Mechanical Demands of the Hang Power Clean and Jump Shrug: A Joint-level Perspective

The purpose of this study was to investigate the joint- and load-dependent changes in the mechanical demands of the lower extremity joints during the hang power clean (HPC) and the jump shrug (JS). Fifteen male lacrosse players were recruited from an NCAA DI team, and completed three sets of the HPC and JS at 30%, 50%, and 70% of their HPC 1-Repetition Maximum (1-RM HPC) in a counterbalanced and randomized order. Motion analysis and force plate technology were used to calculate the positive work, propulsive phase duration, and peak concentric power at the hip, knee, and ankle joints. Separate three-way analysis of variances were used to determine the interaction and main effects of joint, load, and lift type on the three dependent variables. The results indicated that the mechanics during the HPC and JS exhibit joint-, load-, and lift-dependent behavior. When averaged across joints, the positive work during both lifts increased progressively with external load, but was greater during the JS at 30% and 50% of 1-RM HPC than during the HPC. The JS was also characterized by greater hip and knee work when averaged across loads. The joint-averaged propulsive phase duration was lower at 30% than at 50% and 70% of 1-RM HPC for both lifts. Furthermore, the load-averaged propulsive phase duration was greater for the hip than the knee and ankle joint. The jointaveraged peak concentric power was the greatest at 70% of 1-RM for the HPC and at 30% to 50% of 1-RM for the JS. In addition, the joint-averaged peak concentric power of the JS was greater than that of the HPC. Furthermore, the load-averaged peak knee and ankle concentric joint powers were greater during the execution of the JS than the HPC. However, the loadaveraged power of all joints differed only during the HPC, but was similar between the hip and knee joints for the JS. Collectively, these results indicate that compared to the HPC the JS is characterized by greater hip and knee positive joint work, and greater knee and ankle peak concentric joint power, especially if performed at 30 and 50% of 1-RM HPC. This study provides important novel information about the mechanical demands of two commonly used exercises and should be considered in the design of resistance training programs that aim to improve the explosiveness of the lower extremity joints

Correlational analysis between joint-level kinetics of countermovement jumps and weightlifting derivatives

The purpose of this study was to investigate the mechanical similarity between net joint moments (NJM) of the countermovement jump (CMJ) and the hang power clean (HPC) and jump shrug (JS). Twelve male Lacrosse players performed three maximal effort CMJs and three repetitions of the HPC and JS at 30%, 50%, and 70% of their HPC one repetition maximum (1-RM). Ground reaction forces and motion capture data were used to calculate the NJM of the hip, knee, and ankle joints during each exercise. Statistical comparison of the peak NJM indicated that NJM during the HPC and JS across all loads were equal to or greater than the NJM during the CMJ (all p < 0.025). In addition, correlation analyses indicated that CMJ hip NJM were associated (all p < 0.025) with HPC hip NJM at 30% and 70% (r = 0.611-0.822) and JS hip NJM at 50% and 70% (r = 0.674-0.739), whereas CMJ knee NJM were associated with HPC knee NJM at 70% (r = 0.638) and JS knee NJM at 50% and 70% (r = 0.664-0.732). Further, CMJ ankle NJM were associated with HPC ankle NJM at 30% and 50% (r = 0.615-0.697) and JS ankle NJM at 30%, 50%, and 70% (r = 0.735-0.824). Lastly, knee and ankle NJM during the JS were greater than during the HPC at 30% and 50% of 1-RM (all p < 0.017). The degree of mechanical similarity between the CMJ and the HPC and JS is dependent on the respective load and joint. [Abstract copyright: © Journal of Sports Science and Medicine.

Local and regional heterogeneity underlying hippocampal modulation of cognition and mood.

While the hippocampus has been classically studied for its role in learning and memory, there is significant support for a role of the HPC in regulating emotional behavior. Emerging research suggests these functions may be segregated along the dorsoventral axis of the HPC. In addition to this regional heterogeneity, within the HPC, the dentate gyrus is one of two areas in the adult brain where stem cells continuously give rise to new neurons. This process can influence and be modulated by the emotional state of the animal, suggesting that adult neurogenesis within the DG may contribute to psychiatric disorders and cognitive abilities. Yet, the exact mechanism by which these newborn neurons influence behavior remains unknown. Here, we will examine the contribution of hippocampal neurogenesis to the output of the HPC, and suggest that the role of neurogenesis may vary along the DV axis. Next, we will review literature indicating that anatomical connectivity varies along the DV axis of the HPC, and that this underlies the functional segregation along this axis. This analysis will allow us to synthesize novel hypotheses for the differential contribution of the HPC to cognition and mood

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Evaluation of DVFS techniques on modern HPC processors and accelerators for energy-aware applications

Energy efficiency is becoming increasingly important for computing systems, in particular for large scale HPC facilities. In this work we evaluate, from an user perspective, the use of Dynamic Voltage and Frequency Scaling (DVFS) techniques, assisted by the power and energy monitoring capabilities of modern processors in order to tune applications for energy efficiency. We run selected kernels and a full HPC application on two high-end processors widely used in the HPC context, namely an NVIDIA K80 GPU and an Intel Haswell CPU. We evaluate the available trade-offs between energy-to-solution and time-to-solution, attempting a function-by-function frequency tuning. We finally estimate the benefits obtainable running the full code on a HPC multi-GPU node, with respect to default clock frequency governors. We instrument our code to accurately monitor power consumption and execution time without the need of any additional hardware, and we enable it to change CPUs and GPUs clock frequencies while running. We analyze our results on the different architectures using a simple energy-performance model, and derive a number of energy saving strategies which can be easily adopted on recent high-end HPC systems for generic applications

System-Level Design of Energy-Proportional Many-Core Servers for Exascale Computing

Continuous advances in manufacturing technologies are enabling the development of more powerful and compact high-performance computing (HPC) servers made of many-core processing architectures. However, this soaring demand for computing power in the last years has grown faster than emiconductor technology evolution can sustain, and has produced as collateral undesirable effect a surge in power consumption and heat density in these new HPC servers, which result on significant performance degradation. In this keynote, I advocate to completely revise the current HPC server architectures. In particular, inspired by the mammalian brain, I propose to design a disruptive three-dimensional (3D) computing server architecture that overcomes the prevailing worst-case power and cooling provisioning paradigm for servers. This new 3D server design champions a new system-level thermal modeling, which can be used by novel proactive energy controllers for detailed heat and energy management in many-core HPC servers, thanks to micro-scale liquid cooling. Then, I will show the impact of new near-threshold computing architectures on server design, and how we can integrate new on-chip microfluidic fuel cell networks to enable energy-scalability in future generations of many-core HPC servers targeting Exascale computing.Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech