Applying Force Plate Technology to Inform Human Performance Programming in Tactical Populations

Force plate assessments, such as countermovement jumps and isometric mid-thigh pulls, examine performances (e.g., jump height, force, power) and movement strategies (e.g., asymmetries, durations), and are best suited to characterize and monitor physical capabilities, not predict injuries. To begin applying force plate technologies, users must first; (1) develop a data management plan to visualize and capture data over time; (2) select appropriate force plates for their scenario; (3) design appropriate testing protocols to ensure valid and reliable data. Force plate assessments may be added to existing testing, serve as separate testing batteries for annual profile testing to compare individuals and understand initial physical capabilities, or for more frequent testing (i.e., monthly or weekly) to monitor training-related adaptations or neuromuscular fatigue. Although these assessments inform evidence-based program designs, human performance practitioners must understand the considerations for conducting appropriate force plate testing, as well as proper visualizations and management of force plate data. Thus, the aim of this review is to provide evidence-based practices for utilizing force plates in tactical populations (e.g., military, firefighters, police). This includes best practices to implement testing for performance profiling, training adaptations, and monitoring neuromuscular fatigue and force asymmetries. Of note, due to the large amount of force-time metrics to choose from, this article provides general examples of important metrics to monitor and training recommendations based on changes to these force-time metrics, followed by specific examples in three case studies.

GigaSOM.jl: High-performance clustering and visualization of huge cytometry datasets

Background The amount of data generated in large clinical and phenotyping studies that use single-cell cytometry is constantly growing. Recent technological advances allow the easy generation of data with hundreds of millions of single-cell data points with >40 parameters, originating from thousands of individual samples. The analysis of that amount of high-dimensional data becomes demanding in both hardware and software of high-performance computational resources. Current software tools often do not scale to the datasets of such size; users are thus forced to downsample the data to bearable sizes, in turn losing accuracy and ability to detect many underlying complex phenomena. Results We present GigaSOM.jl, a fast and scalable implementation of clustering and dimensionality reduction for flow and mass cytometry data. The implementation of GigaSOM.jl in the high-level and high-performance programming language Julia makes it accessible to the scientific community and allows for efficient handling and processing of datasets with billions of data points using distributed computing infrastructures. We describe the design of GigaSOM.jl, measure its performance and horizontal scaling capability, and showcase the functionality on a large dataset from a recent study. Conclusions GigaSOM.jl facilitates the use of commonly available high-performance computing resources to process the largest available datasets within minutes, while producing results of the same quality as the current state-of-art software. Measurements indicate that the performance scales to much larger datasets. The example use on the data from a massive mouse phenotyping effort confirms the applicability of GigaSOM.jl to huge-scale studies.

MENDESAIN MULTIMEDIA PEMBELAJARA DESIGN THE MULTIMEDIA LEARNING OF MATHEMATICS USING GUI MATLAB

GUI (Graphical User Interface) is a MATLAB tool as multimedia or a tool that can be used in learning mathematics. MATLAB is a high-performance programming language for computing science or mathematical problems. MATLAB integrates computation, algorithm building, data acquisition, modeling, simulation and prototyping, data analysis, exploration, visualization. The GUI toolbox available in MATLAB can be used to build multimedia applications for learning mathematics. With graphic objects such as buttons, text boxes, sliders, menus and others, it helps someone design mathematics learning media as desired.

An Empirical Study on Factors related to Distributed Pair Programming

This paper reports students’ perceptions and experiences attending an object-oriented programming course in which they developed software using the Distributed Pair Programming (DPP) technique. Pair programming (PP) is typically performed on one computer, involving two programmers working collaboratively on the same code or algorithm. DPP on the other hand is performed remotely allowing programmers to collaborate from separate locations. PP started in the software industry as a powerful way to train programmers and to improve software quality. Research has shown that PP (and DPP) is also a successful approach to teach programming in academic programming courses. The main focus of PP and DPP research was PP’s effectiveness with respect to student performance and code quality, the investigation of best team formation strategies and studies of students’ attitudes. There are still limited studies concerning relationships between performance, attitudes and other critical factors. We have selected some of the most common factors which can be found in the literature: academic performance, programming experience, student confidence, feelgood factor, partner compatibility and implementation time. The main goal of this study was to investigate correlations between these attributes, while DPP was used as the main programming technique.

Mental Health and Sport Performance Programming in Athletes Who Present Without Pathology: A Case Examination Supporting Optimization

Athletes are exposed to unique stressors that often negatively impact the way they think, behave, and feel in athletic, academic, and social domains. The Optimum Performance Program in Sports (TOPPS), an adaptation of Family Behavior Therapy, is an innovative approach to optimization science that has demonstrated positive outcomes in student-athletes evidencing substance use disorders. However, this approach has yet to be evaluated in athletes who are interested in optimizing their mental health and sport performance, but have no indication of pathology. We describe the administration of TOPPS in a female student-athlete who presented for intervention with no assessed mental health pathology. Although experimental methodology was uncontrolled, many of the methodological features in this examination were advanced. Treatment integrity was reliably assessed and the athlete demonstrated significant improvements in psychometrically validated measurements of mental health and sport performance from baseline to 5-months post-treatment, including psychiatric domains (somatization, obsessive–compulsive, interpersonal sensitivity, depression, anxiety, phobic anxiety, paranoid ideation, and psychoticism), relationships with teammates, family members, coaches, and peers, and measures of sport performance. Future directions are reported in light of the results.