Analysis of statistical model-based optimization enhancements in Generalized Self-Adapting Particle Swarm Optimization framework

This paper presents characteristics of model-based optimization methods utilized within the Generalized Self-Adapting Particle Swarm Optimization (GA– PSO) – a hybrid global optimization framework proposed by the authors. GAPSO has been designed as a generalization of a Particle Swarm Optimization (PSO) algorithm on the foundations of a large degree of independence of individual particles. GAPSO serves as a platform for studying optimization algorithms in the context of the following research hypothesis: (1) it is possible to improve the performance of an optimization algorithm through utilization of more function samples than standard PSO sample-based memory, (2) combining specialized sampling methods (i.e. PSO, Differential Evolution, model-based optimization) will result in a better algorithm performance than using each of them separately. The inclusion of model-based enhancements resulted in the necessity of extending the GAPSO framework by means of an external samples memory - this enhanced model is referred to as M-GAPSO in the paper

Analysis of the work of deep drainage in the village of Cieszów located in the bend of the Bóbr river

Stopień wodny w Krzywańcu piętrzy wody rzeki Bóbr i pozwala na skierowanie wody do kanału derywacyjnego elektrowni wodnej w Dychowie. Powyżej jazu powstał zbiornik na brzegu, którego położona jest wieś Cieszów. W przekroju wsi zwierciadło wody podniosło się o ok. 3,5 m w stosunku do normalnego poziomu wody w rzece przed budową stopnia. Aby nie dopuścić do znacznego podniesienia się wód gruntowych i podtopienia wsi wykonane zostały drenaże i rowy odwadniające. Obecnie, mimo opisanych wyżej urządzeń, obserwuje się wysoki poziom wód gruntowych na terenie zabudowanym wsi, piwnice budynków są stale zalewane wodą.The Krzywaniec barrage dams the waters of the Bóbr River and allows the water to be directed to the derivative canal of the Dychów hydropower plant. On the shore of the reservoir, there is the village of Cieszów. In the cross-section of the village, the water table rose by about 3.5 m in relation to the normal water level in the river before the construction of the barrage. Drainage and drainage ditches were made to prevent a significant rise of groundwater and flooding of the village. Currently, despite the devices described above, a high level of groundwater is observed in the built-up area of the village, and the basements of buildings are constantly flooded with water

HR Max Prediction Based on Age, Body Composition, Fitness Level, Testing Modality and Sex in Physically Active Population

Maximal heart rate (HRmax) is associated mostly with age, but age alone explains the variance in HRmax to a limited degree and may not be adequate to predict HRmax in certain groups. The present study was carried out on 3374 healthy Caucasian, Polish men and women, clients of a sports clinic, mostly sportspeople, with a mean age of 36.57 years, body mass 74.54 kg, maximum oxygen uptake (VO2max, ml∗kg–1 ∗min–1) 50.07. Cardiopulmonary exercise tests (CPET) were carried out on treadmills or cycle ergometers to evaluate HRmax and VO2max. Linear, multiple linear, stepwise, Ridge and LASSO regression modeling were applied to establish the relationship between HRmax, age, fitness level, VO2max, body mass, age, testing modality and body mass index (BMI). Mean HRmax predictions calculated with 5 previously published formulae were evaluated in subgroups created according to all variables. HRmax was univariately explained by a 202.5–0.53∗age formula (R2 = 19.18). The weak relationship may be explained by the similar age with small standard deviation (SD). Multiple linear regression, stepwise and LASSO yielded an R2 of 0.224, while Ridge yielded R2 0.20. Previously published formulae were less precise in the more outlying groups of the studied population, overestimating HRmax in older age groups and underestimating in younger. The 202.5–0.53∗age formula developed in the present study was the best in the studied population, yielding lowest mean errors in most groups, suggesting it could be used in more active individuals. Tanaka’s formula offers the second best overall prediction, while the 220-age formula yields remarkably high mean errors of up to 9 bpm. In conclusion, adding the studied variables in multiple regression models improves the accuracy of prediction only slightly over age alone and is unlikely to be useful in clinical practice