Adaptasi Font Huruf Latin dari Karakter Visual Tipografi Aksara Makassar ‘Lontara\u27

Characters are the part of the culture that determines the level of human thinking as well as the characteristics and identity of an ethnic. In this modern era, communication in Indonesia generally always using the Latin letters with the aim to be used universally.The results of this study are refers to the visual identification of the fonts \u27Angled\u27 which is an innovation of the writing system, that used by the predecessors of Makassar tribes, starting from anatomical structures, visual characteristics, and geometric element studies to be applied as a new form in digital typeface from Lontara characters into Latin script (Roman)

An Innovative Approach Based on Machine Learning to Evaluate the Risk Factors Importance in Diagnosing Keratoconus

Background and objective: Keratoconus is a non-inflammatory corneal condition affecting both eyes and is present in one out of every 2,000 people worldwide. The cornea deforms into a conical shape and thins, resulting in high-order aberrations and gradual vision loss. Risk factor analysis in the degradation of keratoconus is under-researched. Methods: This research work investigates and uses effective machine learning models to gain insight into how much the risk factors of a patient contribute towards the progressive stages of keratoconus, as well as how significant these factors are in the creation of an accurate prediction model. This research demonstrates the value of machine learning approaches on a clinical dataset. This research paper employs several machine learning algorithms to classify the patients' stage of keratoconus using clinical information, such as measurements of the cornea's topography, elevation, and pachymetry taken using pentacam equipment at Sydney's Vision Eye Institute Chatswood. Results: Eight different machine learning techniques were investigated over three variations of a dataset and achieved an average accuracy of 68, 80, and 90% for the risk factor, pentacam, and cumulative datasets, respectively. The results show a significant increase in accuracy and a 97% increase in AUC upon addition of risk factor data compared to the models trained on pentacam data alone. The machine learning methods shown in this paper outperform those in current research. Conclusions: This research highlights the importance of machine learning methods and risk factor data in the diagnosis of keratoconus and highlights the patient's primary optical aid as the strongest risk factor. The goal of this research is to support the work of the ophthalmologists in diagnosing keratoconus and provide better care for the patient

Bis(acetyl­acetonato-κ2 O,O′)(pyridine-κN)zinc(II)

In the title compound, [Zn(C5H7O2)2(C5H5N)], the metal atom has square-pyramidal coordination geometry with the basal plane defined by the four O atoms of the chelating acetyl­acetonate ligands and with the axial position occupied by the pyridine N atom. The crystal packing is characterized by a C—H⋯O hydrogen-bonded ribbon structure approximately parallel to [10]

The Power of Two Choices in Distributed Voting

Distributed voting is a fundamental topic in distributed computing. In pull voting, in each step every vertex chooses a neighbour uniformly at random, and adopts its opinion. The voting is completed when all vertices hold the same opinion. On many graph classes including regular graphs, pull voting requires $\Theta(n)$ expected steps to complete, even if initially there are only two distinct opinions. In this paper we consider a related process which we call two-sample voting: every vertex chooses two random neighbours in each step. If the opinions of these neighbours coincide, then the vertex revises its opinion according to the chosen sample. Otherwise, it keeps its own opinion. We consider the performance of this process in the case where two different opinions reside on vertices of some (arbitrary) sets $A$ and $B$, respectively. Here, $|A| + |B| = n$ is the number of vertices of the graph. We show that there is a constant $K$ such that if the initial imbalance between the two opinions is ?$\nu_0 = (|A| - |B|)/n \geq K \sqrt{(1/d) + (d/n)}$, then with high probability two sample voting completes in a random $d$ regular graph in $O(\log n)$ steps and the initial majority opinion wins. We also show the same performance for any regular graph, if $\nu_0 \geq K \lambda_2$ where $\lambda_2$ is the second largest eigenvalue of the transition matrix. In the graphs we consider, standard pull voting requires $\Omega(n)$ steps, and the minority can still win with probability $|B|/n$.Comment: 22 page

Aspects of Thermodynamics of Deoxidation of Molten Steel with Mn and A1

Employing data from different sources, variations in the relationship between dissolved oxygen and dissolved element for deoxiation by Al, Si and Mn have been asse-ssed. Thermodynamic calculations have been performed for simultaneous deoxidation by Al+Si and Mn have been perf-ormed for simultaneous deoxidation by Ai+Si+Mn and equil-ibrium compositions of the metal have been determined for various compositions of the metal have been determined for various compositions of slag consisting of MnO, SiO2 and Al203. Some calculated values have been compared with those reported in literature

Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images

Corneal images can be acquired using confocal microscopes which provide detailed views of the different layers inside a human cornea. Some corneal problems and diseases can occur in one or more of the main corneal layers: the epithelium, stroma and endothelium. Consequently, for automatically extracting clinical information associated with corneal diseases, identifying abnormality or evaluating the normal cornea, it is important to be able to automatically recognise these layers reliably. Artificial intelligence (AI) approaches can provide improved accuracy over the conventional processing techniques and save a useful amount of time over the manual analysis time required by clinical experts. Artificial neural networks (ANNs), adaptive neuro fuzzy inference systems (ANFIS) and a committee machine (CM) have been investigated and tested to improve the recognition accuracy of the main corneal layers and identify abnormality in these layers. The performance of the CM, formed from ANN and ANFIS, achieves an accuracy of 100% for some classes in the processed data sets. Three normal corneal data sets and seven abnormal corneal images associated with diseases in the main corneal layers have been investigated with the proposed system. Statistical analysis for these data sets is performed to track any change in the processed images. This system is able to pre-process (quality enhancement, noise removal), classify corneal images, identify abnormalities in the analysed data sets and visualise corneal stroma images as well as each individual keratocyte cell in a 3D volume for further clinical analysis

An orthogonal biocatalytic approach for the safe generation and use of HCN in a multistep continuous preparation of chiral O-acetylcyanohydrins

An enantioselective preparation of O-acetylcyanohydrins has been accomplished by a three-step telescoped continuous process. The modular components enabled accurate control of two sequential biotransformations, safe handling of an in situ generated hazardous gas, and in-line stabilization of products. This method proved to be advantageous over the batch protocols in terms of reaction time (40 vs 345 min) and ease of operation, opening up access to reactions which have often been neglected due to safety concerns.We gratefully acknowledge the Deutsche Forschungsgemeinschaft (DFG) within the research training group GRK 1166 “Biocatalysis in non-conventional media (BioNoCo)”, and the EPSRC (Award Nos. EP/K009494/1 and EP/K039520/1)This is the final version of the article. It first appeared from Georg Thieme Verlag KG via http://dx.doi.org/10.1055/s-0035-156064