Blockchain technology, initially known for its applications in the financial industry, has emerged as a promising solution for various other domains. One prominent area for the use of blockchain-based solutions is forensics, specifically the chain of custody maintenance and control. While there have been numerous research projects exploring the use of blockchain technology in digital forensics, limited attention has been given to its application in controlling of the physical evidence chain of custody. In this research, we aim to explore the literature on the use of blockchain technology to solve problems related to the physical evidence chain of custody. Through a systematic literature review (SLR), we analyzed 26 resources discussing blockchain-based solutions for evidence chain of custody issues, based on requirements that could be applied to both physical and digital evidence. The results showed that there is a lack of studies involving the use of blockchain technology to solve problems related to the physical evidence chain of custody, and future research should focus on solving the issue.

Coastal environments are highly dynamic and sensitive to interference and variations caused by the numerous natural and anthropogenic agents. The northern coast of Northeastern Brazil has undergone intense erosion in recent years. However, the construction of the Pecém Port modified the beach and shoreline features differently from the adjacent areas. This article describes using remote-sensing images integrated with a Geographic Information System to evaluate the sedimentary balance and morphological changes observed in the sandy beach area that was affected by the construction of Pecém Port. Two methods were applied to the RapidEye images to quantify the short-time changes that occurred on the coast. The Digital Shoreline Analysis System method showed that the beach width increased west to the port, which was calculated by the Change Polygon Approach determined by intersecting and uniting polygons to estimate the difference between the areas over time. The short-time analysis results showed changing coastal morphology, demonstrating that the anthropic interventions in the region are transforming significantly the natural elements that make up the region landscape. Between 2011 and 2014, the investigated beach stretch of approximately 3 km suffered an accretion process of more than 102,000 m² over 3 years. The high spatial resolution of satellite images, digital processing imaging techniques and geostatistical methods were effective in this study, allowing understanding the recent changes in the area.

Background: Abnormal glucose metabolism is a predictor of worse outcome after acute coronary syndrome (ACS). However, this parameter is not included in risk prediction scores, including GRACE risk score. We sought to evaluate whether the inclusion of blood glucose at admission in a model with GRACE risk score improves risk stratification. Methods: Study of consecutive patients included in a single centre registry of ACS. Our primary endpoint was the occurrence of all-cause mortality at one-year follow-up. The ability of the two logistic regression models (GRACE risk score alone and in combination with blood glucose) to predict death was analysed. Continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI), with corresponding 95% confidence intervals (CIs), were also calculated. Results: We included 2099 patients, with a mean age of 64 (SD=13) years, 69% males. In our sample, 55.1% presented with ST-segment elevation ACS and 13.1% in Killip class ≥ 2. Only 25% were known diabetic at admission. In-hospital mortality was 5.8% and 9.7% at one-year follow-up. The best cut-point for blood glucose was 160 mg/dl (sensitivity 62% and specificity 68%), and 35.2% of the patients had increased levels. This group was elderly, had more prevalence of cardiovascular risk factors, worse renal function and GRACE score as well as more frequently Killip class ≥2. Treatment was similar in both groups besides less frequent use of clopidogrel in high glycaemic patients. The hyperglycaemia group had higher one-year mortality (17.2% vs. 5.6%, p<0.001). Moreover, binary blood glucose remained a predictor of death independently of the GRACE risk score and the presence of diabetes (odds ratio (OR) 1.99, 95% CI 1.40–2.84, p<0.001). The inclusion of blood glucose, as a continuous variable, in a logistic regression model with GRACE score, increased the area under the ROC curve from 0.80 to 0.82 (p=0.018) as well as the goodness-of-fit and was associated with an improvement in both the NRI (37%) and the IDI (0.021), suggesting effective reclassification. Conclusions: A blood glucose level on admission ≥ 160 mg/dl is an independent predictor of mortality in medium-term follow-up. It offers an incremental predictive value when added to the GRACE risk score, although with a modest magnitude of improvement, probably due to the high predictive performance of the GRACE risk score alone.

The development and application of predictive models to distinguish seepage slicks from oil spills are challenging, since Synthetic Aperture Radars (SAR) detect these events as dark spots on the sea surface. Traditional Machine Learning (ML) has been used to discriminate the Oil Slick Source (OSS) as natural or anthropic assuming that the samples employed to train and test the models in the source domain (D_S) follow the same statistical distribution of unknown samples to be predicted in the target domain (D_T). When such assumptions are not held, Transfer Learning (TL) allows the extraction of knowledge from validated models and the prediction of new samples, thus improving performances even in scenarios never seen before. A database with 26 geometric features extracted from 6279 validated oil slicks was used to develop predictive models in the Gulf of Mexico (GoM) and its Mexican portion (GMex). Innovatively, these well-trained models were applied to predict the OSS of unknown events in the GoM, the American (GAm) portion of the GoM, and in the Brazilian continental margin (BR). When the D_S and D_T domains are similar, the TL and generalization are null, being equivalent to the usual ML. However, when domains are different but statically related, TL outdoes ML (58.91%), attaining 87% of global accuracy when using compatible SAR sensors in the D_S and D_T domains. Conversely, incompatible SAR sensors produce domains statistically divergent, causing negative transfers and generalizations. From an operational standpoint, the evidenced generalization capacity of these models to recognize geometric patterns across different geographic regions using TL may allow saving time and budget, avoiding the collection of validated and annotated new training samples, as well as the models re-training from scratch. When looking for new exploratory frontiers, automatic prediction is a value-added product that strengthens the knowledge-driven classifications and the decision-making processes. Moreover, the prompt identification of an oil spill can speed up the response actions to clean up and protect sensitive areas against oil pollution.

Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data.

ESG is a sustainability framework promoting sustainability and responsible business practices relevant to Petrobras, a major Brazilian oil and gas company. In this sense, blockchain emerged as a technology for trustworthy information storage. This work demonstrates the ESG Token solution, built on top of EVM-compatible smart contracts to address data and process transparency, security, availability, and auditability. RBB (Brazilian Blockchain Network) was used to deploy the ESG Token smart contract, and the pluggable architecture allows for separating the blockchain layer from the business solution. Furthermore, ESG Token integrates with GovBR’s digital signature platform, enabling self-declarations of validated identities to enhance accountability.