Search results for: European Standard Classification

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Eva Kotlánová

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After recession that began in 2007 in the United States and subsequently spilled over the Europe we could expect recovery of economic growth. According to the last estimation of economic progress of European countries, this recovery is not strong enough. Among others, it will depend on economic policy, where and in which way, the economic indicators will proceed. Economic theories postulate that the economic subjects prefer stably, continual economic policy without repeated and strong fluctuations. This policy is perceived as support of economic growth. Mostly in crises period, when the government must cope with consequences of recession, the economic policy becomes unpredictable for many subjects and economic policy uncertainty grows, which have negative influence on economic growth. The aim of this paper is to use panel regression to prove or disprove this hypothesis on the example of five largest European economies in the period 2008–2012.

Keywords: economic crises in Europe, economic policy, uncertainty, panel analysis regression

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Authors: Aliaksandr Shuba, Max Welch Guerra, Martin Pekar

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The research is dedicated to the Historiography of European urbanism in the 20th century with its critical analysis of transnational oriented sources in Slavic languages. The goal of this research was to give an overview of Slavic sources on this subject. In the research, historians, who wrote in influential historiographies on architecture and urbanism in the 20th century history in Slavic languages from Eastern, Central and South-eastern Europe, are analysed. The analysis of historiographies in Slavic languages includes diverse sources from around Europe with authors, who examined European Urbanism in the 20th century through a global prism of or their own perspectives. The main publications are from the second half of the 20th century and the early 21st century with Soviet and Post-Soviet discourses. The necessity to analyse Slavic sources was a result of historiography of urbanism establishment as a discipline in the 20th century and by the USSR, Czechslovak, and Yugoslavian academics, who created strong historiographic bases for a development of their urban historiographic schools for wide studies and analysis of architectural and urban ideas and projects with their history in the early 1970s. That is analyzed in this research within Slavic publications, which often have different perspectives and discourses to Anglo-Saxon, and these bibliographic sources can bring a diversity of new ideas in contemporary academic discourse of the European urban historiography. The publications in Slavic languages are analyzed according to the following aspects: where, when, which types, by whom, and to whom the sources were written. The critical analysis of essential sources on the Historiography of European urbanism in the 20th century with an accomplishment through their comparison and interpretation. The authors’ autonomy is analysed as a central point, along with the influence of the Communist Party and state control on the interpretation of the history of urbanism in Central, Eastern and South-eastern Europe with the main dominant topics and ideas from the second half of the 20th century. Cross-national Slavic Historiographic sources and their perspectives are compared to the main transnational Anglo-Saxon Historiographic topics as some of the dominant subjects are hypothetically similar and others have more local or national oriented directions. Some of the dominant subjects, topics, and subtopics are hypothetically similar, while the others have more local or national oriented directions because of the authors’ autonomy and influences of the Communist Party with the state control in Slavic Socialists countries that were illustrated in this research.

Keywords: European urbanism, historiography, different perspectives, 20th century

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Authors: Zoran Šverko, Saša Vlahinić, Nino Stojković, Ivan Markovinović

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In this article, the classification of target letters is performed using data from the EEG P300 Speller paradigm. Neural networks trained with the results of dynamic connectivity analysis between different brain regions are used for classification. Dynamic connectivity analysis is based on the adaptive window size and the imaginary part of the complex Pearson correlation coefficient. Brain dynamics are analysed using the relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient method (RICI-imCPCC). The RICI-imCPCC method overcomes the shortcomings of currently used dynamical connectivity analysis methods, such as the low reliability and low temporal precision for short connectivity intervals encountered in constant sliding window analysis with wide window size and the high susceptibility to noise encountered in constant sliding window analysis with narrow window size. This method overcomes these shortcomings by dynamically adjusting the window size using the RICI rule. This method extracts information about brain connections for each time sample. Seventy percent of the extracted brain connectivity information is used for training and thirty percent for validation. Classification of the target word is also done and based on the same analysis method. As far as we know, through this research, we have shown for the first time that dynamic connectivity can be used as a parameter for classifying EEG signals.

Keywords: dynamic connectivity analysis, EEG, neural networks, Pearson correlation coefficients

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Authors: Jae Ni Jang, Young Uk Kim

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Background: Chat Generative Pre-training Transformer-3 (ChatGPT; San Francisco, California, Open Artificial Intelligence) is an artificial intelligence chatbot based on a large language model designed to generate human-like text. As the usage of ChatGPT is increasing among less knowledgeable patients, medical students, and anesthesia and pain medicine residents or trainees, we aimed to evaluate the accuracy of ChatGPT-3 responses to questions about the American Society of Anesthesiologists (ASA) classification based on patients’ underlying diseases and assess the quality of the generated responses. Methods: A total of 47 questions were submitted to ChatGPT using textual prompts. The questions were designed for ChatGPT-3 to provide answers regarding ASA classification in response to common underlying diseases frequently observed in adult patients. In addition, we created 18 questions regarding the ASA classification for pediatric patients and pregnant women. The accuracy of ChatGPT’s responses was evaluated by cross-referencing with Miller’s Anesthesia, Morgan & Mikhail’s Clinical Anesthesiology, and the American Society of Anesthesiologists’ ASA Physical Status Classification System (2020). Results: Out of the 47 questions pertaining to adults, ChatGPT -3 provided correct answers for only 23, resulting in an accuracy rate of 48.9%. Furthermore, the responses provided by ChatGPT-3 regarding children and pregnant women were mostly inaccurate, as indicated by a 28% accuracy rate (5 out of 18). Conclusions: ChatGPT provided correct responses to questions relevant to the daily clinical routine of anesthesiologists in approximately half of the cases, while the remaining responses contained errors. Therefore, caution is advised when using ChatGPT to retrieve anesthesia-related information. Although ChatGPT may not yet be suitable for clinical settings, we anticipate significant improvements in ChatGPT and other large language models in the near future. Regular assessments of ChatGPT's ASA classification accuracy are essential due to the evolving nature of ChatGPT as an artificial intelligence entity. This is especially important because ChatGPT has a clinically unacceptable rate of error and hallucination, particularly in pediatric patients and pregnant women. The methodology established in this study may be used to continue evaluating ChatGPT.

Keywords: American Society of Anesthesiologists, artificial intelligence, Chat Generative Pre-training Transformer-3, ChatGPT

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Authors: Cosmas Nathanailides, Fotini Kakali, Kostas Karipoglou

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The filleting yield and the chemical composition of farmed sea bass (Dicentrarchus labrax); rainbow trout (Oncorynchus mykiss) and meagre (Argyrosomus regius) was investigated in farmed fish in NW Greece. The results provide an estimate of the quantity of fish required to produce one kilogram of fillet weight, an estimation which is required for the operational management of fish processing companies. Furthermore in this work, the ratio of feed input required to produce one kilogram of fish fillet (FFCR) is presented for the first time as a useful indicator of the ecological footprint of consuming farmed fish. The lowest lipid content appeared in meagre (1,7%) and the highest in trout (4,91%). The lowest fillet yield and fillet yield feed conversion ratio (FYFCR) was in meagre (FY=42,17%, FFCR=2,48), the best fillet yield (FY=53,8%) and FYFCR (2,10) was exhibited in farmed rainbow trout. This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: ARCHIMEDES III. Investing in knowledge society through the European Social Fund.

Keywords: farmed fish, flesh quality, filleting yield, lipid

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Authors: Elhussaien Elshiekh

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A study was performed to management and compare radiation doses and image quality during Lumbar spine PA and Lumbar spine LAT, x- ray radiography using Computed Radiography (CR) and Digital Radiography (DR). Standard exposure factors such as kV, mAs and FFD used for imaging the Lumbar spine anthropomorphic phantom obtained from average exposure factors that were used with CR in five radiology centres. Lumbar spine phantom was imaged using CR and DR systems. Entrance surface air kerma (ESAK) was calculated X-ray tube output and patient exposure factor. Images were evaluated using visual grading system based on the European Guidelines on Quality Criteria for diagnostic radiographic images. The ESAK corresponding to each image was measured at the surface of the phantom. Six experienced specialists evaluated hard copies of all the images, the image score (IS) was calculated for each image by finding the average score of the Six evaluators. The IS value also was used to determine whether an image was diagnostically acceptable. The optimum recommended exposure factors founded here for Lumbar spine PA and Lumbar spine LAT, with respectively (80 kVp,25 mAs at 100 cm FFD) and (75 kVp,15 mAs at 100 cm FFD) for CR system, and (80 kVp,15 mAs at100 cm FFD) and (75 kVp,10 mAs at 100 cm FFD) for DR system. For Lumbar spine PA, the lowest ESAK value required to obtain a diagnostically acceptable image were 0.80 mGy for DR and 1.20 mGy for CR systems. Similarly for Lumbar spine LAT projection, the lowest ESAK values to obtain a diagnostically acceptable image were 0.62 mGy for DR and 0.76 mGy for CR systems. At standard kVp and mAs values, the image quality did not vary significantly between the CR and the DR system, but at higher kVp and mAs values, the DR images were found to be of better quality than CR images. In addition, the lower limit of entrance skin dose consistent with diagnostically acceptable DR images was 40% lower than that for CR images.

Keywords: image quality, dosimetry, radiation protection, optimization, digital radiography, computed radiography

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Authors: Kurt Willems

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The right to education is a fundamental human right, which has been entrenched in many international and regional treaties and national constitutions. Nevertheless, practice shows that many obstacles impede easy access to quality education for refugees. Overall, the material effects of international human rights legislation on improving (irregular) migrants’ access to social rights in the European countries have remained limited due to the lack of guarantees on effective incorporation in the municipal legal order and due to the lack of effective enforcement mechanisms. After the recent refugee crisis in Europe, this issue has grown in importance. The presentation aims to give a brief overview of the most important issues impeding the effective enforceability of the right to education for refugees. I. Do refugees fall within the scope of application of the relevant human rights treaties and to which extent can they invoke human rights treaties in domestic courts to set aside domestic legislation? II. How is the justiciability of the right to education organized in those treaties? III. What is the legal answer to questions raised in practice when dealing with the influx of refugees in Europe: (i) can refugees be placed in separate schools or classes until they can follow the regular curriculum?; (ii) can higher school fees be asked from pupils without legal documents?; (iii) do refugees have a right to be taught in their own native language until they learn to speak the national language? To answer the above questions, the doctrinal and comparative legal method will be used. The normative framework, as interpreted within Europe, will be distilled from the recent and relevant international treaties and European law instruments (in particular the Convention on the Rights of the Child, the European Convention on human rights, the European Social Charter and the International Covenant on Economic, Social and Cultural Rights) and their underlying policy documents, the legal literature, the (limited) European jurisprudence, and the general comments to those treaties. The article is mainly descriptive in nature. Its aim is to serve as a summary of the legal provisions, case law and legal literature on the topic of the right to education for refugees. The research shows that the reasons for the delicate enforceability of the rights to and the rights in education are multifold. The research will categorize the different contributing factors under the following headings: (i) problems related to the justiciability of international law as such; (ii) problems specifically related to the educational field; (iii) problems related to policy issues in the refugee debate. By categorizing the reasons contributing to the difficult enforceability of the right to education and the rights in education for refugees, this research hopes to facilitate the search for solutions to this delicate problem.

Keywords: right to education, refugees, discrimination, enforceability of human rights

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Authors: Alan C. Lin, Ricky N. Joevan

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This paper focuses on developing a system to transfer mold base plates and standard parts faster during the stage of injection mold design. This system not only provides a way to compare the file version, but also it utilizes Siemens NX 10 to isolate the updated information into a single executable file (.dll), and then, the file can be transferred without the need of transferring the whole file. By this way, the system can help the user to download only necessary mold base plates and standard parts, and those parts downloaded are only the updated portions.

Keywords: CAD, injection molding, mold base, data retrieval

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Authors: Arjun G. Koppad

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The study was conducted in Joida taluk of Uttara Kannada district, Karnataka, India, to assess the land use land cover (LULC) and forest aboveground biomass using L band SAR data. The study area covered has dense, moderately dense, and sparse forests. The sampled area was 0.01 percent of the forest area with 30 sampling plots which were selected randomly. The point center quadrate (PCQ) method was used to select the tree and collected the tree growth parameters viz., tree height, diameter at breast height (DBH), and diameter at the tree base. The tree crown density was measured with a densitometer. Each sample plot biomass was estimated using the standard formula. In this study, the LULC classification was done using Freeman-Durden, Yamaghuchi and Pauli polarimetric decompositions. It was observed that the Freeman-Durden decomposition showed better LULC classification with an accuracy of 88 percent. An attempt was made to estimate the aboveground biomass using SAR backscatter. The ALOS-2 PALSAR-2 L-band data (HH, HV, VV &VH) fully polarimetric quad-pol SAR data was used. SAR backscatter-based regression model was implemented to retrieve forest aboveground biomass of the study area. Cross-polarization (HV) has shown a good correlation with forest above-ground biomass. The Multi Linear Regression analysis was done to estimate aboveground biomass of the natural forest areas of the Joida taluk. The different polarizations (HH &HV, VV &HH, HV & VH, VV&VH) combination of HH and HV polarization shows a good correlation with field and predicted biomass. The RMSE and value for HH & HV and HH & VV were 78 t/ha and 0.861, 81 t/ha and 0.853, respectively. Hence the model can be recommended for estimating AGB for the dense, moderately dense, and sparse forest.

Keywords: forest, biomass, LULC, back scatter, SAR, regression

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

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Authors: Marta Kwiatkowska, Paweł Jarosiewicz, Bożena Bukowska

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Glyphosate (N-phosphonomethylglycine) is a non-selected broad spectrum ingredient in the herbicide (Roundup) used for over 35 years for the protection of agricultural and horticultural crops. Glyphosate was believed to be environmentally friendly but recently, a large body of evidence has revealed that glyphosate can negatively affect on environment and humans. It has been found that glyphosate is present in the soil and groundwater. It can also enter human body which results in its occurrence in blood in low concentrations of 73.6 ± 28.2 ng/ml. Research conducted for potential genotoxicity and cytotoxicity can be an important element in determining the toxic effect of glyphosate. Due to regulation of European Parliament 1107/2009 it is important to assess genotoxicity and cytotoxicity not only for the parent substance but also its impurities, which are formed at different stages of production of major substance – glyphosate. Moreover verifying, which of these compounds are more toxic is required. Understanding of the molecular pathways of action is extremely important in the context of the environmental risk assessment. In 2002, the European Union has decided that glyphosate is not genotoxic. Unfortunately, recently performed studies around the world achieved results which contest decision taken by the committee of the European Union. World Health Organization (WHO) in March 2015 has decided to change the classification of glyphosate to category 2A, which means that the compound is considered to "probably carcinogenic to humans". This category relates to compounds for which there is limited evidence of carcinogenicity to humans and sufficient evidence of carcinogenicity on experimental animals. That is why we have investigated genotoxicity and cytotoxicity effects of the most commonly used pesticide: glyphosate and its impurities: N-(phosphonomethyl)iminodiacetic acid (PMIDA) and bis-(phosphonomethyl)amine on human peripheral blood mononuclear cells (PBMCs), mostly lymphocytes. DNA damage (analysis of DNA strand-breaks) using the single cell gel electrophoresis (comet assay) and ATP level were assessed. Cells were incubated with glyphosate and its impurities: PMIDA and bis-(phosphonomethyl)amine at concentrations from 0.01 to 10 mM for 24 hours. Evaluating genotoxicity using the comet assay showed a concentration-dependent increase in DNA damage for all compounds studied. ATP level was decreased to zero as a result of using the highest concentration of two investigated impurities, like bis-(phosphonomethyl)amine and PMIDA. Changes were observed using the highest concentration at which a person can be exposed as a result of acute intoxication. Our survey leads to a conclusion that the investigated compounds exhibited genotoxic and cytotoxic potential but only in high concentrations, to which people are not exposed environmentally. Acknowledgments: This work was supported by the Polish National Science Centre (Contract-2013/11/N/NZ7/00371), MSc Marta Kwiatkowska, project manager.

Keywords: cell viability, DNA damage, glyphosate, impurities, peripheral blood mononuclear cells

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Authors: Ljiljana Seric, Vladimir Divic, Marin Bugaric

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This paper presents methodology for spatial analysis of GIS data that is used for assessing the microlocation risk index from potential damages of high winds. The analysis is performed on freely available GIS data comprising information about wind load, terrain cover and topography of the area. The methodology utilizes the legislation of Eurocode norms for determination of wind load of buildings and constructions. The core of the methodology is adoption of the wind load parameters related to location on geographical spatial grid. Presented work is a part of the Wind Risk Project, supported by the European Commission under the Civil Protection Financial Instrument of the European Union (ECHO). The partners involved in Wind Risk project performed Wind Risk assessment and proposed action plan for three European countries – Slovenia, Croatia and Germany. The proposed method is implemented in GRASS GIS open source GIS software and demonstrated for Case study area of wider area of Split, Croatia. Obtained Wind Risk Index is visualized and correlated with critical infrastructures like buildings, roads and power lines. The results show good correlation between high Wind Risk Index with recent incidents related to wind.

Keywords: Eurocode norms, GIS, spatial analysis, wind distribution, wind risk

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Authors: Yusuf Jundi Sado

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Changes in land use are mostly credited to human actions that result in negative impacts on biodiversity and ecosystem functions. This study aims to analyze the dynamics of land use and land cover changes for sustainable natural resources planning and management. Chilalo-Galama Mountain Range, Ethiopia. This study used Thematic Mapper 05 (TM) for 1986, 2001 and Landsat 8 (OLI) data 2017. Additionally, data from the Central Statistics Agency on human population growth were analyzed. Semi-Automatic classification plugin (SCP) in QGIS 3.2.3 software was used for image classification. Global positioning system, field observations and focus group discussions were used for ground verification. Land Use Land Cover (LU/LC) change analysis was using maximum likelihood supervised classification and changes were calculated for the 1986–2001 and the 2001–2017 and 1986-2017 periods. The results show that agricultural land increased from 27.85% (1986) to 44.43% and 51.32% in 2001 and 2017, respectively with the overall accuracies of 92% (1986), 90.36% (2001), and 88% (2017). On the other hand, forests decreased from 8.51% (1986) to 7.64 (2001) and 4.46% (2017), and grassland decreased from 37.47% (1986) to 15.22%, and 15.01% in 2001 and 2017, respectively. It indicates for the years 1986–2017 the largest area cover gain of agricultural land was obtained from grassland. The matrix also shows that shrubland gained land from agricultural land, afro-alpine, and forest land. Population dynamics is found to be one of the major driving forces for the LU/LU changes in the study area.

Keywords: Landsat, LU/LC change, Semi-Automatic classification plugin, population dynamics, Ethiopia

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Authors: Daniela Monteiro

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Considering the entry into force of the new EU audit reform regarding statutory auditors, in effect in all member states since 2016, this research aims to identify which audit regulation rules are associated with a high-level audit quality on both its dimensions, i.e., the actual quality and the perceived quality, in relation to public interest entities, within the European Union, and whether those rules have the same impact on both dimensions. Its measurement was based on the following proxies: the quality of financial information through earnings management and the impact of qualified opinions on financial costs. We considered in the research regulation subjects such as auditors’ rotation and provision of services (NAS) and also the level of market concentration. The criteria to include these issues in the research was its contemplation of the new rules. We studied the period before the audit reform (2009-2015) when the regulation measures were less uniform. Besides the consideration of both dimensions of audit quality and several regulation measures, we believe our conclusions configure an important contribution to this research field, considering the involvement of the first 15 member states of the European Union. The results consolidate the assumption that the balance between competence and independence is not the only challenge related to the regulation of the audit profession. The evidence demonstrates that the balance between actual and perceived quality is also a relevant matter. The major conclusion is that the challenge is to keep balanced both actual and perceived audit quality whilst ensuring the independence and competence of auditors.

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: Jagna Mucha, Anna Kańciak

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The concept of good governance constitutes a wide perspective for academic discussion, because it provides a substantial theoretical background for settling many practical problems faced contemporarily by the EU. The basic assumptions of good governance have basically remained unchanged since the 90’s, when the concept was introduced by the World Bank, although the scholarly discussions reveal new facets of the said concept, when related to specific domains. The paper discusses the application of the specific elements of the concept of good governance in the field of the international investment law. Specifically, it seeks to demonstrate that the concept of good governance regulates the issue of international investments in that, among others, it requires the application of the alternative dispute resolution in order to make the investment law enforceable in the best possible way.

Keywords: dispute resolution, european union, investments, good governance

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Authors: Chuang-Chang Chang, Keng-Yu Ho, Yu-Jen Hsiao, Hsin-Ni Yang

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Using a sample of detailed ownership data of 1,032 listed commercial bank observations in 30 European countries from 2004 to 2010, we explore what categories of shareholder are more likely to use derivatives and how different types of owners affect the bank value. We find that a shift in equity from bank investors to either non-financial companies or institutional investors have increase incentives to use derivatives. Moreover, we have significant evidence that a shift in equity from bank investors to either family or manager shareholders who attend derivative activities will decrease bank value. However, a shift in equity from bank investors to non-financial companies who use derivative instrument will increase the bank value. Our results are also robustness to address for the potential endogeneity problems.

Keywords: derivative usage, ownership structure, bank value

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Authors: Boulis M. Ibrahim, Iordanis A. Kalaitzoglou

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A micro structural pricing model is proposed in which price components account for learning by incorporating changing expectations of the trading intensity and the risk level of incoming trades. An analysis of European carbon futures transactions finds expected trading intensity to increase the information component and decrease the liquidity component of price changes, but at different rates. Among the results, the expected persistence in trading intensity explains the majority of the auto correlations in the level and the conditional volatility of price changes, helps predict hourly patterns in the bid–ask spread and differentiates between the impact of buy versus sell and continuing versus reversing trades.

Keywords: CO2 emission allowances, market microstructure, duration, price discovery

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Authors: Smriti Agarwal, Ashish Payal, B. V. R. Reddy

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IEEE 802.15.4 is a Low Rate Wireless Personal Area Networks (LR-WPAN) standard combined with ZigBee, which is going to enable new applications in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) domain. In recent years, it has become a popular standard for WSNs. Wireless communication among sensor motes, enabled by IEEE 802.15.4 standard, is extensively replacing the existing wired technology in a wide range of monitoring and control applications. Researchers have proposed a routing framework and mechanism that interacts with the IEEE 802.15.4 standard using software platform. In this paper, we have designed and implemented MAC based routing (MBR) based on IEEE 802.15.4 standard using a hardware platform “SENSEnuts”. The experimental results include data through light and temperature sensors obtained from communication between PAN coordinator and source node through coordinator, MAC address of some modules used in the experimental setup, topology of the network created for simulation and the remaining battery power of the source node. Our experimental effort on a WSN Testbed has helped us in bridging the gap between theoretical and practical aspect of implementing IEEE 802.15.4 for WSNs applications.

Keywords: IEEE 802.15.4, routing, WSN, ZigBee

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Authors: Efthimios Kokkotos, Kostakis Marios, Beis Alexandros, Angelos Patakas, Antonios Avgeris, Vassilios Triantafyllidis

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H, C, and O stable isotope ratios were measured in different olive oils and table olives originating from different regions of Greece. In particular, the stable isotope ratios of different olive oils produced in the Lakonia region (Peloponesse – South Greece) from different varieties, i.e., cvs ‘Athinolia’ and ‘koroneiki’, were determined. Additionally, stable isotope ratios were also measured in different table olives (cvs ‘koroneiki’ and ‘kalamon’) produced in the same region (Messinia). The aim of this study was to provide sufficient isotope ratio data regarding each variety and region of origin that could be used in discriminative studies of oil olives and table olives produced by different varieties in other regions. In total, 97 samples of olive oil (cv ‘Athinolia’ and ‘koroneiki’) and 67 samples of table olives (cvs ‘kalmon’ and ‘koroneiki’) collected during two consecutive sampling periods (2021-2022 and 2022-2023) were measured. The C, H, and O isotope ratios were measured using Isotope Ratio Mass Spectrometry (IRMS), and the results obtained were analyzed using chemometric techniques. The measurements of the isotope ratio analyses were expressed in permille (‰) using the delta δ notation (δ=Rsample/Rstandard-1, where Rsample and Rstandardis represent the isotope ratio of sample and standard). Results indicate that stable isotope ratios of C, H, and O ranged between -28,5+0,45‰, -142,83+2,82‰, 25,86+0,56‰ and -29,78+0,71‰, -143,62+1,4‰, 26,32+0,55‰ in olive oils produced in Lakonia region from ‘Athinolia’ and ‘koroneiki ‘varieties, respectively. The C, H, and O values from table olives originated from Messinia region were -28,58+0,63‰, -138,09+3,27‰, 25,45+0,62‰ and -29,41+0,59‰,-137,67+1,15‰, 24,37+0,6‰ for ‘Kalamon’ and ‘koroneiki’ olives respectively. Acknowledgments: This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH—CREATE—INNOVATE (Project code: T2EDK-02637; MIS 5075094, Title: ‘Innovative Methodological Tools for Traceability, Certification and Authenticity Assessment of Olive Oil and Olives’).

Keywords: olive oil, table olives, Isotope ratio, IRMS, geographical origin

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Due to increasing cost pressure in global markets, artificial intelligence is becoming a technology that is decisive for competition. Predictive quality enables machinery and plant manufacturers to ensure product quality by using data-driven forecasts via machine learning models as a decision-making basis for test results. The use of cross-process Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: predictive quality, hydraulics, machine learning, classification, supervised learning

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Authors: Ke Ren, Huiruo Shi, Linsen Li, Baoshuai Wang, Yu Zhou

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Since some discriminative features are required for ground moving targets classification, we propose a new feature extraction method based on micro-Doppler signature. Firstly, the time-frequency analysis of measured data indicates that the time-frequency spectrograms of the three kinds of ground moving targets, i.e., single walking person, two people walking and a moving wheeled vehicle, are discriminative. Then, a three-dimensional time-frequency feature vector is extracted from the time-frequency spectrograms to depict these differences. At last, a Support Vector Machine (SVM) classifier is trained with the proposed three-dimensional feature vector. The classification accuracy to categorize ground moving targets into the three kinds of the measured data is found to be over 96%, which demonstrates the good discriminative ability of the proposed micro-Doppler feature.

Keywords: micro-doppler, time-frequency analysis, feature extraction, radar target classification

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Authors: Salah Ghamari

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In this study, the ability of self organizing map artificial (SOM) neural networks in clustering the wheat seeds varieties according to morphological properties of them was considered. The SOM is one type of unsupervised competitive learning. Experimentally, five morphological features of 300 seeds (including three varieties: gaskozhen, Md and sardari) were obtained using image processing technique. The results show that the artificial neural network has a good performance (90.33% accuracy) in classification of the wheat varieties despite of high similarity in them. The highest classification accuracy (100%) was achieved for sardari.

Keywords: artificial neural networks, clustering, self organizing map, wheat variety

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Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Tamas Dani

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The author’s research explores the geo-economical role and importance of some small and medium sized states, reviews their adaption strategies in foreign trade and also in foreign affairs in the course of changing into a multipolar world, uses international background. With these, the paper analyses the recent years and the future of ‘Opening towards Eastern foreign economic policies’ from East-Central Europe and parallel with that the ‘Western foreign economy policies’ from Asia, as the Chinese One Belt One Road new silk route plans (so far its huge part is an infrastructural development plan to reach international trade and investment aims). It can be today’s question whether these ideas will reshape the global trade or not. How does the new silk road initiatives and the Eurasian Union reflect the effect of globalization? It is worth to analyse that how did Central and Eastern European countries open to Asia; why does China have the focus of the opening policies in many countries and why could China be seen as the ‘winner’ of the world economic crisis after 2008. The research is based on the following methodologies: national and international literature, policy documents and related design documents, complemented by processing of international databases, statistics and live interviews with leaders from East-Central European countries’ companies and public administration, diplomats and international traders. The results also illustrated by mapping and graphs. The research will find out as major findings whether the state decision-makers have enough margin for manoeuvres to strengthen foreign economic relations. This work has a hypothesis that countries in East-Central Europe have real chance to diversify their relations in foreign trade, focus beyond their traditional partners. This essay focuses on the opportunities of East-Central-European countries in diversification of foreign trade relations towards China and Russia in terms of ‘Eastern Openings’. The effects of the new silk road initiatives and the Eurasian Union to Hungary’s economy with a comparing outlook on East-Central European countries and exploring common regional cooperation opportunities in this area. The essay concentrate on the changing trade relations between East-Central-Europe and China as well as Russia, try to analyse the effects of the new silk road initiatives and the Eurasian Union also. In the conclusion part, it shows how the cooperation is necessary for the East-Central European countries if they want to have a non-asymmetric trade with Russia, China or some Chinese regions (Pearl River Delta, Hainan, …). The form of the cooperation for the East-Central European nations can be Visegrad 4 Cooperation (V4), Central and Eastern European Countries (CEEC16), 3 SEAS Cooperation (or BABS – Baltic, Adriatic, Black Seas Initiative).

Keywords: China, East-Central Europe, foreign trade relations, geoeconomics, geopolitics, Russia

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Authors: Younghoon Kim, Seoung Bum Kim

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One-class classification plays an important role in detecting outlier and abnormality from normal observations. In the previous research, several attempts were made to extend the scope of application of the one-class classification techniques to statistical process control problems. For most previous approaches, such as support vector data description (SVDD) control chart, the design of the control limits is commonly based on the assumption that the proportion of abnormal observations is approximately equal to an expected Type I error rate in Phase I process. Because of the limitation of the one-class classification techniques based on convex optimization, we cannot make the proportion of abnormal observations exactly equal to expected Type I error rate: controlling Type I error rate requires to optimize constraints with integer decision variables, but convex optimization cannot satisfy the requirement. This limitation would be undesirable in theoretical and practical perspective to construct effective control charts. In this work, to address the limitation of previous approaches, we propose the one-class classification algorithm based on the mixed integer programming technique, which can solve problems formulated with continuous and integer decision variables. The proposed method minimizes the radius of a spherically shaped boundary subject to the number of normal data to be equal to a constant value specified by users. By modifying this constant value, users can exactly control the proportion of normal data described by the spherically shaped boundary. Thus, the proportion of abnormal observations can be made theoretically equal to an expected Type I error rate in Phase I process. Moreover, analogous to SVDD, the boundary can be made to describe complex structures by using some kernel functions. New multivariate control chart applying the effectiveness of the algorithm is proposed. This chart uses a monitoring statistic to characterize the degree of being an abnormal point as obtained through the proposed one-class classification. The control limit of the proposed chart is established by the radius of the boundary. The usefulness of the proposed method was demonstrated through experiments with simulated and real process data from a thin film transistor-liquid crystal display.

Keywords: control chart, mixed integer programming, one-class classification, support vector data description

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Authors: Joseph Kimutai Langat

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Tea typology is the science of classifying tea. This study was carried out between November 2023 and July 2024, whose main objective was to investigate the typological classification nomenclature of processed tea in the world, narrowing down to Kenya. Centres of origin, historical background, tea growing region, scientific naming system, market, fermentation levels, processing/ oxidation levels and cultural reasons are used to classify tea at present. Of these, the most common typology is by oxidation, and more specifically, by the production methods within the oxidation categories. While the Asian tea producing countries categorises tea products based on the decreasing oxidation levels during the manufacturing process: black tea, green tea, oolong tea and instant tea, Kenya’s tea typology system is based on the degree of fermentation process, i.e. black tea, purple tea, green tea and white tea. Tea is also classified into five categories: black tea, green tea, white tea, oolong tea, and dark tea. Black tea is the main tea processed and exported in Kenya, manufactured mainly by withering, rolling, or by use of cutting-tearing-curling (CTC) method that ensures efficient conversion of leaf herbage to made tea, oxidizing, and drying before being sorted into different grades. It is from these varied typological methods that this review paper concludes that different regions of the world use different classification nomenclature. Therefore, since tea typology is not standardized, it is recommended that a global tea regulator dealing in tea classification be created to standardize tea typology, with domestic in-country regulatory bodies in tea growing countries accredited to implement the global-wide typological agreements and resolutions.

Keywords: classification, fermentation, oxidation, tea, typology

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Authors: Vera Varazashvili, Murman Tsarakhov, Tamar Mirianashvili, Teimuraz Pavlenishvili, Tengiz Machaladze, Mzia Khundadze

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Standard Gibbs energy of formation ΔGfor(298.15) of lanthanide-iron double oxides of garnet-type crystal structure R3Fe5O12 - RIG (R – are rare earth ions) from initial oxides are evaluated. The calculation is based on the data of standard entropies S298.15 and standard enthalpies ΔH298.15 of formation of compounds which are involved in the process of garnets synthesis. Gibbs energy of formation is presented as temperature function ΔGfor(T) for the range 300-1600K. The necessary starting thermodynamic data were obtained from calorimetric study of heat capacity and by using the semi-empirical method for calculation of ΔH298.15 (formation). Thermodynamic functions for standard temperature – enthalpy, entropy and Gibbs energy - are recommended as reference data for technological evaluations. Through the isostructural series of rare earth-iron garnets the correlation between thermodynamic properties and characteristics of lanthanide ions are elucidated.

Keywords: calorimetry, entropy, heat capacity, Gibbs energy of formation, rare earth iron garnets

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Authors: Aamna Tufail, Catherine Anyanwu

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Introduction/Aims: Neurosensory deficits following third molar removal affect the quality of life markedly. The purpose of this audit was to evaluate the incidence of IDN and LN damage and to compare departmental rates to an established standard. A secondary objective was to provide a descriptive summary of identified cases for clinical learning. Materials and Methods: A retrospective audit was conducted by a telephone survey of 101 patients who had third molar extractions performed under LA, IVS, or GA from January 2019 to June 2020 at a District General Hospital. The results were compared to a clinical standard identified as Cheng et al1. Data collection included mode of surgery, mode of anaesthesia, grade of clinician, assessment of difficulty, severity, and duration of symptoms. Results/Statistics: A total of 101 patients had 136 third molars extracted. Age range was 18-84 years. 44% extractions were under LA, 52% under GA, and 4% under IV sedation. 30% were simple extractions, 68% were surgical removals, 2% were unspecified. 89% extractions were performed by an Associate Specialist, 5% by a consultant, and 6% by unspecified grade of clinician. The rate of IDN injuries was 2.9% (n=4), higher than standard (0.3%). The rate of LN injuries was 0.7% (n=1), same as standard (0.7%). The 5 cases of neurosensory deficits are discussed in detail. Conclusions/Clinical Relevance: The rate of ID nerve injuries was higher than the standard. The rate of LN complications was lower than the standard.

Keywords: inferior dental nerve, lingual nerve, nerve injuries, third molars

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