Effects of observing real, animated and combined model on learning cognitive and motor levels of basketball jump shot in children

Study aim: The purpose of the present study was to investigate the effects of observing the real, animated and combined model demonstrations on cognitive and motor learning of a basketball jump shot. Material and methods: Forty-five subjects with mean age of (11.03 ± 1.7) were randomly divided into three groups: real, animated and combined model demonstrations. Subjects were asked to perform basketball jump shot task during a four-step process. In total, participants shot 150 shots (10 shots in the pretest, 120 shots and 40 observations in the practice phase, and 10 shots in the posttest and the retention test). Accuracy scores and cognitive status were assessed as dependent variables. Results: Results showed that observation of real, animated and combined model demonstrations had significant effects on motor and cognitive learning of a basketball jump shot. Moreover, there was no significant difference between observing animated model and real model demonstrations at motor level, however, animated model demonstration group performed better than real model group at cognitive level. Finally, combined model demonstration group performed better than both animated and real model demonstrations groups. Conclusions: Results suggested that observing animated model demonstration is an appropriate approach for learning basketball jump shot even in children. It is also recommended to use animated model demonstration as a complement to real model.

Hybrid method of intellectual diagnosis and forecasting of complex technical systems

Modern control and diagnostic systems (CDS) usually determine only the technical condition (TC) at the current time, ie the CDS answers the question: a complex technical system (CTS) should be considered operational or not, and may provide little information on performance CTS even in the near future. Therefore, the existing scenarios of CDS operation do not provide for the assessment of the possibility of gradual failures, ie there is no forecasting of the technical condition. The processes of parameter degradation and degradation prediction are stochastic processes, the “behavior” of which is influenced by a combination of external and internal factors, so the deg-radation process can be described as a function that depends on changes in the internal parameters of CTS. The hybrid method involves the following steps. The first is to determine the set of initial characteristics that characterize the CTS vehicle. The second is the establishment of precautionary tolerances of degradation values of the characteristics that characterize the pre-failure technical con-dition of the CTS. The third is to determine the rational composition of informative indicators, which maximally determine the "behavior" of the initial characteristics. The fourth — implementa-tion of multiparameter monitoring, fixation of values of the controlled characteristics, formation of an information array of values of characteristics. Fifth — the adoption of a general model of the process of changing the characteristics of the CTS. Sixth — the formation of a real model of the process of changing the characteristics of Y(t) on the basis of an information array of values of char-acteristics obtained by multi-parameter monitoring. Seventh — forecasting the time of possible oc-currence of the pre-failure state of the CTS, which is carried out by extrapolating the obtained real model of the process of changing the characteristics of Y(t). It is proposed to use two types of mod-els: for medium- and long-term forecasting - polynomial models, for short-term forecasting — a lin-ear extrapolation model. At the final stage, forecast errors are determined for all types of models of degradation of pa-rameters and characteristics. Based on the results of the forecast verification, the models are adjust-ed

Mathematical modeling of production process using cluster analysis methods

Manufacturing process management is a complex problem characterized by decision-making throughout the entire production process and is reduced to finding the best set of possible (alternative) in terms of some criterion or criteria for the use of available resources. The goal of the production process optimization is to analyze a set of alternative production options in complex systems and to determine the most rational possible state depending on the selected management strategies. The system contains many connections between elements and is influenced by random factors. It is not possible to create a real model for the system that is described analytically, but it can be investigated by using simulation modeling. In the mathematical modeling of the production process cluster analysis methods are used which allow to process a large amount of data from all possible states of the system and divide them into similar aggregates according to several characteristics. The analysis of the formed groups is carried out and the regularities that have the most significant impact on the occurrence of states into clusters are revealed.

Influence of On-Sight and Flash Climbing Styles on Advanced Climbers’ Route Completion for Bouldering

Route previewing has been established as a critical parameter in indoor climbing performance, as it could determine the success or failure in ascending the route. We addressed the effect of different types of previews on output climbing performance. Twenty-one advanced climbers (7b and 7c+ climbing grade) were required to complete 18 routes, rated at 6c, according to the French Rating Scale of Difficulty. Each climber previewed the route under three conditions: “No-previewing”, “video-model previewing”, and “real-model previewing”. Output climbing performance was assessed in terms of route completion. The results showed differences on output climbing performance between types of preview. Specifically, the climbers achieved more successful attempts at climbing to the “Top” of the wall when inspecting the route with the “real-model previewing” condition, compared to the other conditions of preview. On the contrary, the climbers displayed more failed attempts in climbing the route with the “on-sight” condition, compared to the “flash” styles (“video-model” and “real-model”). The preview of the route, including performance of a real/video-projected model manipulating climbing holds, seems to increase the opportunities to climb the boulder successfully, attuning climbers to information specifying ascending actions. Climbing coaches should reinforce the design of representative training, using flash styles, to promote movement solutions for route completion.

Seismic Constrained Gravity Inversion: A Reliable Tool to Improve Geophysical Models Away from Seismic Information

The exploitation of gravity fields in order to retrieve information about subsurface geological structures is sometimes considered a second rank method, in favour of other geophysical methods, such as seismic, able to provide a high resolution detailed picture of the main geological horizons. Within the current work we prove, through a realistic synthetic case study, that the gravity field, thanks to the availability of freely of charge high resolution global models and to the improvements in the gravity inversion methods, can represent a valid and cheap tool to complete and enhance geophysical modelling of the Earth’s crust. Three tests were carried out: In the first one a simple two-layer problem was considered, while in tests two and three we considered two more realistic scenarios in which the availability on the study area of constraints derived from 3D or 2D seismic surveys were simulated. In all the considered test cases, in which we try to simulate real-life scenarios, the gravity field, inverted by means of an advanced Bayesian technique, was able to obtain a final solution closer to the (simulated) real model than the assumed a priori information, typically halving the uncertainties in the geometries of the main geological horizons with respect to the initial model.

SURVEYING THE EFFECT OF GAS-VENT HOLE DIAMETER AND CLEARANCE BETWEEN PISTON AND CYLINDER ON AUTOMATIC FIRING SYSTEM OF 23mm ЗY23-2

On the basis of analyzing the real model 23mm ЗY23-2, the paper chooses an alternative physics model, building a mathematical modeling dynamics, set up a system of equations and solve to find the cycle of operation on automatic firing system, change some parameters of cylinder and study on its effect to do automatic firing system; besides surveying the effect of gas-vent hole diameter and clearance between piston and cylinder on automatic firing system of 23mm ЗY23-2. The research method is based on the calculation theory to ensure compliance with the manufacturer's gun design and use documents.

Questions to the Article: Exploring the Shift in International Trends in Mobile Health Research From 2000 to 2020 (Preprint)

UNSTRUCTURED The article published on September 9, 2021, is well-written and of interest, but remains two questions that are required for clarifications, such as the ways (1) to construct the growth curve and (2) to determine the flashpoint(or, say, inflection point, IP) on the growth curve shown in Figure 3. The authors addressed that the year 2015 was a flashpoint on the curve(ie. y = 37e0.3062x, with R2=0.9935) and determined the flash point by the naked eye. Although numerous bibliometric analyses applied the cumulative publications to release information about the growth curve and the IP using a particular formula, none of such research proposed an appropriate way to determine the IP. Accordingly, we are motivated to propose an item response theory (IRT) model(IRT) to determine the IP on a given ogive curve and found that the IP is in 2017 instead of 2015 with R2=0.9797 rather than 0.9935 in the questionable article. Similarly, the real model coefficient and R2 are 0.2942 and 0.9954, respectively, if the growth curve is modeled by the formula(= 37ebx ) in Microsoft Excel using the Solver add-in tool demonstrated in this article. The ITR model used to determine the IP location on cumulative time-series data is recommended to future relevant studies, not merely limited to the bibliometric analysis.

NIMG-19. SYNTHETIC ISOCITRATE DEHYDROGENASE-MUTANT GLIOBLASTOMAS FROM GENERATIVE ADVERSARIAL NETWORK PROVIDE MORPHOLOGIC VARIABILITY AND DIAGNOSTIC PERFORMANCE SIMILAR TO REAL DATA: DEVELOPMENT AND VALIDATION

BACKGROUND Generative adversarial network (GAN) creates synthetic MRI data that may provide morphologic variability to assess molecular characteristics of glioblastomas. PURPOSE To investigate the ability of GAN-based generation of isocitrate dehydrogenase (IDH)-mutant glioblastomas to provide morphologic variability and improve molecular prediction. METHODS GAN was retrospectively trained on 110 IDH-mutant high-grade gliomas. Paired contrast-enhanced T1-weighted and FLAIR synthetic MRI data were generated. Diagnostic models were developed from 80 IDH-wild type glioblastomas and 38 IDH-mutant patients, (real model), 38 IDH-mutant GAN-generated synthetic data (synthetic model), or both combined (augmented model). Two neuroradiologists independently assessed real and morphologic characteristics of contrast-enhancement patterns, the presence of necrosis, and margins and type of non-enhancing region. Significant predictors of IDH mutation were selected from multivariable logistic regression, and diagnostic performance was validated in 44 separate patients, 33 with IDH-wild type and 11 with IDH-mutant glioblastomas. RESULTS Synthetic IDH-mutant glioblastomas were similar to real tumors on Turing tests, with an area under the curve (AUC) of 0.67–0.71. Significant predictors of a more frontal or insular location (odds ratio [OR], 1.34 vs. 1.52; highest P = .04) and distinct non-enhancing tumor margins (OR, 2.68 vs. 3.88; P < .001) were similar for the real and synthetic models, with the synthetic (AUC, 0.958) and augmented (AUC, 0.899) models having higher diagnostic performance than the real model (AUC, 0.864) in the training set. The diagnostic accuracy was higher for the synthetic and augmented models (90.9% [40/44] each for both observers) than for the real model (84.1% [37/44] for one observer and 86.4% [38/44] for the other). CONCLUSIONS The GAN-based synthetic images yield morphologically variable IDH-mutant glioblastomas and may be useful as realistic training sets.

Optimization of FDM 3D Printing Process Parameters on ABS based Bone Hammer using RSM Technique

Rapid prototyping (RP) uses a cycle where a real model is made by explicitly adding material as thin cross-sectional layers. Fused deposition modelling (FDM) 3D printer is being use for synthesis of ABS based bone hammer. Response surface methodology (RSM) based L27 design of experiment were adopted to perform the experiment using four influencing parameters such as layer thickness, infill percentage, orientation and nozzle temperature for the three responses deflection, hardness and weight. Response surface methodology was used for modelling and optimization of considered process parameters. In present investigation, it is evident that bone hammer fabrication process parameters have been optimized on data such as bone hammer weight 19.8091g, hardness 104.5921 BHN, and force of 15 degree deflection 36.0681 N has been produced with RSM prediction with influence of process parameters such as layer thickness 0.250 mm, infill percentage 63.3333, orientation 60 degree, nozzle temperature 240°C.