Instituto Politécnico do Rio de Janeiro

The best conditions for the kinetic study of the ketalization reaction of glycerol with acetone for the production of solketal using zeolite H-BEA (SAR 19) as a catalyst were found through a fractional experimental design. To simplify the heterogeneous kinetics, by means of a smaller number of kinetic parameters to encompass all the kinetic terms toward the products and reagents, a reversible kinetic model was used. From the comparison between the experimental and calculated conversions, it was possible to analyze the accuracy of the estimations, providing a good way to apply statistical treatments to improve the calculated kinetic properties. Thereby, it is possible to calculate the equilibrium constants for a range of reactions performed across different temperatures (40–80 °C) as well as the forward reaction activation energy (44.77 kJ mol–1) and the reverse reaction activation energy (41.40 kJ mol–1). Moreover, 70–76% glycerol conversion was obtained using the same catalyst for five reactions, without wash or performing any other pretreatments in the catalyst between reactions. The solketal product has been studied as a green industrial solvent additive in gasoline and biofuels.

The photoacoustic technique is used to investigate the thermal diffusivity of ${\mathrm{Ge}}_{\mathit{x}}$${\mathrm{Te}}_{1\mathrm{\ensuremath{-}}\mathit{x}}$ glasses as a function of composition. The observed dependence on the composition is explained on the basis of the chemically ordered network model. This interpretation is further supported by x-ray and electrical resistivity measurements.

Abstract The compaction of highly heterogeneous poroelastic reservoirs with the geology characterized by long‐range correlations displaying fractal character is investigated within the framework of the stochastic computational modelling. The influence of reservoir heterogeneity upon the magnitude of the stresses induced in the porous matrix during fluid withdrawal and rock consolidation is analysed by performing ensemble averages over realizations of a log‐normally distributed stationary random hydraulic conductivity field. Considering the statistical distribution of this parameter characterized by a coefficient of variation governing the magnitude of heterogeneity and a correlation function which decays with a power‐law scaling behaviour we show that the combination of these two effects result in an increase in the magnitude of effective stresses of the rock during reservoir depletion. Further, within the framework of a perturbation analysis we show that the randomness in the hydraulic conductivity gives rise to non‐linear corrections in the upscaled poroelastic equations. These corrections are illustrated by a self‐consistent recursive hierarchy of solutions of the stochastic poroelastic equations parametrized by a scale parameter representing the fluctuating log‐conductivity standard deviation. A classical example of land subsidence caused by fluid extraction of a weak reservoir is numerically simulated by performing Monte Carlo simulations in conjunction with finite elements discretizations of the poroelastic equations associated with an ensemble of geologies. Numerical results illustrate the effects of the spatial variability and fractal character of the permeability distribution upon the evolution of the Mohr–Coulomb function of the rock. Copyright © 2004 John Wiley & Sons, Ltd.

Hydrate prevention strategies for offshore flowlines are now moving toward hydrate risk management by delaying its nucleation and growth using water-soluble polymers, known as kinetic hydrate inhibitors (KHIs). This study investigates the natural gas hydrate inhibition performance of three poly(N-isopropylacrylamide) (PNIPAM)-based KHIs [poly(N-isopropylacrylamide-co-acrylic acid (PNIPAM-co-AA), poly(N-isopropylacrylamide-co-cyclopentylamine (PNIPAM-co-Cp), and poly(N-isopropylacrylamide-co-tert-butylamine (PNIPAM-co-C4t)] by determining the hydrate onset time, growth rate, and resistance to flow using a high-pressure autoclave. These data are compared to three control groups [water, Luvicap solution, and polyvinylpyrrolidone (PVP)] under various cooling rates (0.25, 0.033, and 0.017 K/min). The results show that the nucleation of hydrate crystals was delayed in the presence of the KHI candidates as assessed using the onset time at different cooling rates. The effect of the KHI candidate on the hydrate growth characteristics was also studied by determining the initial growth rate and torque changes with an increasing hydrate fraction in the liquid phase. The obtained results confirmed that the synthesized PNIPAM-based KHIs showed a high subcooling temperature, which is comparable to those of commercial KHIs. The modification of the base polymer (PNIPAM-co-AA) improves the kinetic inhibition performance for PNIPAM-co-Cp (13.9, 12.5, and 7.8 K for 0.25, 0.033, and 0.017 K/min cooling rates, respectively) but results in decreased performance for PNIPAM-co-C4t (9.6, 9.9, and 7.6 K for 0.25, 0.033, and 0.017 K/min cooling rates, respectively). After the hydrate onset, PNIPAM-co-C4t showed a slower growth rate and more stable torque during the hydrate formation than PNIPAM-co-Cp, suggesting its potential role as a crystal growth inhibitor. These results suggest that the performance of PNIPAM-based KHIs can be evaluated with the holistic investigation on nucleation and growth of hydrate crystals.

Cloud computing (CC) technologies (viz artificial intelligence (AI), data science, blockchain, “big data” (BD), etc.) are progressively widespread and practically applied. Researchers face the biggest challenges regarding efficient data access and acquisition. The CPS's efficiency will help researchers advance in CC, cyber-attacks (CAs), “cyber threats” (CTs), and “CT intelligence” (CTI). Alongside optimally storing the data, one major caveat is security. The best possible protection can enhance data storage/handling to protect end systems from illegal attacks and thefts. Smart environments (SEs) are collections of sensors, actuators, and numerous computing pieces that ease human life. With the booming SEs, data have notably increased, thus demanding smart, optimal management. Sometimes, data processing is inadequate due to risks and resource costs. Some characteristic security difficulties are “advanced persistent threats” (APTs), denial-of-service (DoS), eclipse, double-spending attacks, and other malware. Thus, advanced AI-reliant anomaly discovery and mitigation tactics can address and disentangle the issues above. Cybersecurity (CS) in SEs, sub-CPss, and CPSs relates to working well with AI-based anomaly detection techniques. This chapter investigates challenges, trends, and prospective design pathways for securely taming networks and CPSs via AI-centered, robust, real-time, and CC-reliant data storage to inhibit unauthorized access and innumerable CAs (e.g., ransomware, spyware, and phishing. This taxonomy bestows readers with an ample AI potential overview to improve CS in different contexts. The chapter inspects future opportunities in emerging CS applications, advanced AI tactics, data representation, and new infrastructures' development for successfully adopting AI-based CS (AICS) in today's digital transformation era and syndemic circumstances.

The kinetic of fatty acids esterification with ethanol utilizing niobium oxide catalyst (79.8% of NbO5 and 19.6% of water) for the production of biodiesel was analyzed through a reversible kinetic modeling for the determination of the kinetic and thermodynamic data of the reactions. The reactions were carried out with three kinds of fatty acids (stearic, palmitic, and lauric) with anhydrous and hydrated ethanol. From the kinetic study it was possible to calculate the theoretical equilibrium data which were compared to experimental data of each reaction. From the comparison between the experimental and calculated conversions it was possible to analyze the accuracy of the estimations, providing a good way to apply statistical treatments in the improvement of the kinetic and thermodynamic properties calculated.

Local perturbations around contours strongly disturb the final result of computer vision tasks. It is common to introduce a priori information in the estimation process. Improvement can be achieved via a deformable model such as the snake model. In recent works, the deformable contour is modeled by means of B-spline snakes which allows local control, concise representation, and the use of fewer parameters. The estimation of the sub-pixel edges using a global B-spline model relies on the contour global determination according to a Maximum Likelihood framework and using the observed data likelihood. This procedure guarantees that the noisiest data will be filtered out. The data likelihood is computed as a consequence of the observation model which includes both orientation and position information. Comparative experiments of this algorithm and the classical spline interpolation have shown that the proposed algorithm outperforms the classical approach for Gaussian and Salt & Pepper noise.

The authors consider symmetric multiserver multiqueue systems in the cases of exhaustive and gated service disciplines, and present exact analytical results for the average server cycle and vacation times, as well as approximate closed-form expressions for the average customer waiting time, thus complementing the results obtained by the same authors (1990) for the one-limited service discipline. Arrival processes at each queue are assumed to be Poisson, with the same rate for all queues; service times and walk times are modeled with independent, identically distributed random variables with arbitrary distributions. The two cases in which at most one server or any number of servers can simultaneously attend a queue are considered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper analyzes two variants of principal component analysis (PCA) for error-concealment: blockwise PCA and clustered blockwise PCA. Realistic communication channels are not error free. Since the signals transmitted on real-world channels are highly compressed, regardless of cause, the quality of images reconstructed from any corrupted data can be very unsatisfactory. Error concealment is intended to ameliorate the impact of channel impairments by utilizing a priori information about typical images in conjunction with available picture redundancy to provide subjectively acceptable renditions of affected picture regions. Some experiments have been performed with the two proposed algorithms and they are shown.

The influence of ion implantation in the thermal properties of silicon wafers at room temperature is investigated using the photoacoustic technique. It is suggested that the observed decrease of the values of both thermal diffusivity and conductivity, as the implantation dose increases, is due to the amorphization of the implanted layer.

The residual stress profiles in speed gears for automotive vehicles were determined. The profiles were measured at the root between two teeth of the gears by x-ray tensometry without the necessity of cutting the pieces. The gears were cemented, quenched and annealed. After this heat treatment, compressive residual stresses were introduced by shot-peening. The profiles indicate the maximum residual stress of -950 MPa (compressive) at a depth of 0.05 mm. The stresses are zero at a depth of 0.25 mm. In the gears without shot-peening the maximum residual stress at the surface is -600 MPa (compressive) and is zero at a depth of 0.30 mm.

Abstract We calculate the impact of a socioeconomic program during 2020 as a measure to mitigate the Coronavirus Disease 2019 (COVID-19) outbreak in Brazil. For each Brazilian State, we estimate the time-dependent reproduction number from daily reports of COVID-19 infections and deaths using a Susceptible-Exposed-Infected-Recovered-like (SEIR-like) model. Then, we analyse the correlations between the reproduction number, the amount of individuals receiving governmental aid, and the index of social isolation based on mobile phone information. We conclude that socioeconomic programs had a significant impact on reducing the accumulated numbers of infections and deaths by allowing those in need to stay at home, adhering to social isolation.

Abstract A stepwise model with lumped mass transfer kinetics (SW‐LM) is applied for chromatographic column modeling in simulated moving bed (SMB) separation of a racemic mixture of the anesthetic ketamine. The SW‐LM model can easily represent the SMB separation process of ketamine considering the retention factors equal to the equilibrium constants of the R ‐ and S ‐enantiomers. The equilibrium data was utilized only in the previous column characterization for the determination of mass transfer kinetic parameters used later in the SMB design. The SW‐LM model achieves similar transient results of enantiomer concentrations in the extract and raffinate compared to a robust equilibrium dispersive model.

Processing variables are known to have a decisive influence on the impact properties of polypopylene-nano-CaCO 3 composites. Therefore, it becomes mandatory to evaluate the effect of extrusion conditions on composite impact resistance. In the present work, a response surface methodology was used to evaluate the effects of screw rotation speed, screw configuration, and nanoparticles content on the polypropylene impact properties. The concentration of the nano-CaCO 3 particles, the screw rotation speed, and the interaction between these entrance factors were the processing variables of greater influence on the impact properties of nano-CaCO 3 /polypropylene composites. The increase of screw rotation speed led to a decrease in the impact resistance, while the increase of filler content promoted an increase of such property. However, the increase of nanoparticles content led to obtaining poor tensile properties. Therefore, the filler content has to be adjusted in order to improve the impact properties without significant loss of mechanical strength.

The complexity of an independent “ cyber-physical system ” ( CPS ) per control, sensing, actuation, computation, and communication demands is hefty. Ongoing scalar expansion and numerous security cyber threats ( CTs ) arise, notably information sharing channels, hardware, and virtual machines ( VMs ) fraud. CPSs are more complex, knowledgeable, and independent. Scientists cannot predict CPS behavior attributable to their intricate interconnections of various virtual and real components. This investigation unveils a CPS-AI structure to support “ artificial intelligence ” ( AI ), discussing safety hazards in different CPSs, CT modeling, and CT security solution downsides. This chapter debates leading-edge adaptive detection and tracking CPS solutions and their methodological constraints, featuring the difficulty of spotting runtime, security, hibernation, or uncertainty vulnerabilities. Network CTs and other safety incidents simplify CPS communication. “ Intrusion prevention security ( IPS ) systems” (IPSSs) cannot entirely identify, avert, or block insignificant risks. Database misbehavior as to safety measures must be reconsidered and appraised. Nonlinear surveillance systems relying on “ neural networks ” ( NNs ) and “ variable structure control ” ( VSC ) may estimate attacks and prevent CTs. Nonlinear monitoring aids in assessing NN and VSC attacks. Request time, accuracy, loss, and reliability analyses must be viable in CPS-AIs.

We report results of measurements on the thermal diffusivity, specific heat and electrical resistivity of hydrogenated-palladium samples. It is shown that repetition of the hydrogenation process with the same sample induces aging effects, which lead to erroneous results. The results for the thermal-property measurements on single-cycled palladium samples reflected a phase transition between \ensuremath{\alpha}-PdH and \ensuremath{\beta}-PdH as the hydrogen concentration is increased.

Here we report on a work concerning signal-tuned spectral Gabor functions, and their application to power-signal disturbance analysis. The signal-tuned spectral Gabor functions allow the representation of spectral signals by means of Gabor functions whose parameters are defined by the signal's inverse Fourier transform. Basing a time-frequency transform on such functions amounts to analyzing the spectral signal by means of its own coding functions, what leads to similar properties as those of the Wigner transform. The resulting Signal-Tuned Spectral Gabor Transform (STSGT) tunes itself to the input signal, allowing the accurate detection of time and frequency events. In the experimental study reported here, the time- frequency spectrograms yielded by three different approaches (STSGT, Wigner transform and S transform) have been taken as input to a multilayer-perceptron classifier. Our results show that the STSGT, besides yielding higher-quality, more informative spectrograms, is able to outperform the Wigner and S transforms in the classification task.

In this paper, a novel technique for blind signal separation based on a combination of second order and higher order approaches is introduced. The problem of blind signal separation was solved in a wavelet domain. The main idea behind this approach is that the mixing signal can be decomposed into a sum of uncorrelated and/or independent sub-bands using the wavelet transform. In the beginning, the observed signal is prewhitened in the time domain then, the initial separation matrix will be estimated from second order statistics decorrelation method in the wavelet domain. The estimating matrix will be used as an initial separating matrix in the higher order statistics method in order to estimate the final separation matrix. The algorithm was tested using natural images. Extensive experiments have confirmed that the use of the proposed procedure provides promising results in separating the image from noisy mixtures of images.

This paper presents the application of the one new approach using Genetic Algorithm in solving One-Dimensional Cutting Stock Problems in order to minimize two objectives, usually conflicting, i.e., the number of processed objects and setup while simultaneously treating them as a single goal. The model problem, the objective function, the method denominated SingleGA10 and the steps used to solve the problem are also presented. The obtained results of the SingleGA10 are compared to the following methods: SHP, Kombi234, ANLCP300 and Symbio10, found in literature, verifying its capacity to find feasible and competitive solutions. The computational results show that the proposed method, which only uses a genetic algorithm to solve these two objectives inversely related, provides good results.

In this paper, two simple principal component regression methods for estimating the optical flow between frames of video sequences according to a pel-recursive manner are introduced. These are easy alternatives to dealing with mixtures of motion vectors in addition to the lack of prior information on spatial-temporal statistics (although they are supposed to be normal in a local sense). The 2D motion vector estimation approaches take into consideration simple image properties and are used to harmonize regularized least square estimates. Their main advantage is that no knowledge of the noise distribution is necessary, although there is a underlying assumption of localized smoothness. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.