Ruthenium(II) Arene Complexes Bearing Tris(pyrazolyl)methanesulfonate Capping Ligands.Electrochemistry, Spectroscopic, and X-ray Structural Characterization

Novel [Ru(L)(Tpms)]Cl and [Ru(L)(Tpms(Ph))]Cl complexes (L = p-cymene, benzene, or hexamethylbenzene, Tpms = tris(pyrazolyl)-methanesulfonate, Tpms(Ph) = tris(3-phenylpyrazoly)methanesulfonate) have been prepared by reaction of [Ru(L)(mu-Cl)(2)](2) with Li[Tpms] and Li[Tpms(Ph)], respectively. [Ru(p-cymene)(Tpms)]BF4 has been synthesized through a metathetic reaction of [Ru(p-cymene)(Tpms)]Cl with AgBF4. [RuCl(cod)(Tpms)] (cod = 1,5-cyclooctadiene) and [RuCl(cod)(Tpms(Ph))] are also reported, being obtained by reaction of [RuCl2(cod)(MeCN)(2)] with Li[Tpms] and Li[Tpms(Ph)], respectively. The structures of the complexes and the coordination modes of the ligands have been established by IR, NMR, and single-crystal X-ray diffraction (for [RuL(Tpms)]X (L = p-cymene or HMB, X = Cl; L = p-cymene, X = BF4)) studies. Electrochemical studies showed that each complex undergoes a single-electron R-II -> R-III oxidation at a potential measured by cyclic voltammetry, allowing to compare the electron-donor characters of the tris(pyrazolyl)methanesulfonate and arene ligands, and to estimate, for the first time, the values of the Lever E-L ligand parameter for Tmps(Ph), HMB, and cod.

Modelling a Rotating Shaft as an Elastically Restrained Bernoulli-Euler Beam

Industrial rotating machines may be exposed to severe dynamic excitations due to resonant working regimes. Dealing with the bending vibration, problem of a machine rotor, the shaft - and attached discs - can be simply modelled using the Bernoulli-Euler beam theory, as a continuous beam subjected to a specific set of boundary conditions. In this study, the authors recall Rayleigh's method to propose an iterative strategy, which allows for the determination of natural frequencies and mode shapes of continuous beams taking into account the effect of attached concentrated masses and rotational inertias, including different stiffness coefficients at the right and the left end sides. The algorithm starts with the exact solutions from Bernoulli-Euler's beam theory, which are then updated through Rayleigh's quotient parameters. Several loading cases are examined in comparison with the experimental data and examples are presented to illustrate the validity of the model and the accuracy of the obtained values.

Optoelectronic properties of a-Si(1-x)C(x)H films grown in hydrogen diluted silane-methane plasma

This work reports on the optoelectronic properties and device application of hydrogenated amorphous silicon carbide (a-Si(1-x)C(x):H) films grown by plasma-enhanced chemical vapour deposition (PECVD). The films with an optical bandgap ranging from about 1.8 to 2.0 eV were deposited in hydrogen diluted silane-methane plasma by varying the radio frequency power. Several n-i-p structures with an intrinsic a-Si(1-x)C(x):H layer of different optical gaps were also fabricated. The optimized devices exhibited a diode ideality factor of 1.4-1.8, and a leakage current of 190-470 pA/cm(2) at -5 V. The density of deep defect states in a-Si(1-x)C(x):H was estimated from the transient dark current measurements and correlated with the optical bandgap and carbon content. Urbach energies for the valence band tail were also determined by analyzing the spectral response within sub-bandgap energy range. (C) 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

a-SiH p-i-n structures with extreme i-layer thickness

We present measurements and numerical simulation of a-Si:H p-i-n detectors with a wide range of intrinsic layer thickness between 2 and 10 pm. Such a large active layer thickness is required in applications like elementary particle detectors or X-ray detectors. For large thickness and depending on the applied bias, we observe a sharp peak in the spectral response in the red region near 700 nm. Simulation results obtained with the program ASCA are in agreement with the measurement and permit the explanation of the experimental data. In thick samples holes recombine or are trapped before reaching the contacts, and the conduction mechanism is fully electron dominated. As a consequence, the peak position in the spectral response is located near the optical band gap of the a-Si:H i-layer. (C) 2009 Elsevier B.V. All rights reserved.

Image quality and dose analysis for a PA chest X-ray: comparison between AEC mode acquisition and manual mode using the 10 kVp ‘rule’

Purpose - To compare the image quality and effective dose applying the 10 kVp rule with manual mode acquisition and AEC mode in PA chest X-ray. Method - 68 images (with and without lesions) were acquired using an anthropomorphic chest phantom using a Wolverson Arcoma X-ray unit. These images were compared against a reference image using the 2 alternative forced choice (2AFC) method. The effective dose (E) was calculated using PCXMC software using the exposure parameters and the DAP. The exposure index (lgM provided by Agfa systems) was recorded. Results - Exposure time decreases more when applying the 10 kVp rule with manual mode (50%–28%) when compared with automatic mode (36%–23%). Statistical differences for E between several ionization chambers' combinations for AEC mode were found (p = 0.002). E is lower when using only the right AEC ionization chamber. Considering the image quality there are no statistical differences (p = 0.348) between the different ionization chambers' combinations for AEC mode for images with no lesions. Considering lgM values, it was demonstrated that they were higher when the AEC mode was used compared to the manual mode. It was also observed that lgM values obtained with AEC mode increased as kVp value went up. The image quality scores did not demonstrate statistical significant differences (p = 0.343) for the images with lesions comparing manual with AEC mode. Conclusion - In general the E is lower when manual mode is used. By using the right AEC ionising chamber under the lung the E will be the lowest in comparison to other ionising chambers. The use of the 10 kVp rule did not affect the visibility of the lesions or image quality.

A class of singular first order differential equations with applications in reaction-diffusion

Agências Financiadoras: FCT e MIUR

A survey in Portuguese X-ray fluoroscopy equipment: dose rates and image quality

X-ray fluoroscopy is essential in both diagnosis and medical intervention, although it may contribute to significant radiation doses to patients that have to be optimised and justified. Therefore, it is crucial to the patient to be exposed to the lowest achievable dose without compromising the image quality. The purpose of this study was to perform an analysis of the quality control measurements, particularly dose rates, contrast and spatial resolution of Portuguese fluoroscopy equipment and also to provide a contribution to the establishment of reference levels for the equipment performance parameters. Measurements carried out between 2007 and 2013 on 143 fluoroscopy equipment distributed by 34 nationwide health units were analysed. The measurements suggest that image quality and dose rates of Portuguese equipment are congruent with other studies, and in general, they are as per the Portuguese law. However, there is still a possibility of improvements intending optimisation at a national level.

Image quality and dose analysis in PA chest x-ray: comparison between AEC and manual mode acquisition using the 10 kVp rule

Purpose: To compare image quality and effective dose when the 10 kVp rule is applied with manual and AEC mode in PA chest X-ray. Methods and Materials: A total of 68 images (with and without lesions) were acquired of an anthropomorphic chest phantom in a Wolverson Arcoma X-ray unit. The images were evaluated against a reference image using image quality criteria and the 2 alternative forced choice (2 AFC) method by five radiographers. The effective dose was calculated using PCXMC software using the exposure parameters and DAP. The exposure index (lgM) was recorded. Results: Exposure time decreases considerably when applying the 10 kVp rule in manual mode (50%-28%) compared to AEC mode (36%-23%). Statistical differences for effective dose between several AEC modes were found (p=0.002). The effective dose is lower when using only the right AEC ionization chamber. Considering image quality, there are no statistical differences (p=0.348) between the different AEC modes for images with no lesions. Using a higher kVp value the lgM values will also increase. The lgM values showed significant statistical differences (p=0.000). The image quality scores did not present statistically significant differences (p=0.043) for the images with lesions when comparing manual with AEC modes. Conclusion: In general, the dose is lower in the manual mode. By using the right AEC ionising chamber the effective dose will be the lowest in comparison to other ionising chambers. The use of the 10 kVp rule did not affect the detectability of the lesions.

A balance between image quality and effective dose in orbital X-ray screening for ferromagnetic IOFBs: a pilot study

Purpose: To investigate whether standard X-ray acquisition factors for orbital radiographs are suitable for the detection of ferromagnetic intra-ocular foreign bodies in patients undergoing MRI. Method: 35 observers, at varied levels of education in radiography, attending a European Dose Optimisation EURASMUS Summer School were asked to score 24 images of varying acquisition factors against a clinical standard (reference image) using two alternative forced choice. The observers were provided with 12 questions and a 5 point Likert scale. Statistical tests were used to validate the scale, and scale reliability was also measured. The images which scored equal to, or better than, the reference image (36) were ranked alongside their corresponding effective dose (E), the image with the lowest dose equal to or better than the reference is considered the new optimum acquisition factors. Results: Four images emerged as equal to, or better than, the reference in terms of image quality. The images were then ranked in order of E. Only one image that scored the same as the reference had a lower dose. The reference image had a mean E of 3.31μSv, the image that scored the same had an E of 1.8μSv. Conclusion: Against the current clinical standard exposure factors of 70kVp, 20mAs and the use of an anti- scatter grid, one image proved to have a lower E whilst maintaining the same level of image quality and lesion visibility. It is suggested that the new exposure factors should be 60kVp, 20mAs and still include the use of an anti-scatter grid.

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.