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The structural parameters of a second low-temperature form of KZnPO4 have been refined using Rietveld analysis of X-ray powder diffraction (XRPD) data. This form of KZnPO4 is isostructural with NH4ZnPO4I and has previously been denoted as KZnPO4II. This article uses the notation δ-KZnPO4, to be consistent with the α, β, and γ notation commonly used for other KZnPO4 phases.
We review a combinatoric approach to the Hodge conjecture for Fermat varieties and announce new cases where the conjecture is true. We show the Hodge conjecture for Fermat fourfolds $ {X}_m^4 $ of degree m ≤ 100 coprime to 6, and also prove the conjecture for $ {X}_{21}^n $ and $ {X}_{27}^n $, for all n.
In this study we compared radiation dose received by organs at risk (OARs) after breast conservation surgery(BCS) and mastectomy in patients with left breast cancer.
Materials and methods
Total 30 patients, 15 each of BCS and mastectomy were included in this study. Planning Computerised Tomography (CT) was done for each patient. Chest wall, whole breast, heart, lungs, LAD, proximal and distal LAD, and contra lateral breast was contoured for each patient. Radiotherapy plans were made by standard tangent field. Dose prescribed was 40Gy/16#/3 weeks. Mean heart dose, LAD, proximal and distal LAD, mean and V5 of right lung, and mean, V5, V10 and V20 of left lung, mean dose and V2 of contra lateral breast were calculated for each patient and compared between BCS and mastectomy patients using student’s T test.
Results
Mean doses to the heart, LAD, proximal LAD and distal LAD were 3.364Gy, 16.06Gy, 2.7Gy, 27.5Gy; and 4.219Gy, 14.653Gy, 4.306Gy, 24.6Gy, respectively for mastectomy and BCS patients. Left lung mean dose, V5, V10 and V20 were 5.96Gy, 16%, 14%, 12.4%; and 7.69Gy, 21%, 18% and 16% in mastectomy and BCS patients, respectively. There was no statistical significant difference in the doses to the heart and left lung between mastectomy and BCS. Mean dose to the right lung was significantly less in mastectomy as compared to BCS, 0.29Gy vs. 0.51Gy, respectively (p = 0.007). Mean dose to the opposite breast was significantly lower in patients with mastectomy than BCS (0.54Gy Vs 0.37Gy, p = 0.007). The dose to the distal LAD was significantly higher than proximal LAD both in BCS (24.6Gy Vs 4.3Gy, p = <0.0001) and mastectomy (27.5Gy Vs 2.7Gy, p = <0.0001) patients.
Conclusion
There was no difference in doses received by heart and left lung between BCS and mastectomy patients. Mean doses to the right lung and breast were significantly less in mastectomy patients.
For the measurement of flow-induced microrotations in flows utilizing the depolarization of phosphorescence anisotropy, suitable luminophores are crucial. The present work examines dyes of the xanthene family, namely Rhodamine B, Eosin Y and Erythrosine B. Both in solution and incorporated in particles, the dyes are examined regarding their luminescent lifetimes and their quantum yield. In an oxygen-rich environment at room temperature, all dyes exhibit lifetimes in the sub-microsecond range and a low intensity signal, making them suitable for sensing fast rotations with sensitive acquisition systems.
The crystal structure of donepezil hydrochloride, form III, has been solved with FOX using laboratory powder diffraction data previously submitted to and published in the Powder Diffraction File. Rietveld refinement with GSAS yielded monoclinic lattice parameters of a = 14.3662(9) Å, b = 11.8384(6) Å, c = 13.5572(7) Å, and β = 107.7560(26)° (C24H30ClNO3, Z = 4, space group P21/c). The Rietveld-refined structure was compared to a density functional theory (DFT)-optimized structure, and the structures exhibit excellent agreement. Layers of donepezil molecules parallel to the (101) planes are maintained by columns of chloride anions along the b-axis, where each chloride anion hydrogen bonds to three donepezil molecules each.
Quaternary selenide, Pb4In2.6Bi3.4Se13 (x = 2.4 member of the Pb4(InxBi6-xSe13 solid solution), was synthesized by a solid-state technique, and its structure was determined using powder X-ray diffraction (XRD). Pb4In2.6Bi3.4Se13 crystallizes in the orthorhombic space group Pbam (No. 55) with Z = 4. Lattice parameters and calculated density were determined to be a = 22.152(5) Å, b = 27.454(5) Å, and c = 4.1354(6) Å, V = 2515.0(11) Å3, and Dx = 7.490 g cm−3. The structure consists of Z-shaped ribbon units and corner-shared infinite one-dimensional [InSe4]∞ chains running parallel to the c-axis. The chains and ribbons are further connected by Pb atoms to form a three-dimensional network. Pb atoms are situated in the center of bicapped trigonal prisms. The compound exhibits a semiconductor feature. The Seebeck coefficient of Pb4In2.6Bi3.4Se13 was found to be −180 μV K−1 at 295 K and −380 μV K−1 at 600 K. Combining the values of Seebeck coefficient, electrical conductivity, and thermal conductivity yield a figure of merit, ZT, of about 0.175 at 700 K. The powder XRD pattern of Pb4In2.6Bi3.4Se13 was also determined.
The crystal structure of daclatasvir dihydrochloride Form N-2 (Daklinza®) has been refined using synchrotron X-ray powder diffraction data and optimized using density functional theory techniques. Daclatasvir dihydrochloride, Form N-2, crystallizes in space group P1 (#1) with a = 7.54808 (15), b = 9.5566 (5), c = 16.2641 (11) Å, α = 74.0642 (24), β = 84.0026 (13), γ = 70.6322 (5)°, V = 1064.150(11) Å3, and Z = 1. The hydrogen bonds were identified and quantified. Strong N–H⋯Cl hydrogen bonds link the cations and anions in chains along the a-axis. The powder pattern has been submitted to ICDD® for inclusion in the Powder Diffraction File™ (PDF®).
The crystal structure of varenicline hydrogen tartrate Form B (Chantix®) has been refined using synchrotron X-ray powder diffraction data and optimized using density functional techniques. Varenicline hydrogen tartrate Form B crystallizes in space group P212121 (#19) with a = 7.07616(2), b = 7.78357(2), c = 29.86149(7) Å, V = 1644.706(6) Å3, and Z = 4. The hydrogen bonds were identified and quantified. Hydrogen bonds link the cations and anions in zig-zag chains along the b-axis. The powder pattern has been submitted to ICDD® for inclusion in the Powder Diffraction File™ (PDF®).
Developing agents capable of commonsense reasoning is an important goal in Artificial Intelligence (AI) research. Because commonsense is broadly defined, a computational theory that can formally categorize the various kinds of commonsense knowledge is critical for enabling fundamental research in this area. In a recent book, Gordon and Hobbs described such a categorization, argued to be reasonably complete. However, the theory’s reliability has not been independently evaluated through human annotator judgments. This paper describes such an experimental study, whereby annotations were elicited across a subset of eight foundational categories proposed in the original Gordon-Hobbs theory. We avoid bias by eliciting annotations on 200 sentences from a commonsense benchmark dataset independently developed by an external organization. The results show that, while humans agree on relatively concrete categories like time and space, they disagree on more abstract concepts. The implications of these findings are briefly discussed.
The crystal structure of palbociclib isethionate has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Palbociclib isethionate crystallizes in space group P-1 (#2) with a = 8.71334(4), b = 9.32119(6), c = 17.73725(18) Å, α = 80.0260(5), β = 82.3579(3), γ = 76.1561(1)°, V = 1371.282(4) Å3, and Z = 2. The crystal structure is dominated by cation⋯anion and cation⋯cation hydrogen bonds, which result in layers roughly parallel to the (104) plane. Both hydrogen atoms on the protonated nitrogen atom of the pyrimidine ring participate in strong hydrogen bonds to the anions. One proton binds to the sulfonate group, while the other bonds to the hydroxyl group of the isethionate anion. The hydroxyl group of the anion acts as a donor to a ketone oxygen atom in the cation. There are also strong N–H⋯N hydrogen bonds, which occur in pairs linking the cations into dimers with rings having a graph set R2,2(8). The powder pattern has been submitted to ICDD® for inclusion in the Powder Diffraction File™.
The direct derivation (DD) method is a technique for quantitative phase analysis (QPA). It can be characterized by the use of the total sums of scattered/diffracted intensities from individual components as the observed data. The crystal structure parameters are required when we calculate the intensities of reflections or diffraction patterns. Intensity can, however, be calculated only with the chemical composition data if it is not of individual reflections but of a total sum of diffracted/scattered intensities for that material. Furthermore, it can be given in a form of the scattered intensity per unit weight. Therefore, we can calculate the weight proportion of a component material by dividing the total sum of observed scattered/diffracted intensities by the scattered intensity per unit weight. The chemical composition data of samples under investigation are known in almost all cases at the stage of QPA. Thus, a technical problem is how to separate the observed diffraction pattern of a mixture into individual component patterns. Various pattern decomposition techniques currently available can be used for separating the pattern of a mixture. In this report, the theoretical background of the DD method and various techniques for pattern decompositions are reviewed along with the examples of applications.
This brief introductory chapter outlines the broad coverage of the book and its intended contribution in the context of other available sources. It is recognized that there are many excellent books covering the “mechanics of materials,” often with a strong bias towards metals, but relatively few that are focused strongly on testing procedures designed to reveal details about how they deform plastically. There are in fact many subtleties concerning metal plasticity and the information about it obtainable via various types of test. No attempt is made in this chapter to convey any of these, but the scene is set in terms of outlining the absolute basics of elastic and plastic deformation.
Hardness test procedures of various types have been in use for many decades. They are usually quick and easy to carry out, the equipment required is relatively simple and cheap, and there are portable machines that allow in situ measurements to be made on components in service. The volume being tested is relatively small, so it’s possible to map the hardness number across surfaces, exploring local variations, and to obtain values from thin surface layers and coatings. The main problem with hardness is that it’s not a well-defined property. The value obtained during testing of a given sample is different for different types of test, and also for the same test with different conditions. Identical hardness numbers can be obtained from materials exhibiting a wide range of yielding and work hardening characteristics. The reasons for this are well established. There have been many attempts to extract meaningful plasticity parameters, particularly the yield stress, from hardness numbers, but these are mostly based on neglect of work hardening. In practice, materials that exhibit no work hardening at all are rare and indeed quantification of the work hardening behavior of a metal is a central objective of plasticity testing. The status of hardness testing is thus one of being a technique that is convenient and widely used, but the results obtained from it should be regarded as no better than semi-quantitative. There are procedures and protocols in which they are accorded a higher significance than this, but this is an unsound approach.
Testing in (uniaxial) compression is sometimes an attractive alternative to tensile testing. Specimens can be simpler in shape and smaller, since there is no gripping requirement. The key question is whether corresponding information can be obtained. In general, it can, but there is sometimes a perception that at least some materials behave differently under compression – i.e. that there is tensile-compressive asymmetry in their response. In fact, this is largely a myth: at least in the majority of cases, the underlying plasticity response is symmetrical (and indeed the von Mises (deviatoric) stress, which is normally taken to be the determinant of the response, is identical in the two cases). However, there are important caveats to append to this statement. For example, if the material response is indeed dependent on the hydrostatic component of the stress, as it might be for porous materials and for those in which a phase transformation occurs during loading, then asymmetry is possible. Also, while the underlying plasticity response is usually the same, the compressive stress–strain curve is often affected by friction between sample and platen (leading to barreling). Conversely, the necking that is likely to affect the tensile curve cannot occur in compression, although some kind of buckling or shearing instability is possible. It’s also important to distinguish the concept of tension/compression asymmetry from that of the Bauschinger effect (a sample pre-loaded in tension exhibiting a different response if then loaded in compression).
Mechanical testing on a very fine scale, particularly indentation, has become extremely popular. Sophisticated equipment has been developed, often with accompanying software that facilitates the extraction of properties such as stiffness, hardness and other plasticity parameters. The region being tested can be very small – down to sub-micron dimensions. However, strong caveats should be noted concerning such measurements, particularly relating to plasticity. Some of these concern various potential sources of error, such as the effects of surface roughness, oxide films, uncertainty about the precise geometry of the indenter tip etc. Moreover, even if these can be largely eliminated, extraneous effects tend to arise when (plastically) deforming a small region that is constrained by surrounding (elastic) material. They are often grouped together under the heading of “size effects,” with a clear tendency observed for material to appear harder as the scale of the testing is reduced. Various explanations for this have been put forward, some based on dislocation characteristics, but understanding is incomplete and compensating for them in a systematic way does not appear to be viable. A similar level of uncertainty surrounds the outcome of fine scale uniaxial compression testing, although the conditions, and the sources of error, are rather different from those during nanoindentation. Despite the attractions of these techniques, and the extensive work done with them, they are thus of limited use for the extraction of meaningful mechanical properties (related to plasticity).
Comprehensive treatment of metal plasticity requires an understanding of the fundamental nature of stresses and strains. A stress can be understood at a basic level as a force per unit area on which it acts, while a strain is an extension divided by an original length. However, the limitations of these definitions rapidly become clear when considering anything other than very simple loading situations. Analysis of various practical situations can in fact be rigorously implemented without becoming embroiled in mathematical complexity, most commonly via usage of commercial (finite element) numerical modeling packages. However, there are various issues involved in such treatments, which need to be appreciated by practitioners if outcomes are to be understood in detail. This chapter covers the necessary fundamentals, relating to stresses and strains, and to their relationship during elastic (reversible) deformation. How this relationship becomes modified when the material undergoes plastic (permanent) deformation is covered in the following chapter.