Abstract Improving feed efficiency (FE) is essential for the swine industry’s economic and environmental sustainability. Genetic selection, particularly through estimating breeding values for feed conversion ratio (EBV_FCR), is a common strategy to enhance FE. However, the biological mechanisms underlying phenotypic variations in FE between pigs with different EBV_FCR values are not fully understood. This study investigates these mechanisms by examining growth performance, nutrient and energy digestibility, and fecal microbiota composition and functionality of pigs at the nursery stage. The study involved 128 pigs, weaned at 21 days (±2 days) and with an initial body weight of 6.87 kg (±0.34 kg). These pigs, selected from dam and sire lines with divergent EBV_FCR values, were randomly assigned to 32 pens with four pigs each. Pigs were fed a corn and soybean meal-based diet, divided into two feeding phases of 2 weeks each, under similar rearing conditions. Results indicated no significant differences in average daily feed intake (ADFI), average daily body weight gain (ADG), or feed efficiency (FE, gain:feed) between pigs from different EBV_FCR lines (P > 0.05). Similarly, nutrient digestibility showed no significant variation (P > 0.05). While the overall fecal microbiota taxonomic composition was similar between the groups, there was a trend toward higher beta diversity in the microbiota of pigs from parents with lower EBV_FCR (high efficiency pigs, H pigs) (P < 0.083). Carbohydrate and amino acid metabolism were predominant in all pigs, regardless of genetic background, with similar predicted microbiota functionality across groups. The study concluded that genetic differences based on parents divergent EBV_FCR did not affect growth performance, nutrient utilization, or microbiota characteristics at the nursery stage. This suggests that while EBV_FCR based genetic selection does not impact early-stage performance or microbiome responses, its effects may differ in older pigs, warranting further research.
In this paper, we discuss an efficient and effective index mechanism to do the string matching with k mismatches, by which we will find all the substrings in a target string s having at most k positions different from a pattern string r. The main idea is to transform s to a BWT-array as index, denoted as BWT(s), and search r against it. During the process, the precomputed mismatch information of r will be utilized to speed up the BWT(s)'s navigation. In this way, the time complexity can be reduced to O(kn' + n + mlogm), where m = |r|, n = |s|, and n' is the number of leaf nodes of a tree structure, called a mismatching tree, produced during a search of BWT(s). Extensive experiments have been conducted, which show that our method for this problem is promising.
The fashion and luxury industry dates back to the nineteenth and earlier centuries. Today, the industry is a multibillion enterprise involved in producing, marketing and selling various products. These include clothes, shoes, jewelry, accessories and luxury goods. Many companies have grown and developed within this industry over the centuries. One of the iconic and renowned luxury brands is Chanel company. This paper will focus on Chanel's marketing strategy, paying attention to aspects of its production line, pricing, market share, and marketing strategies, such as advertising, among other elements. This research will help gain insights into different aspects of the company, especially its role in becoming one of the brand leaders in the fashion industry. The research can provide insights into brand positioning, marketing mix, consumer psychology, and other factors contributing to its success. The research will employ primary and secondary data sources, such as the analysis of company financial and marketing reports and a review of relevant literature. The paper will also use comparative analysis by looking at earlier trends and cultures that have helped shape the fashion industry and the Chanel company. This paper finds that Chanel's marketing strategy is carefully analyzed to differentiate itself from other brands and secure a sizeable market share. Looking at some of these aspects of its differentiation will provide insights into its successes and market domination.
The concept of matchings originated in group theory to address a linear algebra problem related to canonical forms for symmetric tensors. In an abelian group $(G,+)$, a matching is a bijection $f: A \to B$ between two finite subsets $A$ and $B$ of $G$ such that $a + f(a) \notin A$ for all $a \in A$. A group $G$ has the matching property if, for every two finite subsets $A, B \subset G$ of the same size with $0 \notin B$, there exists a matching from $A$ to $B$. In prior work, matroid analogues of results concerning matchings in groups were introduced and established. This paper serves as a second sequel, extending that line of inquiry by investigating paving, Panhandle, and Schubert matroids through the lens of matchability. While some proofs draw upon earlier findings on the matchability of sparse paving matroids, the paper is designed to be self-contained and accessible without reference to the preceding sequel. Our approach combines tools from both matroid theory and additive number theory.
Measuring heterogeneous influence across nodes in a network is critical in network analysis. This paper proposes an Inward and Outward Network Influence (IONI) model to assess nodal heterogeneity. Specifically, we allow for two types of influence parameters; one measures the magnitude of influence that each node exerts on others (outward influence), while we introduce a new parameter to quantify the receptivity of each node to being influenced by others (inward influence). Accordingly, these two types of influence measures naturally classify all nodes into four quadrants (high inward and high outward, low inward and high outward, low inward and low outward, high inward and low outward). To demonstrate our four-quadrant clustering method in practice, we apply the quasi-maximum likelihood approach to estimate the influence parameters, and we show the asymptotic properties of the resulting estimators. In addition, score tests are proposed to examine the homogeneity of the two types of influence parameters. To improve the accuracy of inferences about nodal influences, we introduce a Bayesian information criterion that selects the optimal influence model. The usefulness of the IONI model and the four-quadrant clustering method is illustrated via simulation studies and an empirical example involving customer segmentation.
This paper presents a multi-criteria economics, environment, and thermodynamics assessment for a combined cooling, heating, and power (CCHP) system consist of a heat recovery system, a small absorption chiller, a 5kW PEMFC stack, gas compressor, and a humidifier. This study presents an improved version of a new optimization technique called improved emperor penguin optimization (IEPO) algorithm for optimizing the system efficiency. After simulations, the system is analyzed in terms of energy and exergy efficiencies, annual cost, and pollutant emission reduction. Simulation results declare low operating temperature develop GHG emission reduction, high relative humidity, pressure of inlet gases, and system exergy performance. The results of the IEPO algorithm are the compared with the standard EPO and also NSGA-II as a widely used optimization algorithm for showing the superiority of the algorithm.
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