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: Assembly lines are production flow systems wherein activities are organised around a line consisting of various workstations through which the product flows. At each station, the product is assembled through a subset of operations. The assembly line balancing problem (ALBP) consists of allocating operations between stations to maximise the system efficiency. In this study, a variable-depth local search algorithm is proposed for solving simple assembly line balancing problems (SALBPs), which are the most widely studied versions of the ALBP. Although the state-of-the-art techniques for solving the SALBP consist of exact enumeration-based methods or heuristics, this paper proposes a local search-based heuristic using variable-length sequences that allow the solution space to be efficiently explored. The proposed algorithm improves the best solution known for multiple instances reported in the literature, indicating that its efficiency is comparable to those of the state-of-the-art method for solving the SALBP. Moreover, the characteristics of the instances for which the proposed procedure provides a better solution than previously reported construction procedures are investigated.

Abstract: Araucanian herring, Strangomera bentincki, is ecologically and economically important. Its complexity, like that of other pelagic fish, arises from seasonal population changes related to distribution with different spatial dynamics and demographic fractions, subject to strong environmental and fishing exploitation variations. This implies the necessity for a thorough understanding of biological processes, which are interpreted with the help of various activities, and directly or indirectly allow to infer and deliver adequate indicators. These activities facilitate a correct technical analysis and consistent conclusions for resource management and administration. In this context, the present study identified and addressed the need to integrate information on Araucanian herring lengths made available in historical series from commercial fleet fishing and sources such as special monitoring, hydroacoustic cruises, and monitoring during closed seasons. The study focused on methodologies widely used in biostatistics that allow analyzing the feasibility of integrating data from different origins, focused on evaluating the correct management of size structures that vary by origin, sample size, and volumes extracted. We call this tool meta-estimation. It estimates the integration of biological-fishery size indicators that originated mainly from commercial fishing and research fisheries for central-south pelagic fishery with data of catch between January and July 2018.

Abstract: This paper proposes the use of multi-stage stochastic programming with recourse for optimised strategic open-pit mine planning. The key innovations are, firstly, that a branching tree of geostatistical simulations is developed to take account of uncertainty in ore grades, and secondly, scenario reduction techniques are applied to keep the trees to a manageable size. Our example shows that different mine plans would be optimal for the downside case when the deposit turns out to be of lower grade than expected compared to when it is of higher grade than expected. Our approach further provides th

Abstract: It is showed that complex scalar fields with a self-interaction potential may propagate along null geodesics on isotropic flat Friedmann-Lemaitre-Robertson-Walker universes with different time-dependent scale factors. This effect appears for certain kinds of self-interactions only, for different forms of potentials, and even for the massive case.

Abstract: Gallic acid (GA) is a polyphenols compound commonly present in wastewater that immensely affects aquatic and human life. GA is also responsible for the inhibitory effects on the microbial activity in the soil, thereby decreasing the fertility of the soil. Therefore, the removal of GA from the wastewater is necessary to combat such issues. The present study focused on the synthesis of reduced graphene oxide (rGO) incorporated bimetallic (Cu/Bi) based nanorods (r-GO-Cu/Bi-NRs) and their photocatalytic applications. Incorporating GO within the CuBi2O4-NRs might decrease the bandgap value, thereby increasing the interfacial charge transfer. Moreover, GO increased the reactive sites and oxygen defects onto the r-GO-Cu/Bi-NRs that led to the separation rate of the photo-induced charge carriers and migration, thereby enhancing the photodegradation ability of the synthesized r-GO-Cu/Bi-NRs. The synthesis process of the r-GO-Cu/Bi-NRs is facile, novel, and economically viable for the photocatalytic degradation of organic pollutants.

Abstract: Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis is a concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources.
Features:
- Easy-to-follow step-by-step concepts and methods
- Every chapter is introduced in a very gentle and intuitive way so students can understand the WHYs, WHAT-IFs, WHAT-IS-THIS-FORs, HOWs, etc. by themselves
- Practical programming exercises in Python for each chapter
- Includes theory and practice for every chapter, summaries, practical coding exercises for target problems, QA, and sample code and data available for download at https://www.routledge.com/Atkinson-Abutridy/p/book/9781032249797

Abstract: This paper presents a strategy for modelling dynamic soil-structure interaction (DSSI) using the spectral element method (SEM) with a Discontinuous Galerkin approach, calibrated by micro-vibrations. The proposed methodology allows not only to adjust the vibration frequencies of the structure but also the observed vibration modes. First, models of two structural shear wall buildings with basements in medium dense sandy soils are developed to estimate empirical modal characteristics and calibrate the structural subdomain and low-strain site properties. Convenient 3D arrays of multiple seismic sensors are used to obtain the environmental vibrations measurements. Afterwards, an optimization process is conducted to calibrate volumetric models of structures. This optimization is performed by preserving the most relevant modal frequencies and shapes to achieve an equivalent dynamic response. Finally, structural models are placed into a neighbouring soil model (soil subdomain), approximating nonlinear soil behaviour by an equivalent linear strategy. Using this complete soil-structure interaction model, relevant engineering performance parameters are assessed via simulations of buildings subjected to a plane wave excitation. The results show the significant effect DSSI have in shear-wall buildings with basements and the importance of considering the flexibility of the foundation in the interpretation of the results. In general, results indicate that DSSI effects are strongly dependent on the input frequency content, which might cause a reduction of the inter-story drifts. Furthermore, a significant period lengthening of the studied structures up to 47% is found, as well as a considerable decrease in story shear up to 220% and a maximum lateral roof displacement reduction of 34% when compared against fixed base referential responses.

Abstract: Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geo-statistics. While best linear prediction has been well understood in univariate spatial statistics, the literature for the multivariate case has been elusive so far. The new challenges provided by modern spatial datasets, being typ-ically multivariate, call for a deeper study of cokriging. In particular, we deal with the problem of misspecified cokriging prediction within the framework of fixed domain asymptotics. Specifically, we provide conditions for equivalence of measures associated with multivariate Gaussian random fields, with index set in a compact set of a d-dimensional Euclidean space. Such conditions have been elusive for over about 50 years of spatial statistics. We then focus on the multivariate Matern and Generalized Wendland classes of matrix valued covariance functions, that have been very popular for having parameters that are crucial to spatial interpolation, and that control the mean square differentiability of the associated Gaussian process. We provide sufficient conditions, for equivalence of Gaussian measures, relying on the covariance parameters of these two classes. This enables to identify the parameters that are crucial to asymptotically equivalent interpolation in multivariate geostatistics. Our findings are then illustrated through simulation studies.

Abstract: Convexification based on convex envelopes is ubiquitous in the non-linear optimization literature. Thanks to considerable efforts of the optimization community for decades, we are able to compute the convex envelopes of a considerable number of functions that appear in practice, and thus obtain tight and tractable approximations to challenging problems. We contribute to this line of work by considering a family of functions that, to the best of our knowledge, has not been considered before in the literature. We call this family ray-concave functions. We show sufficient conditions that allow us to easily compute closed-form expressions for the convex envelope of ray-concave functions over arbitrary polytopes. With these tools, we are able to provide new perspectives to previously known convex envelopes and derive a previously unknown convex envelope for a function that arises in probability contexts.