The two-stage Bayesian approach integrates prior knowledge from generic data in the first stage and updates it with specific data using Bayes' theorem in the second stage. Focusing on the first stage, previous work assumed a time-dependent failure intensity function based on the two-parameter Weibull distribution, with independent prior distributions for the scale alpha and shape beta parameters. The estimation of these prior distributions considered non-homogeneous failure count over total observed time data as generic information, referred to as the KT model. The present work introduces a model that estimates prior distributions using non-homogeneous and time-to-failure data (TTF model) and then compares its performance with the KT model. The TTF model leverages failure time data to estimate independent priors for the Weibull parameters through Particle Swarm Optimization (PSO), enabling a more detailed and flexible reliability assessment. Simulations using theoretical priors were conducted across various dataset configurations, with multiple PSO runs. Results demonstrated that the TTF model provides consistent and accurate estimates, closely aligning with the KT model while offering more precise distributions due to its use of detailed failure data. Although computational time increased for larger datasets, parallel computing proved effective in mitigating processing delays. These findings highlight the TTF model's potential as a robust tool for failure prediction and maintenance planning in reliability engineering.
This study provides the first baseline data on PAHs in the mangrove oyster Crassostrea gasar from contrasting production systems in two Brazilian Amazon conservation areas, including submerged aquaculture in Augusto Corrêa and aquaculture and intertidal natural banks in Curuçá. Mantle, gill, and visceral mass showed Σ16PAHs concentrations from 5.0 to 45.6 ng g−1 dry weight, with low molecular weight PAHs accounting for 77% of the total burden. Σ16PAHs concentrations in the mantle and visceral mass tended to be higher than in gills, indicating preferential accumulation in metabolically active tissues. Wild oysters from natural banks exhibited the highest Σ16PAHs concentration, with tissue partitioning patterns likely related to prolonged sediment-associated exposure. Farmed oysters showed low contamination and no significant differences between farming sites. Concentrations of high molecular weight PAHs and benzo[a]pyrene toxic equivalency remained below regulatory thresholds for all samples, indicating no carcinogenic risk associated with oyster consumption. PAH accumulation profile in wild oysters highlights the importance of environmental monitoring to ensure the long-term sustainability and consumer safety in this ecologically and socioeconomically relevant region.
Power reduction in photovoltaic modules may be caused by environmental conditions, faults, and degradation mechanisms, whose correct identification is essential to improve the reliability, safety, and energy yield of photovoltaic systems. This article proposes a classification approach for power-reduction scenarios based on parametric deviations extracted from a Global Nonlinear Model (GNLM) derived from the Single-Diode Model (SDM). The methodology starts with the construction of a GNLM representative of a healthy reference photovoltaic module, capable of describing its electrical behavior under different irradiance and temperature conditions. For each evaluated experimental current–voltage (I–V) curve, selected SDM parameters associated with this healthy reference model are adjusted to approximate the electrical behavior of the analyzed module. The deviations required for this adjustment, calculated with respect to the healthy reference condition, are used as diagnostic markers for clustering-based classification. Different power-reduction conditions were evaluated, including soiling, short-circuited bypass diodes, open-diode conditions under different shading levels, front-glass breakage, and partial shading (PS). To reduce the overlap between scenarios with distinct electrical behaviors, the samples were initially separated according to the number of maxima in the power–voltage (P–V) curve, denoted by Npeak, distinguishing cases with Npeak=1 from those with Npeak>1. Based on these diagnostic markers, two strategies are investigated according to the set of SDM parameters used in the classification process. Strategy 1 uses deviations in the diode ideality factor (n) and photogenerated current (Iph), achieving the best performance, with an overall accuracy of 99.6%. Strategy 2 uses deviations in the n, series resistance (Rs), and shunt resistance (Rsh), achieving an overall accuracy of 98.3%.
Evaluating airport efficiency through a single analytical dimension, whether operational throughput, airline response, service quality, or environmental sustainability, risks misrepresenting overall infrastructure performance. This study examines dimensional divergence by estimating four Data Envelopment Analysis (DEA) models for Brazil's twenty-nine largest airports from 2000 to 2024: an operational infrastructure-conversion model, a tactical airline-response model, a two-stage network model accounting for flight delays and cancellations, and a socio-environmental network model incorporating pollutant emissions for the 2018–2023 window. Applying within-year Spearman rank correlations, Kendall's coefficient of concordance, and a percentile-based dissonance index evaluated via a double bootstrap that re-estimates the DEA frontiers within each replication, with airport and year fixed effects, we demonstrate that single-dimension rankings conceal fundamental operational trade-offs. While operational throughput and tactical airline responses exhibit close alignment (mean Spearman ρ = 0.85), correlation with social effectiveness drops to 0.55, and environmental agreement falls to 0.40. Furthermore, system concordance is highly state-dependent, collapsing from about 0.86 in pre-crisis years to 0.52–0.63 during 2020–2021, when regulatory clarity is most critical. Crucially, dimensional dissonance is associated with physical scale rather than with ownership structure; governance regimes fail to predict dissonance even under within-airport fixed effects, whereas large capacity-constrained hubs are associated with systematic penalties when operational delays and cancellations are priced. Ultimately, unidimensional benchmarking introduces predictable structural bias into performance assessment, which indicates that regulatory incentives and corporate benchmarking need multi-model architectures to capture hub saturation and regional network realities accurately.
Abstract Cosplay is a fan production that allows its participants to express their subjective values through dressing up as characters from media products. Such phenomenon exemplifies the intricate dynamics of consumer relations since cosplay explores multiple aspects of consumer culture theory. Thus, drawing from fan studies and Foucauldian frameworks, the aim of the research is analyzing how cosplayer subjectivity is constituted. In this sense, the study performs an Ethnographic Foucauldian Genealogy, based on a corpora constituted of autoethnography, netnography, online ethnographic interviews and virtual ethnomethodology data. The results evidenced two subject-forms: Nailers, representing the position taken by cosplayers who take on the role of exemplary representing the cosplay phenomenon; and Guardians, highlighting how some cosplayers feel responsible for maintaining a community of practitioners that allows those interested in cosplay to feel comfortable. Both subjectivities share an ethic named as Love to fandom. Such ethic guide cosplayers to produce truths about the ways in which they interact or show solidarity between peers based on their own condition as fans, legitimizing autonomous and complementary subjectivities, and enhancing and ensuring experiences with cosplay practice. Thus, the study expands the possibilities of living love through consumption – here, inherent to fans. It is a love established and maintained by the consumers themselves both through the intense relationship with the products consumed and through interactions with their peers.
