Optimization of carbon nanotube ultracapacitor for cell design

We report a methodology to optimize vertically grown carbon nanotube (CNT) ultracapacitor (CNU) geometrical features such as CNT length, electrode-to-electrode separation, and CNT packing density. The electric field and electrolyte ionic motion within the CNU are critical in determining the device performance. Using a particle-based model (PBM) based on the molecular dynamics techniques we developed and reported previously, we compute the electric field in the device, keep track of the electrolyte ionic motion in the device volume, and evaluate the CNU electrical performance as a function of the aforementioned geometrical features. We show that the PBM predicts an optimal CNT density. Electrolyte ionic trapping occurs in the high CNT density regime, which limits the electrolyte ions from forming a double layer capacitance. In this regime, the CNU capacitance does not increase with the CNT packing density as expected, but dramatically decreases. Our results compare well with existing experimental data and the PBM methodology can be applied to an ultracapacitor built from any metallic electrode materials, as well as the vertical CNTs studied here

Carbon Nanotube Ultracapacitor Characteristics and Cell Design

A model of carbon nanotube (CNT) ultracapacitor (CNU) as a high-performance energy storage device is developed based on simulations of electrolyte ion motions between cathode and anode. Using a molecular dynamics (MD) approach, the equilibrium positions of electrode charges interacting through Coulomb potential are determined, which in turn yield the equipotential surface and electric field associated with the capacitor. With an applied AC voltage, the current is computed from the nanotube and electrolyte particle distribution and interaction, resulting in a frequency-dependent CNU impedance. From the current and impedance profiles, the Nyquist and Cyclic Voltammetry plots are then extracted. Results of these calculations compare well with existing experimental data. A lumped-element equivalent circuit for the CNU is proposed and the impedance computed from this circuit correlates well with the simulated and measured impedances. Further, a methodology is developed to optimize vertically grown carbon nanotube CNU geometrical features such as CNT length, electrode-to-electrode separation, and CNT packing density. The electric field and electrolyte ionic motion within the CNU are critical in determining device performance. Using a particle-based model (PBM) based on MD techniques, developed for this purpose, the electric field in the device is computed, the electrolyte ionic motion in the device volume is tracked, and the CNU electrical performance as a function of the aforementioned geometrical features is determined. Interestingly, the PBM predicts an optimal CNT packing density for the UC electrodes. Electrolyte ionic trapping occurs in the high CNT density regime, which limits the electrolyte ions from forming a double layer capacitance. In this regime, the CNU capacitance does not increase with the CNT packing density as expected, but decreases significantly. The results compare well with existing experimental data and the PBM methodology can be applied to an ultracapacitor built from any metallic electrode materials, as well as vertically aligned CNTs studied here

Integration of Temporal Abstraction and Dynamic Bayesian Networks in Clinical Systems. A preliminary approach

Abstraction of temporal data (TA) aims to abstract time-points into higher-level interval concepts and to detect significant trends in both low-level data and abstract concepts. TA methods are used for summarizing and interpreting clinical data. Dynamic Bayesian Networks (DBNs) are temporal probabilistic graphical models which can be used to represent knowledge about uncertain temporal relationships between events and state changes during time. In clinical systems, they were introduced to encode and use the domain knowledge acquired from human experts to perform decision support. A hypothesis that this study plans to investigate is whether temporal abstraction methods can be effectively integrated with DBNs in the context of medical decision-support systems. A preliminary approach is presented where a DBN model is constructed for prognosis of the risk for coronary artery disease (CAD) based on its risk factors and using as test bed a dataset that was collected after monitoring patients who had positive history of cardiovascular disease. The technical objectives of this study are to examine how DBNs will represent the abstracted data in order to construct the prognostic model and whether the retrieved rules from the model can be used for generating more complex abstractions

A statistical analysis of sounding derived indices and parameters for extreme and non-extreme thunderstorm events over Cyprus

The main purpose of this study is to provide a simple statistical analysis of several stability indices and parameters for extreme and non-extreme thunderstorm events during the period 1997 to 2001 in Cyprus. For this study, radiosonde data from Athalassa station (35°1´ N, 33°4´ E) were analyzed during the aforementioned period. The stability indices and parameters set under study are the K index, the Total Totals (TT) index, the Convective Available Potential Energy related parameters such as Convective Available Potential Energy (CAPE), Downdraft CAPE (DCAPE) and the Convective Inhibition (CIN), the Vorticity Generator Parameter (VGP), the Bulk Richardson Number (BRN), the BRN Shear and the Storm Relative Helicity (SRH). An event is categorized as extreme, if primarily, CAPE was non zero and secondary, if values of both the K and the TotalTotals (TT) indices exceeded 26.9 and 50, respectively. The cases with positive CAPE but lower values of the other indices, were identified as non-extreme. By calculating the median, the lower and upper limits, as well as the lower and upper quartiles of the values of these indices, the main characteristics of their distribution were determined

The cold frontal depression that affected the area of Cyprus between 28 and 29 January 2008

The baroclinic depression that affected the area of Cyprus during the cold period, between 28 and 29 January 2008 was thoroughly studied and is presented in the present paper. A small perturbation on a northwesterly flow to the north of Cyprus has initiated the generation of the depression and in 24 h this developed into a deep baroclinic system. This depression was associated with intense weather phenomena, such as heavy thunderstorms with hail and near gale force winds. Strong cold advection resulted in a significant temperature decrease; precipitation even in lower altitudes was in the form of snow, while the accumulated rainfall corresponded to the 25% of the monthly normal. January 2008 is considered as a dry month, despite the fact that, on the average, January is considered as the wettest month of the year. In this study, the evolution and development of the depression was investigated from synoptic, dynamic, energetic and thermodynamic perspectives, in order to enhance our knowledge on the life cycle and behaviour of similar depressions over the area with extreme characteristics

Synoptic, thermodynamic and agroeconomic aspects of severe hail events in Cyprus

Hail is a hazardous weather element often accompanying a thunderstorm, as a result of either thermal instability or instability associated with baroclinic synoptic-scale systems (i.e. frontal depressions). Nevertheless, instability of any kind and thunderstorm activity does not always lead to the formation of hail of adequate size to reach the ground. The broader the knowledge concerning hail events the better the understanding of the underlying thermodynamic and dynamic mechanisms, as well as the physical processes associated with its formation. <br><br> In the present study, the severe hail events that were recorded in Cyprus during the ten-year period from 1996 until 2005 were examined, first by grouping them into two clusters, namely, the "thermal instability cluster" and the "frontal depression cluster". Subsequently, the spatial and temporal evolution of the synoptic, dynamic and thermodynamic characteristics of these hail events was studied in depth. Also, the impact of hailstorms on the local economy of the island is presented in terms of the compensations paid by the Agricultural Insurance Organization of the country

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare trajectories from medical records: A deep learning approach

Preliminary verification results of the DWD limited area model LME and evaluation of its storm forecasting skill over the area of Cyprus

A preliminary verification and evaluation is made of the forecast fields of the non-hydrostatic limited area model LME of the German Weather Service (DWD), for a recent three month period. For this purpose, observations from two synoptic stations in Cyprus are utilized. In addition, days with depressions over the area were selected in order to evaluate the model's forecast skill in storm forecasting

Do you see what I see? Images of the COVID-19 pandemic through the lens of Google

During times of crisis, information access is crucial. Given the opaque processes behind modern search engines, it is important to understand the extent to which the “picture” of the Covid-19 pandemic accessed by users differs. We explore variations in what users “see” concerning the pandemic through Google image search, using a two-step approach. First, we crowdsource a search task to users in four regions of Europe, asking them to help us create a photo documentary of Covid-19 by providing image search queries. Analysing the queries, we find five common themes describing information needs. Next, we study three sources of variation - users’ information needs, their geo-locations and query languages - and analyse their influences on the similarity of results. We find that users see the pandemic differently depending on where they live, as evidenced by the 46% similarity across results. When users expressed a given query in different languages, there was no overlap for most of the results. Our analysis suggests that localisation plays a major role in the (dis)similarity of results, and provides evidence of the diverse “picture” of the pandemic seen through Google

Corrigendum to ‘The detection and discrimination of human body fluids using ATR FT-IR spectroscopy’ [Forensic Sci. Int. 252 (2015) e10–e16]

Blood, saliva, semen and vaginal secretions are the main human body fluids encountered at crime scenes. Currently presumptive tests are routinely utilised to indicate the presence of body fluids, although these are often subject to false positives and limited to particular body fluids. Over the last decade more sensitive and specific body fluid identification methods have been explored, such as mRNA analysis and proteomics, although these are not yet appropriate for routine application. This research investigated the application of ATR FT-IR spectroscopy for the detection and discrimination of human blood, saliva, semen and vaginal secretions. The results demonstrated that ATR FT-IR spectroscopy can detect and distinguish between these body fluids based on the unique spectral pattern, combination of peaks and peak frequencies corresponding to the macromolecule groups common within biological material. Comparisons with known abundant proteins relevant to each body fluid were also analysed to enable specific peaks to be attributed to the relevant protein components, which further reinforced the discrimination and identification of each body fluid. Overall, this preliminary research has demonstrated the potential for ATR FT-IR spectroscopy to be utilised in the routine confirmatory screening of biological evidence due to its quick and robust application within forensic science