On the Body Image and Standard Score Scale for Ideal Body of Women in Ho Chi Minh, Vietnam

Developing the shape of human bodies is both one of the important tasks of the sports industry in each country in the world and the need of each individual, especially women. So, what body image is considered standard? We have researched and initially built a rating scale for each of the basic body standards indicators of women including 7/9 body standards indicators. At the same time, it also developed a standard to evaluate the body image of women's body shape through the ratio between waist measurement and indicators such as standing height, bust measurement, and hips measurement. During this study, we used common methods such as reference methods related to research objectives; expert interviews; anthropometric; Statistical mathematics. The study has developed a scale to evaluate the indicators of body beautiful image for each criterion of ideal body standards. This is the basis for them to be able to calculate the measurements of each fitness criterion to exercise in proportion to their height so that they have an ideal body standard, as well as a source of reference for other athletes, trainers, body image trainers, physical education teachers, or researchers on women's health in Vietnam. From there, it helps the practitioner know the correct rings needed have to work out based on his height. This is the basis for them to be able to calculate the measurements of each fitness criterion to exercise in proportion to their height so that they have an ideal body standard.

Human self-selection as a mechanism of human societal evolution: A critique of the cultural selection argument

Natural selection is the main mechanism that drives the evolution of species, including human societies. Under natural selection, human species responds through genetic and cultural adaptations to internal and external selection pressures for survival and reproductive success. However, this theory is ineffective in explaining human societal evolution in the Holocene and a cultural selection argument has been made to remedy the theory. The present article provides a critique of the cultural selection argument and proposes an alternative conception that treats human self-selection as an emergent mechanism of human societal evolution characterized by a new type of selection pressure and a separate fitness criterion. Specifically, the evolution of human societies is divided into two major periods, each driven by a different mode of selection: natural selection acting on genes and cultures for survival and reproductive success prior to the Neolithic Revolution, and human self-selection acting on cultures – and potentially genes as well – for thrival and prosperous living after the Neolithic Revolution. The conditions for the transition from the first mode of selection to the second and the implications of this transition for social research are also discussed.

Monitoring Players’ Readiness Using Predicted Heart-Rate Responses to Soccer Drills

Purpose: To examine the ability of multivariate models to predict the heart-rate (HR) responses to some specific training drills from various global positioning system (GPS) variables and to examine the usefulness of the difference in predicted vs actual HR responses as an index of fitness or readiness to perform. Method: All data were collected during 1 season (2016–17) with players’ soccer activity recorded using 5-Hz GPS and internal load monitored using HR. GPS and HR data were analyzed during typical small-sided games and a 4-min standardized submaximal run (12 km·h−1). A multiple stepwise regression analysis was used to identify which combinations of GPS variables showed the largest correlations with HR responses at the individual level (HRACT, 149 [46] GPS/HR pairs per player) and was further used to predict HR during individual drills (HRPRED). Then, HR predicted was compared with actual HR to compute an index of fitness or readiness to perform (HRΔ, %). The validity of HRΔ was examined while comparing changes in HRΔ with the changes in HR responses to a submaximal run (HRRUN, fitness criterion) and as a function of the different phases of the season (with fitness being expected to increase after the preseason). Results: HRPRED was very largely correlated with HRACT (r = .78 [.04]). Within-player changes in HRΔ were largely correlated with within-player changes in HRRUN (r = .66, .50–.82). HRΔ very likely decreased from July (3.1% [2.0%]) to August (0.8% [2.2%]) and most likely decreased further in September (−1.5% [2.1%]). Conclusions: HRΔ is a valid variable to monitor elite soccer players’ fitness and allows fitness monitoring on a daily basis during normal practice, decreasing the need for formal testing.

PMCNS

Novelty search is an evolutionary approach in which the population is driven towards behavioural innovation instead of towards a fixed objective. The use of behavioural novelty to score candidate solutions precludes convergence to local optima. However, in novelty search, significant effort may be spent on exploration of novel, but unfit behaviours. We propose progressive minimal criteria novelty search (PMCNS) to overcome this issue. In PMCNS, novelty search can freely explore the behaviour space as long as the solutions meet a progressively stricter fitness criterion. We evaluate the performance of our approach by evolving neurocontrollers for swarms of robots in two distinct tasks. Our results show that PMCNS outperforms fitness-based evolution and pure novelty search, and that PMCNS is superior to linear scalarisation of novelty and fitness scores. An analysis of behaviour space exploration shows that the benefits of novelty search are conserved in PMCNS despite the evolutionary pressure towards progressively fitter behaviours.

Genetic Programming

Genetic programming (GP) is a sub-area of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function. Like genetic algorithms, GP uses a representation related to some computational model, but in GP, fitness is tied to task performance by specific program semantics. Instead of strings or permutations, genetic programs are most commonly represented as variable-sized expression trees in imperative or functional programming languages, as grammars (O’Neill & Ryan, 2001), or as circuits (Koza et al., 1999). GP uses patterns from biological evolution to evolve programs: • Crossover: Exchange of genetic material such as program subtrees or grammatical rules • Selection: The application of the fitness criterion to choose which individuals from a population will go on to reproduce • Replication: The propagation of individuals from one generation to the next • Mutation: The structural modification of individuals To work effectively, GP requires an appropriate set of program operators, variables, and constants. Fitness in GP is typically evaluated over fitness cases. In data mining, this usually means training and validation data, but cases can also be generated dynamically using a simulator or directly sampled from a real-world problem solving environment. GP uses evaluation over these cases to measure performance over the required task, according to the given fitness criterion.

Genetic Programming

Genetic programming (GP) is a subarea of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function.

Genetic Programming

Genetic programming (GP) is a subarea of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function.