Due to piezoelectric softening and dissipative nonlinearities, the piezoelectric cantilever energy harvester exhibits nonlinear hysteresis when subjected to large excitation. These nonlinearities have brought significant challenges to the modeling and response prediction of randomly excited piezoelectric energy harvesting systems. In this study, the voltage responses of the nonlinear piezoelectric cantilever energy harvester under random excitation are initially assumed to follow the Gaussian distribution which is experimentally validated later. The equivalent linear transfer function are derived from the approximate linearization of the stiffness and damping using the statistical linearization (SL) technique. The mathematical expectations of the voltage responses are calculated from the multivariate normal distributions. Frequency sweep experiments are conducted to a cantilever energy harvester to identify the nonlinear piezoelectric material properties. The statistically linearized model was experimentally validated under random base acceleration excitation by comparing the probability density function of the predicted voltage responses and average power against the experimental measurements. The advantage of the SL technique lies in allowing one to use an iterative procedure to estimate the equivalent linear terms while the analytical expressions are unattainable because of the complex nonlinearity in the governing equations. The results show that the prediction of the SL model to the random base acceleration excitation agrees with experimental measurements with a broadband frequency range, although only the fundamental mode of the beam is considered.
Continued star formation over the lifetime of a galaxy suggests that gas is steadily flowing in from the circumgalactic medium. Also, cosmological simulations of large-scale structure formation imply that gas is accreted onto galaxies from the halo inside which they formed. Direct observations are difficult, but in recent years observational indications of gas inflows from a circumgalactic medium were obtained. Here we suggest an indirect observational probe: looking for large-scale (exceeding few kpc) turbulence caused by the accretion. As a specific example we consider an accretion flow coplanar with the galaxy disk, and argue that Kelvin-Helmholtz turbulence will be generated. We employ a semi-analytic model of turbulence and derive the expected turbulence power spectrum. The latter turns out to be of a distinctive shape that can be compared with observational power spectra. As an illustrative example we use parameters of the Milky Way galaxy.
We conducted an analysis of the process of GW breaking from an energy perspective using the output from a high-resolution compressible atmospheric model. The investigation focused on the energy conversion and transfer that occur during the GW breaking. The total change in kinetic energy and the amount of energy converted to internal energy and potential energy within a selected region were calculated.Prior to GW breaking, part of the potential energy is converted into kinetic energy, most of which is transported out of the chosen region. After the GW breaks and turbulence develops, part of the potential energy is converted into kinetic energy, most of which is converted into internal energy.The calculations for the transfer of kinetic energy among GWs, turbulence, and the BG in a selected region, as well as the contributions from various interactions (BG-GW, BG-turbulence, and GW-turbulence), are performed. At the point where the GW breaks, turbulence is generated. As the GW breaking process proceeds, the GWs lose energy to the background. At the start of the GW breaking, turbulence receives energy through interactions between GWs and turbulence, and between the BG and turbulence. Once the turbulence has accumulated enough energy, it begins to absorb energy from the background while losing energy to the GWs.The probabilities of instability are calculated during various stages of the GW-breaking process. The simulation suggests that the propagation of GWs results in instabilities, which are responsible for the GW breaking. As turbulence grows, it reduces convective instability.
In this Perspective, we summarize the status of technological development for large-area and low-noise substrate-transferred GaAs/AlGaAs (AlGaAs) crystalline coatings for interferometric gravitational-wave (GW) detectors. These topics were originally presented as part of an AlGaAs Workshop held at American University, Washington, DC, from 15 August to 17 August 2022, bringing together members of the GW community from the laser interferometer gravitational-wave observatory (LIGO), Virgo, and KAGRA collaborations, along with scientists from the precision optical metrology community, and industry partners with extensive expertise in the manufacturing of said coatings. AlGaAs-based crystalline coatings present the possibility of GW observatories having significantly greater range than current systems employing ion-beam sputtered mirrors. Given the low thermal noise of AlGaAs at room temperature, GW detectors could realize these significant sensitivity gains while potentially avoiding cryogenic operation. However, the development of large-area AlGaAs coatings presents unique challenges. Herein, we describe recent research and development efforts relevant to crystalline coatings, covering characterization efforts on novel noise processes as well as optical metrology on large-area (∼10 cm diameter) mirrors. We further explore options to expand the maximum coating diameter to 20 cm and beyond, forging a path to produce low-noise mirrors amenable to future GW detector upgrades, while noting the unique requirements and prospective experimental testbeds for these semiconductor-based coatings.
Mastery-based testing is an assessment scheme that encourages students to learn from their mistakes and develop an understanding of material before moving on. For an entry-level course with large enrollment like College Algebra, this could allow some students to move at an appropriate pace for themselves. This paper outlines the changes made to make College Algebra at the University of Florida an asynchronous mastery-based course for the 500+ students who take the course per year and concludes with a series of lessons learned from the endeavor.
Additively manufactured (AM) composites based on short carbon fibers possess strength and stiffness far less than their continuous fiber counterparts due to the fiber’s small aspect ratio and inadequate interfaces with the epoxy matrix. This investigation presents a route for preparing hybrid reinforcements for AM that comprise short carbon fibers and nickel-based metal-organic frameworks (Ni-MOFs). The porous MOFs furnish the fibers with tremendous surface area. Additionally, the MOFs growth process is non-destructive to the fibers and easily scalable. This investigation also demonstrates the viability of using Ni-based MOFs as a catalyst for growing multi-walled carbon nanotubes (MWCNTs) on carbon fibers. The changes to the fiber were examined via electron microscopy, X-ray scattering techniques, and Fourier-transform infrared spectroscopy (FTIR). The thermal stabilities were probed by thermogravimetric analysis (TGA). Tensile and dynamic mechanical analysis (DMA) tests were utilized to explore the effect of MOFs on the mechanical properties of 3D-printed composites. Composites with MOFs exhibited improvements in stiffness and strength by 30.2% and 19.0%, respectively. The MOFs enhanced the damping parameter by 700%.
CUSP is a powerful formalism that recovers, from first principles and with no free parameter, all the macroscopic properties of dark matter haloes found in cosmological N-body simulations and unveils the origin of their characteristic features. Since it is not restricted by the limitations of simulations, it covers the whole mass and redshift ranges. In the present Paper we use CUSP to calculate the mass–scale relations holding for halo density profiles fitted to the usual NFW and Einasto functions in the most relevant cosmologies and for the most usual mass definitions. We clarify the origin of these relations and provide accurate analytic expressions holding for all masses and redshifts. The performance of those expressions is compared to that of previous models and to the mass–concentration relation spanning more than 20 orders of magnitude in mass at z = 0 obtained in recent simulations of a 100 GeV WIMP universe.
This study presents three different machine learning (ML) models to estimate the flight block time for commercial airlines. The models rely only on explanatory variables that airlines would know when they were planning their schedules several months prior to the actual flight. Historical Actual Block Time (ABT) data is collected for one-way flights in seven airport pairs in the domestic U.S. market for 2019. The variability of the ABT and its components for these airport pairs is presented. The main features that affect this variability are investigated. The results confirm that seasonality, aircraft type, departure/arrival hour, and airport congestion are significant variables in partially explaining ABT variations. Overall, the results show that the considered ML models have limited capability in predicting the ABT. The prediction accuracy of these ML models is compared against a benchmarking scenario, where the median value of the historical ABT is used as an estimate for the block time. It is found that the median-based approach provides better performance compared to the ML models. A sensitivity analysis is performed to evaluate the risk of flight delay against different levels of block time padding. The possible trade-off between the block time padding and the expected flight delays is presented. Finally, the study evaluates and compares different airlines' block time padding strategies in the different airport pairs. Results show significant discrepancies among airlines concerning setting the scheduled block time (SBT) in the same airport pair. However, it is difficult to confirm whether airlines are purposely adopting padding strategies, or they lack the ability to optimize their block time padding against expected on-time performance.
The trend of the era of the Internet of Everything has promoted the integration of various industries and the Internet of Things (IoT) technology, and the scope of influence of the IoT is developing in a wider and deeper level. With the extension of the fields involved, the in-depth progress of the IoT is facing a bottleneck. For example, the security of IoT network and software have problems that are difficult to reconcile. Graph-powered learning methods such as graph embedding and graph neural network (GNN) are expected. How to use the graph learning method in IoT is a question that has to be discussed in relation to the future of the Internet of Things. This paper comprehensively discusses related research and summarizes the progress of using graph-powered learning to promote the network anomaly detection, malware detection, IoT device management, service recommendation and other aspects of IoT. And discuss the results of using graph theory and graph-powered learning methods according to the IoT fields such as smart transportation, Industrial Internet of Things (IIoT), Social Internet of Things (SIoT), smart medical care, smart home, smart grid, and smart city. Finally, in view of the existing issues and trends, this paper proposes future research directions including city various predictions, dynamics and heterogeneity, semantic analysis, resource consumption, point cloud, digital twins, and remote sensing.
The recent literature on antecedents of civil wars focuses primarily on the escalation of non-violent movements to civil wars. Still, it remains silent on why some terrorist campaigns manage to turn their violent campaign into a sustained insurgency. By filling this lacuna, we provide empirically supported explanations for this puzzle. Specifically, we explore the effects of three factors on a terror group’s chance to escalate the civil war, which are 1) how the state responds to the group’s terror campaign, 2) how the group responds to the state’s counterterrorism strategies, 3) and the state’s relations with other states. By testing our theory with recently released data on terrorist groups, we find that a terror group’s campaign is more likely to escalate to a civil war when 1) the state uses more repression against the terror group and 2) the group diversifies its attack portfolio. In addition, we also find that a terror group is less likely to escalate its campaign to a civil war if the state engages in an interstate rivalry, and the state responds to the group with higher spending on public goods, specifically social welfare goods. The results provide implications for future studies on terrorism and civil war.
An examination is conducted of airline strategies during the covid-19 pandemic using data from the United States. Our findings show that airlines pursued diverse strategies in terms of route entry and retention, pricing, and load factors. At the route level, a more detailed examination is conducted of the performance of a middle-seat blocking strategy designed to increase the safety of air travel. We show that this strategy (i.e., not making middle seats available to passengers) likely resulted in revenue losses for carriers, an estimated US $3,300 per flight. This revenue loss provides an indication as to why the middle seat blocking strategy was discontinued by all US airlines despite ongoing safety concerns.
Electron density irregularities in the ionosphere modify the phase and amplitude of trans-ionospheric radio signals. We aim to characterize the spectral and morphological features of E- and F-region ionospheric irregularities likely to produce these fluctuations or “scintillations”. To characterize them, we use a three-dimensional radio wave propagation model—“Satellite-beacon Ionospheric scintillation Global Model of upper Atmosphere” (SIGMA), along with the scintillation measurements observed by a cluster of six Global Positioning System (GPS) receivers called Scintillation Auroral GPS Array (SAGA) at Poker Flat, AK. An inverse method is used to derive the parameters that describe the irregularities by estimating the best fit of model outputs to GPS observations. We analyze in detail one E-region and two F-region events during geomagnetically active times and determine the E- and F-region irregularity characteristics using two different spectral models as input to SIGMA. Our results from the spectral analysis show that the E-region irregularities are more elongated along the magnetic field lines with rod-shaped structures, while the F-region irregularities have wing-like structures with irregularities extending both along and across the magnetic field lines. We also found that the spectral index of the E-region event is less than the spectral index of the F-region events. Additionally, the spectral slope on the ground at higher frequencies is less than the spectral slope at irregularity height. This study describes distinctive morphological and spectral features of irregularities at E- and F-regions for a handful of cases performed using a full 3D propagation model coupled with GPS observations and inversion.
Ability to quantify variations in magnetic field topology and density within Jupiter's magnetosphere is an important step in understanding the overall structure and dynamics. The Juno spacecraft has provided a rich data set in the dawnside magnetosphere. The recent Grid Agnostic MHD for Extended Research Applications (GAMERA) global simulation study by Zhang et al. (2021, https://doi.org/10.1126/sciadv.abd1204) showed a highly structured plasmadisc with closed magnetic field lines mapped between the outer dawn‐tail flank and the high‐latitude polar region. To test these model predictions, we examined Juno's magnetic field data and electron/energetic particle data to categorize portions of orbits 1–15 into one of three regions based on plasma confinement: the flux pileup region, the intermediate region, and the plasmadisc region. For each region we examined periodicities from magnetic field fluctuations and particle density fluctuations on the 1–10 hr time scale. Periodicities on this time scale could relate to internal (e.g., plasmadisc structure) or external processes (e.g., Kelvin‐Helmholtz vortices). Similar analysis was performed on the GAMERA simulation with the data split into two regions, an outer (150 > R > 60) region and an inner (R < 60) region. Finally, using published density moments from Huscher et al. (2021, https://doi.org/10.1029/2021JA029446), we compared the relative density variations of the Juno moments and the GAMERA simulation to further understand the overall structure and dynamics of the plasmadisc. The agreement between data and simulation supports the existence of such a highly structured plasmadisc.
A cooperative task consisting of multiple agents has the advantages of reducing cost, enhancing configurability, and increasing robustness in a large set of mission applications. A distributed strategy, applied using intelligence and adaptability characteristics, can provide the extended potential to achieve high levels of mission protection, especially under disturbances that might lead to operation degradation. This paper describes the design and development of a novel bioinspired distributed adaptive control architecture designed to increase the resilience of multiagent systems. The architecture is formulated using nonlinear bounded functions that characterize the immune system responses of living organisms. Numerical simulations are performed to evaluate the capabilities of this architecture to solve a consensus problem under bounded disturbances. Stability analysis is presented using the Lyapunov direct method to estimate the radius of convergence of the global tracking error under a time-varying disturbance. The proposed distributed controller successfully ensures that consensus is achieved among all agents while simultaneously mitigating the effect of disturbances.
The current technologies for developing quiet rotor noise in urban canyons are reviewed. Several passive noise control approaches are discussed with their limitations in reducing both tonal and broadband noise. Blade tip modifications are seen to be one of the more successful in reducing tonal noise, with serrations at the trailing edge useful in reducing trailing edge broadband noise. Due to the adverse performance limitations of passive control, several optimization approaches are reviewed to discuss the possible improvements in performance of rotors. Additionally, a few legacy control technologies for helicopters are discussed. Active control technologies are investigated. The overall outlook and challenges to these methods are discussed with an eye on Advanced Air Mobility Vehicles (AAM).
This study intends to bridge the unattended research gap and add to the knowledge base of ‘human resource management’ regarding the relationships between abusive supervision, and individual organizational citizenship behaviour (OCBI), through the mediation of ‘employee well-being’. For the given purpose, a sample of 250 cases was selected to collect data from non-managerial hotel employees from the metropolitan cities of Pakistan. Given responses were analysed in Smart PLS 3.0. Structural equation modelling (PLS-SEM) was used to conduct the necessary tests regarding measurement model and structural model assessment. The study found statistical support for three of the four hypotheses, confirming the deleterious role of abusive supervision in general and the intervening role of employee well-being. The findings have concluded that abusive supervision is harmful for workplaces, particularly when it comes to employees’ citizenship behaviours. Finally, the predictive relevance and r-squared values for the underlying model were also confirmed.
This article presents two new chemical plume tracing (CPT) algorithms for using on autonomous underwater vehicles (AUVs) to locate hydrothermal vents. We aim to design effective CPT navigation algorithms that direct AUVs to trace emitted hydrothermal plumes to the hydrothermal vent. Traditional CPT algorithms can be grouped into two categories, including bio-inspired and engineering-based methods, but they are limited by either search inefficiency in turbulent flow environments or high computational costs. To approach this problem, we design a new CPT algorithm by fusing traditional CPT methods. Specifically, two deep reinforcement learning (RL) algorithms, including double deep Q-network (DDQN) and deep deterministic policy gradient (DDPG), are employed to train a customized deep neural network that dynamically combines two traditional CPT algorithms during the search process. Simulation experiments show that both DDQN- and DDPG-based CPT algorithms achieve a high success rate (>90%) in either laminar or turbulent flow environments. Moreover, compared to traditional moth-inspired method, the averaged search time is improved by 67% for the DDQN- and 44% for the DDPG-based CPT algorithms in turbulent flow environments.
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Head of institution
P. Barry Butler, Ph.D.