Abstract: To adequately meet the nutritional needs of broilers, it is necessary to know the values of apparent metabolizable energy corrected by the nitrogen balance (AMEn) of the feedstuffs. To determine AMEn values, biological assays, feedstuff composition tables, or prediction equations are used as a function of the chemical composition of feedstuffs, the latter using statistical methodologies such as multiple linear regression, neural networks, and Bayesian networks (BN). BN is a statistical and computational methodology that consists of graphical (graph) and probabilistic models of quantitative and/or qualitative variables. Ensembles of BN in the area of broiler nutrition are expected, as there is no research showing their AMEn prediction performance. The purpose of this article is to propose and use ensembles of hybrid Bayesian networks (EHBNs) and obtain prediction equations for the AMEn of feedstuffs used in broiler nutrition from their chemical compositions. We trained 100, 1,000, and 10,000 EHBN, and in this way, empirical distributions were found for the coefficients of the covariates (crude protein, ether extract, mineral matter, and crude fiber). Thus, the mean, median, and mode of these distributions were calculated to build prediction equations for AMEn. It is observed that the method for obtaining the coefficients of the covariates discussed in this article is an unprecedented proposal in the field of broiler nutrition. The data used to obtain the equations were obtained by meta-analysis, and the data for the validation of the equations were obtained from metabolic tests. The proposed equations were evaluated by precision measures such as the mean square error (MSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The best equations for predicting AMEn were derived from the mean or mode coefficients for the 10,000 EHBN results. In conclusion, the methodology used is a good tool to obtain prediction equations for AMEn as a function of the chemical composition of their feedstuffs. The coefficients were found to differ from those found by other methodologies, such as the usual neural network or multiple linear regressions. The field of broiler nutrition contributed with new equations and with a never-applied methodology and differentiated in obtaining its coefficients by empirical distributions.

Abstract: Visual representation as a means of communication uses elements to build a narrative. We propose using computer analysis to perform a quantitative analysis of the elements used in the visual creations that have been produced in reference to the epidemic, using 927 images compiled from The Covid Art Museum's Instagram account. This process has been carried out with techniques based on deep learning to detect objects contained in each study image. The research reveals the elements that are repeated in images to create narratives and the relations of association that are established in the sample. The predominant discourses in the sample do not show concern for the effects of illness. On the contrary, the impact and effects of confinement, through the prominent presence of elements such as human figures, windows, and buildings, are the most expressed experiences in the creations analyzed.

Abstract: The compact mm-wavelength signal in the central cavity of the PDS 70 disc, revealed by deep ALMA observations, is aligned with unresolved H alpha emission, and is thought to stem from a circumplanetary disc (CPD) around PDS 70c. We revisit the available ALMA data on PDS 70c with alternative imaging strategies, and with special attention to uncertainties and to the impact of the so-called 'JvM correction', which is thought to improve the dynamic range of restored images. We also propose a procedure for the alignment and joint imaging of multi-epoch visibility data. We find that the JvM correction exaggerates the peak signal-to-noise of the data, by up to a factor of 10. In the case of PDS 70, we recover the detection of PDS 70c from the 2019 July data, but only at 8 sigma. However, its non-detection in 2017 Dec. suggests that PDS 70c is variable by at least 42 per cent +/- 13 per cent over a 1.75 yr time-span, so similar to models of the H alpha variability. We also pick up fine structure in the inner disc, such that its peak is offset by similar to 0 ''.04 from the disc centre. The inner disc is variable too, which we tentatively ascribe to Keplerian rotation as well as intrinsic morphological changes.

Abstract: Various numerical methods have been extensively studied and used for reliability analysis over the past several decades. However, how to understand the effect of numerical uncertainty (i.e., numerical error due to the discretization of the performance function) on the failure probability is still a challenging issue. The active learning probabilistic integration (ALPI) method offers a principled approach to quantify, propagate and reduce the numerical uncertainty via computation within a Bayesian framework, which has not been fully investigated in context of probabilistic reliability analysis. In this study, a novel method termed `Parallel Adaptive Bayesian Quadrature' (PABQ) is proposed on the theoretical basis of ALPI, and is aimed at broadening its scope of application. First, the Monte Carlo method used in ALPI is replaced with an importance ball sampling technique so as to reduce the sample size that is needed for rare failure event estimation. Second, a multi-point selection criterion is proposed to enable parallel distributed processing. Four numerical examples are studied to demonstrate the effectiveness and efficiency of the proposed method. It is shown that PABQ can effectively assess small failure probabilities (e.g., as low as 10(-7)) with a minimum number of iterations by taking advantage of parallel computing.

Abstract: We study the estimation and prediction of Gaussian processes with spacetime covariance models belonging to the dynamical generalized Wendland (DGW) family, under fixed-domain asymptotics. Such a class is nonseparable, has dynamical compact supports, and parameterizes differentiability at the origin similarly to the space-time Matern class.
Our results are presented in two parts. First, we establish the strong consistency and asymptotic normality for the maximum likelihood estimator of the microergodic parameter associated with the DGW covariance model, under fixed-domain asymptotics. The second part focuses on optimal kriging prediction under the DGW model and an asymptotically correct estimation of the mean squared error using a misspecified model. Our theoretical results are, in turn, based on the equivalence of Gaussian measures under some given families of space-time covariance functions, where both space or time are compact. The technical results are provided in the online Supplementary material.

Abstract: Diversifying the energy components of a country's transport sector is essential to guarantee the fuel supply to consumers and increase the market dynamics and competitiveness. Among the known alternative fuels, biogas is a renewable source and after upgrading to biomethane, it presents a similar composition to natural gas (>90% of CH4; 35-40 MJ m(-3)). In addition, it can be produced from a wide variety of biological resources and at different scales In this study, two scenarios have been developed that evaluate the use of liquefied biomethane (LBM) as a diesel replacement option in the freight sector of an area of 248,223 km(2) (equivalent to the area of the UK). Sugarcane vinasse (SVC) and Municipal Solid Waste (MSW) were the sole feedstocks for biogas production. The first scenario, non-restricted scenario (NRS), covered the entire territory while, the second scenario, restricted scenario (RS), includes only the area where gas pipelines are available. An economic assessment of the entire biogas value chain including, biogas production units, purification, transport and end-use was performed. The minimum selling price (MSP) of biomethane throughout the biogas chain was then estimated. LBM is estimated to be a cost-effective and affordable fuel choice compared to diesel. The technical potential of biogas production by the sugarcane mills and landfills of Sao Paulo state can replace up to half of the diesel consumed in the territory. The minimum distances and optimal locations methodology indicated the need for 120 liquefaction plants in the NRS, 35 injection points in the RS, and 7 refuelling stations to supply LBM throughout the state of Sao Paulo. The units for CO2 removal had the greatest influence on capital costs (similar to 60%) in both scenarios. Expenditure associated with the gas injection operation and its transport comprised more than 90% of the operating costs of the RS. Electricity purchasing represented the highest share of the operating costs at biogas purification (20%-30%) and biomethane liquefaction (65%-91%) units. Personnel costs are observed along the entire biogas chain, especially, in the biomethane transport step (40%), indicating an opportunity to generate wealth, jobs, and income. Despite our projections for the cost-effective and competitive supplies of LBM as a diesel replacement fuel, policy support measures such as a feed-in tariff, are likely to be necessary in order to overcome non-technical barriers and gain wider acceptability.

Abstract: Human interactions and perceptions about health risk are essential to understand the evolution over the course of a pandemic. We present a Susceptible-Exposed-Asymptomatic-Infectious-Recovered-Susceptible mathematical model with quarantine and social-distance-dependent transmission rates, to study COVID-19 dynamics. Human activities are split across different location settings: home, work, school, and elsewhere. Individuals move from home to the other locations at rates dependent on their epidemiological conditions and maintain a social distancing behavior, which varies with their location. We perform simulations and analyze how distinct social behaviors and restrictive measures affect the dynamic of the disease within a population. The model proposed in this study revealed that the main focus on the transmission of COVID-19 is attributed to the “home” location setting, which is understood as family gatherings including relatives and close friends. Limiting encounters at work, school and other locations will only be effective if COVID-19 restrictions occur simultaneously at all those locations and/or contact tracing or social distancing measures are effectively and strictly implemented, especially at the home setting.

Abstract: This paper develops a novel procedure to conduct a Freight Origin-Destination Synthesis (FODS) that jointly estimates the trip distribution, mode choice, and the empty trips by truck and rail that provide the best match to the observed freight traffic counts. Four models are integrated: (1) a gravity model for trip distribution, (2) a binary logit model for mode choice, (3) a Noortman and Van Es' model for truck, and (4) a Noortman and Van Es' model for rail empty trips. The estimation process entails an iterative minimization of a nonconvex objective function, the summation of squared errors of the estimated truck and rail traffic counts with respect to the five model parameters. Of the two methods tested to address the nonconvexity, an interior point method with a set of random starting points (Multi-Start algorithm) outperformed the Ordinary Least Squared (OLS) inference technique. The potential of this methodology is examined using a hypothetical example of developing a nationwide freight demand model for Bangladesh. This research improves the existing FODS techniques that use readily available secondary data such as traffic counts and link costs, allowing transportation planners to evaluate policy outcomes without needing expensive freight data collection. This paper presents the results, model validation, limitations, and future scope for improvements.

Abstract: We investigate a range of techniques for the acceleration of Calderon (operator) preconditioning in the context of boundary integral equation methods for electromagnetic transmission problems. Our objective is to mitigate as far as possible the high computational cost of the barycentrically-refined meshes necessary for the stable discretisation of operator products. Our focus is on the well-known PMCHWT formulation, but the techniques we introduce can be applied generically. By using barycentric meshes only for the preconditioner and not for the original boundary integral operator, we achieve significant reductions in computational cost by (i) using “reduced” Calderon preconditioners obtained by discarding constituent boundary integral operators that are not essential for regularisation, and (ii) adopting a “bi-parametric” approach [1,2] in which we use a lower quality (cheaper) H-matrix assembly routine for the preconditioner than for the original operator, including a novel approach of discarding far-field interactions in the preconditioner. Using the boundary element software Bempp (www.bempp.com), we compare the performance of different combinations of these techniques in the context of scattering by multiple dielectric particles. Applying our accelerated implementation to 3D electromagnetic scattering by an aggregate consisting of 8 monomer ice crystals of overall diameter 1cm at 664GHz leads to a 99% reduction in memory cost and at least a 75% reduction in total computation time compared to a non-accelerated implementation. Crown Copyright (C) 2022 Published by Elsevier Inc. All rights reserved.

Abstract: A promising sustainable energy storage characteristic is achieved in redox additive electrolyte by developing strict blend of one dimensional (1D) and two dimensional (2D) structures. Hydrothermal reaction is followed to obtain the desired morphology. Two dimensional (2D) reduced graphene oxide (rGO) is added into the redox reaction between potassium permanganate and sodium nitrite to obtain nanocomposite comprising 1D and 2D blended structures of MnO2/rGO. Their structures and morphologies are studied by XRD, Raman and HRTEM analyses, respectively. The pseudocapacitive behaviour is studied in a redox additive electrolyte comprising KOH and K3Fe(CN)(6). The effect of electrolytic concentration was studied by varying the concentration of K3Fe(CN)6. The specific capacity is considerably enhanced up to 1741 F/g, 8.75 A/g with increase in concentration of K3Fe (CN)6. The role of redox couple [Fe(CN)(6)](3)-/[Fe(CN)(6)](4)-played a key role in adding the charge movement across the electrode which tuned well with the manganese ions to obtain one of the most promising pseudo-capacitances from the developed 1D and 2D blended structures of MnO2/rGO. For in-depth analysis of Fe ions movement, a symmetric supercapacitor cell is constructed to achieve a commendable specific capacitance of 216 F/g at 3.75 A/g. Prolong cycling hinted decreasing electrolytic interfacial layers resulting in fast reversible ki-netics of Fe(III) -> Fe(II) ions to achieve astonishing capacity retention of 127% after 3000 cycles.