Search results for: spectroscopy data analysis

Authors: W. S. Hsu, S. W. Chen, Y. T. Ku, Y. Chiang, J. R. Wang , J. H. Yang, C. Shih

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ALOHA code was used to calculate the concentration under the CO₂ storage burst condition for Maanshan nuclear power plant (NPP) in this study. Five main data are input into ALOHA code including location, building, chemical, atmospheric, and source data. The data from Final Safety Analysis Report (FSAR) and some reports were used in this study. The ALOHA results are compared with the failure criteria of R.G. 1.78 to confirm the habitability of control room. The result of comparison presents that the ALOHA result is below the R.G. 1.78 criteria. This implies that the habitability of control room can be maintained in this case. The sensitivity study for atmospheric parameters was performed in this study. The results show that the wind speed has the larger effect in the concentration calculation.

Keywords: PWR, ALOHA, habitability, Maanshan

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Authors: Varutorn Tulnawat, Padcharee Phasuk

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This paper investigates the issue of China’s dumping on border trade between Thailand and Laos. From the pass mostly, the border trade goods are traditional textile and garment mainly served locals and tourists which majority of traders is of small and medium size. In the present day the competition is fierce, the volume of trade has expanded far beyond its original intent. The major competitors in Thai-Laos border trade are China, Vietnam and also South Korea. This research measures and compares the efficiency and ability to survive the onslaught of Thai and Laos firm along Thailand (Nong Kai province) and Laos (Vientiane) border. Two attack strategies are observed, price cutting and incense such as full facilitation for big volume order. Data Envelopment Analysis (DEA) is applied to data surveyed from 90 Thai and Laos entrepreneurs. The expected results are the proportion of efficiency and inefficiency firms. Points of inefficiency and suggested improvement are also discussed.

Keywords: border trade, dea, textile, garment

Procedia PDF Downloads 245

Authors: Yogita Mishra, Arijit Roy, Dhruval Bhavsar

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The study estimates the nitrogen concentration in selected vegetation community’s i.e. chir pine (pinusroxburghii) by using hyperspectral satellite data and also identified the appropriate spectral bands and nitrogen indices. The Short Wave InfraRed reflectance spectrum at 1790 nm and 1680 nm shows the maximum possible absorption by nitrogen in selected species. Among the nitrogen indices, log normalized nitrogen index performed positively and negatively too. The strong positive correlation is taken out from 1510 nm and 760 nm for the pinusroxburghii for leaf nitrogen concentration and leaf nitrogen mass while using NDNI. The regression value of R² developed by using linear equation achieved maximum at 0.7525 for the analysis of satellite image data and R² is maximum at 0.547 for ground truth data for pinusroxburghii respectively.

Keywords: hyperspectral, NDNI, nitrogen concentration, regression value

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Authors: Alireza Khaloo, Asghar Gholizadeh-Vayghan

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The present work focuses on the characterization of the physicochemical properties of kaolinitic clays in both raw and calcined (i.e., dehydroxylated) states. The properties investigated included the dehydroxylation temperature, chemical composition and crystalline phases, band types, kaolinite content, vitreous phase, and reactive and unreactive silica and alumina. The thermogravimetric analysis, X-ray diffractometry and infrared spectroscopy results suggest that full dehydroxylation takes place at 639°C, converting kaolinite to reactive metakaolinite (Si₂Al₂O₇). Application of higher temperatures up to 800 °C leads to complete decarbonation of the calcite phase, and the kaolinite converts to mullite at temperatures exceeding 957 °C. Calcination at 639°C was found to cause a 50% increase in the vitreous content of kaolin. Statistically meaningful increases in the reactivity of silica, alumina, calcite and sodium carbonate in kaolin were detected as a result of such thermal treatment. Such increases were found to be 11%, 47%, 240% and 10%, respectively. The ferrite phase, however, showed a 36% decline in reactivity. The proposed approach can be used as an analytical method to determine the viability of the source of kaolinite and proper physical and chemical modifications needed to enhance its suitability for geopolymer production.

Keywords: physicochemical properties, dehydroxylation, kaolinitic clays, kaolinite content, vitreous phase, reactivity

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Authors: Qiao-Yu Warren Cai

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Due to the rise of big data, building corpora and using them to analyze ChineseL2 learners’ language output has become a trend. Various empirical research has been conducted using Chinese corpora built by different academic institutes. However, most of the research analyzed the data in the Chinese corpora usingcorpus-based qualitative content analysis with descriptive statistics. Descriptive statistics can be used to make summations about the subjects or samples that research has actually measured to describe the numerical data, but the collected data cannot be generalized to the population. Comte, a Frenchpositivist, has argued since the 19th century that human beings’ knowledge, whether the discipline is humanistic and social science or natural science, should be verified in a scientific way to construct a universal theory to explain the truth and human beings behaviors. Inferential statistics, able to make judgments of the probability of a difference observed between groups being dependable or caused by chance (Free Geography Notes, 2015)and to infer from the subjects or examples what the population might think or behave, is just the right method to support Comte’s argument in the field of TCSOL. Also, inferential statistics is a core of quantitative research, but little research has been conducted by combing corpora with inferential statistics. Little research analyzes the differences in Chinese L2 learners’ language corpus output errors by using theOne-way ANOVA so that the findings of previous research are limited to inferring the population's Chinese errors according to the given samples’ Chinese corpora. To fill this knowledge gap in the professional development of Taiwanese TCSOL, the present study aims to utilize the One-way ANOVA to analyze corpus output errors of Chinese L2 learners from America, Myanmar, and Singapore. The results show that no significant difference exists in ‘shì (是) sentence’ and word order errors, but compared with Americans and Singaporeans, it is significantly easier for Myanmar to have ‘sentence blends.’ Based on the above results, the present study provides an instructional approach and contributes to further exploration of how Chinese L2 learners can have (and use) learning strategies to lower errors.

Keywords: Chinese corpus, error analysis, one-way analysis of variance, Chinese L2 learners, Americans, myanmar, Singaporeans

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Authors: Haojie Wang, Xun Li, Rui Yin

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In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

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Authors: Tanuj Joshi

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Smart transportation is fast emerging as a solution to modern cities’ approach mobility issues, delayed emergency response rate and high congestion on streets. Present day scenario with Google Maps, Waze, Yelp etc. demonstrates how information and communications technologies controls the intelligent transportation system. This intangible and invisible infrastructure is largely guided by the big data analytics. On the other side, the exponential increase in Indian urban population has intensified the demand for better services and infrastructure to satisfy the transportation needs of its citizens. No doubt, India’s huge internet usage is looked as an important resource to guide to achieve this. However, with a projected number of over 40 billion objects connected to the Internet by 2025, the need for systems to handle massive volume of data (big data) also arises. This research paper attempts to identify the ways of exploiting the big data variables which will aid commuters on Indian tracks. This study explores real life inputs by conducting survey and interviews to identify which gaps need to be targeted to better satisfy the customers. Several experts at Mumbai Metropolitan Region Development Authority (MMRDA), Mumbai Metro and Brihanmumbai Electric Supply and Transport (BEST) were interviewed regarding the Information Technology (IT) systems currently in use. The interviews give relevant insights and requirements into the workings of public transportation systems whereas the survey investigates the macro situation.

Keywords: smart transportation, mobility issue, Mumbai transportation, big data, data analysis

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Authors: Omar Alzeley, Sergey Utev

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Minimizing a constrained multivariate function is the fundamental of Machine learning, and these algorithms are at the core of data mining and data visualization techniques. The decision function that maps input points to output points is based on the result of optimization. This optimization is the central of learning theory. One approach to complex systems where the dynamics of the system is inferred by a statistical analysis of the fluctuations in time of some associated observable is time series analysis. The purpose of this paper is a mathematical transition from the autoregressive model of classical time series to the matrix formalization of quantum theory. Firstly, we have proposed a quantum time series model (QTS). Although Hamiltonian technique becomes an established tool to detect a deterministic chaos, other approaches emerge. The quantum probabilistic technique is used to motivate the construction of our QTS model. The QTS model resembles the quantum dynamic model which was applied to financial data. Secondly, various statistical methods, including machine learning algorithms such as the Kalman filter algorithm, are applied to estimate and analyses the unknown parameters of the model. Finally, simulation techniques such as Markov chain Monte Carlo have been used to support our investigations. The proposed model has been examined by using real and simulated data. We establish the relation between quantum statistical machine and quantum time series via random matrix theory. It is interesting to note that the primary focus of the application of QTS in the field of quantum chaos was to find a model that explain chaotic behaviour. Maybe this model will reveal another insight into quantum chaos.

Keywords: machine learning, simulation techniques, quantum probability, tensor product, time series

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Authors: L. Bikulciene, E. Venskaityte, G. Jarusevicius

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Non-stationary and nonlinear signals generated by living complex systems defy traditional mechanistic approaches, which are based on homeostasis. Previous our studies have shown that the evaluation of the interactions of physiological signals by using special analysis methods is suitable for observation of physiological processes. It is demonstrated the possibility of using deep physiological model, based interpretation of the changes of the human body’s functional states combined with an application of the analytical method based on matrix theory for the physiological signals analysis, which was applied on high risk cardiac patients. It is shown that evaluation of cardiac signals interactions show peculiar for each individual functional changes at the onset of hemodynamic restoration procedure. Therefore we suggest that the alterations of functional state of the body, after patients overcome surgery can be complemented by the data received from the suggested approach of the evaluation of functional variables interactions.

Keywords: cardiac diseases, complex systems theory, ECG analysis, matrix analysis

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Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

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Authors: Kumar Shashvat, Amol P. Bhondekar

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In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

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This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: Shady Adib, Vladimir Vinogradov, Peter Gosling

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Digital Twin (DT) technology is a new technology that appeared in the early 21st century. The DT is defined as the digital representation of living and non-living physical assets. By connecting the physical and virtual assets, data are transmitted smoothly, allowing the virtual asset to fully represent the physical asset. Although there are lots of studies conducted on the DT concept, there is still limited information about the ability of the DT models for monitoring and detecting unexpected changes in structural behaviour in real time. This is due to the large computational efforts required for the analysis and an excessively large amount of data transferred from sensors. This paper aims to develop the DT concept to be able to detect the abnormal changes in structural behaviour in real time using advanced modelling techniques, deep learning algorithms, and data acquisition systems, taking into consideration model uncertainties. finite element (FE) models were first developed offline to be used with a reduced basis (RB) model order reduction technique for the construction of low-dimensional space to speed the analysis during the online stage. The RB model was validated against experimental test results for the establishment of a DT model of a two-dimensional truss. The established DT model and deep learning algorithms were used to identify the location of damage once it has appeared during the online stage. Finally, the RB model was used again to identify the damage severity. It was found that using the RB model, constructed offline, speeds the FE analysis during the online stage. The constructed RB model showed higher accuracy for predicting the damage severity, while deep learning algorithms were found to be useful for estimating the location of damage with small severity.

Keywords: data acquisition system, deep learning, digital twin, model uncertainties, reduced basis, reduced order model

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Authors: Victor Chinecherem Ejeke, Raphael Eze Nnam

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The main objective of this research is to produce biodiesel from waste vegetable oil using activated eggshell waste as solid catalysts. A transesterification reaction was performed for the conversion to biodiesel. Waste eggshells were calcined at 700°C, 800°C and 900°C for a time period of 3hrs for the preparation of the renewable catalyst. The calcined waste eggshell catalyst was characterized using X-Ray Florescence (XRF) Spectroscopy, which revealed CaO as the major constituent (90.86%); this was further confirmed by X-Ray Diffraction (XRD) and Fourier Transform Infrared (FTIR) analyses. The prepared catalyst was used for transesterification reaction and the effects of calcination temperature (700 to 900°C), Deep Eutectic Solvent DES loading (3 to 18 wt. %), Waste Egg Shell (WES) catalyst loading (6 to 14 wt. %) on the conversion to biodiesel were studied. The yield of biodiesel using a waste eggshell catalyst (91%) is comparable to conventional catalyst like sodium hydroxide with a yield of 80-90%. The maximum biodiesel production yield was obtained at a specific oil-to methanol molar ratio of 1:10, a temperature of 65°C and a catalyst loading of 14g-wt%. The biodiesel produced was characterized as being composed of methyl Tetradecanoate (C₁₄H₂₈O₂) 30.92% using the Gas Chromatographic (GC-MS) analysis. The fuel properties of the biodiesel (Flashpoint 138ᵒC) were comparable to commercial diesel, and hence it can be used in compression-ignition engines. The results indicated that the catalysts derived from waste eggshell had high potential to be used as biodiesel production catalysts in transesterification of waste vegetable oil with the advantage of reusability and also not requiring water washing steps.

Keywords: waste vegetable oil, catalyst , biodiesel , waste egg shell

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Authors: Yuqing Zou, Chunrui Zou, Yichong Cao

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Previous research has revealed relationships between self-efficacy (SE), engagement, and academic achievement among students in Western countries, but these relationships remain unknown in college math courses among college students in China. In addition, previous research has shown that test anxiety has a direct effect on engagement and academic achievement. However, how test anxiety affects the relationships between SE, engagement, and academic achievement is still unknown. In this study, the authors aimed to explore the mediating roles of behavioral engagement (BE), emotional engagement (EE), and cognitive engagement (CE) in the association between SE and academic achievement and the moderating role of test anxiety in college math courses. Our hypotheses are that the association between SE and academic achievement was mediated by engagement and that test anxiety played a moderating role in the association. To explore the research questions, the authors collected data through self-reported surveys among 147 students at a northwestern university in China. Self-reported surveys were used to collect data. The motivated strategies for learning questionnaire (MSLQ) (Pintrich, 1991), the metacognitive strategies questionnaire (Wolters, 2004), and the engagement versus disaffection with learning scale (Skinner et al., 2008) were used to assess SE, CE, and BE and EE, respectively. R software was used to analyze the data. The main analyses used were reliability and validity analysis of scales, descriptive statistics analysis of measured variables, correlation analysis, regression analysis, and structural equation modeling (SEM) analysis and moderated mediation analysis to look at the structural relationships between variables at the same time. The SEM analysis indicated that student SE was positively related to BE, EE, and CE and academic achievement. BE, EE, and CE were all positively associated with academic achievement. That is, as the authors expected, higher levels of SE led to higher levels of BE, EE, and CE, and greater academic achievement. Higher levels of BE, EE, and CE led to greater academic achievement. In addition, the moderated mediation analysis found that the path of SE to academic achievement in the model was as significant as expected, as was the moderating effect of test anxiety in the SE-Achievement association. Specifically, test anxiety was found to moderate the association between SE and BE, the association between SE and CE, and the association between EE and Achievement. The authors investigated possible mediating effects of BE, EE, and CE in the associations between SE and academic achievement, and all indirect effects were found to be significant. As for the magnitude of mediations, behavioral engagement was the most important mediator in the SE-Achievement association. This study has implications for college teachers, educators, and students in China regarding ways to promote academic achievement in college math courses, including increasing self-efficacy and engagement and lessening test anxiety toward math.

Keywords: academic engagement, self-efficacy, test anxiety, academic achievement, college math courses, behavioral engagement, cognitive engagement, emotional engagement

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Authors: Sonam Kumari

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Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, possesses a remarkable feature of entering into and emerging from a persistent state. The mechanism by which Mtb switches from the dormant state to the replicative form is still poorly characterized. Proteome studies have given us an insight into the role of certain proteins in giving stupendous virulence to Mtb, but numerous dotsremain unconnected and unaccounted. The WhiB family of proteins is one such protein that is associated with developmental processes in actinomycetes.Mtb has seven such proteins (WhiB1 to WhiB7).WhiB proteins are transcriptional regulators; their conserved C-terminal HTH motif is involved in DNA binding. They regulate various essential genes of Mtbby binding to their promoter DNA. Biophysical Analysis of the effect of DNA binding on WhiB proteins has not yet been appropriately characterized. Interaction with DNA induces conformational changes in the WhiB proteins, confirmed by steady-state fluorescence and circular dichroism spectroscopy. ITC has deduced thermodynamic parameters and the binding affinity of the interaction. Since these transcription factors are highly unstable in vitro, their stability and solubility were enhanced by the co-expression of molecular chaperones. The present study findings help determine the conditions under which the WhiB proteins interact with their interacting partner and the factors that influence their binding affinity. This is crucial in understanding their role in regulating gene expression in Mtbandin targeting WhiB proteins as a drug target to cure TB.

Keywords: tuberculosis, WhiB proteins, mycobacterium tuberculosis, nucleic acid binding

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Authors: Yuvraj S. Malghe, Atul B. Lavand

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In recent years, semiconductor photocatalytic degradation processes have attracted a lot of attention and are used widely for the destruction of organic pollutants present in waste water. Among various semiconductors, titanium dioxide (TiO2) is the most popular photocatalyst due to its excellent chemical stability, non-toxicity, relatively low cost and high photo-oxidation power. It has been known that zinc oxide (ZnO) with band gap energy 3.2 eV is a suitable alternative to TiO2 due to its high quantum efficiency, however it corrodes in acidic medium. Unfortunately TiO2 and ZnO both are active only in UV light due to their wide band gaps. Sunlight consist about 5-7% UV light, 46% visible light and 47% infrared radiation. In order to utilize major portion of sunlight (visible spectrum), it is necessary to modify the band gap of TiO2 as well as ZnO. This can be done by several ways such as semiconductor coupling, doping the material with metals/non metals. Doping of TiO2 using transition metals like Fe, Co and non-metals such as N, C or S extends its absorption wavelengths from UV to visible region. In the present work, we have synthesized ZnO-TiO2 nanocomposite using reverse microemulsion method. Visible light photocatalytic activity of synthesized nanocomposite was investigated for degradation of aqueous solution of malachite green (MG). To increase the photocatalytic activity of ZnO-TiO2 nanocomposite, it is decorated with C and Fe. Pure, carbon (C) doped and carbon, iron(C, Fe) co-doped nanosized ZnO-TiO2 nanocomposites were synthesized using reverse microemulsion method. These composites were characterized using, X-ray diffraction (XRD), Energy dispersive X-ray spectroscopy (EDX), Scanning electron microscopy (SEM), UV visible spectrophotometery and X-ray photoelectron spectroscopy (XPS). Visible light photocatalytic activities of synthesized nanocomposites were investigated for degradation of aqueous malachite green (MG) solution. C, Fe co-doped ZnO-TiO2 nanocomposite exhibit better photocatalytic activity and showed threefold increase in photocatalytic activity. Effect of amount of catalyst, pH and concentration of MG solution on the photodegradation rate is studied. Stability and reusability of photocatalyst is also studied. C, Fe decorated ZnO-TiO2 nanocomposite shows threefold increase in photocatalytic activity.

Keywords: malachite green, nanocomposite, photocatalysis, titanium dioxide, zinc oxide

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Authors: Wei Yaming

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Objective: In order to compare the stress of employees sent overseas with (GRAS) before and after, we used the resource-oriented music listening intervention in this study. We also collected pertinent experimental data. Methods: The experiment involved 47 employees who were sent abroad by the Chinese side. They completed the stress scale test and documented it before the intervention. They tested for stress after five interventions and performed one-on-one interviews. Quantitative data and SPSS software were used to analyze relationships between stress reduction and resource-oriented music listening, as well as Pearson's correlation, multiple regression levels, and ANOVA. For the qualitative analysis, content analysis of one-on-one interviews was performed. Results: A comparison of data from before and after demonstrates how resource-focused music listening activities can lessen and relieve stress in remote workers. In the qualitative study, stress is broken down into six categories: relationship stress, health stress, emotional stress, and frustration stress. External pressures include work pressure and cultural stress. And it has been determined that listening to music that is resource-oriented can better ease internal stress (health, emotion, and dissatisfaction). Conclusion: The Guide Resource-oriented Music Listening (GROML) Program appears to have had some effect on the participants' stress levels. The resources that the participants encountered while listening to music are bravery, calm, letting go, and relaxing.

Keywords: resource-oriented, music listening, oversea dispatch employees, work stress

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Authors: Othmane Touri, Sanaa El Filali, El Habib Benlahmar

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Natural Language Processing (NLP) has gained significant attention lately. It has proved its ability to analyze and extract insights from unstructured text data in various languages. It is found that one of the most popular NLP applications is sentiment analysis which aims to identify the sentiment expressed in a piece of text, such as positive, negative, or neutral, in multiple languages. While there are several multilingual NLP models available for sentiment analysis, there is a need to investigate their effectiveness in different contexts and applications. In this study, we aim to investigate the effectiveness of different multilingual NLP models for sentiment analysis on a dataset of online product reviews in multiple languages. The performance of several NLP models, including Google Cloud Natural Language API, Microsoft Azure Cognitive Services, Amazon Comprehend, Stanford CoreNLP, spaCy, and Hugging Face Transformers are being compared. The models based on several metrics, including accuracy, precision, recall, and F1 score, are being evaluated and compared to their performance across different categories of product reviews. In order to run the study, preprocessing of the dataset has been performed by cleaning and tokenizing the text data in multiple languages. Then training and testing each model has been applied using a cross-validation approach where randomly dividing the dataset into training and testing sets and repeating the process multiple times has been used. A grid search approach to optimize the hyperparameters of each model and select the best-performing model for each category of product reviews and language has been applied. The findings of this study provide insights into the effectiveness of different multilingual NLP models for Multilingual Sentiment Analysis and their suitability for different languages and applications. The strengths and limitations of each model were identified, and recommendations for selecting the most performant model based on the specific requirements of a project were provided. This study contributes to the advancement of research methods in multilingual NLP and provides a practical guide for researchers and practitioners in the field.

Keywords: NLP, multilingual, sentiment analysis, texts

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Authors: Mehdi Rahimi-Nasrabadi, Seied Mahdi Pourmortazavi

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Taguchi robust design as a statistical method was applied for optimization of the process parameters in order to tunable, simple and fast synthesis of europium (III) tungstate nanoparticles. Europium (III) tungstate nanoparticles were synthesized by a chemical precipitation reaction involving direct addition of europium ion aqueous solution to the tungstate reagent solved in aqueous media. Effects of some synthesis procedure variables i.e., europium and tungstate concentrations, flow rate of cation reagent addition, and temperature of reaction reactor on the particle size of europium (III) tungstate nanoparticles were studied experimentally in order to tune particle size of europium (III) tungstate. Analysis of variance shows the importance of controlling tungstate concentration, cation feeding flow rate and temperature for preparation of europium (III) tungstate nanoparticles by the proposed chemical precipitation reaction. Finally, europium (III) tungstate nanoparticles were synthesized at the optimum conditions of the proposed method and the morphology and chemical composition of the prepared nano-material were characterized by means of X-Ray diffraction, scanning electron microscopy, transmission electron microscopy, FT-IR spectroscopy, and fluorescence.

Keywords: europium (III) tungstate, nano-material, particle size control, procedure optimization

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Authors: Isa Muhammad Zumo, Musa Lawan

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This study analyzes the land use/land cover changes in Damaturu metropolis from 1986 to 2005. LandSat TM Images of 1986, 1999, and 2005 were used. Built-up lands, agric lands, water body and other lands were created as themes within ILWIS 3.4 software. The images were displayed in False Colour Composite (FCC) for a better visualization and identification of the themes created. Training sample sets were collected based on the ground truth data during field the checks. Statistical data were then extracted from the classified sample set. Area in hectares for each theme was calculated for each year and the result for each land use/land cover types for each study year was compared. From the result, it was found out that built-up areas have a considerable increase from 37.71 hectares in 1986 to 1062.72 hectares in 2005. It has an annual increase rate of approximately 0.34%. The results also reveal that there is a decrease of 5829.66 hectares of other lands (vacant lands) from 1986 to 2005.

Keywords: land use, changes, analysis, environmental pollution

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Authors: Omar Drissi, Nadia El Harfaoui, Khalid Nouneh, Rachid Hsissou, Badre Daoudi

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The pulp endures of having a lower importance, incompletely because of the way that it has been less studied, and it has been recognized as a pivotal product got from biomass that can be utilized in different fields. The current research focuses on pulp of Argania spinosa (L). To this end, the aim is to study the characteristics and properties of Argan pulp, such as shape, chemical and macromineral composition. As a result, X-Ray Fluorescence (XRF), Fourier transform infrared spectroscopy (FTIR), and Scanning Electron Microscopy (SEM) were used in the research.

Keywords: argania spinose, argan pulp, argan bio-waste, green synthesis, silver nanoparticles, valorization

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Authors: S. C. Lin, W. W. Cheng, M. S. Wu

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This study attempted to investigate the connection between teacher professional development program and students’ Learning. This study took Readers’ Theater Teaching Program (RTTP) for professional development as an example to inquiry how participants apply their new knowledge and skills learned from RTTP to their teaching practice and how the impact influence students learning. The goals of the RTTP included: 1) to enhance teachers RT content knowledge; 2) to implement RT instruction in teachers’ classrooms in response to their professional development. 2) to improve students’ ability of reading fluency in professional development teachers’ classrooms. This study was a two-year project. The researchers applied mixed methods to conduct this study including qualitative inquiry and one-group pretest-posttest experimental design. In the first year, this study focused on designing and implementing RTTP and evaluating participants’ satisfaction of RTTP, what they learned and how they applied it to design their English reading curriculum. In the second year, the study adopted quasi-experimental design approach and evaluated how participants RT instruction influenced their students’ learning, including English knowledge, skill, and attitudes. The participants in this study composed two junior high school English teachers and their students. Data were collected from a number of different sources including teaching observation, semi-structured interviews, teaching diary, teachers’ professional development portfolio, Pre/post RT content knowledge tests, teacher survey, and students’ reading fluency tests. To analyze the data, both qualitative and quantitative data analysis were used. Qualitative data analysis included three stages: organizing data, coding data, and analyzing and interpreting data. Quantitative data analysis included descriptive analysis. The results indicated that average percentage of correct on pre-tests in RT content knowledge assessment was 40.75% with two teachers ranging in prior knowledge from 35% to 46% in specific RT content. Post-test RT content scores ranged from 70% to 82% correct with an average score of 76.50%. That gives teachers an average gain of 35.75% in overall content knowledge as measured by these pre/post exams. Teachers’ pre-test scores were lowest in script writing and highest in performing. Script writing was also the content area that showed the highest gains in content knowledge. Moreover, participants hold a positive attitude toward RTTP. They recommended that the approach of professional learning community, which was applied in RTTP was benefit to their professional development. Participants also applied the new skills and knowledge which they learned from RTTP to their practices. The evidences from this study indicated that RT English instruction significantly influenced students’ reading fluency and classroom climate. The result indicated that all of the experimental group students had a big progress in reading fluency after RT instruction. The study also found out several obstacles. Suggestions were also made.

Keywords: teacher’s professional development, program evaluation, readers’ theater, english reading instruction, english reading fluency

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Authors: Desta Kebede Ayana, Lies Sercu, Demelash Mengistu

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The study is set in Ethiopia, a sub-Saharan multilingual, multiethnic African country, which has seen a significant increase in the number of universities in recent years. The aim of this growth is to provide access to education for all cultural and linguistic groups across the country. However, there are challenges in promoting intercultural competence among students in this diverse context. The aim of the study is to investigate the interculturalization of Ethiopian Higher Education Institutions as perceived by university lecturers and administrators. In particular, the study aims to determine the level of support for this educational innovation and gather suggestions for its implementation and institutionalization. The researchers employed semi-structured interviews with administrators and lecturers from two large Ethiopian universities to gather data. Thematic analysis was utilized for coding and analyzing the interview data, with the assistance of the NVIVO software. The findings obtained from the grounded analysis of the interview data reveal that while there are opportunities for interculturalization in the curriculum and campus life, support for educational innovation remains low. Administrators and lecturers also emphasize the government's responsibility to prioritize interculturalization over other educational innovation goals. The study contributes to the existing literature by examining an under-researched population in an under-researched context. Additionally, the study explores whether Western perspectives of intercultural competence align with the African context, adding to the theoretical understanding of intercultural education. The data for this study was collected through semi-structured interviews conducted with administrators and lecturers from two large Ethiopian universities. The interviews allowed for an in-depth exploration of the participants' views on interculturalization in higher education. Thematic analysis was applied to the interview data, allowing for the identification and organization of recurring themes and patterns. The analysis was conducted using the NVIVO software, which aided in coding and analyzing the data. The study addresses the extent to which administrators and lecturers support the interculturalization of Ethiopian Higher Education Institutions. It also explores their suggestions for implementing and institutionalizing intercultural education, as well as their perspectives on the current level of institutionalization. The study highlights the challenges in interculturalizing Ethiopian universities and emphasizes the need for greater support and prioritization of intercultural education. It also underscores the importance of considering the African context when conceptualizing intercultural competence. This research contributes to the understanding of intercultural education in diverse contexts and provides valuable insights for policymakers and educational institutions aiming to promote intercultural competence in higher education settings.

Keywords: administrators, educational change, Ethiopia, intercultural competence, lecturers

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Authors: Valentina Futoryanova, Hugh Hunt

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One of the common failure modes of the diesel engine turbochargers is high cycle fatigue of the turbine wheel blades. Mistuning of the blades due to the casting process is believed to contribute to the failure mode. Laser vibrometer is used to characterize mistuning for a population of turbine wheels through the analysis of the blade response to piezo speaker induced noise. The turbine wheel design under investigation is radial and is typically used in 6-12 L diesel engine applications. Amplitudes and resonance frequencies are reviewed and summarized. The study also includes test results for a paddle wheel that represents a perfectly tuned system and acts as a reference. Mass spring model is developed for the paddle wheel and the model suitability is tested against the actual data. Randomization is applied to the stiffness matrix to model the mistuning effect in the turbine wheels. Experimental data is shown to have good agreement with the model.

Keywords: vibration, radial turbines, mistuning, turbine blades, modal analysis, periodic structures, finite element

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Authors: Stefan K. Behfar

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The rapid growth of the Ethereum network has brought forth the urgent need for scalable analysis methods to handle the increasing volume of blockchain data. In this research, we propose efficient methodologies for making Ethereum network analysis scalable. Our approach leverages a combination of graph-based data representation, probabilistic sampling, and parallel processing techniques to achieve unprecedented scalability while preserving critical network insights. Data Representation: We develop a graph-based data representation that captures the underlying structure of the Ethereum network. Each block transaction is represented as a node in the graph, while the edges signify temporal relationships. This representation ensures efficient querying and traversal of the blockchain data. Probabilistic Sampling: To cope with the vastness of the Ethereum blockchain, we introduce a probabilistic sampling technique. This method strategically selects a representative subset of transactions and blocks, allowing for concise yet statistically significant analysis. The sampling approach maintains the integrity of the network properties while significantly reducing the computational burden. Graph Convolutional Networks (GCNs): We incorporate GCNs to process the graph-based data representation efficiently. The GCN architecture enables the extraction of complex spatial and temporal patterns from the sampled data. This combination of graph representation and GCNs facilitates parallel processing and scalable analysis. Distributed Computing: To further enhance scalability, we adopt distributed computing frameworks such as Apache Hadoop and Apache Spark. By distributing computation across multiple nodes, we achieve a significant reduction in processing time and enhanced memory utilization. Our methodology harnesses the power of parallelism, making it well-suited for large-scale Ethereum network analysis. Evaluation and Results: We extensively evaluate our methodology on real-world Ethereum datasets covering diverse time periods and transaction volumes. The results demonstrate its superior scalability, outperforming traditional analysis methods. Our approach successfully handles the ever-growing Ethereum data, empowering researchers and developers with actionable insights from the blockchain. Case Studies: We apply our methodology to real-world Ethereum use cases, including detecting transaction patterns, analyzing smart contract interactions, and predicting network congestion. The results showcase the accuracy and efficiency of our approach, emphasizing its practical applicability in real-world scenarios. Security and Robustness: To ensure the reliability of our methodology, we conduct thorough security and robustness evaluations. Our approach demonstrates high resilience against adversarial attacks and perturbations, reaffirming its suitability for security-critical blockchain applications. Conclusion: By integrating graph-based data representation, GCNs, probabilistic sampling, and distributed computing, we achieve network scalability without compromising analytical precision. This approach addresses the pressing challenges posed by the expanding Ethereum network, opening new avenues for research and enabling real-time insights into decentralized ecosystems. Our work contributes to the development of scalable blockchain analytics, laying the foundation for sustainable growth and advancement in the domain of blockchain research and application.

Keywords: Ethereum, scalable network, GCN, probabilistic sampling, distributed computing

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Authors: Mulatu Kassie Birhanu

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Electro-oxidation of glycerol is an important process to convert the less price glycerol into other expensive (essential) and energy-rich chemicals. In this study, nickel was electro-deposited on laboratory-made carbon ceramic electrode (CCE) substrate using electrochemical techniques that is cyclic voltammetry (CV) to prepare an electro-catalyst (Ni/CCE) for electro-oxidation of glycerol. Carbon ceramic electrode was prepared from graphite and methyl tri-methoxy silane (MTMOS) through the processes called hydrolysis and condensation with methanol in acidic media (HCl) by a sol-gel technique. Physico-chemical characterization of bare CCE and modified (deposited) CCE (Ni/CCE) was measured and evaluated by Fourier Transform Infrared spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and X-ray diffraction (XRD). Electro-oxidation of glycerol was performed in 0.1 M glycerol in alkaline media (0.5 M NaOH). High-Performance Liquid Chromatography (HPLC) technique was used to identify and determine the concentration of glycerol, reaction intermediates and oxidized products of glycerol after its electro-oxidation is performed. The conversion (%) of electro-oxidation of glycerol during 9-hour oxidation was 73% and 36% at 1.8V and 1.6V vs. RHE, respectively. Formate, oxalate, glycolate and glycerate are the main oxidation products of glycerol with selectivity (%) of 75%, 8.6%, 1.1% and 0.95 % at 1.8 V vs. RHE and 55.4%, 2.2%, 1.0% and 0.6% at 1.6 V vs. RHE respectively. The result indicates that formate is the main product in the electro-oxidation of glycerol on Ni/CCE using the indicated applied potentials.

Keywords: carbon-ceramic electrode, electrodeposition, electro-oxidation, Methyltrimethoxysilane

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Authors: Xu Feifei

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The prevalence of COVID-19 currently is the biggest challenge to improving people's quality of life. Its impact on the health-related quality of life (HRQoL) is highly uncertain and has not been summarized so far. The aim of the present systematic review was to assess and provide an up-to-date analysis of the impact of the COVID-19 pandemic on the HRQoL of participants who have been infected, have not been infected but isolated, frontline, with different diseases, and the general population. Therefore, an electronic search of the literature in PubMed databases was performed from 2019 to July 2022 (without date restriction). PRISMA guideline methodology was employed, and data regarding the HRQoL were extracted from eligible studies. Articles were included if they met the following inclusion criteria: (a) reports on the data collection of the health state utility values (HSUVs) related to COVID-19 from 2019 to 2021; (b) English language and peer-reviewed journals; and (c) original HSUV data; (d) using EQ-5D tool to quantify the HRQoL. To identify studies that reported the effects on COVID-19, data on the proportion of overall HSUVs of participants who had the outcome were collected and analyzed using a one-group meta-analysis. As a result, thirty-two studies fulfilled the inclusion criteria and, therefore, were included in the systematic review. A total of 45295 participants and provided 219 means of HSUVs during COVID-19 were included in this systematic review. The range of utility is from 0.224 to 1. The study included participants from Europe (n=16), North America (n=4), Asia (n=10), South America (n=1), and Africa (n=1). Twelve articles showed that the HRQoL of the participants who have been infected with COVID-19 (range of overall HSUVs from 0.6125 to 0.863). Two studies reported the population of frontline workers (the range of overall HSUVs from 0.82 to 0.93). Seven of the articles researched the participants who had not been infected with COVID-19 but suffered from morbidities during the pandemic (range of overall HSUVs from 0.5 to 0.96). Thirteen studies showed that the HRQoL of the respondents who have not been infected with COVID-19 and without any morbidities (range of overall HSUVs from 0.64 to 0.964). Moreover, eighteen articles reported the outcomes of overall HSUVs during the COVID-19 pandemic in different population groups. The estimate of overall HSUVs of direct COVID-19 experience population (n=1333) was 0.751 (95% CI 0.670 - 0.832, I2 = 98.64%); the estimate of frontline population (n=610) was 0.906 ((95% CI 0.854 – 0.957, I2 = 98.61%); participants with different disease (n=132) were 0.768 (95% CI 0.515 - 1.021, I2= 99.26%); general population without infection history (n=29,892) was 0.825 (95% CI 0.766 - 0.885, I2 =99.69%). Conclusively, taking into account these results, this systematic review might confirm that COVID-19 has a negative impact on the HRQoL of the infected population and illness population. It provides practical value for cost-effectiveness model analysis of health states related to COVID-19.

Keywords: COVID-19, health-related quality of life, meta-analysis, systematic review, utility value

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Authors: F. Sangiorgio, J. Silfwerbrand, G. Mancini

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Geometric and mechanical properties all influence the resistance of RC structures and may, in certain combination of property values, increase the risk of a brittle failure of the whole system. This paper presents a statistical and probabilistic investigation on the resistance of RC beams designed according to Eurocodes 2 and 8, and subjected to multiple failure modes, under both the natural variation of material properties and the uncertainty associated with cross-section and transverse reinforcement geometry. A full probabilistic model based on JCSS Probabilistic Model Code is derived. Different beams are studied through material nonlinear analysis via Monte Carlo simulations. The resistance model is consistent with Eurocode 2. Both a multivariate statistical evaluation and the data clustering analysis of outcomes are then performed. Results show that the ultimate load behaviour of RC beams subjected to flexural and shear failure modes seems to be mainly influenced by the combination of the mechanical properties of both longitudinal reinforcement and stirrups, and the tensile strength of concrete, of which the latter appears to affect the overall response of the system in a nonlinear way. The model uncertainty of the resistance model used in the analysis plays undoubtedly an important role in interpreting results.

Keywords: modelling, Monte Carlo simulations, probabilistic models, data clustering, reinforced concrete members, structural design

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Authors: F. Rashno Seydari, M. Nikafrooz

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This paper intends to study needs analysis of hearing-impaired students’ teachers in elementary schools all over Iran. The subjects of this study were 275 teachers who were teaching hearing-impaired students in elementary schools. The participants were selected by a quota sampling method. To collect the data, questionnaires of training needs consisting of 41 knowledge items and 31 performance items were used. The collected data were analyzed by using SPSS software in the form of descriptive analyses (frequency and mean) and inferential analyses (one sample t-test, paired t-test, independent t-test, and Pearson correlation coefficient). The findings of the study indicated that teachers generally have considerable needs in knowledge and performance domains. In 32 items out of the total 41 knowledge domain items and in the 27 items out of the total 31 performance domain items, the teachers had considerable needs. From the quantitative point of view, the needs of the performance domain were more than those of the knowledge domain, so they have to be considered as the first priority in training these teachers. There was no difference between the level of the needs of male and female teachers. There was a significant difference between the knowledge and performance domain needs and the teachers’ teaching experience, 0.354 and 0.322 respectively. The teachers who had been trained in working with hearing-impaired students expressed more training needs (both knowledge and performance).

Keywords: educational needs analysis, teachers of hearing impaired students, knowledge domain, function domain

Procedia PDF Downloads 96