Synthesis, characterization and antimicrobial effect of Cu(ii) and Zn(ii) compounds derived from 1,2,4-triazoles

Compounds of Cu(II) and Zn(II) with 3-R-1H-1,2,4-triazole-5-amine {R = methyl (mta), phenyl (pta)} were prepared and characterized by infrared spectroscopy (IR), multinuclear NMR (1H, 13C), electronic spectroscopy (UV-VIS), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), microanalyses and melting point. Dimeric, monomeric, and polymeric materials were synthesized in this work. The Zn(II)-1,2,4-triazole derivatives have the metal ion at the center of two geometric arrangements, being a tetrahedron for the complex-7 and 9, and an octahedron for the complex-8. The only polymeric material, complex-7, was characterized by the DSC analytical data. The Decomposition reaction of complex-8 in chloroform produced the complex-9, a aminoguanidine derivative, and the Zn(II)-benzoate compound. The IR and UV-VIS of Cu(II)-mta derivatives revealed two possible geometric patterns for the metallic ion; a distorted bipyramidal trigonal geometry for compounds 10 and 11 in solution, and in the solid state, the same geometry for complex-10, but for complex-11, the IR data suggest a distorted octahedral geometry. The biological assay of the 1,2,4-triazole compounds and their metal derivatives against Gram-positive and Gram-negative bacteria shown the compounds of Zn(II) as the only active materials with values of MIC within the range of 133.5 µM (83.3 µg / mL) to 360.7 µM (166.6 µg / mL).

Formal grammar methodology for digital visualization of Islamic geometric patterns

In the oriental practice of art and architecture, and among the regions under their influence, Islamic geometric patterns (IGPs) have been widely used, not only due to aesthetics and decoration but also to make it possible to cover wide flat surfaces, curved surface of domes, and perforated surfaces of window and partitions, with perfectly tessellated shapes. However, with advances in time and technology, these techniques could not connect to the new technologies and benefit from the capacities of digitalization. Recent progress in science and technology tends to open new doors to study geometrical patterns by digitalizing the old ones and developing new variations. This study looks at formal grammar and computer science to introduce a new approach to digital visualization of available IGPs, particularly, star patterns. We investigate the potentials of developing a re-writing system for simulation of IGPs to provide a flexible platform, which allows introducing IGP to CAD/CAM software without previous knowledge on their design or drawing techniques. This methodology allows designers to directly develop various scenarios of IGP applications and implement them on related CAD/CAM tools. Formal language and grammar theories, based on applied mathematics are contributing to the advancements of computer science and digital modeling. They can provide an opportunity to express relational definition and written equivalents of the geometries by using strings and symbols. It is supposed that by using the formal grammar frameworks, certain languages could be developed to visualize IGPs in a machine-friendly way, and consequently, this computational interpretation of IGPs facilitates their application and further developments, for example, regards to digital fabrication. The presented method of IGP visualization is developed as a C#-based add-on for Grasshopper in Rhino3D, one of the main modeling tools used by architects and product designers.

Intelligent Restoration of Historical Parametric Geometric Patterns by Zernike Moments and Neural Networks

Historical Islamic ornaments include a fantastic treasury of geometric and mathematical algorithms. Inevitably, restoration of these ornaments in periodic patterns consisting of repeated elements has been faced following and substituting the other available similar ingredients instead of vanished parts. Still, the prediction of parametric, quasi, or non-periodic patterns, where components are not identical, needs to be carried out in a more challenging process than the periodic ones due to shape, scale, or angle of rotation alteration. Intelligent restoration could facilitate the forecasting of damaged parts in such geometric patterns that an algorithm has changed their geometric characteristics. In some architectural heritage, geometric patterns include a parametric algorithm like parametric patterns in the ceiling of Sheikh Lotfollahmosque in Isfahan, Iran, and the dominant structure of Persian domes Karbandi. In this article, the aim is to propose a new method for the smart restoration of the parametric geometric patterns in which, by having access to the image of the existing patterns, the vanished parts could be reconstructed spontaneously. Our approach is based on image processing by detecting boundaries of deterioration, finding every individual element, and extracting features of detected individual patterns via Zernike moments. The order of individual patterns starts from the farthest pattern to detected deterioration. Then by creating a time series, the Back-propagation neural network would be trained by extracted features, and the vanished patterns’ features could be predicted and reconstructed. Eventually, the reconstructed and real patterns are compared to determine differences between them by mean-squared error and to evaluate the performance of our method. To validate the process, a parametric geometric pattern is designed by the assumption that some parts are disappeared. The proposed method’s results, in this case, hold an efficient performance with the accuracy of 92.99%. Furthermore, Sheikh Lotfollah’s patterns and Naseredin Mirza mansion’s patterns as two real cases are tested by the proposed method, representing reliable and suitable performance results.

Likeness-making and the evolution of cognition

Paleontological evidence suggests that human artefacts with intentional markings might have originated already in the Lower Paleolithic, up to 500.000 years ago and well before the advent of ‘behavioural modernity’. These markings apparently did not serve instrumental, tool-like functions, nor do they appear to be forms of figurative art. Instead, they display abstract geometric patterns that potentially testify to an emerging ability of symbol use. In a variation on Ian Hacking’s speculative account of the possible role of “likeness-making” in the evolution of human cognition and language, this essay explores the central role that the embodied processes of making and the collective practices of using such artefacts might have played in early human cognitive evolution. Two paradigmatic findings of Lower Paleolithic artefacts are discussed as tentative evidence of likenesses acting as material scaffolds in the emergence of symbolic reference-making. They might provide the link between basic abilities of mimesis and imitation and the development of modern language and thought.

(De)Constructing the Madonna’s Cloth of Honor

The present study centers on pieced textiles included in Marian paintings of the Proto-Renaissance era rendered in Tuscany. The complex geometric patterns of these cloths mimic those found in the Islamic textiles that were then being imported into Europe, consumed by the aristocracy, and later imitated by Italian cloth makers. On a basic level, their colors and patterning reference the virtues of the Virgin, her mission to bring about the Incarnation of Christ, her selflessness, virtuous character, and majesty. They also contribute to her humanization since these are material objects that belonged in the aristocratic domestic setting and which were familiar to the patrons who paid for the works. On a deeper level, they provide complex layers of meaning, some of which derive from Moorish iconography. They reference the perfection of God’s creation and the promise of an affable afterlife. They also evoke the remote lands where the lives of the Virgin and Christ unfolded. By inserting pieced cloths into Marian iconography, artists were following a long established tradition of utilizing the piecing technique in Early Medieval sacred practice, an issue that until now has not been recognized.

Atypical and Non-spontaneous Attentional Control in “just Look” Tasks for Face Recognition in ASD Children Revealed by Gaze Tracking Pupillometry

We hypothesized that abnormalities in social interaction and executive function may be related to fluctuations in pupil diameter, which reflect norepinephrine activity in terms of attentional function. We adopted “just look” tasks to examine spontaneous changes in attention. Twenty children with autism spectrum disorder (ASD) and 39 typically developing (TD) controls participated. Intragroup comparisons of differences in pupil diameter changes during a shift from a scrambled image to the original image (task 1-a), fixation on faces, letters, and geometric patterns (task 1-b), and pupil diameter changes during a shift from a nonsense image to a face-like image (task 2) were performed. In task 1-a, ASD children had prolonged pupil dilation after the shift in images, whereas the pupil contracted in TD children, indicating deficits in attentional disengagement in ASD children. In task 1-b, ASD children preferred geometric patterns over faces. In task 2, the rate of pupillary dilatation during the shift in images was lower in ASD children than in TD children. Therefore, ASD children appear to have abnormalities in spontaneous attention to faces, which function automatically in TD children. In conclusion, atypical attentional function may contribute to the manifestation of abnormalities in social interaction and executive control in ASD.

Uses for Incomplete Ammonite Sutures: Lateral Lobe and Second Saddle as Markers of Sutural Complexity

Ammonoid sutures are geometric patterns formed by the intersection of the septa and the shell wall, and have long been a diagnostic tool for ammonite researchers for such applications as species identification, taxonomic relationships, ontogenetic change, functional and evolutionary morphology, determination of ecological niche, and other aspects of ammonoid paleobiology. Researchers interested in a variety of paleobiological questions related to ammonoids have almost always required access to the entire hemisuture. Without access to specimens in museum or institutional collections, researchers must rely on previously published illustrations and photographs of ammonoid sutures. However, due to the perspective in photographs, distortion of the marginal elements of suture geometry occurs due to shell curvature near the venter and umbilicus when photographed in profile. The revised approach described here, which we refer to as the Lateral Lobe Saddle, or LLS approach, makes use of only the lateral lobe and second saddle S2 (lateral lobe-second saddle pairs, or LLS) which lie in the central, mid-whorl undistorted sector of a suture line as viewed in lateral, profile shell photos and illustrations. The factors by which fractal dimension of LLS data convert to fractal dimension of the standard hemisuture measurements are largely consistent within genera. The LLS method’s non-requirement of a full hemisuture also facilitates comparisons among sutures within an ontogenetic sequence, or sutures from multiple ammonite taxa where ventral and umbilical sutural elements are hidden by whorl overlap or poor preservation.

Replacing Chemical Intuition by Machine Learning: a Mixed Design of Experiments - Reinforcement Learning Approach to the Construction of Training Sets for Model Hamiltonians

Model Hamiltonians based on the so-called cluster expansion (CE), which consist of a linear fit of parameters corresponding to geometric patterns, provide an efficient and rigorous means to quickly evaluate the energy of diverse arrangements of adsorbate mixtures on reactive surfaces as typically relevant for heterogeneous catalysis. However, establishing the model Hamiltonian is a tedious task, requiring the construction and optimization of many geometries. Today, most of these geometries are constructed by hand, based on chemical intuition or random choices. Hence, the quality of the training set is unlikely to be optimal and its construction is not reproducible. Herein, we propose a reformulation of the construction of the training set as a strategy-based game, aiming at an efficient exploration of the relevant patterns constituting the model Hamiltonian. Based on this reformulation, we exploit a typical active learning solution for machine-learning such a strategy game: an upper confidence tree (UCT) based framework. However, in contrast to standard games, evaluating the true score is computationally expensive, as it requires a costly geometry optimization. Hence, we augment the UCT with a pre-exploration step inspired by the variance-based Design of Experiments (DoE) methods. This novel mixed UCT+DoE framework allows to automatically construct a well adapted training set, minimizing computational cost and user-intervention. As a proof of principle, we apply our UCT+DoE approach on the CO oxidation reaction on Pd(111), for which a relevant model Hamiltonian has been established previously. The results demonstrate the effectiveness of the custom built UCT and its significant benefits on a DoE-based approach.