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Our primary objective in this study was to investigate the offensive strategies employed in the attack phase of men's volleyball, specifically focusing on side-out as stratified by the type of confrontation that was determined by the opponent's team performance. We analyzed 5524 attacking actions during 22 games of the 12 teams that participated in the Volleyball Men's Superliga (season 2021-2022). Based on their final rankings in the championship, we classified these teams into three tiers: high-performance, intermediate-performance, and low-performance. Subsequently, we examined the dynamics of these matches using Social Network Analysis. We found that the opponent teams' performance levels did not influence the game dynamics. Notably, the eigenvector values were prominently higher for Attack Zones 2 and 4, wherein the middle-blocker jumped to attack close to the setter across all networks. Thus, setters opted for traditional and low-risk strategies to minimize errors, disregarding available information about the skill level of the opposing team, making their offensive tactics predictable., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The present research objective was to analyze the offensive phase from Complex I in high-level male volleyball teams in a macro- and micro-level view, through the inter e intra-team variability analysis of eight best teams of the 2018 Men's Volleyball World Championship over the social network analysis and eigenvector centrality. The sample consisted of 22 matches and 2,743 offensive actions, resulting in 8 sub-networks with 368 nodes and 6221 edges. The results showed from macro view the variables that presented highest centrality values were Attack Zone 4 (range 0.56-0.90), Attack Tempo 2 (0.65-0.87), Power Attack (0.62-0.94), No Touch Block (0.61-1), Attack Effect Continuity (0.59-0.94), and Middle Blocker Centralized (0.60-0.95). In a micro view, Reception Effect, Play Position, Reception Zone, and Block Composition showed high variability in each sub-network. The intra- and inter-team variability presented the importance of to respect each team idiosyncrasies and to consider the different approaches to the game and success., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Rocha et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A quantitative structure-activity relationship (QSAR) study of twenty flavonoid derivatives with antimutagenic activity against 3-nitrofluoranthene (3-NFA) was performed by Partial Least Squares (PLS), using Ordered Predictors Selection (OPS) algorithm for variable selection. Four descriptors (PJI2, Mor27m, G1e and R4u+) were selected and a good model (n = 19; R(2) = 0.747; SEC = 0.332; PRESS(cal) = 1.768; F((2,27)) = 23.585; Q(LOO)(2) = 0.590; SEV = 0.388; PRESS(val) = 2.858; R(pred)(2) = 0.591; SEP = 0.394; ARE(pred) = 5.230%; k = 1.005; k' = 0.990; |R(02) - R'(02)| = 0.109) was built with two latent variables describing 83.410% of the original information. Leave-N-out cross validation (LNO) and y-randomization were performed in order to confirm the robustness of the model. The topological descriptors selected indicate that the antimutagenic activity against 3-NFA depends on molecular size, shape and Sanderson electronegativity of flavonoids. The proposed model may provide a better understanding of the antimutagenic activity of flavonoids and can be used as a guidance for proposition of new chemopreventive agents., (Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.)