Search results for: hybrid working models

Authors: M. Şimşek

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Rapidly changing factors that affect daily life also affect operational environment and the way military leaders fulfill their missions. With the help of technological developments, traditional linearity of conflict and war has started to fade away. Furthermore, mission domain has broadened to include traditional threats, hybrid threats and new challenges of cyber and space. Considering the future operational environment, future military leaders need to adapt themselves to the new challenges of the future battlefield. But how to decide what kind of features of leadership are required to operate and accomplish mission in the new complex battlefield? In this article, the main aim is to provide answers to this question. To be able to find right answers, first leadership and leadership components are defined, and then characteristics of future operational environment are analyzed. Finally, leadership features that are required to be successful in redefined battlefield are explained.

Keywords: future operational environment, leadership, leadership components

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Authors: Jacqueline Rose T. Alipo-on, Francesca Isabelle F. Escobar, Myles Joshua T. Tan, Hezerul Abdul Karim, Nouar Al Dahoul

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Electrocardiogram (ECG) signal analysis and processing are crucial in the diagnosis of cardiovascular diseases, which are considered one of the leading causes of mortality worldwide. However, the traditional rule-based analysis of large volumes of ECG data is time-consuming, labor-intensive, and prone to human errors. With the advancement of the programming paradigm, algorithms such as machine learning have been increasingly used to perform an analysis of ECG signals. In this paper, various deep learning algorithms were adapted to classify five classes of heartbeat types. The dataset used in this work is the synthetic MIT-BIH Arrhythmia dataset produced from generative adversarial networks (GANs). Various deep learning models such as ResNet-50 convolutional neural network (CNN), 1-D CNN, and long short-term memory (LSTM) were evaluated and compared. ResNet-50 was found to outperform other models in terms of recall and F1 score using a five-fold average score of 98.88% and 98.87%, respectively. 1-D CNN, on the other hand, was found to have the highest average precision of 98.93%.

Keywords: heartbeat classification, convolutional neural network, electrocardiogram signals, generative adversarial networks, long short-term memory, ResNet-50

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Authors: Amandeep Sharma, Digvaijay Verma, Ruplal Choudhary

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Food processing is an activity across the globe that help in better handling of agricultural produce, including dairy, meat, and fish. The operations carried out in the food industry includes raw material quality authenticity; sorting and grading; processing into various products using thermal treatments – heating, freezing, and chilling; packaging; and storage at the appropriate temperature to maximize the shelf life of the products. All this is done to safeguard the food products and to ensure the distribution up to the consumer. The approaches to develop predictive models based on mathematical or statistical tools or empirical models’ development has been reported for various milk processing activities, including plant maintenance and wastage. Recently AI is the key factor for the fourth industrial revolution. AI plays a vital role in the food industry, not only in quality and food security but also in different areas such as manufacturing, packaging, and cleaning. A new conceptual model was developed, which shows that smaller sample size as only spectra would be required to predict the other values hence leads to saving on raw materials and chemicals otherwise used for experimentation during the research and new product development activity. It would be a futuristic approach if these tools can be further clubbed with the mobile phones through some software development for their real time application in the field for quality check and traceability of the product.

Keywords: predictive modlleing, ann, ai, food

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Authors: Ugwu Elias Ikechukwu

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Discussions on bilingualism have always dwelt on how the mother tongue interferes with the target language. This interference is considered a serious problem in second language learning. Usually, the interference has been phonological. But the objective of this research is to explore how the target language interferes with the mother tongue. In the case of the Igbo language, it interferes with English mostly at the phonological level while English interferes with Igbo at the realm of vocabulary. The result is a new language \"Engligbo\" which is a hybrid of English and Igbo. The Igbo language spoken by about 25 million people is one of the three most prominent languages in Nigeria. This paper discusses the phenomenal Engligbo, and other implications for Igbo learners of English. The method of analysis is descriptive. A number of recommendations were made that would help teachers handle problems arising from such mutual interferences.

Keywords: reciprocal interferences, bilingualism, implications, language pedagogy

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Authors: Andrey Khalov

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The rapid growth of unstructured data necessitates advanced methods for transforming raw information into structured knowledge, particularly in domain-specific contexts such as IT service management and outsourcing. This paper presents a methodology for automatically constructing domain ontologies using the DOLCE framework as the base ontology. The research focuses on expanding ITIL-based ontologies by integrating concepts from ITSMO, followed by the extraction of entities and relationships from domain-specific texts through transformers and statistical methods like formal concept analysis (FCA). In particular, this work introduces an R-GNN-based approach for ontology mapping, enabling more efficient entity extraction and ontology alignment with existing knowledge bases. Additionally, the research explores transfer learning techniques using pre-trained transformer models (e.g., DeBERTa-v3-large) fine-tuned on synthetic datasets generated via large language models such as LLaMA. The resulting ontology, termed IT Ontology (ITO), is evaluated against existing methodologies, highlighting significant improvements in precision and recall. This study advances the field of ontology engineering by automating the extraction, expansion, and refinement of ontologies tailored to the IT domain, thus bridging the gap between unstructured data and actionable knowledge.

Keywords: ontology mapping, knowledge graphs, R-GNN, ITIL, NER

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Authors: S.K. Bhandari, E. Veneklaas, L. McCaw, R. Mazanec, K. Whitford, M. Renton

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Eucalyptus marginata, E. diversicolor and Corymbia calophylla form widespread forests in south-west Western Australia (SWWA). These forests have economic and ecological importance, and therefore, tree growth and sustainable management are of high priority. This paper aimed to analyse and model the growth of these species at both stand and individual levels, but this presentation will focus on predicting the growth of E. Marginata at the individual tree level. More specifically, the study wanted to investigate how well individual E. marginata tree growth could be predicted by considering the diameter and height of the tree at the start of the growth period, and whether this prediction could be improved by also accounting for the competition from neighbouring trees in different ways. The study also wanted to investigate how many neighbouring trees or what neighbourhood distance needed to be considered when accounting for competition. To achieve this aim, the Pearson correlation coefficient was examined among competition indices (CIs), between CIs and dbh growth, and selected the competition index that can best predict the diameter growth of individual trees of E. marginata forest managed under different thinning regimes at Inglehope in SWWA. Furthermore, individual tree growth models were developed using simple linear regression, multiple linear regression, and linear mixed effect modelling approaches. Individual tree growth models were developed for thinned and unthinned stand separately. The developed models were validated using two approaches. In the first approach, models were validated using a subset of data that was not used in model fitting. In the second approach, the model of the one growth period was validated with the data of another growth period. Tree size (diameter and height) was a significant predictor of growth. This prediction was improved when the competition was included in the model. The fit statistic (coefficient of determination) of the model ranged from 0.31 to 0.68. The model with spatial competition indices validated as being more accurate than with non-spatial indices. The model prediction can be optimized if 10 to 15 competitors (by number) or competitors within ~10 m (by distance) from the base of the subject tree are included in the model, which can reduce the time and cost of collecting the information about the competitors. As competition from neighbours was a significant predictor with a negative effect on growth, it is recommended including neighbourhood competition when predicting growth and considering thinning treatments to minimize the effect of competition on growth. These model approaches are likely to be useful tools for the conservations and sustainable management of forests of E. marginata in SWWA. As a next step in optimizing the number and distance of competitors, further studies in larger size plots and with a larger number of plots than those used in the present study are recommended.

Keywords: competition, growth, model, thinning

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Authors: Yang Gon Seo, Chang-Joon Kim, Dae Hyeok Kim

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It is estimated that 1.5 billion tires are produced worldwide each year which will eventually end up as waste tires representing a major potential waste and environmental problem. Pyrolysis has been great interest in alternative treatment processes for waste tires to produce valuable oil, gas and solid products. The oil and gas products may be used directly as a fuel or a chemical feedstock. The solid produced from the pyrolysis of tires ranges typically from 30 to 45 wt% and have high carbon contents of up to 90 wt%. However, most notably the solid have high sulfur contents from 2 to 3 wt% and ash contents from 8 to 15 wt% related to the additive metals. Upgrading tire pyrolysis products to high-value products has concentrated on solid upgrading to higher quality carbon black and to activated carbon. Hydrogen sulfide and ammonia are one of the common malodorous compounds that can be found in emissions from many sewages treatment plants and industrial plants. Therefore, removing these harmful gasses from emissions is of significance in both life and industry because they can cause health problems to human and detrimental effects on the catalysts. In this work, pyrolytic carbon black from waste tires was used to develop adsorbent with good adsorption capacity for removal of hydrogen and ammonia. Pyrolytic carbon blacks were prepared by pyrolysis of waste tire chips ranged from 5 to 20 mm under the nitrogen atmosphere at 600℃ for 1 hour. Pellet-type adsorbents were prepared by a mixture of carbon black, metal oxide and sodium hydroxide or hydrochloric acid, and their adsorption capacities were estimated by using the breakthrough curve of a continuous fixed bed adsorption column at ambient condition. The adsorbent was manufactured with a mixture of carbon black, iron oxide(III), and sodium hydroxide showed the maximum working capacity of hydrogen sulfide. For ammonia, maximum working capacity was obtained by the adsorbent manufactured with a mixture of carbon black, copper oxide(II), and hydrochloric acid.

Keywords: adsorbent, ammonia, pyrolytic carbon black, hydrogen sulfide, metal oxide

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Authors: Hsin Lee, Hsuan Lee

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Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

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Authors: František Šebek, Petr Kubík, Jindřich Petruška, Jiří Hůlka

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Present study is aimed on the cutting process of circular cross-section rods where the fracture is used to separate one rod into two pieces. Incorporating the phenomenological ductile fracture model into the explicit formulation of finite element method, the process can be analyzed without the necessity of realizing too many real experiments which could be expensive in case of repetitive testing in different conditions. In the present paper, the steel AISI 1045 was examined and the tensile tests of smooth and notched cylindrical bars were conducted together with biaxial testing of the notched tube specimens to calibrate material constants of selected phenomenological ductile fracture models. These were implemented into the Abaqus/Explicit through user subroutine VUMAT and used for cutting process simulation. As the calibration process is based on variables which cannot be obtained directly from experiments, numerical simulations of fracture tests are inevitable part of the calibration. Finally, experiments regarding the cutting process were carried out and predictive capability of selected fracture models is discussed. Concluding remarks then make the summary of gained experience both with the calibration and application of particular ductile fracture criteria.

Keywords: ductile fracture, phenomenological criteria, cutting process, explicit formulation, AISI 1045 steel

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Authors: Asep Andi Suryandi, Katri Yulianto, Dewi Rianti Mandasari

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The development of environmentally friendly vehicles increasingly attracted the attention of almost all countries in the world, including Indonesia. There are various types of environmentally friendly vehicles, such as: electric vehicles, hybrid, and fuel gas. The Electric vehicle has been developed in Indonesia, a private or public vehicle. But many electric vehicles had been developed using the battery as a power source, while the battery technology for electric vehicles still constraints in capacity, dimensions of the battery itself and charging system. Trolley bus is one of the electric buses with the main power source of the network catenary / overhead line with trolley pole as the point of contact. This paper will discuss the design and manufacture electrical system in Trolleybus.

Keywords: trolley bus, electric propulsion system, design, manufacture, electric vehicle

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Authors: Mohsen Azarmjoo, Yasaman Azarmjoo, Zahra Alikhani Koopaei

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This article introduces devices that can transfer sunlight to buildings that do not have access to direct sunlight during the day. The transmission and reflection of sunlight are done through the movement of movable mirrors. The focus of this article is on two models of sun tracker systems designed and built by the Macad team. In fact, this article will reveal the distinction between the two Macad devices and the previously built competitor device. What distinguishes the devices built by the Macad team from the competitor's device is the different mode of operation and the difference in the location of the sensors. Given that the devices have the same results, the Macad team has tried to reduce the defects of the competitor's device as much as possible. The special feature of the second type of device built by the Macad team has enabled buildings with different construction positions to use sun tracking systems. This article will also discuss diagrams of the path of sunlight transmission and more details of the device. It is worth mentioning that fixed mirrors are also placed next to the main devices. So that the light shining on the first device is reflected to these mirrors, this light is guided within the light receiver space and is transferred to the different parts around by steel sheets built in the light receiver space, and finally, these spaces benefit from sunlight.

Keywords: design, construction, mechatronic device, sun tracker system, sun tracker, sunlight

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Authors: Chiedu C. Ifekandu, Olusegun Sangowawa, Jean E. Njab

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Background: Health care providers (HCPs) in Nigeria receive little or no training of the healthcare needs of men who have sex with men (MSM) limiting the quality and effectiveness of comprehensive HIV prevention and treatment services. Consequently, most MSM disguise themselves to access services which limit the quality of care provided partly due to challenges related to stigma and discrimination, and breach of confidentiality. Objective: To assess the knowledge of healthcare providers on effective intervention for MSM. Methods: We trained 122 HIV focal persons drawn from 60 health facilities from twelve Nigerian states. , the participants were requested to complete a pre-training questionnaire to assess their level of working experience with key populations as a baseline. Participants included male and female doctors, nurses and counselors/testers. A test was administered to measure their knowledge on MSM sexual risk practices, HIV prevention and healthcare needs and also to assess their attitudes (including homophobia) and beliefs and how it affects service uptake by key populations particularly MSM prior and immediately after the training to ascertain the impact of the training. Results: The mean age of the HCP was 38 years +/- SD Of the 122 HCPs (45 % female, 55 % male; 85 % counsellor/testers; 15 % doctors and nurses; 92 % working in government facilities) from 42 health facilities were trained, of which 105 attempted the test questions. At the baseline, few HCPs reported any prior sensitivity training on MSM. Most of the HCPs had limited knowledge of MSM sexual health needs. Over 90% of the HCPs believed that homosexuality is a mental illness. 8 % do not consider MSM, FSW and PWID as key populations for HIV infection. 45 % lacked knowledge on MSM anal sexual practices. The post-test showed that homophobic attitudes had decreased significantly by the end of the training; the health care providers have acquired basic knowledge compared to the pre-test. Conclusions: Scaling up MSM sensitivity training for Nigerian HCPs is likely to be a timely and effective means to improve their understanding of MSM-related health issues, reduce homophobic sentiments and enhance their capacity to provide responsive HIV prevention, treatment and care services in a supportive and non-stigmatizing environment.

Keywords: healthcare providers, key population, men who have sex with men, HCT

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Authors: Derek O Reilly, Piotr Milczarski, Shane Dowdall, Artur Hłobaż, Krzysztof Podlaski, Hiram Bollaert

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Learning through creation of contextual games is a very promising way/tool for interdisciplinary and international group projects. During 2013 and 2014 we took part and organized two intensive students projects in different conditions. The projects enrolled 68 students and 12 mentors from 5 countries. In the paper we want to share our experience how to strengthen the chances to succeed in short (12-15 days long) student projects. In our case almost all teams prepared working prototype and the results were highly appreciated by external experts.

Keywords: contextual games, mobile games, GGULIVRR, walkabout, Erasmus intensive programme

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Authors: Rafat Fazeli, Reza Fazeli

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This paper focuses on the study of the welfare state and social wage in the leading liberal economy of the United States. The welfare state acquired a broad acceptance as a major socioeconomic achievement of the liberal democracy in the Western industrialized countries during the postwar boom period. The modern and modified vision of capitalist democracy offered, on the one hand, the possibility of high growth rate and, on the other hand, the possibility of continued progression of a comprehensive system of social support for a wider population. The economic crises of the 1970s, provided the ground for a great shift in economic policy and ideology in several Western countries, most notably the United States and the United Kingdom (and to a lesser extent Canada under Prime Minister Brian Mulroney). In the 1980s, the free market oriented reforms undertaken under Reagan and Thatcher greatly affected the economic outlook not only of the United States and the United Kingdom, but of the whole Western world. The movement which was behind this shift in policy is often called neo-conservatism. The neoconservatives blamed the transfer programs for the decline in economic performance during the 1970s and argued that cuts in spending were required to go back to the golden age of full employment. The agenda for both Reagan and Thatcher administrations was rolling back the welfare state, and their budgets included a wide range of cuts for social programs. The question is how successful were Reagan and Thatcher’s efforts to achieve retrenchment? The paper involves an empirical study concerning the distributive role of the welfare state in the two countries. Other studies have often concentrated on the redistributive effect of fiscal policy on different income brackets. This study examines the net benefit/ burden position of the working population with respect to state expenditures and taxes in the postwar period. This measurement will enable us to find out whether the working population has received a net gain (or net social wage). This study will discuss how the expansion of social expenditures and the trend of the ‘net social wage’ can be linked to distinct forms of economic and social organizations. This study provides an empirical foundation for analyzing the growing significance of ‘social wage’ or the collectivization of consumption and the share of social or collective consumption in total consumption of the working population in the recent decades. The paper addresses three other major questions. The first question is whether the expansion of social expenditures has posed any drag on capital accumulation and economic growth. The findings of this study provide an analytical foundation to evaluate the neoconservative claim that the welfare state is itself the source of economic stagnation that leads to the crisis of the welfare state. The second question is whether the increasing ideological challenges from the right and the competitive pressures of globalization have led to retrenchment of the American welfare states in the recent decades. The third question is how social policies have performed in the presence of the rising inequalities in the recent decades.

Keywords: the welfare state, social wage, The United States, limits to growth

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Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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Authors: Jefferson Hernandez, Juan Padilla

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Estimation of price elasticity of demand is a valuable tool for the task of price settling. Given its relevance, it is an active field for microeconomic and statistical research. Price elasticity in the industry of oil and gas, in particular for fuels sold in gas stations, has shown to be a challenging topic given the market and state restrictions, and underlying correlations structures between the types of fuels sold by the same gas station. This paper explores the Lotka-Volterra model for the problem for price elasticity estimation in the context of fuels; in addition, it is introduced multivariate random effects with the purpose of dealing with errors, e.g., measurement or missing data errors. In order to model the underlying correlation structures, the Inverse-Wishart, Hierarchical Half-t and LKJ distributions are studied. Here, the Bayesian paradigm through Markov Chain Monte Carlo (MCMC) algorithms for model estimation is considered. Simulation studies covering a wide range of situations were performed in order to evaluate parameter recovery for the proposed models and algorithms. Results revealed that the proposed algorithms recovered quite well all model parameters. Also, a real data set analysis was performed in order to illustrate the proposed approach.

Keywords: price elasticity, volume, correlation structures, Bayesian models

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Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

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Authors: Josephine K. Mule, Levi Obonyo

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Technology has changed human life over time. The COVID-19 pandemic has altered the work set-up, the school system, the shopping experience, church attendance, and even the way athletes train in Kenya. Although the use of technology to communicate and maintain interactions has been on the rise in the last 30 years, the uptake during the COVID-19 pandemic has been unprecedented. Traditionally, ‘paid’ work has been considered to take place outside the “home house” but COVID-19 has resulted in what is now being referred to as “the world’s largest work-from-home experiment” with up to 43 percent of employees working at least some of the time remotely. This study was conducted on 90 respondents from across remote work set-ups, school systems, merchants and customers of online shopping, church leaders and congregants and athletes, and their coaches. Data were collected by questionnaires and interviews that were conducted online. The data is based on the first three months since the first case of coronavirus was reported in Kenya. This study found that the use of technology is in the center of working remotely with work interactions being propelled on various online platforms including, Zoom, Microsoft Teams, and Google Meet, among others. The school system has also integrated the use of technology, including students defending their thesis/dissertations online and university graduations being conducted virtually. Kenya is known for its long-distance runners, due to the directives to reduce interactions; coaches have taken to providing their athletes with guidance on training on social media using applications such as WhatsApp. More local stores are now offering the shopping online option to their customers. Churches have also felt the brunt of the situation, especially because of the restrictions on crowds resulting in online services becoming more popular in 2020 than ever before. Artists, innovatively have started online musical concerts. The findings indicate that one of the outcomes in the Kenyan society that is evident as a result of the COVID-19 period is a population that is using technology more to communicate and get work done. Vices that have thrived in this season where the use of technology has increased, include the spreading of rumors on social media and cyberbullying. The place of technology seems to have been cemented by demand during this period.

Keywords: communication, coronavirus, COVID-19, Kenya, technology

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Authors: Vladimir S. Timofeev

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In this article the problem of distributional moments estimation is considered. The new approach of moments estimation based on usage of the characteristic function is proposed. By statistical simulation technique, author shows that new approach has some robust properties. For calculation of the derivatives of characteristic function there is used numerical differentiation. Obtained results confirmed that author’s idea has a certain working efficiency and it can be recommended for any statistical applications.

Keywords: characteristic function, distributional moments, robustness, outlier, statistical estimation problem, statistical simulation

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Authors: Erick Pruchnicki, Nikhil Padhye

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Plates are important engineering structures which have attracted extensive research since the 19th century. The subject of this work is statical analysis of a linearly elastic homogenous plate under small deformations. A 'thin plate' is a three-dimensional structure comprising of a small transverse dimension with respect to a flat mid-surface. The general aim of any plate theory is to deduce a two-dimensional model, in terms of mid-surface quantities, to approximately and accurately describe the plate's deformation in terms of mid-surface quantities. In recent decades, a common starting point for this purpose is to utilize series expansion of a displacement field across the thickness dimension in terms of the thickness parameter (h). These attempts are mathematically consistent in deriving leading-order plate theories based on certain a priori scaling between the thickness and the applied loads; for example, asymptotic methods which are aimed at generating leading-order two-dimensional variational problems by postulating formal asymptotic expansion of the displacement fields. Such methods rigorously generate a hierarchy of two-dimensional models depending on the order of magnitude of the applied load with respect to the plate-thickness. However, in practice, applied loads are external and thus not directly linked or dependent on the geometry/thickness of the plate; thus, rendering any such model (based on a priori scaling) of limited practical utility. In other words, the main limitation of these approaches is that they do not furnish a single plate model for all orders of applied loads. Following analogy of recent efforts of deploying Fourier-series expansion to study convergence of reduced models, we propose two-dimensional model(s) resulting from truncation of the potential energy and rigorously prove the convergence of these two-dimensional plate models to the parent three-dimensional linear elasticity with increasing truncation order of the potential energy.

Keywords: plate theory, Fourier-series expansion, convergence result, Legendre polynomials

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Authors: Jonathan Josephs-Spaulding, Hannah Rettig, Simon Graspeunter, Jan Rupp, Christoph Kaleta

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Background: Recurrent urinary tract infections (rUTIs) are a global cause of emergency room visits and represent a significant burden for public health systems. Therefore, metatranscriptomic approaches to investigate metabolic exchange and crosstalk between uropathogenic Escherichia coli (UPEC), which is responsible for 90% of UTIs, and collaborating pathogens of the urogenital microbiome is necessary to better understand the pathogenetic processes underlying rUTIs. Objectives: This study aims to determine the level in which uropathogens optimize the host urinary metabolic environment to succeed during invasion. By developing patient-specific metabolic models of infection, these observations can be taken advantage of for the precision treatment of human disease. Methods: To date, we have set up an rUTI patient cohort and observed various urine-associated pathogens. From this cohort, we developed patient-specific metabolic models to predict bladder microbiome metabolism during rUTIs. This was done by creating an in silico metabolomic urine environment, which is representative of human urine. Metabolic models of uptake and cross-feeding of rUTI pathogens were created from genomes in relation to the artificial urine environment. Finally, microbial interactions were constrained by metatranscriptomics to indicate patient-specific metabolic requirements of pathogenic communities. Results: Metabolite uptake and cross-feeding are essential for strain growth; therefore, we plan to design patient-specific treatments by adjusting urinary metabolites through nutritional regimens to counteract uropathogens by depleting essential growth metabolites. These methods will provide mechanistic insights into the metabolic components of rUTI pathogenesis to provide an evidence-based tool for infection treatment.

Keywords: recurrent urinary tract infections, human microbiome, uropathogenic Escherichia coli, UPEC, microbial ecology

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Authors: Robin Devillers, Chantal van Dijk

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Children face ambiguities in everyday language use. Particularly ambiguity in pronoun resolution can be challenging, whereas adults can rapidly identify the antecedent of the mentioned pronoun. Two main factors underlie this process, namely the accessibility of the referent and the syntactic cues of the pronoun. After 200ms, adults have converged the accessibility and the syntactic constraints, while relieving cognitive effort by considering contextual cues. As children are still developing their cognitive capacity, they are not able yet to simultaneously assess and integrate accessibility, contextual cues and syntactic information. As such, they fail to identify the correct referent and possibly fixate more on the competitor in comparison to adults. In this study, Dutch while-clauses were used to investigate the interpretation of pronouns by children. The aim is to a) examine the extent to which 7-10 year old children are able to utilise discourse and syntactic information during online and offline sentence processing and b) analyse the contribution of individual factors, including age, working memory, condition and vocabulary. Adult and child participants are presented with filler-items and while-clauses, and the latter follows a particular structure: ‘Anna and Sophie are sitting in the library. While Anna is reading a book, she is taking a sip of water.’ This sentence illustrates the ambiguous situation, as it is unclear whether ‘she’ refers to Anna or Sophie. In the unambiguous situation, either Anna or Sophie would be substituted by a boy, such as ‘Peter’. The pronoun in the second sentence will unambiguously refer to one of the characters due to the syntactic constraints of the pronoun. Children’s and adults’ responses were measured by means of a visual world paradigm. This paradigm consisted of two characters, of which one was the referent (the target) and the other was the competitor. A sentence was presented and followed by a question, which required the participant to choose which character was the referent. Subsequently, this paradigm yields an online (fixations) and offline (accuracy) score. These findings will be analysed using Generalised Additive Mixed Models, which allow for a thorough estimation of the individual variables. These findings will contribute to the scientific literature in several ways; firstly, the use of while-clauses has not been studied much and it’s processing has not yet been identified. Moreover, online pronoun resolution has not been investigated much in both children and adults, and therefore, this study will contribute to adults and child’s pronoun resolution literature. Lastly, pronoun resolution has not been studied yet in Dutch and as such, this study adds to the languages

Keywords: pronouns, online language processing, Dutch, eye-tracking, first language acquisition, language development

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Authors: Abderrahmane Hamdi, Julie Chalon, Benoit Dodin, Philippe Champagne

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In this report, we present a simple route to realize robust, hydrophobic, and highly transparent coatings using organic polysilazane (durazane) and zinc oxide nanoparticles (ZnO). These coatings were deposited by spraying the mixture solution on glass slides. Thus, the properties of the films were characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), UV–vis-NIR spectrophotometer, and water contact angle method. This sprayable polymer mixed with ZnO nanoparticles shows high transparency for visible light > 90%, a hydrophobic character (CA > 90°), and good mechanical and chemical stability. The coating also demonstrates excellent self-cleaning properties, which makes it a promising candidate for commercial use.

Keywords: coatings, durability, hydrophobicity, organic polysilazane, self-cleaning, transparence, zinc oxide nanoparticles

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Authors: Mehravar Javid, Sadrieh Khajavi Mazanderani, Kelly Gleischman, Zoe Andris

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The present study was conducted with the aim of investigating the effectiveness of transactional analysis group training on self-knowledge and Its dimensions (self, child, and adult) in counselors working in public and private high schools in Tehran. Counseling has become an important job for society, and there is a need for consultants in organizations. Providing better and more efficient counseling is one of the goals of the education system. The personal characteristics of counselors are important for the success of the therapy. In TA, humans have three ego states, which are named parent, adult, and child, and the main concept in the transactional analysis is self-state, which means a stable feeling and pattern of thinking related to behavioral patterns. Self-knowledge, considered a prerequisite to effective communication, fosters psychological growth, and recognizing it, is pivotal for emotional development, leading to profound insights. The research sample included 30 working counselors (22 women and 8 men) in the academic year 2019-2020 who achieved the lowest scores on the self-knowledge questionnaire. The research method was quasi-experimental with a control group (15 people in the experimental group and 15 people in the control group). The research tool was a self-awareness questionnaire with 29 questions and three subscales (child, parent, and adult Ego state). The experimental group was exposed to transactional analysis training for 10 once-weekly 2-hour sessions; the questionnaire was implemented in both groups (post-test). Multivariate covariance analysis was used to analyze the data. The data showed that the level of self-awareness of counselors who received transactional analysis training is higher than that of counselors who did not receive any training (p<0.01). The result obtained from this analysis shows that transactional analysis training is an effective therapy for enhancing self-knowledge and its subscales (Adult ego state, Parent ego state, and Child ego state). Teaching transactional analysis increases self-knowledge, and self-realization and helps people to achieve independence and remove irresponsibility to improve intra-personal and interpersonal relationships.

Keywords: ego state, group, transactional analysis, self-knowledge

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Delara Soltani, Sajad Alimohammadi, Tim Persoons

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Microfluidic technology reveals a new area of research in drug delivery, biomedical diagnostics, and the food and chemical industries. Mixing is an essential part of microfluidic devices. There is a need for fast and homogeneous mixing in microfluidic devices. On the other hand, mixing is difficult to achieve in microfluidic devices because of the size and laminar flow in these devices. In this study, a hybrid passive micromixer of a curved channel with obstacles inside the channel is designed. The computational fluid dynamic method is employed to solve governing equations. The results show that using obstacles can improve mixing efficiency in spiral micromixers. the effects of Reynolds number, number, and position of baffles are investigated. In addition, the effect of baffles on pressure drop is presented. this novel micromixer has the potential to utilize in microfluidic devices.

Keywords: CFD, micromixer, microfluidics, spiral, reynolds number

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Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

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Authors: Chokri Slim

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The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims.

Keywords: credit risk management, multiple linear regression, principal components analysis, artificial neural networks, support vector machines

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Authors: Alberto Mínguez-Martínez, Jesús De Vicente Y Oliva

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It seems that we live in a world in which the trend in industrial environments is the miniaturization of systems and materials and the fabrication of parts at the micro-and nano-scale. The problem arises when manufacturers want to study the quality of their production. This characteristic is becoming crucial due to the evolution of the industry and the development of Industry 4.0. As Industry 4.0 is based on digital models of production and processes, having accurate measurements becomes capital. At this point, the metrology field plays an important role as it is a powerful tool to ensure more stable production to reduce scrap and the cost of non-conformities. The most extended measuring instruments that allow us to carry out accurate measurements at these scales are optical microscopes, whether they are traditional, confocal, focus variation microscopes, profile projectors, or any other similar measurement system. However, the accuracy of measurements is connected to the traceability of them to the SI unit of length (the meter). The fact of providing adequate traceability to 2D and 3D dimensional measurements at micro-and nano-scale in industrial environments is a problem that is being studied, and it does not have a unique answer. In addition, if commercial material standards for micro-and nano-scale are considered, we can find that there are two main problems. On the one hand, those material standards that could be considered complete and very interesting do not give traceability of dimensional measurements and, on the other hand, their calibration is very expensive. This situation implies that these kinds of standards will not succeed in industrial environments and, as a result, they will work in the absence of traceability. To solve this problem in industrial environments, it becomes necessary to have material standards that are easy to use, agile, adaptive to different forms, cheap to manufacture and, of course, traceable to the definition of meter with simple methods. By using these ‘customized standards’, it would be possible to adapt and design measuring procedures for each application and manufacturers will work with some traceability. It is important to note that, despite the fact that this traceability is clearly incomplete, this situation is preferable to working in the absence of it. Recently, it has been demonstrated the versatility and the utility of using laser technology and other AM technologies to manufacture customized material standards. In this paper, the authors propose to manufacture a customized material standard using an ultraviolet laser system and a method to calibrate it. To conclude, the results of the calibration carried out in an accredited dimensional metrology laboratory are presented.

Keywords: industrial environment, material standards, optical measuring instrument, traceability

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Authors: Stuti Modi, Aditi Kambli

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This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.

Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution

Procedia PDF Downloads 186