University of Massachusetts Amherst

About: University of Massachusetts Amherst is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 37274 authors who have published 83965 publications receiving 3834996 citations. The organization is also known as: UMass Amherst & Massachusetts State College.

Biomechanics of baseball pitching A preliminary report

Abstract: Fifteen professional major league pitchers were filmed with high speed cinematography. One hundred forty-seven pitches were analyzed using an electromagnetic digitizer and a microcomputer. Three phases of throwing were studied: cocking, acceleration, and follow-through. The cocking phase is the period of time between the initiation of the windup and the moment at which the shoulder is in maximum external rotation. This phase occurs in approximately 1500 ms, and the shoulder is brought into an extreme position of external rotation. The acceleration phase and the initial stages of the follow-through phase produce extraordinary demands on the shoulder and elbow. The acceleration phase begins with the throwing shoulder in the position of maximum external rotation and terminates with ball release. This phase occurs in approximately 50 ms, and peak angular velocities averaging 6,180 deg/sec for shoulder internal rotation and 4,595 deg/sec for elbow extension were measured. The follow-through phase begins at ball release and continues until the motion of throwing has ceased. This phase occurs in approximately 350 ms.

Top ten fundamental challenges of biomass pyrolysis for biofuels.

Abstract: Pyrolytic biofuels have technical advantages over conventional biological conversion processes since the entire plant can be used as the feedstock (rather than only simple sugars) and the conversion process occurs in only a few seconds (rather than hours or days). Despite decades of study, the fundamental science of biomass pyrolysis is still lacking and detailed models capable of describing the chemistry and transport in real-world reactors is unavailable. Developing these descriptions is a challenge because of the complexity of feedstocks and the multiphase nature of the conversion process. Here, we identify ten fundamental research challenges that, if overcome, would facilitate commercialization of pyrolytic biofuels. In particular, developing fundamental descriptions for condensed-phase pyrolysis chemistry (i.e., elementary reaction mechanisms) are needed since they would allow for accurate process optimization as well as feedstock flexibility, both of which are critical to any modern high-throughput process. Despite the benefits to pyrolysis commercialization, detailed chemical mechanisms are not available today, even for major products such as levoglucosan and hydroxymethylfurfural (HMF). Additionally, accurate estimates for heat and mass transfer parameters (e.g., thermal conductivity, diffusivity) are lacking despite the fact that biomass conversion in commercial pyrolysis reactors is controlled by transport. Finally, we examine methods for improving pyrolysis particle models, which connect fundamental chemical and transport descriptions to real-world pyrolysis reactors. Each of the ten challenges is presented with a brief review of relevant literature followed by future directions which can ultimately lead to technological breakthroughs that would facilitate commercialization of pyrolytic biofuels.

Explainable Machine Learning for Scientific Insights and Discoveries

Abstract: Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article, we review explainable machine learning in view of applications in the natural sciences and discuss three core elements that we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas.

Neural theory of association and concept-formation

Abstract: The present paper looks for possible neural mechanism underlying such high-level brain functioning as association and concept-formation. Primitive neural models of association and concept-formation are presented, which will elucidate the distributed and multiply superposed manner of retaining knowledge in the brain. The models are subject to two rules of self-organization of synaptic weights, orthogonal and covariance learning. The convergence of self-organization is proved, and the characteristics of these learning rules are shown. The performances, especially the noise immunity, of the association net and concept-formation net are analyzed.