Predicting Inflation Component Drivers in Nigeria: A Stacked Ensemble Approach

Our study examined the disaggregation of inflation components in Nigeria using the stacked ensemble approach, a machine learning algorithm capable of compensating the weakness of a base learner with the strength of another. This approach gives flexibility of a synergistic performance of stacking each base learner and produces a formidable model that yields the highest level of accuracy and best predictive ability. We analyzed the test data, out-of-sample, and our results show a strong accuracy in predicting inflation. Our results further show that food CPI is the most important driver for headline, urban, and rural inflation while bread and cereals is the most important driver for food inflation. However, biscuits, agric rice, garri white are among the top main drivers of bread and cereal inflation. We note that some CPI items that mostly drive inflation have lower weights while others have higher weights therefore, focusing entirely on CPI weights as a policy guide will stymied a successful control of inflation in Nigeria. In addition, ignoring CPI items with lower weights in policy intervention will make inflation difficult to control. Above all, adequate trace of the source of inflation to the least sub-component of each component will help address or formulates an appropriate policy to confront inflation problems in Nigeria.JEL: C53, E37

Temporal loudness weights: Primacy effects, loudness dominance and their interaction

Loudness judgments of sounds varying in level across time show a non-uniform temporal weighting, with increased weights assigned to the beginning of the sound (primacy effect). In addition, higher weights are observed for temporal components that are higher in level than the remaining components (loudness dominance). In three experiments, sounds consisting of 100- or 475-ms Gaussian wideband noise segments with random level variations were presented and either none, the first, or a central temporal segment was amplified or attenuated. In Experiment 1, the sounds consisted of four 100-ms segments that were separated by 500-ms gaps. Previous experiments did not show a primacy effect in such a condition. In Experiment 2, four- or ten-100-ms-segment sounds without gaps between the segments were presented to examine the interaction between the primacy effect and level dominance. As expected, for the sounds with segments separated by gaps, no primacy effect was observed, but weights on amplified segments were increased and weights on attenuated segments were decreased. For the sounds with contiguous segments, a primacy effect as well as effects of relative level (similar to those in Experiment 1) were found. For attenuation, the data indicated no substantial interaction between the primacy effect and loudness dominance, whereas for amplification an interaction was present. In Experiment 3, sounds consisting of either four contiguous 100-ms or 475-ms segments, or four 100-ms segments separated by 500-ms gaps were presented. Effects of relative level were more pronounced for the contiguous sounds. Across all three experiments, the effects of relative level were more pronounced for attenuation. In addition, the effects of relative level showed a dependence on the position of the change in level, with opposite direction for attenuation compared to amplification. Some of the results are in accordance with explanations based on masking effects on auditory intensity resolution.

Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification

The graph neural network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered. Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN. This study proposes an ensemble model called Boosting-GNN, which uses GNNs as the base classifiers during boosting. In Boosting-GNN, higher weights are set for the training samples that are not correctly classified by the previous classifiers, thus achieving higher classification accuracy and better reliability. Besides, transfer learning is used to reduce computational cost and increase fitting ability. Experimental results indicate that the proposed Boosting-GNN model achieves better performance than graph convolutional network (GCN), GraphSAGE, graph attention network (GAT), simplifying graph convolutional networks (SGC), multi-scale graph convolution networks (N-GCN), and most advanced reweighting and resampling methods on synthetic imbalanced datasets, with an average performance improvement of 4.5%.

Application of AHP for the Weighting of Sustainable Development Indicators at the Subnational Level

This article presents an indicator weighting method for constructing composite indices to assess sustainable development at the subnational level. The study uses an analytic hierarchy process (AHP), which is considered relevant, since it establishes links between the indicators that make up the different sustainable development goals (SDG). For this purpose, 28 indicators defined by experts constitute the base to evaluate the progress towards sustainable development of the Aburrá Valley region, located in Antioquia, Colombia. The results show that health, employment, and education indicators obtained higher weights, while environmental indicators received the most reduced weights. Likewise, the model proves to be consistent using a consistency ratio, which generates the possibility of replicating this model at different subnational levels.

Relationship of some metabolic Hormones with Increased Live Body Weight of Male and Female of Karakul and Awassi Lambs

This study was conducted in the animal field of the Al-Kafeel Company from November 2019 till May 2020 to investigate the possible association between growth hormone (GH) and thyroxine (T4) with the growth traits in Awassi and Karakul sheep. The total number of animals which used in the study was 60 lambs, 28 (13 males and 15 females) from Karakul and 32 (18 males and 14 females) from Awassi. Blood samples were collected at birth, weaning, and six months of age, and both GH and T4 concentrations were measured. Results showed higher values of T4 for Karakul as compared with Awassi at birth and weaning. Karakul breed was also exhibited significantly higher values of GH over Awassi breed only at weaning, while, no significant differences were observed at birth and six months of age. Karakul lambs showed higher weights as compared with Awassi lambs at all studied periods. A highly significant (p < 0.01) positive correlation was observed between T4 concentration and the weight of lambs at most studied periods. Whereas, GH did not exhibit any correlation with growth traits measured in both breeds. The elevated T4 might be one of the reasons for superiority of Karakul over Awassi breed in live body weights. This high correlation between T4 and growth traits could be used in the early selection of lambs to improve the weights of sheep at marketing.

Improving mechanical properties of the UIC test bench

Brakes are one of the most important components of vehicles. This is due to their function of decelerating, regulating vehicle speed and keeping vehicles stationary. The correct function and reliability of this system is essential for safe operation and vehicles driving. In our case, we deal with the brakes of rail vehicles. As trainsets achieve higher weights and speeds, great emphasis is also placed on improving the friction components of brake systems, which must meet the strict parameters set by the International Union of Railways (UIC) and must be tested on specialized approved test benches, before being put into service. As the demands on the development of friction components increase, so do the demands on their testing. From this point of view, there is necessary constant improvement in the measuring lane of the brake bench. It is necessary to improve the measuring and control technology as well as the mechanical components. This paper deals with the study of mechanical components in terms of operational dynamics. The main idea of the work is to create a virtual model of the brake bench in order to simulate the operating states and determine the critical modes with a possible adverse impact on the course of measurement and control. During the measurement, the monitored parameter is an important simulated mass, which must meet a precisely determined tolerance for the success of the measurement.

PSXV-26 Late-Breaking: Effects of feeding haylage on the reproductive performance of 2-year-old beef cattle raised in a sustainable production system at Berea college farm

Effects of winter feeding haylage on the growth and reproductive performance of late weaned, summer breeding, rotationally grazed, selectively bred mixed breed cattle (n =90) raised at Berea College Farm during 2015 to 2021 were evaluated. Cattle were grouped based on the year in which they turned 2 years old (2017, 2018, 2019, 2020 and 2021). The 2017 group alone was not fed haylage. Initial data indicated that maintenance of a pre-ruminant gastrointestinal tract during the early developmental years under our management is the main factor impacting cattle performance. Indeed, despite similar birth weights, weaning and yearling weights were numerically higher in 2018 and 2019 compared to 2017 cattle. Significantly higher weaning and yearling weights (264.47 vs 229.37kg and 306.60 vs 253.03kg; P &lt; 0.05) were observed in 2020 versus 2017 cattle, respectively. Additionally, 2018 (426.83kg) but not 2019 (387.38kg) cattle had higher (P &lt; 0.05) liveweights than 2017 (398.93kg) cattle at yearling pregnancy check. At the 2nd breeding, 2018 cattle maintained numerically higher weights than 2019. However, the higher liveweights observed for 2018 cattle compared to that of 2017 cattle at the yearling pregnancy check, was reversed in the following year at the 2-year-old pregnancy check. Consequently, pregnancy rates at the yearling pregnancy check did not differ (89.47 vs 91.67%) but numerically lower retention (31.8 vs 50%) and pregnancy rates (50 vs 75%) were observed for 2018 cattle than 2017 cattle by the 2-year-old pregnancy check, respectively. Additionally, 55% the 2018 cattle displayed ovarian activity and 50% of the 2019 cattle displayed estrus prior bull introduction. In 2019 cattle, 92% were cyclic before introduction of the bull and a 91% estrus response was detected during the breeding season. Feeding haylage promoted growth and reproductive performance of cattle but apparently did not alleviate 2019 drought induced dystocia occurrences during 2020 and 2021 calving seasons.

Prediction of longitudinal and transverse profiles of pressure flushing cones using artificial intelligence and data pre-processing

One of the most critical issues in dam reservoir management is the determination of sediment level after flushing operation. Artificial intelligence (AI) methods have recently been considered in this context. The present study adopts four AI approaches, including the Feed-Forward Neural Network (FFNN), Cascade Feed-Forward Neural Network (CFFNN), Gene Expression Programming (GEP), and Bayesian Networks (BNs). Experimental data were exploited to train and test the models. The results revealed that the models were able to estimate the post-flushing sediment level accurately. FFNN outperformed the other models. Furthermore, the importance of model inputs was determined using the τ-Kendall (τ–k), Random Forest (RF), and Shannon Entropy (SE) pre-processing methods. The initial level of sediment was found to be the most important input, while the orifice output flow rate was observed to have the lowest importance in modeling. Finally, inputs of higher weights were introduced to the FFNN model (as the best predictive model), and the analysis of the results indicated that the exclusion of less important input variables would have no significant impact on model performance.

Blood Biomarker Profile Alterations in Newborn Canines: Effect of the Mother′s Weight

This study aims to determine the effect of the weight of bitches on liveborn and stillbirth puppies from eutocic births, and physiological blood alterations during the first minute postpartum. A total of 52 female dogs were evaluated and distributed in four categories: C1 (4.0–8.0 kg, n = 19), C2 (8.1–16.0 kg, n = 16), C3 (16.1–32.0 kg, n = 11), and C4 (32.1–35.8 kg, n = 6). The dams produced 225 liveborn puppies and 47 were classified as stillbirth type II. Blood samples were taken from the umbilical vein to evaluate the concentration of gases, glucose, lactate, calcium, hematocrit levels, and blood pH. The liveborn puppies in C2, C3, and C4 had more evident physiological alterations (hypercapnia, acidosis) than those in C1 (p < 0.05). These signs indicate a process of transitory asphyxiation. The stillborn pups in all four categories had higher weights than their liveborn littermates. C3 and C4 had the highest mean weights (419.86 and 433.79 g, respectively) and mortality rates (C3 = 20.58%, C4 = 24.58%). Results suggest that if the weight of the bitch is >16.1 kg in eutocic births, there is a higher risk of intrapartum physiological alterations and death. The results of this study allowed us to identify that the weight of dams before birth determines the weight of the puppies at birth.