Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models

This article discusses the problem of handwriting recognition in Kazakh and Russian languages. This area is poorly studied since in the literature there are almost no works in this direction. We have tried to describe various approaches and achievements of recent years in the development of handwritten recognition models in relation to Cyrillic graphics. The first model uses deep convolutional neural networks (CNNs) for feature extraction and a fully connected multilayer perceptron neural network (MLP) for word classification. The second model, called SimpleHTR, uses CNN and recurrent neural network (RNN) layers to extract information from images. We also proposed the Bluechet and Puchserver models to compare the results. Due to the lack of available open datasets in Russian and Kazakh languages, we carried out work to collect data that included handwritten names of countries and cities from 42 different Cyrillic words, written more than 500 times in different handwriting. We also used a handwritten database of Kazakh and Russian languages (HKR). This is a new database of Cyrillic words (not only countries and cities) for the Russian and Kazakh languages, created by the authors of this work.
Aggregation-free process for functional CdSe/CdS core/shell quantum dots

Particle aggregation is implicated in a preparation of water-soluble functional nanoparticles and there is currently great interest in aggregation-free and water-soluble nanoparticles with a specific functionality. In this report, we present the aggregation-free surface modification process that provides water-soluble and functional quantum dots below 10 nm in hydrodynamic diameter. Our strategy is based on the reasoning that particle aggregation occurs due to polychelating internanoparticle hydrogen bonding interactions through surface molecules when they are no longer ionized. Therefore, the very strategy is to interrupt internanoparticle hydrogen bonding interactions by placing long chain hydrocarbon pillars (mercaptoundecanoic acid) which are bonded to functional molecules (folic acid or polyethylene glycol derivative) at appropriate distances on the CdSe/CdS core/shell quantum dots (QDs). The remaining QD surface area is then replaced by short-chain hydrophilic molecules (mercaptopropionic acid) endo.
Research on three L2 writing conditions: Students’ perceptions and use of background languages when writing in English

Abstract This paper gives an account of Norwegian upper secondary school students’ self-reported use of linguistic resources while composing a text in English (L2) under three different writing conditions, i.e. English-only, translation, and translanguaging. After writing a text in English, 200 students answered a questionnaire about their use of background languages as well as their perceptions of the assigned writing condition. A combination of quantitative and qualitative approaches to the analysis of the questionnaire data was employed to capture how the students use their background languages and what they consider to be relative advantages and disadvantages of the assigned writing condition. The results indicate: (i) a strong presence of English as a metacognitive language of choice in all three writing conditions, (ii) students’ strategic use of L1 to generate ideas and structure information, and (iii) students’ willingness to experiment with languages to enhance certain aspects of their writing. By integrating translation and translanguaging into the drafting stage of writing in a target language, the present study contributes to the empirical research that embraces bi- and multilingual approach to English writing instruction in modern language classrooms as they become more linguistically and culturally diverse.
Introduction: Governing Markets as Knowledge Commons

This is a preliminary draft of an introductory chapter to an edited volume titled Governing Markets as Knowledge Commons: The New Entrepreneurial History of Shared Social Infrastructures. We lay down a theoretical framework for the analysis of the production, reproduction, and transformation of intellectual and legal infrastructures that enable market interactions using the Governing Knowledge Commons research program. A distinctive contribution of this volume is conceptualizing these market-supporting knowledge structures as shared goods. There are four building blocks around which the edited volume revolves: First, markets are cultural, they depend on various kinds of knowledge some of which are governed as commons; markets coincide with a form of culture that we call civilization. Secondly, civilization relies on shared goods which are – unlike physical commons – produced and reproduced by contributions and sharing. The more one uses a shared good, the more there is of it. Thirdly, the knowledge that constitutes our civilization includes various kinds of intellectual and legal infrastructures. These infrastructures are a part of a capital structure: they are economic inputs. Finally, entrepreneurs sustain and marginally change the shared intellectual and legal infrastructures by abiding by them as well as through slight evasions, and justifications.
Using action research to develop data literacy in initial teacher education

There is a call for higher education institutions and professional learning providers to support pre-service teachers (PST) in developing competency in data literacy through clinical (classroom) practice. However, PST rarely have the opportunity to collect and analyze data that is relevant to their own classroom practice. In this paper, we put forward the results of a study that centered on PST conducting their own action research project during a ten-week professional experience placement in a high school. The aim of the PST’s action research studies was to demonstrate that their data-driven pedagogical decisions had a positive impact upon their students’ learning outcomes. It was found that while all of the PST could develop a research question and collect data, they needed more explicit training in action research and data literacy skills to develop a range of competencies that would support them in making nuanced data-driven learning and teaching decisions in the classroom.
An upper tropospheric ‘ozone river’ from Africa to India during the 2008 Asian post-monsoon season

We have used ozone data from the Infrared Atmospheric Sounding Interferometer to follow an event of ozone-enriched air-masses in the upper troposphere from eastern Africa to northern India. The ozone transport (hereafter called ‘ozone river’ or O3R) occurred during the Asian post-monsoon season in 2008 and was associated with Rossby wave propagation. The persistence of the O3R in a narrow channel was confirmed by MOZAIC airborne data over the northwestern Indian coast. The regions of origin of the O3R were identified by a transport analysis based on the Lagrangian model FLEXPART. The Lagrangian simulations combined with potential vorticity fields indicate that stratospheric intrusions are not likely to be the most important contributor to the observed O3 enhancements. A high-resolution Eulerian model, Meso-NH, with tagged tracers was used to discriminate between African biomass burning, lightnings and Indian anthropogenic pollution as potential sources of precursors for the O3R. Lightning NOx emissions, associated with convective clouds over Africa, were found to be the principal contributor to the ozone enhancement over the Indian Ocean taking advantage of a northeastward jet. This case study illustrates African lightning emissions as an important source for enhanced O3 in the upper troposphere over the Indian Ocean region during the post-monsoon season.
Structure of Barium Stearate Films at the Air/Water Interface Investigated by Polarization Modulation Infrared Spectroscopy and π−A Isotherms

Polarization modulation infrared reflection absorption spectroscopy (PM-IRRAS) and π−A isotherms were applied to investigate in situ the fragile stearic acid/Ba2+ (BaSt) system on the aqueous BaCl2 subphase in a Langmuir trough. The spectra were recorded at 294−274 K, pH = 9.5 and 6.3, and 4 cm-1 resolution with excellent signal-to-noise ratios. At pH = 6.3 the stearic acid was not totally deprotonated and no genuine unit cells were detected. The mixed COOH/COO- monolayer had CH2 antisymmetric (νas(CH2)) and symmetric (νs(CH2)) bands at 2917 and 2851 cm-1, respectively, indicating a predominantly trans zigzag chain conformation. At pH = 9.5 the stearic acid was totally deprotonated after spreading and formed a monolayer with ionic interaction between the COO- group and the subphase Ba2+ ion. Later the monolayer spontaneously reorganized into a three-dimensional structure, where the COO group and the barium atom formed a chelating bidentate coordination. This reorganization took 11 min to complete at 294 K.
Semi-Empirical Methods to Predict the Physical Properties of Ionic Liquids: An Overview of Recent Developments

The field of ionic liquids (ILs) has shown rapid growth in recent years. Much of this work has involved the synthesis of new ILs and their application in an ever-increasing number of areas. In contrast, there have been relatively few studies that investigate and attempt to predict the fundamental physical properties of ILs, which are extremely important for their applications. The quantitative prediction of the physical properties of unknown salts remains an important goal in IL research. This will allow the design of new ILs with specific properties tailored for particular applications, without the need for time-consuming trial and error syntheses. Recently, several studies have shown that it is possible to make predictions of the physical properties of ILs e.g. melting points, conductivities, viscosities, densities, surface tensions and refractive indices. This paper gives an overview of these semi-empirical methods and makes some comparisons regarding the accuracy of their predictions and their applicability to predicting the properties of unknown salts.
Dynamic force spectroscopy of oppositely charged polyelectrolyte brushes

Ion pairing is the main driving force in the formation of polyelectrolyte complexes, which find widespread use in micellar assemblies, drug carriers, and coatings. In this paper we examine the actual ion pairing forces in a polyelectrolyte complex between two oppositely charged polyelectrolyte brushes. Using colloidal probe AFM, we study the effect of salt on the strength of the model complex. We find a strong dependence of attractive force on salt concentration up to a critical salt concentration beyond which no attraction can be measured. By changing the salt concentration, the time allowed for formation of the complex, and the rate at which the complex is disrupted, we are able to measure the kinetics of complex formation and to estimate the ion pairing forces in equilibrium polyelectrolyte complexes. We use a kinetic model for the dynamics of complex disruption as a function of disruption rate to find a typical force of ion pairing in the complex. The model shows that our model polyelectrolyte complex.
Motions of single molecules inserted in a self-assembled monolayer matrix of a bicyclo[2.2.2]octane derivative on Au(111)

The motion of single conjugated molecules inserted in a self-assembled monolayer (SAM) matrix composed of a spherical bicyclo[2.2. 2]octane derivative was observed using scanning tunnelling microscopy at room temperature. Two kinds of conjugated molecules, i.e. benzenemethanethiol and 4-biphenylmethanethiol, were inserted in the SAM matrix. It was revealed that the isolated single molecules exhibited two types of motions. One was the change in tilt of the molecular axis at a fixed position, and the other was lateral motion in the SAM matrix. These motions of the single molecules due to thermal energy at room temperature were most likely caused by large and mobile vacancies of single molecular defects in the SAM matrix.
The State of the Art of Spatial Interfaces for 3D Visualization

We survey the state of the art of spatial interfaces for 3D visualization. Interaction techniques are crucial to data visualization processes and the visualization research community has been calling for more research on interaction for years. Yet, research papers focusing on interaction techniques, in particular for 3D visualization purposes, are not always published in visualization venues, sometimes making it challenging to synthesize the latest interaction and visualization results. We therefore introduce a taxonomy of interaction technique for 3D visualization. The taxonomy is organized along two axes: the primary source of input on the one hand and the visualization task they support on the other hand. Surveying the state of the art allows us to highlight specific challenges and missed opportunities for research in 3D visualization. In particular, we call for additional research in: (1) controlling 3D visualization widgets to help scientists better understand their data, (2) 3D interaction techniques for dissemination, which are under‐explored yet show great promise for helping museum and science centers in their mission to share recent knowledge, and (3) developing new measures that move beyond traditional time and errors metrics for evaluating visualizations that include spatial interaction.
Fabrication-Tolerant Broadband Air-Filled SIW Isolated Power Dividers/Combiners

This article introduces two-way broadband air-filled substrate integrated waveguide (AFSIW) isolated power dividers/combiners based on surface mounted absorbing materials. The proposed structures take advantage of the AFSIW multilayer feature. The absorber implementation is found highly tolerant of variations in dimensions and positioning. Three different power dividers/combiners topologies are analyzed theoretically, simulatively, and experimentally. The measurement results validate the broadband responses of the fabricated Ka-band demonstrators. The proposed power dividers/combiners are intended to exploit high-power AFSIW system on substrate (up to few hundred watts).
Improved analysis and modelling of soil diffuse reflectance spectra using wavelets

Diffuse reflectance spectroscopy using visible (vis), near‐infrared (NIR) and mid‐infrared (mid‐IR) energy can be a powerful tool to assess and monitor soil quality and function. Mathematical pre‐processing techniques and multivariate calibrations are commonly used to develop spectroscopic models to predict soil properties. These models contain many predictor variables that are collinear and redundant by nature. Partial least squares regression (PLSR) is often used for their analysis. Wavelets can be used to smooth signals and to reduce large data sets to parsimonious representations for more efficient data storage, computation and transmission. Our aim was to investigate their potential for the analyses of soil diffuse reflectance spectra. Specifically we wished to: (i) show how wavelets can be used to represent the multi‐scale nature of soil diffuse reflectance spectra, (ii) produce parsimonious representations of the spectra using selected wavelet coefficients and (iii) improve the regression analysis for prediction of soil organic carbon (SOC) and clay content. We decomposed soil vis‐NIR and mid‐IR spectra using the discrete wavelet transform (DWT) using a Daubechies’s wavelet with two vanishing moments. A multiresolution analysis (MRA) revealed their multi‐scale nature. The MRA identified local features in the spectra that contain information on soil composition. We illustrated a technique for the selection of wavelet coefficients, which were used to produce parsimonious multivariate calibrations for SOC and clay content. Both vis‐NIR and mid‐IR data were reduced to less than 7% of their original size. The selected coefficients were also back‐transformed. Multivariate calibrations were performed by PLSR, multiple linear regression (MLR) and MLR with quadratic polynomials (MLR‐QP) using the spectra, all wavelet coefficients, the selected coefficients and their back transformations. Calibrations by MLR‐QP using the selected wavelet coefficients produced the best predictions of SOC and clay content. MLR‐QP accounted for any nonlinearity in the data. Transforming soil spectra into the wavelet domain and producing a smaller representation of the data improved the efficiency of the calibrations. The models were computed with reduced, parsimonious data sets using simpler regressions.
Evolutionary Multitask Ensemble Learning Model for Hyperspectral Image Classification

Recently, ensemble learning paradigm has shown great potential to achieve better prediction performance in the hyperspectral image classification. However, in the traditional methods, each classifier independently searches for the optimal spectral feature subspace in series and some important features are searched repeatedly, which leads to high computing redundancy and low effective utilization of features. In this article, an evolutionary multitask ensemble learning model (EMT_EL) for hyperspectral image classification is designed. First, the model formulates the spectral feature subspaces generation into a multitask optimization problem to concurrently search for optimal feature subspaces for multiple classifiers, which would be capable to select more informative and representative feature subspaces effectively. Second, seeking the optimal feature subspace for one base classifier can assist in the optima-seeking process for some other base classifiers via sharing the useful features, which can accelerate converge toward the direction of the optimal feature subspace, avoid trapping in local optimal subspace and improve searching capability. Third, randomization-enhanced genetic operators are designed for effective and reasonable feature selection, which can facilitate the exchange of information and improve the joint searching efficiency of the feature subspace. Eventually, the quality of generated spectral feature subspaces for each base classifier is improved and the feature sharing can parse HSI data by knowing which spectral features are important. Experimental results demonstrate that the proposed method can generate the appropriate feature subspace for each base classifier, thus it has outstanding classification performance on the different hyperspectral datasets.
Optimal Supervisory Control of Two Phases Intersection Using Hybrid-based Model Approach

In the present paper, a hybrid approach that combines modular modelling and Timed Petri Nets model is proposed, the main interest of this combination is to ensure optimal supervisory control of urban traffic flows in two phases intersections of medium-sized cities. Each intersection is considered as a multi-agent system, composed of all agents who represent all physical spaces. The behaviours of interconnected agents are supervised by the Timed Petri Nets model that controls the traffic light cycles according to an optimal timing plan defined by the Continuous Genetic Algorithm. A real two phases intersection in Sidi Bel-Abbes City is presented as an example. The proposed timing plan is compared with the current one using SUMO software for the most important objective functions. Also, the proposed model's behaviour is compared with TSIS-CORSIM software's model, and all obtained experiments results show the effectiveness of the approach and its possibility to be extended and applied to a many-phases intersection.
New Approach in Genetic Algorithm for RNA Secondary Structure Prediction

RNA secondary structure problem is one of the most important fields in computational molecular biology. Ribonucleic Acid (RNA) has important structural and functional roles in the cell and plays roles in many stages of protein synthesis. The structure of RNA largely determines its function. Many methods can be used to predict the secondary structure of an RNA molecule. One of the methods is the dynamic programming approach. However, the dynamic programming approach usually takes too much time. Genetic algorithm is an evolutionary approach for solving space layout and optimization problems. Due to some drawbacks in genetic algorithm, several modifications are performed on this algorithm. When the advantages of GA are combined with advantages of another algorithm then this approach is called Hybrid Genetic Algorithm. In this paper we introduce a hybrid Genetic Algorithm with Fuzzy Logic. From this hybrid algorithm we apply the problem of predicting the secondary structure of Ribonucleic Acid. Problem predicted secondary structure that we mentioned methods based on thermodynamics, to find secondary structure with minimum energy. With the results of the algorithms found by the hybrid algorithm we introduce, we hope to contribute to the molecular biology data warehouse for molecular biology research. It also introduces a new approach to genetic algorithms.
Kinetics of separating multicomponent mixtures by nondispersive solvent extraction: Ni and Cd

A model for nonsteady metals separation using nondispersive solvent extraction presented explicitly accounts for selective separation of multicomponent solutions and concentration of separated components in a back-extraction phase. The separation process comprises extraction and back-extraction steps carried out in two different hollow-fiber modules, connected through the organic phase, together with three homogenization tanks. The model is based on a set of coupled differential equations describing mass balances of metallic solutes in the fluid phases. To be applied in its simplest form, it requires the knowledge of three characteristic parameters, two parameters related to the chemical reactions, and one mass-transport parameter. The latter describes the mass-transport rate through pores of the hollow-fiber membrane filled with the organic phase. The mathematical model was checked against the kinetic results of the separation of Cd/Ni mixtures working with high-concentration solutions and obtaining the characteristic parameters of this system.
Electrical Properties of Cobalt Monoxide

The electrical conductivity of polycrystalline CoO1+x was measured as a function of temperature and partial pressure of oxygen, P(O2). The range of temperature was 900°—1450°C and the range of partial pressure was 1 − 10−12 atm. The plot of the conductivity against partial pressure of oxygen gave two distinct regions: Region A, where the conductivity is proportional to P(O2)¼, and Region B, where the conductivity is proportional to P(O2)⅙. From these results, together with considerations of ionic radii, we concluded that the dominant defects in CoO1+x at high temperatures are cation vacancies, with the majority being singly ionized in Region A and doubly ionized in Region B.A combination of gravimetric and electrical measurements showed that the mobility of the holes contributed by these vacancies rises exponentially with temperature. The activation energy of mobility is Eu=0.3 eV. At 1350°C, the mobility is about 0.4 cm2 V−1·sec−1. These values are discussed in terms of polaron theory.The sum of the enth.
Iodine‐Azide Detection System for Dipeptides in Thin‐Layer Chromatography

Abstract Dipeptides were derivatized with phenyl thiocarbamyl (PITC) to sulfur‐containing compounds. The reaction was performed directly on the chromatographic plate in normal phase chromatography. The detection system was based on an iodine‐azide reaction that was induced by sulfur compounds. The procedure involved spraying the developed plate with sodium azide and starch solution and, subsequently, exposing the plate to iodine vapor. Due to the catalytic effect of C˭S bonds, white spots appeared on the violet‐gray background. The method was found to be competitive with the other detection techniques and its sensitivity range was as low as pmol per spot.