Critical speeds and forced response solutions for active magnetic bearing turbomachinery, part 2

The need for better performance of turbomachinery with active magnetic bearings has necessitated a study of such systems for accurate prediction of their vibrational characteristics. A modification of existing transfer matrix methods for rotor analysis is presented to predict the response of rotor systems with active magnetic bearings. The position of the magnetic bearing sensors is taken into account and the effect of changing sensor position on the vibrational characteristics of the rotor system is studied. The modified algorithm is validated using a simpler Jeffcott model described previously. The effect of changing from a rotating unbalance excitation to a constant excitation in a single plane is also studied. A typical eight stage centrifugal compressor rotor is analyzed using the modified transfer matrix code. The results for a two mass Jeffcott model were presented previously. The results obtained by running this model with the transfer matrix method were compared with the results of the Jeffcott analysis for the purposes of verification. Also included are plots of amplitude versus frequency for the eight stage centrifugal compressor rotor. These plots demonstrate the significant influence that sensor location has on the amplitude and critical frequencies of the rotor system

Discovering gated recurrent neural network architectures

Reinforcement Learning agent networks with memory are a key component in solving POMDP tasks. Gated recurrent networks such as those composed of Long Short-Term Memory (LSTM) nodes have recently been used to improve state of the art in many supervised sequential processing tasks such as speech recognition and machine translation. However, scaling them to deep memory tasks in reinforcement learning domain is challenging because of sparse and deceptive reward function. To address this challenge first, a new secondary optimization objective is introduced that maximizes the information (Info-max) stored in the LSTM network. Results indicate that when combined with neuroevolution, Info-max can discover powerful LSTM-based memory solutions that outperform traditional RNNs. Next, for the supervised learning tasks, neuroevolution techniques are employed to design new LSTM architectures. Such architectural variations include discovering new pathways between the recurrent layers as well as designing new gated recurrent nodes. This dissertation proposes evolution of a tree-based encoding of the gated memory nodes, and shows that it makes it possible to explore new variations more effectively than other methods. The method discovers nodes with multiple recurrent paths and multiple memory cells, which lead to significant improvement in the standard language modeling benchmark task. The dissertation also shows how the search process can be speeded up by training an LSTM network to estimate performance of candidate structures, and by encouraging exploration of novel solutions. Thus, evolutionary design of complex neural network structures promises to improve performance of deep learning architectures beyond human ability to do so.Computer Science

A Statistical Model for Ascertaining the Influence and Reliability of Weather Parameters on Incidence of Blossom Blight in Mango (Mangifera indica L.)

A statistical model was developed to study the influence and reliability of weatlier parameters on incidence of blossom blight in Mango (Mangifera indica) and subsequently to predict their incidence. Results showed that preceding week's weather variables viz., maximum and minimum temperature, evaporation, rainfall, morning and evening relative humidity and wind speed were found to collectively predict blossom blight incidence to the extent of 94.3 per cent. Further, as a measure of goodness-of-fit, the coefficient of determination (R2) and mean squared error were used to evaluate the empirical model developed by using above variables. Validation test showed that the model developed using relative humidity at 07.30 h (X3), evaporation (X5) and wind speed (X6) (Y = 883.4 - 8.065 X3-11.506 X5 -33.619 X6) could predict the incidence to the extent of 75.7%. This model is useful in determining the role of climatic factors in disease appearance and progression and devising suitable management strategy

A sense of embodiment is reflected in people's signature size

BACKGROUND: The size of a person's signature may reveal implicit information about how the self is perceived although this has not been closely examined. METHODS/RESULTS: We conducted three experiments to test whether increases in signature size can be induced. Specifically, the aim of these experiments was to test whether changes in signature size reflect a person's current implicit sense of embodiment. Experiment 1 showed that an implicit affect task (positive subliminal evaluative conditioning) led to increases in signature size relative to an affectively neutral task, showing that implicit affective cues alter signature size. Experiments 2 and 3 demonstrated increases in signature size following experiential self-focus on sensory and affective stimuli relative to both conceptual self-focus and external (non-self-focus) in both healthy participants and patients with anorexia nervosa, a disorder associated with self-evaluation and a sense of disembodiment. In all three experiments, increases in signature size were unrelated to changes in self-reported mood and larger than manipulation unrelated variations. CONCLUSIONS: Together, these findings suggest that a person's sense of embodiment is reflected in their signature size

Understanding Tourism Dynamics Using GIS and Forecasting Model in Banswara District, Rajasthan, India

Understanding the tourism dynamics of an area involves a rigorous literature review, interviewing the local people and experts, and visiting the sites. This also involves getting acquainted with the transport network of the area to know about the accessibility to the tourism destination, which ultimately helps in preparing a suitable plan for tourism development by creating tourist circuits. Forecasting tourism demand with the help of past data helps put a better picture in front of planners and administration so they can plan more efficiently. Thus, a proper understanding of tourism dynamics requires a holistic vision. The present study tried to understand the tourism dynamics of Banswara district in Rajasthan state, India, by using forecasting techniques and GIS. This twin methodology used in this study highlights the fact that Banswara tourism is at its incipient stage and that there is a substantial prospect of religious, historical, and nature-based tourism in this area. Few of the sites are well connected with the road network, and thus, by taking connectivity into account, tourist circuits are designed

Bouncing Back from Bankruptcy: Narratives of Entrepreneurial Antifragility

Drawing on antifragility as a lens, we explore UK-based entrepreneurs’ accounts of their behaviours and choices to theorise how they managed to bounce back from bankruptcy to restart their new firms. Our findings suggest that antifragility as an entrepreneurial competence enables once bankrupted business owners to identify potentialities and limits of restarting and managing a new venture in ways otherwise overlooked by others. In this way, we illuminate how antifragility as an organising capability could be harnessed by developing a process model that highlights the core stages of entrepreneurial recovery post-bankruptcy.

Self heating Effects in GaN High Electron Mobility Transistor for Different Passivation Material

In this paper effect of self-heating has been studied of AlGaN/GaN high electron mobility transistor (HEMT) for different passivation layers which is promising device for high power at high frequencies. The different passivation layers used are aluminium oxide (Al2O3), silicon nitride (SiN) and silicon dioxide (SiO2). The device GaN HEMT has been simulated and characterised for its thermal behaviour by the distribution of lattice temperature inside the device using device simulation tool ATLAS from SILVACO. The transfer and output characteristics with and without self-heating has been studied for electrical characterisation. The channel temperature for different passivation observed is 448 K, 456 K and 471 K forAl2O3, SiN and SiO2 respectively. The observed different temperatures are due to difference in their thermal conductivity. This channel temperature information is critical to study the reliability of the device at high power levels

Assessment of Antioxidant Properties in Fruits of Myrica esculenta: A Popular Wild Edible Species in Indian Himalayan Region

Crude extract of Myrica esculenta fruits, a wild edible species of Indian Himalayan Region, was evaluated for phenolic compounds and antioxidant properties. Results revealed significant variation in total phenolic and flavonoid contents across populations. Among populations, total phenolic content varied between 1.78 and 2.51 mg gallic acid equivalent/g fresh weight (fw) of fruits and total flavonoids ranged between 1.31 and 1.59 mg quercetin equivalent/g fw. Antioxidant activity determined by 2,2′-azinobis(3-ethylbenzothiazoline-6-sulphonic acid) radical scavenging, 1,1-diphenyl-2-picrylhydrazyl radical scavenging and ferric reducing antioxidant power (FRAP) exhibited considerable antioxidant potential and showed significant positive correlation with total phenolic and total flavonoids content. High performance liquid chromatography analysis revealed significant variation (P <  .01) in phenolic compounds (i.e., gallic acid, catechin, hydroxybenzioc acid and ρ-coumaric acid) across populations. This study provides evidences to establish that consumption of M. esculenta fruits while providing relished taste would also help in reduction of free radicals. Therefore, this wild edible species deserves promotion in the region through horticulture and forestry interventions