Search results for: 3D plant data

Authors: Amanda Madden

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The purpose of this research was to identify students understanding of well-being and perceptions of the effective components of school-based well-being programs they have participated in during their time in secondary school. With one in four adolescents suffering from some form of mental health disorder, which has the potential to directly impact their academic ability, schools have moved towards a more holistic approach to education, resulting in the growth of school-based well-being programs. There is limited research on the effectiveness of school-based well-being programs, with fewer studies examining students’ perspectives on their well-being. A mixed-method design was utilized, framed by a social constructivist methodology. Quantitative data was collected through a researcher-developed self-report survey, and qualitative data were collected through one-on-one interviews and a semi-structured focus group undertaken with Year 12 students from three independent co-educational schools in Western Australia. Preliminary findings indicate that participants have experienced a minimal impact, either positively or negatively, on their well-being from school-based well-being programs. The data detailed that adolescents consider happiness, positive attitude, good physical health, balance, emotional fulfillment and confidence components of well-being. The findings also highlighted sports, positive family relationships, positive friendships, and pets positively enhanced well-being. This research suggests that researchers and educational leaders should consider students’ understanding of well-being in the development of school-based well-being assessments and interventions. Students are the recipients of school-based well-being programs and are best placed to inform what they will and will not respond to in the determination of appropriate well-being content.

Keywords: wellbeing, school based wellbeing, adolescents, wellbeing interventions

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Authors: Dino Carpentras, Paul Maher, Caoimhe O'Reilly, Michael Quayle

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Opinion dynamics is dedicated to modeling the dynamic evolution of people's opinions. Models in this field are based on a micro-dynamic rule, which determines how people update their opinion when interacting. Despite the high number of new models (many of them based on new rules), little research has been dedicated to experimentally validate the rule. A few studies started bridging this literature gap by experimentally testing the rule. However, in these studies, participants are forced to express their opinion as a number instead of using natural language. Furthermore, some of these studies average data from experimental questions, without testing if differences existed between them. Indeed, it is possible that different topics could show different dynamics. For example, people may be more prone to accepting someone's else opinion regarding less polarized topics. In this work, we collected data from 200 participants on 5 unpolarized topics. Participants expressed their opinions using natural language ('agree' or 'disagree') and the certainty of their answer, expressed as a number between 1 and 10. To keep the interaction based on natural language, certainty was not shown to other participants. We then showed to the participant someone else's opinion on the same topic and, after a distraction task, we repeated the measurement. To produce data compatible with standard opinion dynamics models, we multiplied the opinion (encoded as agree=1 and disagree=-1) with the certainty to obtain a single 'continuous opinion' ranging from -10 to 10. By analyzing the topics independently, we observed that each one shows a different initial distribution. However, the dynamics (i.e., the properties of the opinion change) appear to be similar between all topics. This suggested that the same micro-dynamic rule could be applied to unpolarized topics. Another important result is that participants that change opinion tend to maintain similar levels of certainty. This is in contrast with typical micro-dynamics rules, where agents move to an average point instead of directly jumping to the opposite continuous opinion. As expected, in the data, we also observed the effect of social influence. This means that exposing someone with 'agree' or 'disagree' influenced participants to respectively higher or lower values of the continuous opinion. However, we also observed random variations whose effect was stronger than the social influence’s one. We even observed cases of people that changed from 'agree' to 'disagree,' even if they were exposed to 'agree.' This phenomenon is surprising, as, in the standard literature, the strength of the noise is usually smaller than the strength of social influence. Finally, we also built an opinion dynamics model from the data. The model was able to explain more than 80% of the data variance. Furthermore, by iterating the model, we were able to produce polarized states even starting from an unpolarized population. This experimental approach offers a way to test the micro-dynamic rule. This also allows us to build models which are directly grounded on experimental results.

Keywords: experimental validation, micro-dynamic rule, opinion dynamics, update rule

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Authors: Elizabeth Hartney, Keith Borkowsky, Jo Axe, Doug Hamilton

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The aim of this research is to develop a grounded theory of educational leadership development, using an approach to initiating and maintaining professional growth in school principals and vice principals termed generative dialogue. The research was conducted in a relatively affluent, urban school district in Western Canada. Generative dialogue interviews were conducted by a team of consultants, and anonymous data in the form of handwritten notes were voluntarily submitted to the research team. The data were transcribed and analyzed using grounded theory. The results indicate that a key focus of educational leadership development is focused on navigating relationships within the school setting and that the generative dialogue process is helpful for principals and vice principals to explore how they might do this. Applicability and limitations of the study are addressed.

Keywords: generative dialogue, school principals, grounded theory, leadership development

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Authors: Kim Kennedy, Daren Gibson, Stephanie Flukes, Chandra Diwakarla, Lisa Spalding, Leanne Pilkington, Andrew Redfern

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Introduction: It is well known that patients with Head and Neck Cancer (HNC) are at increased risk of subsequent head and neck cancers due to various aetiologies. Aim: We sought to determine the factors contributing to an increased risk of subsequent HNC primaries, and also to evaluate whether Aboriginal patients are at increased risk. Methods: We performed a retrospective cohort analysis of 320 HNC patients from a single centre in Western Australia, identifying 80 Aboriginal patients and 240 non-Aboriginal patients matched on a 1:3 ratio by site, histology, rurality, and age. We collected patient data including smoking and alcohol consumption, tumour and treatment data, and data on subsequent HNC primaries. Results: A subsequent HNC primary was seen in 37 patients (11.6%) overall. There was no significant difference in the rate of second primary HNCs between Aboriginal patients (12.5%) and nonAboriginal patients (11.2%) (p=0.408). Subsequent HNCs, were strongly associated with smoking and alcohol consumption however, with 95% of patients with a second primary being ever-smokers, and 54% of patients with a second primary having a history of excessive alcohol consumption. In the 37 patients with multiple HNC primaries, there were a total of 57 HNCs, with 29 patients having two primaries, six patients having 3 HNC primaries, one patient with four, and one with six. 54 out of the 57 cancers were in ever smokers (94.7%). There were only two multiple HNC primaries in a never smoker, non-drinker, and these cases were of unknown etiology with HPV/p16 status unknown in both cases. In the whole study population, there were 32 HPV-positive HNCs, and 67 p16-positive HNCs, with only two 2 nd HNCs in a p16-positive case, giving a rate of 3% in the p16+ population, which is actually much lower than the rate of second primaries seen in the overall population (11.6%), and was highest in the p16-negative population (15.7%). This suggests that p16-positivity is not a strong risk factor for subsequent primaries, and in fact p16-negativity appeared to be associated with increased risk, however this data is limited by the large number of patients without documented p16 status (45.3% overall, 12% for oropharyngeal, and 59.6% for oral cavity primaries had unknown p16 status). Summary: Subsequent HNC primaries were strongly associated with smoking and alcohol excess. Second and later HNC primaries did not appear to occur at increased rates in Aboriginal patients compared with non-Aboriginal patients, and p16-positivity did not predict increased risk, however p16-negativity was associated with an increased risk of subsequent HNCs.

Keywords: head and neck cancer, multiple primaries, aboriginal, p16 status, smoking, alcohol

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Authors: Elcin Timur Cakmak, Ayse Oguzlar

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This study presents the results of the tweets sent by Twitter users on social media about the Russia-Ukraine war by bigram and trigram methods. On February 24, 2022, Russian President Vladimir Putin declared a military operation against Ukraine, and all eyes were turned to this war. Many people living in Russia and Ukraine reacted to this war and protested and also expressed their deep concern about this war as they felt the safety of their families and their futures were at stake. Most people, especially those living in Russia and Ukraine, express their views on the war in different ways. The most popular way to do this is through social media. Many people prefer to convey their feelings using Twitter, one of the most frequently used social media tools. Since the beginning of the war, it is seen that there have been thousands of tweets about the war from many countries of the world on Twitter. These tweets accumulated in data sources are extracted using various codes for analysis through Twitter API and analysed by Python programming language. The aim of the study is to find the word sequences in these tweets by the n-gram method, which is known for its widespread use in computational linguistics and natural language processing. The tweet language used in the study is English. The data set consists of the data obtained from Twitter between February 24, 2022, and April 24, 2022. The tweets obtained from Twitter using the #ukraine, #russia, #war, #putin, #zelensky hashtags together were captured as raw data, and the remaining tweets were included in the analysis stage after they were cleaned through the preprocessing stage. In the data analysis part, the sentiments are found to present what people send as a message about the war on Twitter. Regarding this, negative messages make up the majority of all the tweets as a ratio of %63,6. Furthermore, the most frequently used bigram and trigram word groups are found. Regarding the results, the most frequently used word groups are “he, is”, “I, do”, “I, am” for bigrams. Also, the most frequently used word groups are “I, do, not”, “I, am, not”, “I, can, not” for trigrams. In the machine learning phase, the accuracy of classifications is measured by Classification and Regression Trees (CART) and Naïve Bayes (NB) algorithms. The algorithms are used separately for bigrams and trigrams. We gained the highest accuracy and F-measure values by the NB algorithm and the highest precision and recall values by the CART algorithm for bigrams. On the other hand, the highest values for accuracy, precision, and F-measure values are achieved by the CART algorithm, and the highest value for the recall is gained by NB for trigrams.

Keywords: classification algorithms, machine learning, sentiment analysis, Twitter

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Authors: Hoda Rahmani, Gary Weckman

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The body of research examining the integration of drones into various industries is expanding rapidly. Despite progress made in addressing the cybersecurity concerns for commercial drones, knowledge deficits remain in determining potential occupational hazards and risks of drone use to employees’ well-being and health in the workplace. This creates difficulty in identifying key approaches to risk mitigation strategies and thus reflects the need for raising awareness among employers, safety professionals, and policymakers about workplace drone-related accidents. The purpose of this study is to investigate the prevalence of and possible risk factors for drone-related mishaps by comparing the application of drones in construction with manufacturing industries. The chief reason for considering these specific sectors is to ascertain whether there exists any significant difference between indoor and outdoor flights since most construction sites use drones outside and vice versa. Therefore, the current research seeks to examine the causes and patterns of workplace drone-related mishaps and suggest possible ergonomic interventions through data collection. Potential ergonomic practices to mitigate hazards associated with flying drones could include providing operators with professional pieces of training, conducting a risk analysis, and promoting the use of personal protective equipment. For the purpose of data analysis, two data mining techniques, the random forest and association rule mining algorithms, will be performed to find meaningful associations and trends in data as well as influential features that have an impact on the occurrence of drone-related accidents in construction and manufacturing sectors. In addition, Spearman’s correlation and chi-square tests will be used to measure the possible correlation between different variables. Indeed, by recognizing risks and hazards, occupational safety stakeholders will be able to pursue data-driven and evidence-based policy change with the aim of reducing drone mishaps, increasing productivity, creating a safer work environment, and extending human performance in safe and fulfilling ways. This research study was supported by the National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant #T42OH008432.

Keywords: commercial drones, ergonomic interventions, occupational safety, pattern recognition

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Authors: Yazan M. Alatrash, Han-ok Kang, Hyun-gi Yoon, Shen Zhang, Juhyeon Yoon

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Flow coastdown phenomena are very important to secure nuclear fuel integrity during loss of off-site power accidents. In this study, primary coolant flow coastdown phenomena are investigated for the Jordan Research and Training Reactor (JRTR) using a simulation software package, Modular Modelling System (MMS). Two MMS models are built. The first one is a simple model to investigate the characteristics of the primary coolant pump only. The second one is a model for a simulation of the Primary Coolant System (PCS) loop, in which all the detailed design data of the JRTR PCS system are modelled, including the geometrical arrangement data. The same design data for a PCS pump are used for both models. Coastdown curves obtained from the two models are compared to study the PCS loop coolant inertia effect on a flow coastdown. Results showed that the loop coolant inertia effect is found to be small in the JRTR PCS loop, i.e., about one second increases in a coastdown half time required to halve the coolant flow rate. The effects of different flywheel inertia on the flow coastdown are also investigated. It is demonstrated that the coastdown half time increases with the flywheel inertia linearly. The designed coastdown half time is proved to be well above the design requirement for the fuel integrity.

Keywords: flow coastdown, loop inertia, modelling, research reactor

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Authors: Pietro D'Ambrosio, Roberta D'Ambrosio

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The upgrading of the architectural and urban heritage of the urban historic centers almost always involves the planning for the reuse and refunctionalization of the structures. Such interventions have complexities linked to the need to take into account the urban and social context in which the structure and its intrinsic characteristics such as historical and artistic value are inserted. To these, of course, we have to add the need to make a preliminary estimate of recovery costs and more generally to assess the economic and financial sustainability of the whole project of re-socialization. Particular difficulties are encountered during the pre-assessment of costs since it is often impossible to perform analytical surveys and structural tests for both structural conditions and obvious cost and time constraints. The methodology proposed in this work, based on a multidisciplinary and data-driven approach, is aimed at obtaining, at very low cost, reasonably priced economic evaluations of the interventions to be carried out. In addition, the specific features of the approach used, derived from the predictive analysis techniques typically applied in complex IT domains (big data analytics), allow to obtain as a result indirectly the evaluation process of a shared database that can be used on a generalized basis to estimate such other projects. This makes the methodology particularly indicated in those cases where it is expected to intervene massively across entire areas of historical city centers. The methodology has been partially tested during a study aimed at assessing the feasibility of a project for the reuse of the monumental complex of San Massimo, located in the historic center of Salerno, and is being further investigated.

Keywords: evaluation, methodology, restoration, reuse

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Authors: Zohra Aziz Ali Manjiyani, Renju Thomas Jacob, Keerthan Kumar

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This project develops a hand movement monitoring system, which feeds the data into the computer and gives the 3D image rotation according to the direction of the tilt and hence monitoring the movement of the hand in context to its tilt. Advancement of MEMS Technology has enabled us to get very small and low-cost accelerometer ICs which is based on capacitive principle. Accelerometer based Tilt sensor ADXL335 is used in this paper, based on MEMS technology and the project emphasis on the development of the MEMS-based accelerometer to measure the tilt, interfacing the hardware with the LabVIEW and showing the 3D rotation to the user, which is in his understandable form and tilt data can be saved in the computer. It provides an experience of working on emerging technologies like MEMS and design software like LabVIEW.

Keywords: MEMS accelerometer, tilt sensor ADXL335, LabVIEW simulation, 3D animation

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Authors: Tomas Trainys, Algimantas Venckauskas

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Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: Mouhamadou Diop, Mohamed I. Hassan

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Molten metallic flow in metallurgical plant is highly turbulent and presents a complex coupling with heat transfer, phase transfer, chemical reaction, momentum transport, etc. Molten silicon flow has significant effect in directional solidification of multicrystalline silicon by affecting the temperature field and the emerging crystallization interface as well as the transport of species and impurities during casting process. Owing to the complexity and limits of reliable measuring techniques, computational models of fluid flow are useful tools to study and quantify these problems. The overall objective of this study is to investigate the potential of a traveling magnetic field for an efficient operating control of the molten metal flow. A multidimensional numerical model will be developed for the calculations of Lorentz force, molten metal flow, and the related phenomenon. The numerical model is implemented in a laboratory-scale silicon crystallization furnace. This study presents the potential of traveling magnetic field approach for an efficient operating control of the molten flow. A numerical model will be used to study the effects of magnetic force applied on the molten flow, and their interdependencies. In this paper, coupled and decoupled, steady and unsteady models of molten flow and crystallization interface will be compared. This study will allow us to retrieve the optimal traveling magnetic field parameter range for crystallization furnaces and the optimal numerical simulations strategy for industrial application.

Keywords: multidimensional, numerical simulation, solidification, multicrystalline, traveling magnetic field

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Authors: Carlos Roberto Valêncio, Fábio Renato de Almeida, Thatiane Kawabata, Leandro Alves Neves, Julio Cesar Momente, Mario Luiz Tronco, Angelo Cesar Colombini

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By running transactions under the Snapshot isolation we can achieve a good level of concurrency, specially in databases with high-intensive read workloads. However, Snapshot is not immune to all the problems that arise from competing transactions and therefore no serialization warranty exists. We propose in this paper a technique to obtain data consistency with Snapshot by using some special triggers that we named Daemon Triggers. Besides keeping the benefits of the Snapshot isolation, the technique is specially useful for those database systems that do not have an isolation level that ensures serializability, like Firebird and Oracle. We describe all the anomalies that might arise when using the Snapshot isolation and show how to preclude them with Daemon Triggers. Based on the methodology presented here, it is also proposed the creation of a new isolation level: Daemon Snapshot.

Keywords: data consistency, serialization, snapshot, isolation

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Authors: Agah Tugrul Korucu

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Significant changes have taken place for the better in the bulk of information and in the use of technology available in the field of education induced by technological changes in the 21st century. It is mainly the job of the teachers and pre-service teachers to integrate information and communication technologies into education by means of conveying the use of technology to individuals. While the pre-service teachers are conducting lessons by using technology, the methods they have developed are important factors for the requirements of the lesson and for the satisfaction levels of the students. The study of this study is to examine the satisfaction levels of pre-service teachers as regards e-learning in a technological environment in which there are lesson activities conducted through an online learning environment in terms of various variables. The study group of the research is composed of 156 pre-service teachers that were students in the departments of Computer and Teaching Technologies, Art Teaching and Pre-school Teaching in the academic year of 2014 - 2015. The qualitative research method was adopted for this study; the scanning model was employed in collecting the data. “The Satisfaction Scale regarding the E-learning Process”, developed by Gülbahar, and the personal information form, which was developed by the researcher, were used as means of collecting the data. Cronbach α reliability coefficient, which is the internal consistency coefficient of the scale, is 0.91. SPSS computerized statistical package program and the techniques of medium, standard deviation, percentage, correlation, t-test and variance analysis were used in the analysis of the data.

Keywords: online learning environment, integration of information technologies, e-learning, e-learning satisfaction, pre-service teachers

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Authors: K. Matušínská, Z. Bednarčík, M. Klepek

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This paper examines international marketing in business practice of Czech exporting small and medium-sized enterprises (SMEs) with regard to the strategic perspectives. Research was focused on Czech exporting SMEs from Moravian-Silesia region and their behaviour on international markets. For purpose of collecting data, a questionnaire was given to 262 SMEs involved in international business. Statistics utilized in this research included frequency, mean, percentage, and chi-square test. Data were analysed by Statistical Package for the Social Sciences software. The research analysis disclosed that there is certain space for improvement in strategic marketing especially in marketing research, perception of cultural and social differences, product adaptation and usage of marketing communication tools.

Keywords: international marketing, marketing mix, marketing research, small and medium-sized enterprises, strategic marketing

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Authors: Aouissi Nora, Meksem Leila

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This work focused on the study of air pollution generated by the transport sector in the region of Annaba. Our study is based on two parts: the first one concerns an epidemiological investigation in the area of Annaba situated in the east Algerian coast, which deals with the development of the fleet and its impact on public health. To get a more precise idea of the impact of road traffic on public health, we consulted the computing center office of the National Social Insurance Fund. The information we were given by this office refers to the number of reported asthma and heart disease after medical examination during the period 2006-2010. The second part was devoted to the study of the toxicity of exhaust gases on some physical and biochemical parameters of durum wheat (Triticum durum Desf.). After germination and three-leaf stage, the pots are placed in a box of volume (0,096 m3) having an input which is linked directly to the exhaust pipe of a truck, and an outlet to prevent asphyxiation plant. The experience deals with 30 pots: 10 pots are exposed for 5 minutes to exhaust smoke; the other 10 are exposed for 15 minutes, and the remaining 10 for 30 minutes. The epidemiological study shows that the levels of pollutants emitted by the fleet are responsible for the increase of people respiratory and cardiovascular diseases. As for biochemical analyses of vegetation, they clearly show the toxicity of pollutants emitted by the exhaust gases, with an increase in total protein, proline and stimulation of detoxification enzyme (catalase).

Keywords: air pollution, toxicity, epidemiology, biochemistry

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Authors: Nattawadee Sinpattanawong

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Oral presentation has been used mostly in business communication. The business presentation is carrying out through an audio and visual presentation material such as statistical documents, projectors, etc. Common examples of business presentation are intra-organization and sales presentations. The study aims at investigating functions, strategies and contents of assessors’ verbal feedback on presenters’ oral presentations and exploring presenters’ learning process and specific views and expectations concerning assessors’ verbal feedback related to the delivery of the oral presentation. This study is designed as a descriptive qualitative research; four master students and one teacher in English for Business and Industry Presentation Techniques class of public university will be selected. The researcher hopes that any understanding how assessors’ verbal feedback on oral presentations and learning process may illuminate issues for other people. The data from this research may help to expand and facilitate the readers’ understanding of assessors’ verbal feedback on oral presentations and learning process in their own situations. The research instruments include an audio recorder, video recorder and an interview. The students will be interviewing in order to ask for their views and expectations concerning assessors’ verbal feedback related to the delivery of the oral presentation. After finishing data collection, the data will be analyzed and transcribed. The findings of this study are significant because it can provide presenters knowledge to enhance their learning process and provide teachers knowledge about providing verbal feedback on student’s oral presentations on a business context.

Keywords: business context, learning process, oral presentation, verbal feedback

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Authors: Mohammad Abdullah-Al-Wadud

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In the current era of automation and artificial intelligence, different systems have been increasingly keeping on depending on decision-making capabilities of machines. Such systems/applications may range from simple classifiers to sophisticated surveillance systems based on traditional sensors and related equipment which are becoming more common in the internet of things (IoT) paradigm. However, the available data for such problems are usually imprecise and incomplete, which leads to uncertainty in decisions made based on traditional probability-based classifiers. This requires a robust fusion framework to combine the available information sources with some degree of certainty. The theory of evidence can provide with such a method for combining evidence from different (may be unreliable) sources/observers. This talk will address the employment of the Dempster-Shafer Theory of evidence in some practical applications.

Keywords: decision making, dempster-shafer theory, evidence fusion, incomplete data, uncertainty

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Authors: Yousef Abdulaziz Almolhem

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Geographically, the most Arabic countries locate in areas confined to arid or semiarid regions. For this reason, most of our countries have adopted the seawater desalination as a strategy to overcome this problem. For example, the water supply of AUE, Kuwait, and Saudi Arabia is almost 100% from the seawater desalination plants. Many areas in Saudia Arabia and other countries in the world suffer from lack of fresh water which hinders the development of these areas, despite the availability of saline water and high solar radiation intensity. Furthermore, most developing countries do not have sufficient meteorological data to evaluate if the solar radiation is enough to meet the solar desalination. A mathematical model was developed to simulate and predict the thermal behavior of the solar still which used direct solar energy for distillation of seawater. Measurement data were measured in the Environment and Natural Resources Department, Faculty of Agricultural and Food sciences, King Faisal University, Saudi Arabia, in order to evaluate the present model. The simulation results obtained from this model were compared with the measured data. The main results of this research showed that there are slight differences between the measured and predicted values of the elements studied, which is resultant from the change of some factors considered constants in the model such as the sky clearance, wind velocity and the salt concentration in the water in the basin of the solar still. It can be concluded that the present model can be used to estimate the average total solar radiation and the thermal behavior of the solar still in any area with consideration to the geographical location.

Keywords: mathematical model, sea water, distillation, solar radiation

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Authors: Tsige Reda

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Essential oil of Thymus vulgaris was extracted by means of hydro-distillation. This study was done to investigate the chemical composition, antibacterial and antioxidant activities. The chemical composition of the essential oils was determined using gas chromatography coupled to mass spectroscopy (GC-MS). Using disc diffusion assay the antibacterial activity was assessed on one Gram-positive bacteria and one Gram-negative bacteria. The percentage oil yield of the essential oil was found to be 0.97 ± 0.08% (w/w) with yellow color. The physicochemical constants of the oil were also noted. The phytochemical screening of the plant extract revealed the presence of tannins, saponins, phenol, flavonoids, terpenoids, steroids and alkaloids. A total of 18 chemical constituents were identified by Gas Chromatography-Mass Spectroscopy analysis representing 100% of the total essential oil of Thymus vulgaris, with thymol (31.977%), o-cymene (29.992%), and carvacrol (14.541%). Previous studies have revealed that the thymol, o-cymen and carvacrol components of Thymus vulgaris are responsible for their biological activities. Thymus vulgaris have been used traditionally to treat a wide variety of infections. Based on the extensive use and lack of scientific evidence, a study was embarked upon to determine its bioactivity. The essential oil of Thymus vulgaris leaves exhibited higher activity towards the Gram-positive bacteria (Staphylococcus aurous) than the Gram-negative bacteria (Escherichia coli) and also has good antioxidant activity, and can be used medicinal and therapeutic applications. This activity may be due to the high amount of thymol, o-cymen and carvacrol.

Keywords: hydro-distillation, Thymus vulgaris, essential oil composition, phytochemical screening, physicochemical constants, antioxidant activity, antibacterial activity

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Authors: Ayesha Aihetasham, Najma Arshad, Sobia Khan

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Damage causes by subterranean termites are of major concern today. Termites majorly treated with pesticides resulted in several problems related to health and environment. For this reason, plant-derived natural products specifically essential oils have been evaluated in order to control termites. The aim of the present study was to investigate the antitermitic potential of six essential oils on Heterotermes indicola subterranean termite. No-choice bioassay was used to assess the termiticidal action of essential oils. Further, gut from each set of treated termite group was extracted and analyzed for reduction in number of protozoa and bacteria by protozoal count method using haemocytometer and viable bacterial plate count (dilution method) respectively. In no-choice bioassay it was found that Foeniculum vulgare oil causes high degree of mortality 90 % average mortality at 10 mg oil concentration (10mg/0.42g weight of filter paper). Least mortality appeared to be due to Citrus sinensis oil (43.33 % average mortality at 10 mg/0.42g). The highest activity verified to be of Foeniculum vulgare followed by Eruca sativa, Trigonella foenum-graecum, Peganum harmala, Syzygium cumini and Citrus sinensis. The essential oil which caused maximum reduction in number of protozoa was P. harmala followed by T. foenum-graecum and E. sativa. In case of bacterial count E. sativa oil indicated maximum decrease in bacterial number (6.4×10⁹ CFU/ml). It is concluded that F. vulgare, E. sativa and P. harmala essential oils are highly effective against H. indicola termite and its gut microflora.

Keywords: bacterial count, essential oils, Heterotermes indicola, protozoal count

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Authors: Mimoza Canga, Irene Malagnino, Giulia Malagnino, Alketa Qafmolla, Ruzhdie Qafmolla, Vito Antonio Malagnino

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The goal of the present study was to analyze the risk factors regarding dental fear. This observational study was conducted during the period of February 2020 - April 2022 in Albania. The sample was composed of 200 participants, of which 40% were males and 60% were females. The participants' age range varied from 35 to 75 years old. We divided them into four age groups: 35-45, 46-55, 56-65, and 66-75 years old. Statistical analysis was performed using IBM SPSS Statistics 23.0. Data were scrutinized by the Post Hoc LSD test in analysis of variance (ANOVA). The P ≤ 0.05 values were considered significant. Data analysis included Confidence Interval (95% CI). The prevailing age range in the sample was mostly from 55 to 65 years old, 35.6% of the patients. In all, 50% of the patients had extreme fear about the fact that the dentist may be infected with Covid-19, 12.2% of them had low dental fear, and 37.8% had extreme dental fear. However, data collected from the current study indicated that a large proportion of patients 49.5% of them had high dental fear regarding the dentist not respecting the quarantine due to COVID-19, in comparison with 37.2% of them who had low dental fear and 13.3% who had extreme dental fear. The present study confirmed that 22.2% of the participants had an extreme fear of poor hygiene practices of the dentist that have been associated with the transmission of COVID-19 infection, 57.8% had high dental fear, and 20% of them had low dental fear. The present study showed that 50% of the patients stated that another factor that causes extreme fear was that the patients feel pain after interventions in the oral cavity. Strong associations were observed between dental fear and pain 95% CI; 0.24-0.52, P-value ˂ .0001. The results of the present study confirmed strong associations between dental fear and the fact that the dentist may be infected with Covid-19 (95% CI; 0.46-0.70, P-value ˂ .0001). Similarly, the analysis of the present study demonstrated that there was a statistically significant correlation between dental fear and poor hygiene practices of the dentist with 95% CI; 0.82-1.02, P-value ˂ .0001. On the basis of our statistical data analysis, the dentist did not respect the quarantine due to COVID-19 having a significant impact on dental fear with a P-value of ˂ .0001. This study shows important risk factors that significantly increase dental fear.

Keywords: Covid-19, dental fear, pain, past dreadful experiences

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Authors: Salwa Enneifer

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The research is intended to investigate Tunisian students’ own perception of the difficulties they encounter in the writing task. To achieve this objective, a questionnaire was administered to students enrolled in the ‘Faculty of Letters Arts and Humanities’ in Kairouan, in Tunisia. Students were classified into three groups: first-, second-, and third-year students. The researcher used 120 questionnaires filled in by the students as data for this study; moreover, 30 students participated in a semi-structured interview to complete the data. The questionnaire results revealed that Tunisian EFL students faced spelling and grammar difficulties. ANOVA also revealed that the first-year students did not recognise that Arabic and English greatly differ in their respective punctuation systems. The second-year class, however, was fully aware of this difference. Additionally, the interview shed light on other aspects or different difficulties experienced by students in writing: a cruel ‘lack of vocabulary’, Arabic language interference, the organisation of the essay and especially the academic essay, and difficulty with writing an argumentative essay.

Keywords: difficulties, writing, Tunisian, EFL students

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Authors: P. Bublíkova, P. Halodova, H. K. Namburi, J. Stodolna, J. Duchon, O. Libera

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Neutron radiation-induced microstructural changes cause degradation of mechanical properties and the lifetime reduction of reactor internals during nuclear power plant operation. Investigating the effects of neutron irradiation on mechanical properties of the irradiated material (hardening, embrittlement) is challenging and time-consuming. Although the fast neutron spectrum has the major influence on microstructural properties, the thermal neutron effect is widely investigated owing to Irradiation-Assisted Stress Corrosion Cracking firstly observed in BWR stainless steels. In this study, 300-series austenitic stainless steels used as material for NPP's internals were examined after neutron irradiation at ~ 15 dpa. Although several nanoindentation experimental publications are available to determine the mechanical properties of ion irradiated materials, less is available on neutron irradiated materials at high dpa tested in hot-cells. In this work, we present particular methodology developed to determine the mechanical properties of neutron irradiated steels by nanoindentation technique. Furthermore, radiation-induced damage in the specimens was investigated by High Resolution - Transmission Electron Microscopy (HR-TEM) that showed the defect features, particularly Frank loops, cavity microstructure, radiation-induced precipitates and radiation-induced segregation. The results of nanoindentation measurements and associated nanoscale defect features showed the effect of irradiation-induced hardening. We also propose methodologies to optimized sample preparation for nanoindentation and microscotructural studies.

Keywords: nanoindentation, thermal neutrons, radiation hardening, transmission electron microscopy

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Authors: Charalampos Saridakis, Stelios Tsafarakis

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Although environmental concern is on the rise across Europe, current market data indicate that adoption rates of environmentally friendly vehicles remain extremely low. Against this background, the aim of this paper is to a) assess preferences of European consumers for clean-fuel cars and their characteristics and b) design car lines that optimize the combination of fuel types among models in the line-up. In this direction, the authors introduce a new evolutionary mechanism and implement it to stated-preference data derived from a large-scale choice-based conjoint experiment that measures consumer preferences for various factors affecting clean-fuel vehicle (CFV) adoption. The proposed two-step methodology provides interesting insights into how new and existing fuel-types can be combined in a car line that maximizes customer satisfaction.

Keywords: clean-fuel vehicles, product line design, conjoint analysis, choice experiment, differential evolution

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Authors: Alia Alghosoun, Nabil El Moçayd, Mohammed Seaid

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Modeling dam-break flows over non-flat beds requires an accurate representation of the topography which is the main source of uncertainty in the model. Therefore, developing robust and accurate techniques for reconstructing topography in this class of problems would reduce the uncertainty in the flow system. In many hydraulic applications, experimental techniques have been widely used to measure the bed topography. In practice, experimental work in hydraulics may be very demanding in both time and cost. Meanwhile, computational hydraulics have served as an alternative for laboratory and field experiments. Unlike the forward problem, the inverse problem is used to identify the bed parameters from the given experimental data. In this case, the shallow water equations used for modeling the hydraulics need to be rearranged in a way that the model parameters can be evaluated from measured data. However, this approach is not always possible and it suffers from stability restrictions. In the present work, we propose an adaptive optimal control technique to numerically identify the underlying bed topography from a given set of free-surface observation data. In this approach, a minimization function is defined to iteratively determine the model parameters. The proposed technique can be interpreted as a fractional-stage scheme. In the first stage, the forward problem is solved to determine the measurable parameters from known data. In the second stage, the adaptive control Ensemble Kalman Filter is implemented to combine the optimality of observation data in order to obtain the accurate estimation of the topography. The main features of this method are on one hand, the ability to solve for different complex geometries with no need for any rearrangements in the original model to rewrite it in an explicit form. On the other hand, its achievement of strong stability for simulations of flows in different regimes containing shocks or discontinuities over any geometry. Numerical results are presented for a dam-break flow problem over non-flat bed using different solvers for the shallow water equations. The robustness of the proposed method is investigated using different numbers of loops, sensitivity parameters, initial samples and location of observations. The obtained results demonstrate high reliability and accuracy of the proposed techniques.

Keywords: erodible beds, finite element method, finite volume method, nonlinear elasticity, shallow water equations, stresses in soil

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Authors: Sun H. Hwang, Young E. Lee, Yunhan Ga, Gilwon Yoon

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EEG is a very complex signal with noises and other bio-potential interferences. EOG is the most distinct interfering signal when EEG signals are measured and analyzed. It is very important how to process raw EEG signals in order to obtain useful information. In this study, the EEG signal processing techniques such as EOG filtering and outlier removal were examined to minimize unwanted EOG signals and other noises. The two different mental states of resting and focusing were examined through EEG analysis. A focused state was induced by letting subjects to watch a red dot on the white screen. EEG data for 32 healthy subjects were measured. EEG data after 60-Hz notch filtering were processed by a commercially available EOG filtering and our presented algorithm based on the removal of outliers. The ratio of beta wave to theta wave was used as a parameter for determining the degree of focusing. The results show that our algorithm was more appropriate than the existing EOG filtering.

Keywords: EEG, focus, mental state, outlier, signal processing

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Authors: Osama Elsarrar, Marjorie Darrah, Richard Devin

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This paper discusses the idea of capturing an expert’s knowledge in the form of human understandable rules and then inserting these rules into a dynamic cell structure (DCS) neural network. The DCS is a form of self-organizing map that can be used for many purposes, including classification and prediction. This particular neural network is considered to be a topology preserving network that starts with no pre-structure, but assumes a structure once trained. The DCS has been used in mission and safety-critical applications, including adaptive flight control and health-monitoring in aerial vehicles. The approach is to insert expert knowledge into the DCS before training. Rules are translated into a pre-structure and then training data are presented. This idea has been demonstrated using the well-known Iris data set and it has been shown that inserting the pre-structure results in better accuracy with the same training.

Keywords: neural network, self-organizing map, rule extraction, rule insertion

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Authors: Sadia Munir, Diana White, Aya Albahri, Pratiwi Hastania, Eltahir Mohamed, Mahmood Khan, Fathima Mohamed, Ayat Kadhi, Haila Saleem

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Preeclampsia is a major complication of pregnancy. Prediction and management of preeclampsia is a challenge for obstetricians. To our knowledge, no major progress has been achieved in the prevention and early detection of preeclampsia. There is very little known about the clear treatment path of this disorder. Preeclampsia puts both mother and baby at risk of several short term- and long term-health problems later in life. There is huge health service cost burden in the health care system associated with preeclampsia and its complications. Preeclampsia is divided into two different types. Early onset preeclampsia develops before 34 weeks of gestation, and late onset develops at or after 34 weeks of gestation. Different genetic and environmental factors, prognosis, heritability, biochemical and clinical features are associated with early and late onset preeclampsia. Prevalence of preeclampsia greatly varies all over the world and is dependent on ethnicity of the population and geographic region. To authors best knowledge, no published data on preeclampsia exist in Qatar. In this study, we are reporting the incidence of preeclampsia in Qatar. The purpose of this study is to compare the incidence and risk factors of both early onset and late onset preeclampsia in Qatar. This retrospective longitudinal cohort study was conducted using data from the hospital record of Women’s Hospital, Hamad Medical Corporation (HMC), from May 2014-May 2016. Data collection tool, which was approved by HMC, was a researcher made extraction sheet that included information such as blood pressure during admission, socio demographic characteristics, delivery mode, and new born details. A total of 1929 patients’ files were identified by the hospital information management when they apply codes of preeclampsia. Out of 1929 files, 878 had significant gestational hypertension without proteinuria, 365 had preeclampsia, 364 had severe preeclampsia, and 188 had preexisting hypertension with superimposed proteinuria. In this study, 78% of the data was obtained by hospital electronic system (Cerner) and the remaining 22% was from patient’s paper records. We have gone through detail data extraction from 560 files. Initial data analysis has revealed that 15.02% of pregnancies were complicated with preeclampsia from May 2014-May 2016. We have analyzed difference in the two different disease entities in the ethnicity, maternal age, severity of hypertension, mode of delivery and infant birth weight. We have identified promising differences in the risk factors of early onset and late onset preeclampsia. The data from clinical findings of preeclampsia will contribute to increased knowledge about two different disease entities, their etiology, and similarities/differences. The findings of this study can also be used in predicting health challenges, improving health care system, setting up guidelines, and providing the best care for women suffering from preeclampsia.

Keywords: preeclampsia, incidence, risk factors, maternal

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Authors: Sandra Landy

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Traumatic experiences in youth are a major risk factor for future suicidality. With suicide steadily increasing over the last 20 years as one of the top three leading causes of death in children and adolescents, it is essential to examine the aspects of trauma that contribute to suicidality. A quantitative secondary data analysis of a prospective, multicenter 24-month observational study of youth who have experienced traumatic experiences was utilized to determine the relationship between bullying and suicide attempts, cyberbullying and suicide attempts, and number of traumas and suicide attempts. Data was analyzed with the Spearman-rank correlation test to determine the relationships. Findings supported past research establishing a relationship between bulling, including cyberbullying, and suicide attempts, as well as increasing number of traumatic experiences and suicide attempts. Further large scale studies may be beneficial to support these findings.

Keywords: adolescent(s), suicide, trauma, bullying, cyberbullying

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Authors: Maxim A. Kadatskiy, Konstantin V. Khishchenko

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Iron alloys are widespread components in various types of structural materials which are exposed to intensive thermal and mechanical loads. Various quantum-statistical cell models with the approximation of self-consistent field can be used for the prediction of the behavior of these materials under extreme conditions. The application of these models is even more valid, the higher the temperature and the density of matter. Results of Hugoniot calculation for iron alloys in the framework of three quantum-statistical (the Thomas–Fermi, the Thomas–Fermi with quantum and exchange corrections and the Hartree–Fock–Slater) models are presented. Results of quantum-statistical calculations are compared with results from other reliable models and available experimental data. It is revealed a good agreement between results of calculation and experimental data for terra pascal pressures. Advantages and disadvantages of this approach are shown.

Keywords: alloy, Hugoniot, iron, terapascal pressure

Procedia PDF Downloads 330