Complexity Leadership: The First Two Decades

Complexity leadership, complex adaptive leadership, and adaptive leadership theories are related but separate streams of leadership research dating back four decades. This article reviews the first two decades. The research team searched academic literature within the business discipline for journal articles related to complex adaptive leadership, complexity leadership, and adaptive leadership, resulting in a sample of 778 articles. The researchers used multiple methods to analyze the articles, eventually conducting deductive analysis on a subset of nine articles published between 1982-2002. Analysis from the sample revealed frustration by some leadership scholars over the ability of leadership theory to address practical leadership problems. Therefore, scholars called for and began to develop novel approaches beyond concepts of leader-follower influence. Scholars turned their attention to understanding the role of leadership within VUCA contexts. They began to conceive of organizations as open systems and to describe characteristics that leaders would need to be successful in complex adaptive systems. These early attempts set the stage for scholars to apply complexity theory to the study of leadership

Assessing the Potential of Classical Q-learning in General Game Playing

After the recent groundbreaking results of AlphaGo and AlphaZero, we have seen strong interests in deep reinforcement learning and artificial general intelligence (AGI) in game playing. However, deep learning is resource-intensive and the theory is not yet well developed. For small games, simple classical table-based Q-learning might still be the algorithm of choice. General Game Playing (GGP) provides a good testbed for reinforcement learning to research AGI. Q-learning is one of the canonical reinforcement learning methods, and has been used by (Banerjee $\&$ Stone, IJCAI 2007) in GGP. In this paper we implement Q-learning in GGP for three small-board games (Tic-Tac-Toe, Connect Four, Hex)\footnote{source code: https://github.com/wh1992v/ggp-rl}, to allow comparison to Banerjee et al.. We find that Q-learning converges to a high win rate in GGP. For the $\epsilon$-greedy strategy, we propose a first enhancement, the dynamic $\epsilon$ algorithm. In addition, inspired by (Gelly $\&$ Silver, ICML 2007) we combine online search (Monte Carlo Search) to enhance offline learning, and propose QM-learning for GGP. Both enhancements improve the performance of classical Q-learning. In this work, GGP allows us to show, if augmented by appropriate enhancements, that classical table-based Q-learning can perform well in small games.Comment: arXiv admin note: substantial text overlap with arXiv:1802.0594

Fatigue Testing of a Composite Propeller Blade using Fiber-Optic Strain Sensors

The performance of surface-mounted extrinsic Fabry-Perot interferometric (EFPI) sensors during a seventeen-million-cycle, high-strain fatigue test is reported. Fiber-optic strain measurements did not degrade during the test. The sensors were applied to a composite propeller blade subject to a constant axial load and a cyclic bending load. Strain measurements were taken at four blade locations using two types of EFPI sensors and co-located electrical resistance strain gages. Static and dynamic strain measurements were taken daily during the 65 days of this standard propeller-blade test. All fiber-optic sensors survived the fatigue test while most of the resistive gages failed. The suitability of fiber-optic monitoring for fatigue testing and other high-cycle monitoring is demonstrated

Enhanced Peculiar Velocities in Brane-Induced Gravity

The mounting evidence for anomalously large peculiar velocities in our Universe presents a challenge for the LCDM paradigm. The recent estimates of the large scale bulk flow by Watkins et al. are inconsistent at the nearly 3 sigma level with LCDM predictions. Meanwhile, Lee and Komatsu have recently estimated that the occurrence of high-velocity merging systems such as the Bullet Cluster (1E0657-57) is unlikely at a 6.5-5.8 sigma level, with an estimated probability between 3.3x10^{-11} and 3.6x10^{-9} in LCDM cosmology. We show that these anomalies are alleviated in a broad class of infrared-modifed gravity theories, called brane-induced gravity, in which gravity becomes higher-dimensional at ultra large distances. These theories include additional scalar forces that enhance gravitational attraction and therefore speed up structure formation at late times and on sufficiently large scales. The peculiar velocities are enhanced by 24-34% compared to standard gravity, with the maximal enhancement nearly consistent at the 2 sigma level with bulk flow observations. The occurrence of the Bullet Cluster in these theories is 10^4 times more probable than in LCDM cosmology.Comment: 15 pages, 6 figures. v2: added reference

Shimura curve computations via K3 surfaces of Neron-Severi rank at least 19

It is known that K3 surfaces S whose Picard number rho (= rank of the Neron-Severi group of S) is at least 19 are parametrized by modular curves X, and these modular curves X include various Shimura modular curves associated with congruence subgroups of quaternion algebras over Q. In a family of such K3 surfaces, a surface has rho=20 if and only if it corresponds to a CM point on X. We use this to compute equations for Shimura curves, natural maps between them, and CM coordinates well beyond what could be done by working with the curves directly as we did in ``Shimura Curve Computations'' (1998) = Comment: 16 pages (1 figure drawn with the LaTeX picture environment); To appear in the proceedings of ANTS-VIII, Banff, May 200

An Inquiry into the Aviation Management Education Paradigm Shift

Working adults with four-year degrees from accredited colleges or universities earn, on average, almost three times more than individuals without a degree. This pay gap led Newcomer and his colleagues to study attitudes of aviation and aerospace managers towards education. That study found that managers valued education in new hires, even though they did not deem it critical to their own positions. That finding indicated a potential paradigm shift towards the perceived value of education in the industry. In the current qualitative, phenomenological research, we interviewed 14 managers from various capacities within the aviation and aerospace industries to determine the relative importance of education, certification, and experience when hiring or selecting new team members. The results indicated that managers value experience most when making staffing decisions. Next, they value certification or education, depending on the technical or managerial role. A majority of the managers did express that their attitudes towards the value of education had become stronger over their careers. The study has hiring implications for aviation and aerospace managers, as well as employees, in terms of what to focus on in interviews and in reviewing candidate credentials

Intelligent Strain Sensing on a Smart Composite Wing using Extrinsic Fabry-Perot Interferometric Sensors and Neural Networks

Strain prediction at various locations on a smart composite wing can provide useful information on its aerodynamic condition. The smart wing consisted of a glass/epoxy composite beam with three extrinsic Fabry-Perot interferometric (EFPI) sensors mounted at three different locations near the wing root. Strain acting on the three sensors at different air speeds and angles-of-attack were experimentally obtained in a closed circuit wind tunnel under normal conditions of operation. A function mapping the angle of attack and air speed to the strains on the three sensors was simulated using feedforward neural networks trained using a backpropagation training algorithm. This mapping provides a method to predict the stall condition by comparing the strain available in real time and the predicted strain by the trained neural network