Ink discrimination based on co-occurrence analysis of visible and infrared images

Inks found in Byzantine manuscripts are semitransparent pigments and their examination and analysis provide an invaluable source of information on the authenticity and dating of manuscripts and the number of authors involved. However, inks are difficult to characterize because their intensity depends on the amount of liquid spread during scripting and the reflective properties of the support. Most existing methods for the analysis of ink materials are based on destructive testing techniques that require the physicochemical sampling of data. Such methods cannot be widely used because of the historical and cultural value of the manuscripts. In this work we show that manuscript inks can be represented through a mixture of Gaussian functions and can be characterised using co-occurrence matrices

Ink recognition based on statistical classification methods

Statistical classification methods can be applied to images of historical manuscripts in order to characterize the various kinds of inks used. As these methods do not require destructive sampling they can be applied to the study of old and fragile manuscripts. Analysis of manuscript inks based on statistical analysis can be applied in situ, to provide important information for the authenticity, dating and origin of manuscripts. This paper describes a methodology and related algorithms used to interpret the photometric properties of inks and produce computational models which classify diverse types of inks found in Byzantine-era manuscripts. Various optical properties of these inks are extracted by the analysis of digital images taken in the visible and infrared regions of the electromagnetic spectrum. The inks are modelled based on their grey-level and colour information using a mixture of Gaussian functions and classified using Bayes' decision rule

Polarization power spectra and dust cloud morphology

In the framework of studies of the CMB polarization and its Galactic foregrounds, the angular power spectra of thermal dust polarization maps have revealed an intriguing E/B asymmetry and a positive TE correlation. In interpretation studies of these observations, magnetized ISM dust clouds have been treated as filamentary structures only; however, sheet-like shapes are also supported by observational and theoretical evidence. In this work, we study the influence of cloud shape and its connection to the local magnetic field on angular power spectra of thermal dust polarization maps. We simulate realistic filament-like and sheet-like interstellar clouds, and generate synthetic maps of their thermal dust polarized emission using the software $Asterion$. We compute their polarization power spectra in multipole range $\ell \in [100,500]$ and quantify the E/B power asymmetry through the $R_{EB}$ ratio, and the correlation coefficient $r^{TE}$ between T and E modes. We quantify the dependence of $R_{EB}$ and $r^{TE}$ values on the offset angle (between longest cloud axis and magnetic field) and inclination angle (between line-of-sight and magnetic field) for both cloud shapes embedded either in a regular or a turbulent magnetic field. We find that both cloud shapes cover the same regions of the ($R_{EB}$, $r^{TE}$) parameter space. The dependence on inclination and offset angles are similar for both shapes although sheet-like structures generally show larger scatter. In addition to the known dependence on the offset angle, we find a strong dependence of $R_{EB}$ and $r^{TE}$ on the inclination angle. The fact that filament-like and sheet-like structures may lead to polarization power spectra with similar ($R_{EB}$, $r^{TE}$) values complicates their interpretation. In future analyses, this degeneracy should be accounted for as well as the connection to the magnetic field geometry.Comment: 16 pages, 24 figures. Accepted for publication by A&

Anticommutative extension of the Adler map

We construct a noncommutative (Grassmann) extension of the well-known Adler Yang–Baxter map. It satisfies the Yang–Baxter equation, it is reversible and birational. Our extension preserves all the properties of the original map except the involutivity

Prioritization of responsive maintenance tasks via machine learning-based inference

Maintenance task prioritization is essential for allocating resources. It is estimated that almost 1/3 of the maintenance cost is wasted to unnecessary activities. Task prioritization is based on risk assessment that takes into account the probability of failure and the criticality of an asset. The criticality analysis is defined by the asset owner based on several parameters, among them safety, downtime cost, productivity, whilst the probability of failure is determined based on deterioration models, regular manual inspections, or installed sensors. Currently, the latter is an extremely complicated and labour intensive procedure, when multiple and different types of assets need to be managed. This paper proposes an innovative method that exploits the advances in mobile communications, social networking, Internet of Things and machine learning to address this shortcoming. This approach brings building elements and assets online using asset tags with an online ‘asset profile’ linked to it. Users of assets are able to scan these tags using a mobile phone app to not only see the information about those assets, but also enter ‘comments’ describing issues and problems on the profiles. These comments are processed through machine learning-based inference methods to estimate the probability that a failure has occurred. This paper validates the proposed method using historical data collected from the Estate Management, of the University of CambridgeInnovate U

Role of atmospheric indices in describing shoreline variability along the Atlantic coast of Europe

The data set consists of time series atmospheric indicates, wave conditions and beach morphology for three sites on the Atlantic coast of EuropeThe project investigate the connections between atmospheric indicates, wave conditions and beach change over a 15-20 year period for three beach on the Atlantic coast of Europ

Scoring functions and enrichment: a case study on Hsp90.

BACKGROUND: The need for fast and accurate scoring functions has been driven by the increased use of in silico virtual screening twinned with high-throughput screening as a method to rapidly identify potential candidates in the early stages of drug development. We examine the ability of some the most common scoring functions (GOLD, ChemScore, DOCK, PMF, BLEEP and Consensus) to discriminate correctly and efficiently between active and non-active compounds among a library of approximately 3,600 diverse decoy compounds in a virtual screening experiment against heat shock protein 90 (Hsp90). RESULTS: Firstly, we investigated two ranking methodologies, GOLDrank and BestScorerank. GOLDrank is based on ranks generated using GOLD. The various scoring functions, GOLD, ChemScore, DOCK, PMF, BLEEP and Consensus, are applied to the pose ranked number one by GOLD for that ligand. BestScorerank uses multiple poses for each ligand and independently chooses the best ranked pose of the ligand according to each different scoring function. Secondly, we considered the effect of introducing the Thr184 hydrogen bond tether to guide the docking process towards a particular solution, and its effect on enrichment. Thirdly, we considered normalisation to account for the known bias of scoring functions to select larger molecules. All the scoring functions gave fairly similar enrichments, with the exception of PMF which was consistently the poorest performer. In most cases, GOLD was marginally the best performing individual function; the Consensus score usually performed similarly to the best single scoring function. Our best results were obtained using the Thr184 tether in combination with the BestScorerank protocol and normalisation for molecular weight. For that particular combination, DOCK was the best individual function; DOCK recovered 90% of the actives in the top 10% of the ranked list; Consensus similarly recovered 89% of the actives in its top 10%. CONCLUSION: Overall, we demonstrate the validity of virtual screening as a method for identifying new leads from a pool of ligands with similar physicochemical properties and we believe that the outcome of this study provides useful insight into the setting up of a suitable docking and scoring protocol, resulting in enrichment of 'target active' compounds

Cyclic voltammetry peaks due to deep level traps in Si nanowire array electroes

When metal-assisted chemical etching (MACE) is used to increase the effective surface area of Si electrodes for electrochemical capacitors, it is often found that the cyclic voltammetry characteristics contain anodic and cathodic peaks. We link these peaks to the charging-discharging dynamics of deep level traps within the nanowire system. The trap levels are associated with the use of Ag in the MACE process that can leave minute amounts of Ag residue within the nanowire system to interact with the H2O layer surrounding the nanowires in a room temperature ionic liquid. The influence of the traps can be removed by shifting the Fermi level away from the trap levels via spin-on doping. These results in lower capacitance values but improved charge-discharge cycling behavior. Low-frequency noise measurements proof the presence or absence of these deep level traps

The Role of School Discipline from the Students’ Point of View

Regarding the topic of discipline management in the educational practice, there are legitimate concerns and many pedagogical questions that need to be addressed, given that the attainment of discipline is a significant issue in schools. The main purpose of this research is to determine whether primary and secondary school students have comprehended the role of discipline and rules in school and, in particular, in educational practice. In addition, related issues are investigated, such as the student’s undisciplined or problematic behavior, the causes of indiscipline phenomena, as well as, the pedagogical means available to the teacher for creating propitious conditions of communication and relationship with the students, which contribute to the effective management of both the educational process and the challenging matters related to the attainment of classroom discipline and in general, of school discipline. As it is indicated by the findings of this research, the responses of the students of primary and secondary school enlighten the questions addressed in the questionnaire. This paper concludes with the research outcomes

Stress inhomogeneity effect on fluid-induced fracture behavior into weakly consolidated granular systems.

We study the effect of stress inhomogeneity on the behavior of fluid-driven fracture development in weakly consolidated granular systems. Using numerical models we investigate the change in fracture growth rate and fracture pattern structure in unconsolidated granular packs (also referred to as soft-sands) as a function of the change in the confining stresses applied to the system. Soft-sands do not usually behave like brittle, linear elastic materials, and as a consequence, poroelastic models are often not applicable to describe their behavior. By making a distinction between "cohesive" and "compressive" grain-grain contact forces depending on their magnitude, we propose an expression that describes the fluid opening pressure as a function of the mean value and the standard deviation of the "compressive stress" distribution. We also show that the standard deviation of this distribution can be related with the extent to which fracture "branches" reach into the material.BP International Centre for Advanced Materials (BP-ICAM