Inference and dynamic decision-making for deteriorating systems with probabilistic dependencies through Bayesian networks and deep reinforcement learning

In the context of modern environmental and societal concerns, there is an increasing demand for methods able to identify management strategies for civil engineering systems, minimizing structural failure risks while optimally planning inspection and maintenance (I&M) processes. Most available methods simplify the I&M decision problem to the component level due to the computational complexity associated with global optimization methodologies under joint system-level state descriptions. In this paper, we propose an efficient algorithmic framework for inference and decision-making under uncertainty for engineering systems exposed to deteriorating environments, providing optimal management strategies directly at the system level. In our approach, the decision problem is formulated as a factored partially observable Markov decision process, whose dynamics are encoded in Bayesian network conditional structures. The methodology can handle environments under equal or general, unequal deterioration correlations among components, through Gaussian hierarchical structures and dynamic Bayesian networks. In terms of policy optimization, we adopt a deep decentralized multi-agent actor-critic (DDMAC) reinforcement learning approach, in which the policies are approximated by actor neural networks guided by a critic network. By including deterioration dependence in the simulated environment, and by formulating the cost model at the system level, DDMAC policies intrinsically consider the underlying system-effects. This is demonstrated through numerical experiments conducted for both a 9-out-of-10 system and a steel frame under fatigue deterioration. Results demonstrate that DDMAC policies offer substantial benefits when compared to state-of-the-art heuristic approaches. The inherent consideration of system-effects by DDMAC strategies is also interpreted based on the learned policies

Computational performance of risk-based inspection methodologies for offshore wind support structures

Offshore wind turbines are dynamically responding structures reaching around 70 million of stress cycles per year due to the combined action of waves and wind loading. Therefore, the assessment of fatigue deterioration becomes crucial. Besides, fatigue assessment is characterized by large uncertainties associated with both fatigue loads and strength. Inspections can be undertaken to detect potential cracks and therefore improve our belief about the condition of the structure. However, offshore wind inspections are costly and complex operations, involving the deployment of ROVs or divers for the case of underwater components. Risk-based inspection aims to identify the optimal maintenance policy by balancing the risk of structural failure against maintenance efforts (inspections and repairs). Introduction of a risk-based inspection plan can lead to reductions in the expected life-cycle costs as already demonstrated in the Oil & Gas sector. Inspection planning is a complex sequential decision problem where the decision about whether to go or not for an inspection must consider the outcomes from the previous inspections. In theory, it is possible to find the optimal policy by solving a pre-posterior decision analysis. Nevertheless, for the real case of an offshore wind structure standing a lifetime of 20 years, it is not possible to solve a decision tree which is exponentially growing over time and it becomes computationally intractable. Due to the computational limitations, assumptions are generally introduced within the risk-based analysis leading to approximate optimal policies. Traditional risk-based inspection techniques encompass FORM/SORM or Monte Carlo simulations to estimate and update the probability of failure as well as the inclusion of heuristic decision rules to solve the decision problem. However, novel methods and algorithms have been proposed recently to improve the computational efficiency of the risk-based analyses such as Dynamic Bayesian Networks (DBNs) or Partially Observable Markov Decision Processes (POMDPs). The aim of this work is to compare the existing risk-based inspection planning methodologies applicable to offshore wind structures. The computational performance and life-cycle expected costs corresponding to the different methodologies are explored. Additionally, the challenges which risk-based inspection planning is facing in the present are presented and potential solutions are suggested, for instance, on how to incorporate the correlation between structural components or “system-effects” into the risk-based analysis. In order to explore the main aspects involved during the application of the existing risk-based methodologies, the following step are pursued: 1) identification of the most relevant random variables within the deterioration models, 2) calibration of SN/Miner’s fatigue model to a fracture mechanics model, 3) comparison of the methods available for updating the failure probability when information from inspections is available and 4) comparison of the methods available to solve the pre-posterior decision problem corresponding to inspection planning. The optimal inspection plan for an offshore wind tubular joint is then identified by employing the different risk-based methodologies. Thereby, the methodologies are reviewed in terms of: 1) computational time to set up the model, 2) computation time required by the simulation and 3) obtained life-cycle expected costs. The results highlight the computational advantages of modern methods such as DBNs or POMDP which facilitate the identification of more optimal inspection policies

IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL

We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning (MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a platform for benchmarking the scalability of cooperative MARL methods in real-world engineering applications. In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition. Specifically, each agent plans inspections and repairs for a specific system component, aiming to minimise maintenance costs while cooperating to minimise system failure risk. With IMP-MARL, we release several environments including one related to offshore wind structural systems, in an effort to meet today's needs to improve management strategies to support sustainable and reliable energy systems. Supported by IMP practical engineering environments featuring up to 100 agents, we conduct a benchmark campaign, where the scalability and performance of state-of-the-art cooperative MARL methods are compared against expert-based heuristic policies. The results reveal that centralised training with decentralised execution methods scale better with the number of agents than fully centralised or decentralised RL approaches, while also outperforming expert-based heuristic policies in most IMP environments. Based on our findings, we additionally outline remaining cooperation and scalability challenges that future MARL methods should still address. Through IMP-MARL, we encourage the implementation of new environments and the further development of MARL methods

Farm-wide virtual load monitoring for offshore wind structures via Bayesian neural networks

Offshore wind structures are subject to deterioration mechanisms throughout their operational lifetime. Even if the deterioration evolution of structural elements can be estimated through physics-based deterioration models, the uncertainties involved in the process hurdle the selection of lifecycle management decisions. In this scenario, the collection of relevant information through an efficient monitoring system enables the reduction of uncertainties, ultimately driving more optimal lifecycle decisions. However, a full monitoring instrumentation implemented on all wind turbines in a farm might become unfeasible due to practical and economical constraints. Besides, certain load monitoring systems often become defective after a few years of marine environment exposure. Addressing the aforementioned concerns, a farm-wide virtual load monitoring scheme directed by a fleet-leader wind turbine offers an attractive solution. Fetched with data retrieved from a fully-instrumented wind turbine, a model can be trained and then deployed, thus yielding load predictions of non-fully monitored wind turbines, from which only standard data remains available. In this paper, we propose a virtual load monitoring framework formulated via Bayesian neural networks (BNNs) and we provide relevant implementation details needed for the construction, training, and deployment of BNN data-based virtual monitoring models. As opposed to their deterministic counterparts, BNNs intrinsically announce the uncertainties associated with generated load predictions and allow to detect inaccurate load estimations generated for non-fully monitored wind turbines. The proposed virtual load monitoring is thoroughly tested through an experimental campaign in an operational offshore wind farm and the results demonstrate the effectiveness of BNN models for fleet-leader-based farm-wide virtual monitoring

IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL

We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning (MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a platform for benchmarking the scalability of cooperative MARL methods in real-world engineering applications. In IMP, a multi-component engineering system is subject to a risk of failure due to its components' damage condition. Specifically, each agent plans inspections and repairs for a specific system component, aiming to minimise maintenance costs while cooperating to minimise system failure risk. With IMP-MARL, we release several environments including one related to offshore wind structural systems, in an effort to meet today's needs to improve management strategies to support sustainable and reliable energy systems. Supported by IMP practical engineering environments featuring up to 100 agents, we conduct a benchmark campaign, where the scalability and performance of state-of-the-art cooperative MARL methods are compared against expert-based heuristic policies. The results reveal that centralised training with decentralised execution methods scale better with the number of agents than fully centralised or decentralised RL approaches, while also outperforming expert-based heuristic policies in most IMP environments. Based on our findings, we additionally outline remaining cooperation and scalability challenges that future MARL methods should still address. Through IMP-MARL, we encourage the implementation of new environments and the further development of MARL methods

The step of incorporation of Bacillus coagulans GBI-30 6086 into “requeijão cremoso” processed cheese does not affect metabolic homeostasis of rats

Dairy product consumption is a common habit in Brazil. These products present a good matrix for probiotic incorporation. Thus, in this study the feasibility of producing a probiotic "requeijao cremoso" incorporated with Bacillus coagulans GBI-30 6086 in three different steps and its metabolic effect in an animal model for 2 weeks has been evaluated. Wistar adult health rats were randomized into one to five groups (n = 8 for each group): Control (C); "requeijao cremoso" without probiotic (RC); probiotic inoculated in the milk before pasteurization at 65 degrees C/30 min (RPP); "requeijao cremoso" inoculated before the fusion step and consequently exposed to 90 degrees C/5 min (RPF); and "requeijao cremoso" inoculated after fusion step, i.e., once the product temperature reached 50 degrees C (RPAF). At the end of treatment, analysis of molecular markers of proteins of stress and antioxidant system, HSP 25, 60, 70 and 90, SOD and catalase were performed in the animals' muscles by Western Blot technique. The HSP25, HSP90 and catalase levels of C, RPP, RPF, and RPAF were similar, indicating that the homeostasis remained unchanged. The incorporation of B. coagulans GBI-30 6086 in the "requeijao cremoso" was shown to be stable and the microorganism remained viable in all steps tested. The incorporation of the probiotic strain in the fusion stage facilitated the technological process, since it allowed a better homogenization of the product and did not affect the maintenance of the metabolic homeostasis of rats10CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPsem informação302763/2014-7; 305804/2017-013/21544-9; 18/24540-8; 2019/21188-

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost