Temperature Prediction Model for Bone Drilling Based on Density Distribution and In Vivo Experiments for Minimally Invasive Robotic Cochlear Implantation

Surgical robots have been proposed ex vivo to drill precise holes in the temporal bone for minimally invasive cochlear implantation. The main risk of the procedure is damage of the facial nerve due to mechanical interaction or due to temperature elevation during the drilling process. To evaluate the thermal risk of the drilling process, a simplified model is proposed which aims to enable an assessment of risk posed to the facial nerve for a given set of constant process parameters for different mastoid bone densities. The model uses the bone density distribution along the drilling trajectory in the mastoid bone to calculate a time dependent heat production function at the tip of the drill bit. Using a time dependent moving point source Green's function, the heat equation can be solved at a certain point in space so that the resulting temperatures can be calculated over time. The model was calibrated and initially verified with in vivo temperature data. The data was collected in minimally invasive robotic drilling of 12 holes in four different sheep. The sheep were anesthetized and the temperature elevations were measured with a thermocouple which was inserted in a previously drilled hole next to the planned drilling trajectory. Bone density distributions were extracted from pre-operative CT data by averaging Hounsfield values over the drill bit diameter. Post-operative [Formula: see text]CT data was used to verify the drilling accuracy of the trajectories. The comparison of measured and calculated temperatures shows a very good match for both heating and cooling phases. The average prediction error of the maximum temperature was less than 0.7 °C and the average root mean square error was approximately 0.5 °C. To analyze potential thermal damage, the model was used to calculate temperature profiles and cumulative equivalent minutes at 43 °C at a minimal distance to the facial nerve. For the selected drilling parameters, temperature elevation profiles and cumulative equivalent minutes suggest that thermal elevation of this minimally invasive cochlear implantation surgery may pose a risk to the facial nerve, especially in sclerotic or high density mastoid bones. Optimized drilling parameters need to be evaluated and the model could be used for future risk evaluation.

LIFE TABLE ANALYSIS OF DIAPHORINA CITRI (HEMIPTERA: PSYLLIDAE) INFESTING SWEET ORANGE (CITRUS SINENSIS) IN SAO PAULO

An ecological life table for eggs and nymphs of Diaphorina citri Kuwayama (Hemiptera: Psyllidae) was constructed with data obtained from orange orchards (Citrus sinensis Osbeck) in 2 regions of the State of Sao Paulo, over 4 generations in the period from XI-2006 to V-2007, comprising spring, summer, and fall seasons. Young growing shoots with D. citri eggs present were identified, and live individuals were counted until adult emergence. No predatory arthropods were observed in association with D. citri eggs and nymphs during the study. The mean parasitism of fourth- and fifth-instar nymphs by Tamarixia radiata Waterston (Hymenoptera: Eulophidae) was 2.3%. The durations of the egg-adult period were similar among the 4 generations, ranging from 18.0 to 24.7 d (at mean temperatures ranging from 21.6 to 26.0 degrees C) and followed the temperature requirement models obtained in the laboratory for D. citri. However, survival from the egg to the adult stage for the same period varied considerably from 1.7 to 21.4%; the highest mortalities were observed in the egg and small nymphal (first- to thirdinstar) stages, which were considered to be key phases for population growth of the pest.

Spatiotemporal model or time series model for assessing city-wide temperature effects on mortality?

Most studies examining the temperature–mortality association in a city used temperatures from one site or the average from a network of sites. This may cause measurement error as temperature varies across a city due to effects such as urban heat islands. We examined whether spatiotemporal models using spatially resolved temperatures produced different associations between temperature and mortality compared with time series models that used non-spatial temperatures. We obtained daily mortality data in 163 areas across Brisbane city, Australia from 2000 to 2004. We used ordinary kriging to interpolate spatial temperature variation across the city based on 19 monitoring sites. We used a spatiotemporal model to examine the impact of spatially resolved temperatures on mortality. Also, we used a time series model to examine non-spatial temperatures using a single site and the average temperature from three sites. We used squared Pearson scaled residuals to compare model fit. We found that kriged temperatures were consistent with observed temperatures. Spatiotemporal models using kriged temperature data yielded slightly better model fit than time series models using a single site or the average of three sites' data. Despite this better fit, spatiotemporal and time series models produced similar associations between temperature and mortality. In conclusion, time series models using non-spatial temperatures were equally good at estimating the city-wide association between temperature and mortality as spatiotemporal models.

Role of Temperature in the Biological Activity of a Boreal Forest

In northern latitudes, temperature is the key factor driving the temporal scales of biological activity, namely the length of the growing season and the seasonal efficiency of photosynthesis. The formation of atmospheric concentrations of biogenic volatile organic compounds (BVOCs) are linked to the intensity of biological activity. However, interdisciplinary knowledge of the role of temperature in the biological processes related to the annual cycle and photosynthesis and atmospheric chemistry is not fully understood. The aim of this study was to improve understanding of the role of temperature in these three interlinked areas: 1) onset of growing season, 2) photosynthetic efficiency and 3) BVOC air concentrations in a boreal forest. The results present a cross-section of the role of temperature on different spatial (southern northern boreal), structural (tree forest stand - forest) and temporal (day-season- year) scales. The fundamental status of the Thermal Time model in predicting the onset of spring recovery was confirmed. However, it was recommended that sequential models would be more appropriate tools when the onset of the growing season is estimated under a warmer climate. A similar type of relationship between photosynthetic efficiency and temperature history was found in both southern and northern boreal forest stands. This result draws attention to the critical question of the seasonal efficiency of coniferous species to emit organic compounds under a warmer climate. New knowledge about the temperature dependence of the concentrations of biogenic volatile organic compounds in a boreal forest stand was obtained. The seasonal progress and the inter-correlation of BVOC concentrations in ambient air indicated a link to biological activity. Temperature was found to be the main driving factor for the concentrations. However, in addition to temperature, other factors may play a significant role here, especially when the peak concentrations are studied. There is strong evidence that the spring recovery and phenological events of many plant species have already advanced in Europe. This study does not fully support this observation. In a boreal forest, changes in the annual cycle, especially the temperature requirement in winter, would have an impact on the atmospheric BVOC composition. According to this study, more joint phenological and BVOC field observations and laboratory experiments are still needed to improve these scenarios.

Coupled Temperature-Displacement Modelling of Injection Stretch Blow Moulding of PET bottles using Buckley Model

This study presents a fully coupled temperature–displacement finite element modelling of the injection stretch-blow moulding (ISBM) process of polyethylene terephthalate (PET) bottles using ABAQUS with a view to optimising the process conditions. A physically-based material model (Buckley model) was used to predict the mechanical behaviour of PET at temperatures slightly above its glass transition temperature. A model incorporating heat transfer between the stretch rod, the preform and the mould was built using axisymmetric solid elements. Extensive finite element analyses were carried out to predict the deformation, the distribution and history of strain and temperature during ISBM of a 20 g–330 ml bottle, which was made in an in situ test on a Sidel SB06 machine. Comparisons of numerical results with the measurements demonstrate that the model can satisfactorily model the sidewall thickness and material distributions. It is also shown that significant non-linear differentials exist in temperature and strain in both bottle thickness and length directions during the process. This justifies the employment of a volume approach to accurately predict the final mechanical properties of the bottles governed by the orientation and crystallinity which are highly temperature and strain dependent.

Development of a log-quadratic model to describe microbial inactivation, illustrated by thermal inactivation of Clostridium botulinum

In the commercial food industry, demonstration of microbiological safety and thermal process equivalence often involves a mathematical framework that assumes log-linear inactivation kinetics and invokes concepts of decimal reduction time (DT), z values, and accumulated lethality. However, many microbes, particularly spores, exhibit inactivation kinetics that are not log linear. This has led to alternative modeling approaches, such as the biphasic and Weibull models, that relax strong log-linear assumptions. Using a statistical framework, we developed a novel log-quadratic model, which approximates the biphasic and Weibull models and provides additional physiological interpretability. As a statistical linear model, the log-quadratic model is relatively simple to fit and straightforwardly provides confidence intervals for its fitted values. It allows a DT-like value to be derived, even from data that exhibit obvious "tailing." We also showed how existing models of non-log-linear microbial inactivation, such as the Weibull model, can fit into a statistical linear model framework that dramatically simplifies their solution. We applied the log-quadratic model to thermal inactivation data for the spore-forming bacterium Clostridium botulinum and evaluated its merits compared with those of popular previously described approaches. The log-quadratic model was used as the basis of a secondary model that can capture the dependence of microbial inactivation kinetics on temperature. This model, in turn, was linked to models of spore inactivation of Sapru et al. and Rodriguez et al. that posit different physiological states for spores within a population. We believe that the log-quadratic model provides a useful framework in which to test vitalistic and mechanistic hypotheses of inactivation by thermal and other processes. Copyright © 2009, American Society for Microbiology. All Rights Reserved.

Voltage and Temperature Scalable Standard Cell Leakage Models Based on Stacks for Statistical Leakage Characterization

With extensive use of dynamic voltage scaling (DVS) there is increasing need for voltage scalable models. Similarly, leakage being very sensitive to temperature motivates the need for a temperature scalable model as well. We characterize standard cell libraries for statistical leakage analysis based on models for transistor stacks. Modeling stacks has the advantage of using a single model across many gates there by reducing the number of models that need to be characterized. Our experiments on 15 different gates show that we needed only 23 models to predict the leakage across 126 input vector combinations. We investigate the use of neural networks for the combined PVT model, for the stacks, which can capture the effect of inter die, intra gate variations, supply voltage(0.6-1.2 V) and temperature (0 - 100degC) on leakage. Results show that neural network based stack models can predict the PDF of leakage current across supply voltage and temperature accurately with the average error in mean being less than 2% and that in standard deviation being less than 5% across a range of voltage, temperature.

Multi-model climate change projections for India under representative concentration pathways

Climate projections for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) are made using the newly developed representative concentration pathways (RCPs) under the Coupled Model Inter-comparison Project 5 (CMIP5). This article provides multi-model and multi-scenario temperature and precipitation projections for India for the period 1860-2099 based on the new climate data. We find that CMIP5 ensemble mean climate is closer to observed climate than any individual model. The key findings of this study are: (i) under the business-as-usual (between RCP6.0 and RCP8.5) scenario, mean warming in India is likely to be in the range 1.7-2 degrees C by 2030s and 3.3-4.8 degrees C by 2080s relative to pre-industrial times; (ii) all-India precipitation under the business-as-usual scenario is projected to increase from 4% to 5% by 2030s and from 6% to 14% towards the end of the century (2080s) compared to the 1961-1990 baseline; (iii) while precipitation projections are generally less reliable than temperature projections, model agreement in precipitation projections increases from RCP2.6 to RCP8.5, and from short-to long-term projections, indicating that long-term precipitation projections are generally more robust than their short-term counterparts and (iv) there is a consistent positive trend in frequency of extreme precipitation days (e.g. > 40 mm/day) for decades 2060s and beyond. These new climate projections should be used in future assessment of impact of climate change and adaptation planning. There is need to consider not just the mean climate projections, but also the more important extreme projections in impact studies and as well in adaptation planning.

New application of temperature-dependent modelling of high temperature superconductors: Quench propagation and pulse magnetization

We present temperature-dependent modeling of high-temperature superconductors (HTS) to understand HTS electromagnetic phenomena where temperature fluctuation plays a nontrivial role. Thermal physics is introduced into the well-developed H-formulation model, and the effect of temperature-dependent parameters is considered. Based on the model, we perform extensive studies on two important HTS applications: quench propagation and pulse magnetization. A micrometer-scale quench model of HTS coil is developed, which can be used to estimate minimum quench energy and normal zone propagation velocity inside the coil. In addition, we study the influence of inhomogeneity of HTS bulk during pulse magnetization. We demonstrate how the inhomogeneous distribution of critical current inside the bulk results in varying degrees of heat dissipation and uniformity of final trapped field. The temperature- dependent model is proven to be a powerful tool to study the thermally coupled electromagnetic phenomena of HTS. © 2012 American Institute of Physics.

A study on the mechanism of the abnormal reduction of Eu3+ -> Eu2+ in Sr2B5O9Cl prepared in air at high temperature

This paper reports a new observation of the abnormal reduction of Eu3+ --> Eu2+ in Sr2B5O9Cl when prepared in air at high temperature. A model based on the nature of substitution defects is proposed to explain this abnormal reduction. Electrons, which reduced the Eu3+ ions, are created by the substitution of cations first and then transferred to the target Eu3+ ions via tetrahedral berate anion groups. Codoping experiments are designed and performed. The results of these experiments support the model proposed. (C) 1999 Academic Press.

Cure processing modeling and cure cycle simulation of epoxy-terminated poly(phenylene ether ketone). V: Estimation of temperature distribution during cure process

A numerical method to estimate temperature distribution during the cure of epoxy-terminated poly(phenylene ether ketone) (E-PEK)-based composite is suggested. The effect of the temperature distribution on the selection of cure cycle is evaluated using a suggested alternation criterion. The effect of varying heating rate and thickness on the temperature distribution, viscosity distribution and distribution of the extent of cure reaction are discussed based on the combination of the here-established temperature distribution model and the previously established curing kinetics model and chemorheological model. It is found that, for a thin composite (<=10mm) and low heating rate (<=2.5K/min), the effect of temperature distribution on cure cycle and on the processing window for pressure application can be neglected. Low heating rate is of benefit to reduce the temperature gradient. The processing window for pressure application becomes narrower with increasing thicknesses of composite sheets. The validity of the temperature distribution model and the modified processing window is evaluated through the characterization of mechanical and physical properties of E-PEK-based composite fabricated according to different temperature distribution conditions.

Short, medium and long term load forecasting model and virtual load forecaster based on radial basis neural networks

Artificial neural networks (ANNs) can be easily applied to short-term load forecasting (STLF) models for electric power distribution applications. However, they are not typically used in medium and long term load forecasting (MLTLF) electric power models because of the difficulties associated with collecting and processing the necessary data. Virtual instrument (VI) techniques can be applied to electric power load forecasting but this is rarely reported in the literature. In this paper, we investigate the modelling and design of a VI for short, medium and long term load forecasting using ANNs. Three ANN models were built for STLF of electric power. These networks were trained using historical load data and also considering weather data which is known to have a significant affect of the use of electric power (such as wind speed, precipitation, atmospheric pressure, temperature and humidity). In order to do this a V-shape temperature processing model is proposed. With regards MLTLF, a model was developed using radial basis function neural networks (RBFNN). Results indicate that the forecasting model based on the RBFNN has a high accuracy and stability. Finally, a virtual load forecaster which integrates the VI and the RBFNN is presented.

Domain annihilation due to temperature and thickness gradients in single-crystal BaTiO3

The manner in which 90? ferroelectric-ferroelastic domains respond to changes in temperature has been mapped in BaTiO3 single crystals using atomic force microscopy. Domain periodicity remains unaltered until approximately 2 ? C below the Curie temperature (TC ), whereupon domains coarsened dramatically. This behavior was successfully rationalized by considering the temperature dependence of the parameters associated with standard models of ferroelastic domain formation. However, while successful in describing the expected radical increase in equilibrium period with temperature, the model did not predict the unusual mechanism by which domain coarsening occurred; this was not continuous at a local level but instead involved discrete domain annihilation events. Subsequent insights from a combination of free energy analysis for the system and further experimental data from an analogous situation, in which domain period increases with increasing crystal thickness, suggested that domain annihilation is inevitable whenever a component of the relevant gradient that affects domain period is orientated parallel to the domain walls. Consistent with this thesis, we note that, for the observations presented herein, the thermal gradient possessed a significant component parallel to the domain walls. We suggest that domain annihilation is a general feature of domain structures in gradient fields.

On the evaluation of temperature trends in the tropical troposphere

A series of model experiments with the coupled Max-Planck-Institute ECHAM5/OM climate model have been investigated and compared with microwave measurements from the Microwave Sounding Unit (MSU) and re-analysis data for the period 1979–2008. The evaluation is carried out by computing the Temperature in the Lower Troposphere (TLT) and Temperature in the Middle Troposphere (TMT) using the MSU weights from both University of Alabama (UAH) and Remote Sensing Systems (RSS) and restricting the study to primarily the tropical oceans. When forced by analysed sea surface temperature the model reproduces accurately the time-evolution of the mean outgoing tropospheric microwave radiation especially over tropical oceans but with a minor bias towards higher temperatures in the upper troposphere. The latest reanalyses data from the 25 year Japanese re-analysis (JRA25) and European Center for Medium Range Weather Forecasts Interim Reanalysis are in very close agreement with the time-evolution of the MSU data with a correlation of 0.98 and 0.96, respectively. The re-analysis trends are similar to the trends obtained from UAH but smaller than the trends from RSS. Comparison of TLT, computed from observations from UAH and RSS, with Sea Surface Temperature indicates that RSS has a warm bias after 1993. In order to identify the significance of the tropospheric linear temperature trends we determined the natural variability of 30-year trends from a 500 year control integration of the coupled ECHAM5 model. The model exhibits natural unforced variations of the 30 year tropospheric trend that vary within ±0.2 K/decade for the tropical oceans. This general result is supported by similar results from the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model. Present MSU observations from UAH for the period 1979–2008 are well within this range but RSS is close to the upper positive limit of this variability. We have also compared the trend of the vertical lapse rate over the tropical oceans assuming that the difference between TLT and TMT is an approximate measure of the lapse rate. The TLT–TMT trend is larger in both the measurements and in the JRA25 than in the model runs by 0.04–0.06 K/decade. Furthermore, a calculation of all 30 year TLT–TMT trends of the unforced 500-year integration vary between ±0.03 K/decade suggesting that the models have a minor systematic warm bias in the upper troposphere.