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Background: Coelomic cysts are uncommon benign cyctic lesions of the mediastinum. Complete resection of the cyst with surgical treatment is indicated depending on whether the patient is symptomatic or whether the cyst grows during follow-up, or whether the diagnosis is uncertain. Surgical treatment prevents complications, relieves symptoms, and establishes diagnosis. Video-assisted thoracoscopic surgery (VATS) is the best method for excision of these cysts. This approach is safe and effective, has low morbidity, and shortens the length of hospital stay

A solar illumination design with fluorescent coatings to change in crop growth in vertical farms was investigated using simulations based on numerical analysis. The fluorescent reflectors were used as part of the lighting system to improve the irradiation for photosynthesis over the shelves. The reflectors were based on optical glass (BK7) embedded with fluorescent pigments (K2SiF6:Mn4+) and were used in the light distribution system to increase crop production by improving spatial light distribution and altering the solar spectrum. The optimum number of shelves was identified for three different latitudes, considering a fixed size of solar collector. It was estimated that introducing fluorescent reflectors to the system could lead to an increase in crop production exceeding 35%.

Objective The aim of this study was to investigate the role of 18F-fluorodeoxyglucose positron emission tomography/computerized tomography (FDG-PET/CT) in the diagnosis and treatment of pulmonary hydatid disease and also compare the morphological characteristics of the lesions with SUVmax values and identify complicated disease. Methods Thirty-six patients with a diagnosis of pulmonary hydatid disease who underwent 18F-FDG PET/CT imaging were included in this retrospective study. The size of the lesions, morphological characteristics, accompanying parenchymal and pleural findings, SUVmax and HUmean values and FDG uptake in mediastinal lymph nodes were noted. The relationship between morphologic properties, SUVmax of the lesions and lymphatic FDG uptake was analysed. Results A total of 99 lesions of 36 patients were classified as solid (7.1%), cystic (53.5%), semisolid (20.2%) and cavitary (19.2%). Thirty-two of the lesions were encapsulated, 38 of the lesions had border irregularity. Accompanying consolidation was present in 10 cases, bronchial obstruction in 9, pleural thickening in 28 and effusion in 3. There was positive correlation between lesions HUmean and SUVmax values (r = 0.285). SUVmax values were significantly higher in lesions with irregular borders, solid or semisolid type and presence of consolidation, bronchial obstruction and pleural thickening. Also more lymphatic FDG uptake was detected in this group. Conclusion Higher SUVmax values may be a useful parameter in the diagnosis of complicated pulmonary hydatid disease. FDG-PET may provide guidance for determining the priority of lesion for surgery in cases with multiple lesions and may be helpful to evaluate the response to medical treatment.

Altering wavelength via fluorescent particles is used in various applications. The solution of the broadband radiative transfer equation (RTE) for absorbing and anisotropically scattering a fluorescent medium is presented in this study considering fluorescent cascade, along with a Monte-Carlo-method-based solution of the equation. The path-length-based Monte Carlo method, the dual-stage method, and its modified version, the multi-stage method, which are used for solving the RTE in a fluorescent medium for biomedical and lighting applications, are not capable of accurately solving the broadband RTE with fluorescent cascade. Therefore, a collision-based Monte Carlo method is applied to overcome the limitations of these approaches. An accuracy comparison with the alternative methods is presented along with the flow chart and codes of the proposed method.

Objectives Consolidation/tumour (C/T) ratio means the maximum diameter of the consolidation is divided by the maximum diameter of the tumour and it is predictive for pathologic subtypes and prognosis after resection of the tumour. The purpose of this study is to clarify impact of C/T ratio along with maximum standardized uptake value (SUV) in pathological stage I lung adenocarcinoma. Methods Only patients with pathological stage I lung adenocarcinoma diagnosed by resection were included. Prognostic impact of C/T ratio and SUVmax were analysed by using regression analyses. Results Totally 156 patients (121 males, 35 females) were evaluated retrospectively. Overall survival (OS) and progression free survival (PFS) were higher in patients with C/T ratio >= 0.5 (OS: 46.3 +/- 23.7 vs 30.4 +/- 14.6, P = 0.002 and PFS: 43.0 +/- 25.4 vs 27.8 +/- 15.8, P = 0.005). But PFS and OS curves did not reveal any significant differences with Kaplan-Meier method (P = 0.45 and P = 0.055 respectively). Resection type (limited vs anatomic) and C/T ratio were predictors for OS in multivariate analyses (resection type: HR: 2.21 (1.01-4.83), P = 0.045 and C/T ratio: HR: 0.44 (0.20-0.98), P = 0.045). For PFS, resection type and SUVmax had prognostic significance (resection type: HR: 3.56 (1.64-7.74), P = 0.001 and SUVmax: HR: 1.31 (0.82-2.99), P = .002). Conclusion In pathological stage 1 lung adenocarcinomas, SUVmax and surgery type are important predictors for recurrence rates. For early stage, adenocarcinoma patients with high SUVmax value, tumour size >= 3 cm and high grade subtype, C/T ratio should not be considered significant alone on survival and recurrence.

Optical characterization of nanoparticle aggregates is a complex inverse problem that can be solved by deterministic or statistical methods. Previous studies showed that there exists a different lower size limit of reliable characterization, corresponding to the wavelength of light source used. In this study, these characterization limits are determined considering a light source wavelength range changing from ultraviolet to near infrared (266–1064 nm) relying on numerical light scattering experiments. Two different measurement ensembles are considered. Collection of well separated aggregates made up of same sized particles and that of having particle size distribution. Filippov's cluster-cluster algorithm is used to generate the aggregates and the light scattering behavior is calculated by discrete dipole approximation. A likelihood-free Approximate Bayesian Computation, relying on Adaptive Population Monte Carlo method, is used for characterization. It is found that when the wavelength range of 266–1064 nm is used, successful characterization limit changes from 21–62 nm effective radius for monodisperse and polydisperse soot aggregates.

The thermal properties of the TIP3P and TIP5P water models are investigated using equilibrium and non-equilibrium molecular dynamics techniques in the presence of solid surfaces. The performance of the non-equilibrium technique for rigid molecules is found to depend significantly on the distribution of atomic degrees of freedom. An improved approach to distribute atomic degrees of freedom is proposed for which the thermal conductivity of the TIP5P model agrees more closely with equilibrium molecular dynamics and experimental results than the existing state of the art.

Effect of silicon atomic force microscope probe position and particle spacing on the local absorption of an array of gold nanoparticles placed over a dielectric borosilicate glass surface are evaluated. An improved, vectorized version of discrete dipole approximation coupled with surface interactions is employed throughout the study. It is shown that surface evanescent waves interacting with the system of nanoparticles and atomic force microscope probe result in a near-field coupling between them. This coupling can enhance or reduce the local absorption by the nanoparticles depending on the position of atomic force microscope tip in three-dimensional space and direction of propagation of the surface evanescent wave. The position of the atomic force microscope's tip and spacing that maximize the absorption are identified. This concept can be used for selective heating of nanoparticles placed over a surface that enables precision manufacturing at nanometer scales.

Absorption efficiency profiles and localized surface plasmon resonance (LSPR) wavelengths are reported for metallic core-shell nanoparticles (NPs) placed over a BK7 glass substrate. A numerical study is performed with the vectorized version of the discrete dipole approximation with surface interactions (DDA-SI-v). Gold (Au) and silver (Ag) metallic components are used for the simulations of two different core-shell structures. Absorption enhancement and the hybrid modes of plasmon resonances of the core-shell structures are compared by using a measure that defines a size configuration. It is observed that a small volume fraction of the core sizes results in shell domination over the plasmon response. An additional study is conducted to discern the sensitivity of the refractive index of nanoparticles in different surrounding environments. With a selected core-shell size configuration of Ag-Au pairs, a significant absorption enhancement with a redshift of LSPR wavelength is observed for both Ag core-Au shell and Au core-Ag shell NPs. The absorption behavior of the bare metallic NPs and selected core-shell pairs in proximity to an external probe is also analyzed. The gallium phosphide (GaP) and silicon (Si) AFM tip usage are investigated with transverse electric (TE) and transverse magnetic (TM) wave polarizations. It is observed that the dominance of light polarization on the absorption enhancement of the NPs switches at different wavelengths, where the dielectric transition for tip materials occurs. These findings show the possible targeted uses of metallic core-shell nanoparticles in several areas such as nanomanufacturing, localized heating, bio-sensing, and material detection applications., Accepted for publication by Journal of Quantitative Spectroscopy and Radiative Transfer

A systematic approach based on the principles of supervised learning and design of experiments concepts is introduced to build a surrogate model for estimating the optical properties of fractal aggregates. The surrogate model is built on Gaussian process (GP) regression, and the input points for the GP regression are sampled with an adaptive sequential design algorithm. The covariance functions used are the squared exponential covariance function and the Matern covariance function both with Automatic Relevance Determination (ARD). The optical property considered is extinction efficiency of soot aggregates. The strengths and weaknesses of the proposed methodology are first tested with RDG-FA. Then, surrogate models are developed for the sampled points, for which the extinction efficiency is calculated by DDA. Four different uniformly gridded databases are also constructed for comparison. It is observed that the estimations based on the surrogate model designed with Matern covariance functions is superior to the estimations based on databases in terms of the accuracy of the estimations and the total number of input points they require. Finally, a preliminary surrogate model for S 11 is built to correct RDG-FA predictions with the aim of combining the speed of RDG-FA with the accuracy of DDA., 19 pages, 8 figures

A central concern of molecular dynamics simulations are the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system, and have generally been calculated using either predefined analytical formulas (classical) or quantum mechanical simulations (ab initio). The former can accurately reproduce only a selection of material properties, whereas the latter is restricted to short simulation times and small systems. Machine learning potentials have recently emerged as a third approach to model atomic interactions, and are purported to offer the accuracy of ab initio simulations with the speed of classical potentials. However, the performance of machine learning potentials depends crucially on the description of a local atomic environment. A set of invariant, orthogonal and differentiable descriptors for an atomic environment is proposed, implemented in a neural network potential for solid-state silicon, and tested in molecular dynamics simulations. Neural networks using the proposed descriptors are found to outperform ones using the Behler Parinello and SOAP descriptors currently in the literature., 23 Pages, 5 figures, 2 tables, journal article

Cooling performance enhancement of computer liquid cooling (LC) systems using hexagonal boron nitride (hBN)–water nanofluids is investigated experimentally. Particle volume fractions of 0.1–2% are considered at constant flow rates varying from 0.3 to 2 L/min for two different cold plates (CPs), with and without fins. A commercial closed-loop LC system is also tested to examine performance of hBN–water nanofluids at constant pumping power. It was observed that the thermal performance can be improved by using hBN nanofluids, and higher improvements are achieved for systems with limited convection rates.

Recently, machine learning potentials have been advanced as candidates to combine the high-accuracy of quantum mechanical simulations with the speed of classical interatomic potentials. A crucial component of a machine learning potential is the description of local atomic environments by some set of descriptors. These should ideally be continuous throughout the specified local atomic environment, twice-differentiable with respect to atomic positions and complete in the sense of containing all possible information about the neighborhood. An updated version of the recently proposed Spherical Bessel descriptors satisfies all three of these properties, and moreover is optimally complete in the sense of encoding all configurational information with the smallest possible number of descriptors. The Smooth Overlap of Atomic Position descriptors that are frequently visited in the literature and the Zernike descriptors that are built upon a similar basis are included into the discussion as being the natural counterparts of the Spherical Bessel descriptors, and shown to be incapable of satisfying the full list of core requirements for an accurate description. Aside being mathematically and physically superior, the Spherical Bessel descriptors have also the advantage of allowing machine learning potentials of comparable accuracy that require roughly an order of magnitude less computation time per evaluation than the Smooth Overlap of Atomic Position descriptors, which appear to be the common choice of descriptors in recent studies., Comment: 15 pages, 5 figures, under review for Journal of Chemical Physics

The convective heat transfer and flow behavior of graphene-water nanofluids are studied experimentally by focusing on transitional flow. Graphene-water nanofluids with different particle mass fractions (0.025, 0.1 and 0.2%) are produced following two-step method and using PVP as a surfactant. Thermo-physical characterization is performed by measuring viscosity and thermal conductivity of the nanofluids. Convection characteristics are experimentally studied from laminar to turbulent flow regimes. It is seen that pressure drop increases dramatically in the transition region, and laminar to turbulent transition shifts to lower Reynolds numbers with increasing nanoparticle concentration. The transition initiates at a Reynolds number of 2475 for water, while it initiates at 2315 for the nanofluid with 0.2% particle mass fraction. Increase in mean heat transfer coefficient and Nusselt numbers are nearly identical at different Reynolds numbers and axial positions along the test tube in the laminar flow for nanofluids and water due to dominance of conduction enhancement mechanisms on the heat transfer increase in laminar flow. Beyond laminar flow regime, enhancement of Nusselt number is observed indicating that thermophoresis and Brownian motion are more effective heat transfer augmentation mechanisms. The maximum heat transfer enhancement is observed as 36% for a Reynolds number of 3950.

Hydrodynamic and thermal modeling of nanofluid flow in a uniformly heated circular pipe is considered using single-phase models. Various single-phase models relying on Brownian and dispersion viscosity models are evaluated by comparing heat transfer coefficient, Nusselt number and friction factor with experimental results from literature. Single-phase models are capable of predicting heat transfer of nanofluids better when dispersion models are used. However, they fail to accurately predict surface shear stress when used with standard viscosity models. A new viscosity model based on dispersion viscosity is proposed to improve prediction accuracy of single-phase models for estimating the surface shear stress of laminar nanofluid flow. Results suggest that proposed single-phase dispersion model is capable of accurately predicting heat transfer coefficient and friction factor. (C) 2015 Elsevier Ltd. All rights reserved.

Thermal and rheological behaviors of the graphene-water nanofluids are investigated experimentally, focusing on the hysteresis phenomenon. Nanofluids with particle mass fraction from 0.1 to 2.0% are prepared by using PVP as surface-active material and ultrasonication. Morphological, material and stability characterization are carried out by STEM imaging, Raman spectroscopy, and zeta potential measurements. Rheological behavior of the prepared samples is analyzed at different temperatures and shear rates. Maximum viscosity increase is observed to be 45% for the 2.0% mass concentration at 25 °C. The relative viscosity does not change with temperature, unlike the absolute viscosity that decreases with temperature due to the viscosity change of the base fluid. Moreover, the relative viscosity is nearly constant at low concentrations for all shear rates, and it decreases with increasing shear rate for the mass fractions higher than 1.0%. Therefore, graphene-water nanofluids exhibit Newtonian behavior for particle mass fractions below 1.0% and shear thinning behavior at higher concentrations. Hysteresis is observed when increasing and reducing the shear rates within the same speed ranges for the samples higher than 1% particle mass fraction and above 40 °C. Thermal conductivity is enhanced with increasing concentration and maximum augmentation is observed to be 96% for a mass fraction of 2.0%.

Inverse design of thickness sensitive spectrally selective pigmented coatings that are used in absorbers of solar thermal collectors is considered. The objective is to maximize collection efficiency by achieving high absorptance at solar wavelengths and low emittance at the infrared (IR) wavelengths to minimize heat loss. Radiative properties of these coatings depend on coating thickness, pigment size, concentration, and the optical properties of binder and pigment materials, and a unified radiative transfer model of the pigmented coatings is developed in order to understand the effect of these parameters on the properties. The unified model (UM) relies on Lorenz–Mie theory (LMT) for independent scattering regime in conjunction with extended Hartel theory (EHT) to incorporate the multiple scattering effects, T-matrix method (TMM) for dependent scattering, and effective medium theory (EMT) for very small particles. A simplified version of the UM (SUM) ignoring dependent scattering is also developed for improving computational efficiency. Through the solution of the radiative transfer equation by the four flux method (FFM), spectral properties are predicted. The developed model is used in conjunction with inverse design for estimating design variables yielding the desired spectral emittance of the ideal coating. The nonlinear inverse design problem is solved by optimization by using simulated annealing (SA) method that is capable of finding global minimum regardless of initial guess.

Design of a spectrally selective filter based on one-dimensional Si/SiO2 layers is considered for improved performance of thermo-photovoltaic devices. Spectrally selective filters transmit only the convertible radiation from the emitter as non-convertible radiation leads to a reduction in cell efficiency due to heating. The presented Si/SiO2 based filter concept reflects the major part of the undesired range back to the emitter to minimize energy required for the process and it is adaptable to different types of cells and emitters with different temperatures since its cut-off wavelength can be tuned. While this study mainly focuses on InGaSb based thermo-photovoltaic cell, Si, GaSb, and Ga0.78In0.22As0.19Sb0.81 based cells are also examined. Transmittance of the structure is predicted by rigorous coupled wave approach. Genetic algorithni, which is a global optimization method, is used to find the best possible filter structure by considering the overall efficiency as an objective function that is maximized. The simulations show that significant enhancement in the overall system and device efficiency is possible by using such filters with TPV devices. The methodology described in this paper allows for an improved filter design procedure for selected applications. (C) 2017 Elsevier Ltd. All rights reserved.

Recently developed hexagonal boron nitride (hBN) containing nanofluids are relatively new class of materials, and increase in their thermal conductivity with respect to base fluid is relatively higher than that of viscosity. This study focuses on convective heat transfer characteristics of hBN-water nanofluids, and thermally developing laminar forced convection of hBN nanofluids in a horizontal copper pipe, subjected to constant heat flux boundary condition, is investigated experimentally. hBN-water nanofluids, with a particle volume concentration range of 0.1-1% are considered for a Reynolds numbers' range of 800-1700. Measured thermophysical properties of hBN-water based nanofluids are used in predicting the heat transfer behavior based on measurements. It is observed that the enhancement in the convective heat transfer coefficient of hBN-water nanofluids is proportional to the observed thermal conductivity enhancement. Therefore, there is no abnormal enhancement in the measured Nusselt number, and measured values are in good agreement with predictions by standard laminar thermally developing flow correlations. (C) 2017 Elsevier Ltd. All rights reserved.

Macroscopic modeling of hydrodynamic and thermal behavior of nanofluid flows at the entry region of uniformly heated pipe is studied. Single-phase models with and without thermal dispersion effect, Eulerian-Eulerian, and Eulerian-Mixture two-phase models are evaluated by comparing predicted convective heat transfer coefficients and friction factors with experimental results from literature. Solutions with two different velocity pressure coupling algorithms, Full Multiphase Coupled, and Phase Coupled Semi-Implicit Method for Pressure Linked Equations are also compared in terms of accuracy and computational cost. Dispersion model that uses velocity gradient to define dispersion conductivity is found to be more effective at entry region compared to other single-phase models. However, two-phase models predict convective heat transfer coefficient and friction factor more accurately at the entry region. Moreover, computational cost of Eulerian-Eulerian two-phase model can be reduced up to 50% by implementing Full Multiphase Coupled scheme. (C) 2014 Elsevier Masson SAS. All rights reserved.

Thermal transport in a water–Cu nanocolloid system was investigated using equilibrium molecular dynamics. A systematic analysis of the Green–Kubo calculations is presented to clarify the effect of simulation parameters. Several sources of error were identified and quantified for the thermal conductivity estimations, and the effect of the base fluid potential was investigated. Simulations were carried out with a single copper particle for different diameters and water potentials, and thermal enhancements exceeding both theoretical and experimental results were observed in parallel with some other studies in the literature. The anomalous Green–Kubo thermal enhancement results could be explained by the interfacial dynamics and the neglect of calibrating the interaction potential to satisfy the physically-observed energy flow at the interface.

Controlling the properties of the nanostructures requires use of proper characterization methods. There are many techniques available such as microscopy, time-resolved laser-induced-incandescence or light scattering methods that can be used for characterization of nanostructures. While direct observation such as microscopy is possible for some applications, the indirect characterization is carried out through measurements of structures’ emission or scattering behavior as in the laser-induced-incandescence and light scattering methods. Characterization of soot aggregates is well studied and is often classified as an inverse problem that can be formulated as a parameter estimation problem, where parameters defining the aggregate such as average size and number of nanoparticles are estimated. Estimations based on the scattering behavior can be questionable unless these parameters are directly observed by other methods. Instead of presenting a single estimate, Bayesian credible intervals would be a better option when indirect methods such as light scattering are used. The objective of this work is to investigate the strengths and weaknesses of the Bayesian approach based on numerical light scattering experiments considering soot aggregates as an example. Light scattering behavior of the soot aggregates is obtained with discrete dipole approximation, considering unpolarized light.

A new interlayer potential is developed for interlayer interactions of hexagonal boron nitride sheets, and its performance is compared with other potentials in the literature using molecular dynamics simulations. The proposed potential contains Coulombic and Lennard-Jones 6?12 terms, and is calibrated with recent experimental data including the hexagonal boron nitride interlayer distance and elastic constants. The potentials are evaluated by comparing the experimental and simulated values of interlayer distance, density, elastic constants, and thermal conductivity using non-equilibrium molecular dynamics. The proposed potential is found to be in reasonable agreement with experiments, and improves on earlier potentials in several respects. Simulated thermal conductivity values as a function of the number of layers and of temperature suggest that the proposed LJ 6?12 potential has the ability to predict some phonon behaviour during heat transport in the out-of-plane direction.

This study considers enhancing localized absorption by a gold nanoparticle (NP) placed over a substrate where an atomic force microscope (AFM) tip is in close proximity of the particle. The gold NP and AFM tip are interacting with a surface evanescent wave, resulting a near-field coupling between the tip and NP and consequently enhances the absorption. This concept can be used for selective heating of NPs placed over a surface that enables precise manufacturing at nanometer scales. Different tip positions are considered to identify the optimal tip location and the corresponding enhancement limits. The effects of these interactions on the absorption profiles of dielectric core/gold shell NPs are also studied. It is observed that using core–shell nanoparticles with a dielectric core leads to further enhancement of the absorption efficiency and a more uniform distribution of absorption over the shell. Discrete dipole approximation coupled with surface interactions (DDA-SI) is employed throughout the study, and it is vectorized to improve its computational efficiency. Center for Energy, Environment and Economy at Özyeğin University

Characterization of nanostructures using light scattering experiments without using polarization information and a priori particle size or number measurements is investigated through numerical experiments. The study focuses on particle clusters in the form of carbon nanoparticle aggregates that are generated with Filippov's particle-cluster algorithm. Seven cases of monodisperse aggregates with less than 30 nanoparticles with primary particle radius between 10 and 40 nm are investigated, together with one polydisperse case with lognormal particle size distribution. In all these cases, the scattering behavior of an ensemble of well separated, similar aggregates are represented by the behavior of a single aggregate considering orientation averaging using discrete dipole approximation. A database is developed and used for the solution of the direct problem considering the high computational time required for the solution. The inverse characterization problem is formulated as a least squares minimization. Use of Tabu Search algorithm along with gradient based Levenberg–Marquardt algorithm is investigated as problem topology is prone to multiple extrema. It is found that the proposed method relying on Tabu search algorithm is able to predict the particle size and number for monodisperse aggregates with effective radius larger than 20 nm using a UV light source at a wavelength of 266 nm. The proposed method also characterizes the polydisperse aggregate case with reasonable accuracy.

Hexagonal boron nitride (hBN) is a highly stable dielectric ceramic material that exhibits versatile properties such as, exceptionally high thermal conductivity and good chemical inertness. Due to its layered hexagonal crystal structure, hBN is the softest form among the other polymorphs of BN. Preparation, stability and thermophysical properties of hBN containing DI water, ethylene glycol (EG) and EG-DI water mixture (by volume 50%) based nanofluids are investigated. For this purpose a series of well dispersed, stable nanofluids, containing hBN nanoparticles with a mean diameter of 70 nm, are produced with a two-step method, relying on ultrasonication and use of surface active materials such as sodium dodecyl sulfate (SDS) and polyvinyl pyrrolidone (PVP). The effect of these surfactants on the base fluids' thermophysical properties is measured. The stability is evaluated by quantitative methods such as, time dependent zeta potential, and thermal conductivity measurements. Morphological characterization of nanofluids is completed by qualitative methods such as, SEM (Scanning "Electron Microscopy) and TEM (Transmission. Electron Microscopy). The thermal conductivity enhancement of nanofluids, with particle volume concentration varying between 0.03% and 3%, is investigated experimentally in accordance with their increase in viscosity. It is observed that the hBN nanofluids have remarkably higher thermal conductivity values than their corresponding base fluids, depending on the volume concentration of dispersed nanoparticles. Moreover, water based hBN nanofluids with relatively dilute particle suspensions, exhibits significant increase in thermal conductivity with respect to the viscosity increase. (C) 2016 Elsevier Inc. All rights reserved.

Thermal interfaces are encountered in many thermal management applications and interface materials are used to minimize thermal contact resistance resulting from solid–solid contact. For opto-electronic devices the quality of the thermal interface is critical for removing the generated heat for proper thermal management. Defects in the thermal interface introduce additional thermal resistance in the thermal path, and must be prevented. Detection of defects in the thermal interfaces becomes critical during the assembly process development. Imaging techniques such as X-ray computerized tomography, or scanning acoustic microscopy that require expensive equipment and significant processing time is necessary. Thermal tomography in conjunction to IR thermometry can be used as a lower cost alternative to these techniques. The feasibility of thermal tomography for non-destructive characterization of thermal interfaces is presented by considering different image reconstruction algorithms. The algorithms considered are the iterative perturbation algorithm, Levenberg–Marquardt algorithm and the regularized Newton–Gauss algorithm, and they were found to be capable of characterizing the thermal interface layer.

Due to copyright restrictions, the access to the full text of this article is only available via subscription. Intercoupling of an incident electric field in metal nanoparticles causes asymmetric distribution of surface charges, which eventuates in shifting of the surface plasmon resonance frequency. This feature can be used in tuning the surface plasmon resonance and controlling the light absorption in a desired wavelength. This work provides a theoretical study of the plasmonic properties of complex gold nanostructures on a dielectric substrate where the nanoparticles have different morphologies. For analysis, we have developed a discrete dipole approximation with surface interactions-z, which is the third version of the MATLAB-based DDA-SI toolbox. In this version, lower-upper decomposition of the interaction matrix is used as a preconditioning of the LSQR iterative solver. This method accelerates the DDA-SI calculations by decreasing the total number of iteration steps and decreases the relative residual to achieve more accurate results. In the analysis, nanostructures are assumed to be gold dimers, trimers, and quadrumers with different sizes and elongations of cubical or spherical geometries on a BK7 substrate. The results show that absorption spectra exhibit both red- and blueshifted plasmon resonances in array, depending on the particle shape and elongation. The cubic structure of gold array provides the highest absorption efficiency, while the spherical structures give wider bandwidth; the combination of these structures could be used to design a system with intended features. We demonstrate that the geometrical symmetry plays an important role in the plasmon resonance of gold arrays, and it is shifted when the symmetry of the array is broken. Center for Energy, Environment and Economy at Ozyeoin University

The aim of this study was to evaluate the diagnostic contribution of alveolar dead space fraction (AVDSf) measured using capnography in patients admitted with suspected pulmonary embolism (PE). A total of 58 patients who were admitted to our hospital with suspected PE between October 2006 and January 2008 were included in this study. All patients were assessed using the Wells clinical score, capnography, computed tomographic pulmonary angiography, D-dimer measurement, lower-extremity venous Doppler ultrasonography, and V/Q scintigraphy. Forty patients (69%) had PE based on computed tomographic pulmonary angiography findings. The AVDSf value with the highest sensitivity and specificity, which was at the same time statistically significant, was 0.09. This value was consistent with the AVDSf value obtained using receiver operating characteristic analysis. In our study, the sensitivity of capnography was 70%, with a specificity of 61.1%, positive predictive value of 80%, and negative predictive value of 47.8%. The use of AVDSf in combination with any of the several scoring systems that evaluate clinical likelihood of PE and D-dimer levels resulted in higher sensitivity and specificity rates for the diagnosis of PE.

The rapid thermal processing of semiconductor devices is very temperature sensitive and requires precise temperature measurement. Light pipe radiation thermometers are widely used for temperature control during manufacturing by industry, and there is concern about errors associated with light pipe measurements. Modeling in simplified systems has helped in understanding the signal transport process in light pipes and errors associated with measurements in the past. Considering the small sensor area compared to the size of the semiconductor wafer and the remaining system components, modeling of the complete system has not been done due to the computational demand. A reverse Monte Carlo model can be used efficiently to model the signal transported to the photodetector in conjunction with a thermal model of the system to better characterize the system. The proposed method is demonstrated in a full-scale instrumented system with a light pipe thermometer, and the results are compared against previously published measurements from the system.