Publicações - ciências exatas e da terra
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HEINRICH, TIAGO;
WILL, NEWTON CARLOS;
OBELHEIRO, RAFAEL;
Carlos Alberto Maziero
Palavra-chave:
WebAssembly;
Detecção de intrusão;
Segurança de sistemas
Áreas do conhecimento:
Ciências Exatas e da Terra; Ciência da Computação; Sistemas de Computação; Arquitetura de Sistemas de Computação
resumo ...
The security of Web services for both users and developers is crucial; given that WebAssembly is an emerging format that has seen increasing attention in this environment over the years, new security measures are necessary. Despite this, intrusion detection solutions for WebAssembly applications are mostly confined to static binary analysis. We introduce an innovative method for dynamic WebAssembly intrusion detection through data categorization and machine learning. Our method analyzes communication data extracted from the WebAssembly sandbox to more effectively capture the behavior of applications. Our approach was validated through two strategies, both online and offline, to evaluate the effectiveness of categorical data for intrusion detection. The results obtained demonstrate that both strategies are viable for WebAssembly intrusion detection, showing a high detection rate with low false-negative and false-positive rates.
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NASCIMENTO, LORENA SILVA;
Mauricio Almeida Noernberg
Palavra-chave:
Derrame de óleo;
monitoramento ambiental;
Vigilância costeira;
Rede Social
Áreas do conhecimento:
Ciências Exatas e da Terra; Oceanografia; Oceanografia Física; Oceanografia Costeira;
Ciências Exatas e da Terra; Oceanografia; Oceanografia Biológica; Interação entre os Organismos Marinhos e os Parâmetros Ambientais
resumo ...
ABSTRACT Oil spills cause extensive negative impacts on marine ecosystems. However, detecting...
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D. KRAUSE;
JORGE, JUAN PABLO;
LOMBARDI, OLIMPIA
Palavra-chave:
Quasi-sets;
quantum ontology;
properties
Áreas do conhecimento:
Ciências Humanas; Filosofia; Filosofia da Física;
Ciências Exatas e da Terra; Física; Lógica da Mecânica Quântica
resumo ...
One of the main ontological challenges posed by quantum mechanics is the problem of the indistinguishability of so-called “identical” particles, that is, particles that share the same state-independent properties. In the framework of this philosophical problem, a quasi-set theory was formulated to provide a proper metalanguage to deal with quantum indistinguishability; this theory included certain Urelemente called m-atoms, representing essentially indistinguishable objects. In turn, over the last two decades, the Modal Hamiltonian Interpretation proposed an ontology of properties, totally devoid of objects, where quantum systems are bundles of instances of universal properties. Therefore, the original quasi-set theory, with its m-atoms, does not adequately reflect the structure of an ontology devoid of objects. The purpose to the present article is to introduce a new quasi-set theory that does not include atoms at all: elementary items correspond to properties and are also represented by quasi-sets, which can be only numerically different. The final aim is to apply this new quasi-set theory to the MHI ontology.
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Ana Paula Yumi Nishimura;
Fernando Augusto Pedersen Voll;
KRIEGER, NADIA;
MITCHELL, DAVID ALEXANDER
Palavra-chave:
lipase;
seletividade;
cinética enzimática;
ping pong bi bi;
trimetilolpropano
Áreas do conhecimento:
Ciências Biológicas; Biotecnologia; Biotecnologia Industrial;
Ciências Biológicas; Bioquímica; cinética enzimática;
Ciências Exatas e da Terra; Química; Química Orgânica; Biocatálise
resumo ...
Kinetic models are important tools for guiding the design and optimization of lipase-catalyzed processes. These processes follow the Ping Pong bi bi mechanism, for which mechanistic kinetic equations can be derived. However, when there are several competing reactions, fully mechanistic models contain a large number of parameters, making it difficult to obtain reliable estimates, so simplified models are necessary. We present a two-step approach to developing semi-mechanistic models of such processes. The first step involves the estimation of the selectivities of the enzyme, using profiles for the reaction species plotted against the degree of reaction, while the second step involves empirical fitting to the same data, but plotted as a function of time. We demonstrate this two-step approach through four case studies based on the literature data for the lipase-catalyzed esterification of fatty acids with trimethylolpropane to produce biolubricants. The semi-mechanistic models were able to describe the data well. Our approach has the advantage of allowing selectivities to be estimated without confounding effects from phenomena such as enzyme denaturation and inhibition. It therefore provides a promising framework for developing models of enzyme-catalyzed processes that obey Ping Pong bi bi kinetics.
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João Paulo M. Rodrigues;
Antônio S. N. Aguiar;
ALINE SILVA MUNIZ;
Maria Aparecida Ferreira César Oliveira;
ANGELO ROBERTO DOS SANTOS OLIVEIRA;
Hamilton B. Napolitano
ChemistrySelect,
v. 11,
p. n/a - n/a
2026
Palavra-chave:
phenolic compounds;
biofuels;
biofuel additives;
antioxidant additives
Áreas do conhecimento:
Ciências Exatas e da Terra; Química; Química;
Ciências Exatas e da Terra; Química; Química Orgânica; Síntese Orgânica
resumo ...
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SANTOS, MÁRCIO F.;
MORAES, PEDRO IVO R.;
TAVARES, SÉRGIO R.;
MARTINS, A.S.;
CAPAZ, RODRIGO B.;
WYPYCH, FERNANDO;
Alexandre Amaral Leitão
Palavra-chave:
density-functional theory;
layered double hydroxide
Áreas do conhecimento:
Ciências Exatas e da Terra; Química; Físico-Química; Química Teórica
resumo ...
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WIGGERS, VINICIUS;
ZANIN, SILVIO;
VASCONCELOS, GIOVANI L.;
Marcus W. Beims
Palavra-chave:
finite baths;
termalização
Áreas do conhecimento:
Ciências Exatas e da Terra; Física; Sistemas Complexos
resumo ...
This work investigates how correlated environments influence the relative ( $$T_{\mathrm{eff}}^{(r)}$$ ) and center-of-mass ( $$T_{\mathrm{eff}}^{(R)}$$ ) effective temperatures using the Langevin description. The procedure is to consider N-identical “independent” particles, each coupled to its own thermal bath at temperature $$T_i$$ ( $$i=1,2,\ldots ,N$$ ), where correlations among the baths mediate indirectly the coupling between particles. Using Jacobi coordinates, expressions for $$T_{\mathrm{eff}}^{(R)}$$ and $$T_{\mathrm{eff}}^{(r_i)}$$ are derived, and it is shown that $$T_{\mathrm{eff}}^{(R)}$$ can always be identified with the equilibrium temperature obtained from the first law of thermodynamics when N baths are put in thermal contact. The analysis demonstrates that cross correlations between the environments lead to fluctuations effects which are visible in the expression for $$T_{\mathrm{eff}}^{(R)}$$ , highlighting how microscopic noise correlations reshape macroscopic equilibration. Consequently, the procedure allows one to obtain the kind of bath cross-correlations between Langevin equations which correctly mimics the correlations involved in the first law of thermodynamics.
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DO AMARAL, WANDERLEI;
MARINHO, EMMANUEL SILVA;
REBELO, RICARDO ANDRADE;
CARNEIRO, JOARA NÁLYDA PEREIRA;
DA SILVA, TAÍS GUSMÃO;
DE LIMA, LUCIENE FERREIRA;
MARINHO, MÁRCIA MACHADO;
DE OLIVEIRA, VICTOR MOREIRA;
BRAGA, MARIA FLAVIANA BEZERRA MORAIS;
Luiz Everson da Silva
Palavra-chave:
antifungal activity;
Lamiaceae;
docking
Áreas do conhecimento:
Ciências Exatas e da Terra; Química; Química de Produtos Naturais
resumo ...
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ELEUTÉRIO, CÍNTIA L.;
MENDES, CARLOS F. O.;
Marcus W. Beims;
GALICEANU, MIRCEA;
FILIZOLA, NAZIANO P.
Palavra-chave:
extreme/rare events;
Artificial neural networks;
distance correlation
Áreas do conhecimento:
Ciências Exatas e da Terra; Física; Sistemas Complexos
resumo ...
We propose a novel multi-output convolutional neural network (CNN) framework with station-specific subnets to model and analyze historical low- and high-river stages in the Negro River basin, located in northern South America. The basin is largely covered by tropical forest and experiences strong spatial and seasonal variability in rainfall. The study addresses the challenge of reconstructing accurate waterlevel time series in regions with sparse hydrological observations. Using observed data from five gauging stations—Cucuí, Serrinha, Caracaraí, Santa Maria do Boiaçú, and Moura—the study successfully reconstructs historical waterlevel time series. Quantitative evaluation shows that the subnet-based architecture achieves very low errors (MSE $$\lesssim$$ 0.09) and high distance correlation metrics (DC $$\approx$$ 1.00) during the 2021 flood. We also compare modelling results from subnet-based models with those obtained from individually trained station-specific networks, demonstrating that subnetworks more effectively capture both system-level and station-specific hydrological dynamics. The model captures complex temporal patterns, including sudden decreases, gradual recoveries, and flood rises, demonstrating its ability to represent station-specific hydrological dynamics and system-level responses to extreme floods and droughts. The findings highlight the broader implications of the subnet framework for hydrological prediction under climate variability, particularly for improving early-warning systems and operational monitoring in data-scarce basins. By enhancing the reconstruction of extremes and supporting gap-filling and consistency checking, the method contributes to decision-support strategies for managing future flood and drought risks in the Amazon basin. This visual summary provides a concise overview of the study’s core findings and methodologies. Water level data were collected from five gauging stations in the Negro River basin, covering regions with strong spatial and seasonal variability in rainfall. The data were preprocessed using normalization and formatting suitable for 1D convolutional layers. The study employed CNN-based subnetwork architectures integrating features from all stations, alongside independently trained CNNs for comparison, to evaluate the benefits of spatially shared learning. The graphical abstract illustrates the model’s capability to accurately reconstruct complex temporal dynamics, including sudden decreases, progressive recoveries, and repiquetes, while demonstrating superior generalization relative to individual networks across heterogeneous hydrological regimes. These results underscore the potential of the approach as a data-driven tool for flood and drought monitoring, gap-filling, and consistency checking in regions with sparse hydrological data, supporting operational applications such as early warning systems and water resource management. By leveraging subnetwork architectures, this study addresses the challenges of monitoring large and hydrologically diverse regions like the Amazon, highlighting the importance of integrative models for capturing basin-scale dynamics. CNN-based subnetworks accurately reconstruct historical low- and high-river stages in the Negro River basin. The model captures complex temporal patterns, including sudden decreases, gradual recoveries, and repiquetes. The approach supports monitoring and gap-filling in regions with sparse hydrological data. Integration of multiple stations through subnetworks enhances generalization across heterogeneous hydrological regimes. Subnetworks demonstrate potential for operational use in early warning and water resource management.
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SOARES FIGUEIREDO, ISAQUE;
SOARES FIGUEIRÊDO, ISAIAS;
Marco Andre Argenta
Palavra-chave:
Elementos Finitos
resumo ...
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