Miracle-dz/UniFAR-FTUs huggingface dataset:
Chromato-mass-spectrometric research in Salvia grandiflora L., Salvia pratensis L. and Salvia verticillata L. aboveground organs

The genus Salvia L. has more than 900 species distributed throughout the globe. 21 species are growing in Ukraine. All species of this genus have essential oils. Salvia officinalis and Salvia sclarea have been used in the culture and are widely used in medical practice. The chemical composition of other species of sage and the possibility of their use in pharmaceutical and medical practice are almost not studied. Taking into account the results of chemotaxonomic studies of species of the flora genus of Ukraine, their prevalence and prospects for introduction into the culture, for further studies were selected raw materials of S. grandiflora, S. pratensis and S. verticillata. The aim. The aim of the study was to conduct a chromato-mass spectrometric study of the aboveground organs of S. grandiflora L. , S. pratensis L. and S. verticillata L. to establish the prospects for the use of raw materials of these species in medical and pharmaceutical practice. Materials and methods. The objects of the study were leaves of S. officinalis, leaves, stems and flowers of S. grandiflora, S. pratensis and S. verticillata, which were harvested on the basis of the botanical garden of Ivan Franko National University of Lviv. The research of volatile substances in the objects of the research was carried out by the method of GC-MS on the basis of the Department of Natural Sciences for Foreign Students and Toxicological Chemistry of Zaporizhia State Medical University. Results. As a result of the study, 243 substances were found in the objects of the study, of which 149 were identified. 77 substances were found in the leaves of S. officinalis, 80, 26 and 63 substances in the leaves, stems and flowers of S. grandiflora, respectively, in the leaves , stems and flowers of S. pratensis – 28, 30 and 48 substances, respectively, in leaves, stems and flowers of S. verticillata – 39, 22 and 39 substances, respectively. Dominant compounds among substances of terpenoid nature are: cyclofenchene, camphene, 1,8-cineole, α-thujone, β-thujone, camphor borneol, caryophyllene, humulene, viridiflorol, sabinene, pyranone, β-pinene, phytol, kolavenol, β-copaen, loliolide, pseudolimonene and spatulenol. Among the dominant substances, 8 were detected for the first time in these species: cyclofenchene, viridiflorol, sabinene, pyranone, phytol, kolavenol, loliolide and pseudolimonene. Conclusions. The leaves of S. officinalis, leaves, stems and flowers of S. grandiflora, S. pratensis and S. verticillata of the flora of Ukraine were studied by chromato-mass spectrometric method. As a result of the study, 243 substances were identified, of which 149 were identified. Promising raw materials containing terpene compounds for S. grandiflora there are leaves, and for S. pratensis and S. verticillata – flowers, so they are promising agents for introduction into pharmaceutical practice
OCR Processing of Swedish Historical Newspapers Using Deep Hybrid CNN–LSTM Networks

Deep CNN–LSTM hybrid neural networks have proven to improve the accuracy of Optical Character Recognition (OCR) models for different languages. In this paper we examine to what extent these networks improve the OCR accuracy rates on Swedish historical newspapers. By experimenting with the open source OCR engine Calamari, we are able to show that mixed deep CNN–LSTM hybrid models outperform previous models on the task of character recognition of Swedish historical newspapers spanning 1818–1848. We achieved an average character accuracy rate (CAR) of 97.43% which is a new state–of–the–art result on 19th century Swedish newspaper text. Our data, code and models are released under CC-BY licence.
Institutional Quality and International Capital Flows to the Emerging Economies

The international capital flows and the factors influencing them are imperative in this era of globalization and financial liberalization. This paper empirically examines the role of institutional quality in enticing foreign capital flows in emerging market economies (EMEs). A panel data set for the period 1995-2018 is used for the 24 major EMEs including Argentina, Bangladesh, Brazil, Chile, China, Czech Republic, Greece, Hungary, India, Indonesia, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, Qatar, Romania, Russia, South Africa, Thailand, Turkey, Ukraine, and Venezuela. The system GMM estimation technique of dynamic panel data handling developed by Arellano-Bover (1995) and Blundell-Bond (1998) is employed for the estimation. The empirical results reveal that the FDI inflows are positively and significantly affected by the institutional quality, but the portfolio equity capital inflows are not influenced by any indicator of institutional performance. In other words, the Lucas paradox is explained by the institutional quality only in the case of FDI inflows. The study accomplishes that the policy aiming at attracting FDI flows by improving institutional infrastructure is expedient for the emerging economies.
When Blockchain Creates Shareholder Value: Empirical Evidence from International Firm Announcements

Blockchain is a prominently discussed technology in operations and supply chain management and firms increasingly engage in blockchain initiatives. Yet, an understanding of the technology's financial value remains elusive. Based on 175 firm announcements between 2015 and 2019, we conduct an international event study to estimate the impact of blockchain initiatives on the market value of the firm. We empirically demonstrate that blockchain announcements are associated with a significant average abnormal return of 0.30% on the announcement day, and that there are indications of positive long‐term effects on shareholder value. We further demonstrate how blockchain use case, project, and firm characteristics affect the stock market reaction. Specifically, we find that the stock market reaction to blockchain announcements is less positive when blockchain is used to trace physical objects or to share sensitive data, providing empirical evidence for the risk associated with current challenges in the design of blockchain use cases. Our results also suggest that the involvement of an external information technology service provider in a blockchain project attenuates the positive stock market reaction. Interestingly, more innovative firms do not experience a stronger stock market reaction to blockchain announcements. Leveraging the international scope of our sample, we further shed light on how the firm's competitive (i.e., industry factors) and macro environment (i.e., country factors) affect the stock market reaction. Our findings indicate that the industry's R&D intensity and the country's data restriction level play a crucial role in driving the value attributed to blockchain initiatives.
Poplar agroforestry systems in eastern China enhance the spatiotemporal stability of soil microbial community structure and metabolism

Agroforestry systems provide soil microorganisms with a rich variety of carbon sources and a relatively stable living environment. However, the effects of agroforestry systems on the metabolic dynamics of soil microorganisms have not been well studied, especially in the subsoil. To assess the vertical and seasonal variations in soil microbial metabolic capacity as a consequence of various planting systems, five typical planting systems, including three poplar agroforestry systems, were investigated. The Biolog EcoPlate method was used to determine the vertical and seasonal variations in soil microbial metabolic capacity. The average well colour development, carbon source utilization ability, and microbial diversity index values were higher throughout the soil profile, and highly stable with seasonal changes in the poplar (Populus × euramericana “Nanlin 895”) + wheat (Triticum aestivum L.) + soybean (Glycine max) and poplar + potherb mustard (Brassica juncea var. multiceps) agroforestry systems. Furthermore, the influence of the planting systems and seasonal changes on the metabolic activity of soil microorganisms decreased with an increase in soil depth. Over‐stocking chickens in the forest reduced the metabolic activity of soil microorganisms. It was also found that plants influenced soil microbial metabolism through affecting the available carbon source types. Therefore, agroforestry systems improved the metabolic potential of the soil microbial community. Our results demonstrated that soil microbial communities are affected by the planting system and soil depth. The findings enhance our understanding of the functional diversity of soil microorganisms in agroforestry systems.
FastANI, Mash and Dashing equally differentiate between Klebsiella species

Species of the genus Klebsiella are among the most important multidrug resistant human pathogens, though they have been isolated from a variety of environments. Given the need for quickly and accurately classifying newly sequenced Klebsiella genomes, we compared 982 Klebsiella genomes using different species-delimiting measures: Average Nucleotide Identity (ANI), which is becoming a standard for species delimitation, as well as Mash, Dashing, and DNA compositional signatures, which can be run in a fraction of the time required to run ANI. ROC analyses showed equal quality in species delimitation for ANI, Mash and Dashing (AUC: 0.99), followed by DNA signatures (AUC: 0.96). The groups obtained at optimal cutoffs were largely in agreement with species designation. Using optimized cutoffs, we obtained 17 species-level groups using either ANI, Mash, or Dashing, all containing the same genomes, unlike DNA signatures which broke the dataset into 38 groups. Further use of Mash to map species after adding draft genomes to the dataset also showed excellent results (AUC: 0.99), producing a total of 28 Klebsiella species in the publicly available genome collection. The ecological niches of Klebsiella strains were found to neither be related to species delimitation, nor to protein functional content, suggesting that a single Klebsiella species can have a wide repertoire of ecological functions.
Development of an Intelligent Robotic Manipulator

The presence of hazards to human health in chemical process plant and nuclear waste stores leads to the use of robots and more specifically manipulators in unmanned spaces. Rapid and accurate performance of robotic arm movement and positioning, coupled with a reliable manipulator gripping mechanism for variable orientation and a range of deformable and/or geometric and coloured products, will lead to smarter/intelligent operation of high precision equipment. The aim of the research is to design a more effective robot arm manipulator for use in a glovebox environment utilising control kinematics together with image processing / object recognition algorithms and in particular the work is aimed at improving the movement of the robot arm in the case of unresolved kinematics, seeking improved speed and performance of object recognition along with improved sensitivity of the manipulator gripper mechanism A virtual robot arm and associated workspace was designed within the LabView 2009 environment and prototype gripper arms were designed and analysed within the Solidworks 2009 environment. Visual information was acquired by barrel cameras. Field research determines the location of identically shaped objects, and the object recognition algorithms establish the difference between them. A touch/feel device installed within the gripper arm housing ensures that the applied force is adequate to securely grasp the object without damage, but also to adapt to any slippage whilst the manipulator moves within the robot workspace. The research demonstrates that complex operations can be achieved without the expense of specialised parts/components; and that implementation of control algorithms can compensate for any ambiguous signals or fault conditions that occur through the operation of the manipulator. The results show that system performance is determined by the trade-off between speed and accuracy. The designed system can be further utilised for control of multi-functional robots connected within a production line. The Graphic User Interface illustrated within the thesis can be customised by the supervisor to suit operational needs.
Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features

Abstract Background Autism spectrum disorder (ASD) is characterized by a spectrum of social and communication impairments and rigid and stereotyped behaviors that have a neurodevelopmental origin. Although many imaging studies have reported structural and functional alterations in multiple brain regions, clinically useful diagnostic imaging biomarkers for ASD remain unavailable. Methods In this study, we applied machine learning (ML) models to regional volumetric and cortical thickness data from the largest structural magnetic resonance imaging (sMRI) dataset available from the Enhancing Neuro Imaging Genetics Through Meta‐Analysis (ENIGMA) consortium (1833 subjects with ASD and 1838 without ASD; age range: 1.5–64; average age: 15.6; male/female ratio: 4.2:1). Results The highest classification accuracy on a hold‐out test set was achieved using a stacked Extra Tree Classifier. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.62 (95% confidence interval [CI]: 0.57, 0.68) and the area under the precision‐recall curve was 0.58. Learning curve analysis showed the good fit of the model and suggests that more training examples will not likely benefit model performance. Conclusions Our results suggest that sMRI volumetric and cortical thickness data alone may not provide clinically sufficient useful diagnostic biomarkers for ASD. Developing clinically useful imaging classifiers for ASD will benefit from combining other data modalities or feature types, such as functional MRI data and raw images that can leverage other machine learning (ML) techniques such as convolutional neural networks.
Bruch's membrane opening – minimum rim width measurement after acute primary angle-closure

Purpose To evaluate Bruch's membrane opening – minimum rim width (BMO-MRW) and peripapillary retinal nerve fiber layer thickness (RNFLT) following an acute primary angle-closure attack (APAC). Materials and methods Nine consecutive patients with unilateral APAC were included. Patients with a bilateral attack, with signs of glaucomatous optic nerve damage or evidence of a previous APAC in either eye were excluded. Three months after the attack, all eyes underwent BMO-MRW and RNFLT measurements with SDOCT. APAC eyes were compared to the contralateral eyes. Results Three months after the attack, mean BMO-MRWs were 281.22 ± 56.88 μm and 313.78 ± 43.48 μm (P = 0.009) and mean RNFLTs were 78 ± 15.36 μm vs 95.78 ± 10.81 μm (P = 0.008) in the APAC and contralateral eyes, respectively. RNFLT and BMO-MRW measurements had a strong positive correlation (R = 0.7436, P = 0.013). APAC eyes had a shorter axial length (21.85 ± 1.21 vs 22 ± 1.07, P = 0.042) and shallower anterior chamber depth (2.29 ± 0.21 vs 2.41 ± 0.12, P = 0.039) than contralateral eyes. IOP at presentation showed a strong negative correlation with both BMO-MRW (R = −0.7669, P = 0.009) and RNFLT measurements (R = −0.7723, P = 0.008). Conclusion BMO-MRW and RNFLT measurements are significantly reduced 3 months after an APAC when compared to the contralateral eye. IOP at presentation may have an impact on the reduction of these parameters.
Application of the Acoustic Integration Model (AIM) to predict and minimize environmental impacts

Minimizing and mitigating the potential effect of sound upon the environment is an increasing concern for many activities. Naval operations, seismic exploration, vessel and aircraft operations, and scientific investigations now need to consider the potential effect of underwater acoustic sources. Marine mammals are usually the primary concern, due to their widespread distribution and excellent hearing. Predicting the exposure of marine mammals is complicated by their diving behavior, which causes them to 'sample' many depth strata within the water column. Acoustic propagation and sound received levels are a function of depth as well as range. The Acoustic Integration Model (AIM) addresses this specific complication. A principal component of the central engine of AIM is a movement simulator. Both sound sources and animals, collectively addressed as 'ANIMATS', are programmed to move in location and depth over time in a realistic function. Animal movement is based on documented regional and seasonal behavioral data for each species generated. Acoustic sources and receivers are programmed to move through a virtual acoustic environment based on external environmental databases and radiated sound fields created from a choice of several propagation models. The integration component of the AIM engine then predicts the exposure level of each simulated animal at successive operator-selected time steps. Furthermore, each animat can evaluate its environment at each time step, and can be programmed to alter direction or diving behavior in response to any variable, such as sound level or sea depth. The model therefore allows the user to predict the effects of different operational scenarios and animal response levels, thereby allowing the selection of the alternative that produces the least impact and still meets operation requirements.
No relationship found between female genital cutting and intimate partner violence across six sub-Saharan African countries

ABSTRACT Female genital cutting (FGC) and intimate partner violence (IPV) are highly prevalent forms of violence against women, and the UN has encouraged strengthening policy linkages between FGC and IPV programme work (UN Women, Female genital mutilation/cutting and violence against women and girls. Strengthening the policy linkages between different forms of violence. UN Women, 2017a). However few studies have examined the relationship between these behaviours. This study addresses two research gaps identified by the UN; (a) whether women with FGC are more likely to experience physical or sexual IPV, and (b) whether women’s IPV experiences are associated with their support for FGC. Multilevel multivariate logistic regression models were run using Demographic and Health Survey data from 6 sub-Saharan African countries (Burkina Faso, Ethiopia, The Gambia, Ivory Coast, Kenya and Nigeria, n33,170). Our results show no association between FGC and IPV. Women with FGC are not at greater risk of experiencing IPV, and women who experienced IPV in the preceding year are not more likely to support FGC. Further, the importance of individual and community factors differ; IPV experience is influenced by individual factors, whereas FGC support is more influenced by ethnic group characteristics. Although the proposition that FGC and IPV could be tackled collaboratively is attractive for policy makers, these results are important because they indicate that eradication programmes will be more effective when targeted at specific behaviours.
Information and communication technology, inequality change and regional development in Indonesia

Although the advancement of technology provides numerous opportunities to boost economic growth and development, equal distribution may not be guaranteed. Thus, this study seeks further elaboration as to whether ICT development has a role to play in the inequality in Indonesia. Using municipal level data from 2018, the study provides both linear and non-linear models to be estimated using OLS and 2SLS. Major findings include: (i) the availability of basic ICT infrastructure is strongly linked to the reduction of inequality; (ii) the actual ICT use is positively associated with inequality, albeit at a diminishing rate, revealing a non-linear relationship similar to Kuznets' curve; (iii) the ICT skill variable comprising the education level has direct correlation with ICT use instead of inequality, with an additional score on ICT skill being associated with an increase in ICT use; (iv) the relationship between ICT use and inequality differed depending on the level of economic development, with lower-income regions experiencing the inverted U-shaped relationship as in the original Kuznets curve and higher-income regions experiencing the U-shaped curve.JEL Classification: D63; I24; O10
An intense Nigerian stock exchange market prediction using logistic with back-propagation ANN model

This paper is a continuation of our research work on the Nigerian Stock Exchange Market (NSEM) uncertainties, In our previous work (Magaji et al, 2013) we presented the Naive Bayes and SVM-SMO algorithms as a tools for predicting the Nigerian Stock Exchange Market; subsequently we used the same transformed data of the NSEM and explored the implementation of the Logistic function on Back-propagation algorithm on the WEKA platform, and results obtained, made us to also conclude that the Back-propagation model of Artificial Neural Network (ANN) performed very well and thus it is another algorithm that can effectively and efficiently be used for predicting the Nigerian Stock Exchange Market.
Sokoban: a system object case study

This report presents a case study applying the Distribution of System Responsibilities (DSR) object-oriented design technique. DSR provides a starting point for an object-oriented design by directly manipulating the requirements for that application. The case study applies DSR to the development of a small application, the game Sokoban. The case study provides evidence that DSR does allow object-oriented designs to be created, that it allows reasonable designs to be created, and that any object-oriented design can be created by it.
Neural Network-Based Generation of Sport Summaries: A Preliminary Study

This paper presents a global summarization method for live sport commentaries for which we have a human-written summary available. This method is based on a neural generative summarizer. The amount of data available for training is limited compared to corpora commonly used by neural summarizers. We propose to help the summarizer to learn from a limited amount of data by limiting the entropy of the input texts. This step is performed by a classification into categories derived by a detailed analysis of the human-written summaries. We show that the filtering helps the summarization system to overcome the lack of resources. However, several improving points have emerged from this preliminary study, that we discuss and plan to implement in future work.