MeV.  </p> <p>The counts from the sensor were recorded every 1 second into a computer system which had his time reference corrected by a GNSS pulse per second (PPS) signal. The GNSS was also used to precisely position the ship. The measurements were performed over the Atlantic ocean from January to May 2020, along the ship’s round trip from Lisboa - Cape Verde – Rio de Janeiro – Buenos Aires – Cape Town – Cape Verde - Lisboa.</p> <p>The results show that the gamma radiation time series displays considerable higher counts and larger variability in January compared to the remaining period. Reanalysis data also indicate higher aerosol concentration. This work investigates in detail the association between the temporal evolution of the gamma radiation measurements obtained from the SAIL campaign over the Atlantic Ocean and co-located total aerosol concentration at 550 nm obtained every 3 hours from EAC4(ECMWF Atmospheric Composition Reanalysis 4) data.</p>

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