Search results for: ongoing training

Authors: Ruslan Safarov, Zhanat Shomanova, Yuri Nossenko, Zhandos Mussayev, Ayana Baltabek

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Geochemical maps of distribution of polluting elements V, Cr, Mn, Co, Ni, Cu, Zn, Mo, Cd, Pb on the territory of the Pavlodar city (Kazakhstan), which is an industrial hub were designed. The samples of soil were taken from 100 locations. Elemental analysis has been performed using XRF. The obtained data was used for training of the computational model with gradient boosting algorithm. The optimal parameters of model as well as the loss function were selected. The computational model was used for prediction of polluting elements concentration for 1000 evenly distributed points. Based on predicted data geochemical maps were created. Additionally, the total pollution index Zc was calculated for every from 1000 point. The spatial distribution of the Zc index was visualized using GIS (QGIS). It was calculated that the maximum coverage area of the territory of the Pavlodar city belongs to the moderately hazardous category (89.7%). The visualization of the obtained data allowed us to conclude that the main source of contamination goes from the industrial zones where the strategic metallurgical and refining plants are placed.

Keywords: Pavlodar, geochemical map, gradient boosting, CatBoost, QGIS, spatial distribution, heavy metals

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Authors: Asma A. Alsufyani, Maram A. Alharthi, Maha J. Althobaiti, Manal S. Alharthi, Huda Rizq

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Twitter is one of the most popular microblogging sites that allows users to publish short text messages called 'tweets'. Increasing the number of accounts to follow (followings) increases the number of tweets that will be displayed from different topics in an unclassified manner in the timeline of the user. Therefore, it can be a vital solution for many Twitter users to have their tweets in a timeline classified into general categories to save the user’s time and to provide easy and quick access to tweets based on topics. In this paper, we developed a classifier for timeline tweets trained on a dataset consisting of 3600 tweets in total, which were collected from Saudi Twitter and annotated manually. We experimented with the well-known Bag-of-Words approach to text classification, and we used support vector machines (SVM) in the training process. The trained classifier performed well on a test dataset, with an average F1-measure equal to 92.3%. The classifier has been integrated into an application, which practically proved the classifier’s ability to classify timeline tweets of the user.

Keywords: corpus creation, feature extraction, machine learning, short text classification, social media, support vector machine, Twitter

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Authors: Melissa C. LaDuke

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The overarching purpose of this study was to determine the relationship between the current format of online delivery for Defense Acquisition University (DAU) courses and Air Force Acquisition (AFA) personnel participation. AFA personnel (hereafter named “student”) were particularly of interest, as they have been mandated to take anywhere from 3 to 30 online courses to earn various DAU specialization certifications. Participants in this qualitative case study were AFA personnel who pursued DAU certifications in science and technology management, program/contract management, and other related fields. Air Force personnel were interviewed about their experiences with online courses. The data gathered were analyzed and grouped into 12 major themes. The themes tied into the theoretical framework and spoke to either teacher-centered or student-centered educational practices within Defense Acquisitions University. Based on the results of the data analysis, various factors contributed to student perceptions of DAU courses, including the online course construct and relevance to their job. The analysis also found students want to learn the information presented but would like to be able to apply the information learned in meaningful ways.

Keywords: educational theory, computer-based training, interview, student perceptions, online course design, teacher positionality

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Authors: Naman Solanki, Souraj Mondal

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With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively.

Keywords: computer vision, engagement prediction, deep learning, multi-level classification

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Authors: Awad Elkhadir, Rajab M. Ben Yousef

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Introduction: The production of high-quality medical images by radiology technologists is useful in diagnosing and treating various injuries and diseases. However, the factors affecting the role of radiology technologists in image interpretation in Sudan have not been investigated widely. Methods: Cross-sectional study has been employed by recruiting ten radiology college deans in Sudan. The questionnaire was distributed online, and obtained data were analyzed using Microsoft Excel and IBM-SPSS version 16.0 to generate descriptive statistics. Results: The study results have shown that half of the deans were doubtful about the readiness of Sudan to implement the role of radiology technologist specialist in image interpretation. The majority of them (60%) believed that this issue had been most strongly pushed by researchers over the past decade. The factors affecting the implementation of the radiology technologist specialist role in image interpretation included; education/training (100%), recognition (30%), technical issues (30%), people-related issues (20%), management changes (30%), government role (30%), costs (10%), and timings (20%). Conclusion: The study concluded that there is a need for a change in image interpretation by radiology technologists in Sudan.

Keywords: development, image interpretation, implementation, radiology technologist specialist, Sudan

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Authors: Lily Ranjbar, Haori Yang

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Online education has been around for several decades, but the importance of online education became evident after the COVID-19 pandemic. Eventhough the online delivery approach works well for knowledge building through delivering content and oversight processes, it has limitations in developing hands-on laboratory skills, especially in the STEM field. During the pandemic, many education institutions faced numerous challenges in delivering lab-based courses, especially in the STEM field. Also, many students worldwide were unable to practice working with lab equipment due to social distancing or the significant cost of highly specialized equipment. The laboratory plays a crucial role in nuclear science and engineering education. It can engage students and improve their learning outcomes. In addition, online education and virtual labs have gained substantial popularity in engineering and science education. Therefore, developing virtual labs is vital for institutions to deliver high-class education to their students, including their online students. The School of Nuclear Science and Engineering (NSE) at Oregon State University, in partnership with SpectralLabs company, has developed an Advanced Virtual Radiation Detection and Measurement Lab (AVR-DML) to offer a fully online Master of Health Physics program. It was essential for us to use a system that could simulate nuclear modules that accurately replicate the underlying physics, the nature of radiation and radiation transport, and the mechanics of the instrumentations used in the real radiation detection lab. It was all accomplished using a Realistic, Adaptive, Interactive Learning System (RAILS). RAILS is a comprehensive software simulation-based learning system for use in training. It is comprised of a web-based learning management system that is located on a central server, as well as a 3D-simulation package that is downloaded locally to user machines. Users will find that the graphics, animations, and sounds in RAILS create a realistic, immersive environment to practice detecting different radiation sources. These features allow students to coexist, interact and engage with a real STEM lab in all its dimensions. It enables them to feel like they are in a real lab environment and to see the same system they would in a lab. Unique interactive interfaces were designed and developed by integrating all the tools and equipment needed to run each lab. These interfaces provide students full functionality for data collection, changing the experimental setup, and live data collection with real-time updates for each experiment. Students can manually do all experimental setups and parameter changes in this lab. Experimental results can then be tracked and analyzed in an oscilloscope, a multi-channel analyzer, or a single-channel analyzer (SCA). The advanced virtual radiation detection and measurement laboratory developed in this study enabled the NSE school to offer a fully online MHP program. This flexibility of course modality helped us to attract more non-traditional students, including international students. It is a valuable educational tool as students can walk around the virtual lab, make mistakes, and learn from them. They have an unlimited amount of time to repeat and engage in experiments. This lab will also help us speed up training in nuclear science and engineering.

Keywords: advanced radiation detection and measurement, virtual laboratory, realistic adaptive interactive learning system (rails), online education in stem fields, student engagement, stem online education, stem laboratory, online engineering education

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Authors: Carolina S. Monteiro, Eugénia Pinto, Miguel A. Faria, Sara C. Cunha

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Rice (Oryza sativa) production plays a vital role in reducing hunger and poverty and assumes particular importance in low-income and developing countries. Rice is a sensitive plant, and production occurs strictly where suitable temperature and water conditions are found. Climatic changes are likely to affect worldwide, and some models have predicted increased temperatures, variations in atmospheric CO₂ concentrations and modification in precipitation patterns. Therefore, the ongoing climatic changes threaten rice production by increasing biotic and abiotic stress factors, and crops will grow in different environmental conditions in the following years. Around the world, the effects will be regional and can be detrimental or advantageous depending on the region. Mediterranean zones have been identified as possible hot spots, where dramatic temperature changes, modifications of CO₂ levels, and rainfall patterns are predicted. The actual estimated atmospheric CO₂ concentration is around 400 ppm, and it is predicted that it can reach up to 1000–1200 ppm, which can lead to a temperature increase of 2–4 °C. Alongside, rainfall patterns are also expected to change, with more extreme wet/dry episodes taking place. As a result, it could increase the migration of pathogens, and a shift in the occurrence of mycotoxins, concerning their types and concentrations, is expected. Mycotoxigenic spoilage fungi can colonize the crops and be present in all rice food chain supplies, especially Penicillium species, mainly resulting in ochratoxin A (OTA) contamination. In this scenario, the objectives of the present study are evaluating the effect of temperature (20 vs. 25 °C), CO₂ (400 vs. 1000 ppm), and water stress (0.93 vs 0.95 water activity) on growth and OTA production by a Penicillium nordicum strain in vitro on rice-based media and when colonizing layers of raw rice. Results demonstrate the effect of temperature, CO₂ and drought on the OTA production in a rice-based environment, thus contributing to the development of mycotoxins predictive models in climate change scenarios. As a result, improving mycotoxins' surveillance and monitoring systems, whose occurrence can be more frequent due to climatic changes, seems relevant and necessary. The development of prediction models for hazard contaminants presents in foods highly sensitive to climatic changes, such as mycotoxins, in the highly probable new agricultural scenarios is of paramount importance.

Keywords: climate changes, ochratoxin A, penicillium, rice

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Authors: Siti Aisyah binti Jumpaan

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21ˢᵗ century skills (PAK-21) integration has made its way into Malaysian curriculum when Ministry of Education introduce its implementation since 2016. This study was conducted to explore pre-service teachers’ readiness in integrating 21st century skills in the classroom via the digital storytelling (DST) method and to find gaps between theory and practice that can be integral towards pre-service teachers’ professional growth. Qualitative research method was used in this research involving six respondents who were selected using a purposive sampling method. Their response from interviews and lesson plan analysis were analysed using narrative analysis. The findings showed that pre-service teachers showed a moderate level of readiness in integrating 21st century skills using DST. Pre-service teachers demonstrated high level of preparedness in writing their lesson plan, but their interview revealed that they faced struggles in implementation due to several factors, such as lack of technology and failure to obtain students’ participation. This study further strengthens the need for specialised curriculum for pre-service teachers in teaching 21st century skills via DST.

Keywords: digital storytelling, 21ˢᵗ century skills, preservice teachers, teacher training

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Authors: Abiodun Afolabi, Rufus Adesoji Adedoyin

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This study investigated and compared the effects of gym-based exercise programme (GEBP) and audio-visual guided home-based exercise programme (AVGHBEP) on selected Anthropometric variables (Weight (W), Body Mass Index (BMI), Waist Circumference (WC), Hip Circumference (HC), Thigh Circumference (TC), Waist-Hip-Ratio (WHR), Waist-Height-Ratio (WHtR), Waist-Thigh-Ratio (WTR), Biceps Skinfold Thickness (BSFT), Triceps Skinfold Thickness (TSFT), Suprailliac Skinfold Thickness (SISFT), Subscapular Skinfold Thickness (SSSFT) and Percent Body Fat (PBF)); and Cardiovasular variables (Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP) and Heart Rate (HR)) of overweight and obese students of Federal College of Education (Special), Oyo, Oyo State, Nigeria, with a view to providing information and evidence for GBEP and AVGHBEP in reducing overweight and obesity for promoting cardiovascular fitness. Eighty overweight and obese students (BMI ≥ 25 Kg/m²) were involved in this pretest-posttest quasi experimental study. Participants were randomly assigned into GBEP (n = 40) and AVGBBEP (n = 40) groups. Anthropometric and cardiovascular variables were measured using a weighing scale, height meter, tape measure, skinfold caliper and electronic sphygmomanometer following standard protocols. GBEP and AVGHBEP were implemented following a circuit training (aerobic and resistance training) pattern with a duration of 40-60 minutes, thrice weekly for twelve weeks. GBEP consisted of gymnasium supervised exercise programme while AVGHBEP is a Visual Display guided exercise programme conducted at the home setting. Data were analyzed by Descriptive and Inferential Statistics. The mean ages of the participants were 22.55 ± 2.55 and 23.65 ± 2.89 years for the GBEP group and AVGHBEP group, respectively. Findings showed that in the GBEP group, there were significant reductions in anthropometric variables and adiposity measures of Weight, BMI, BSFT, TSFT, SISFT, SSSFT, WC, HC, TC, WHtR, and PBF at week 12 of the study. Similarly, in the AVGHBEP group, there were significant reductions in Weight, BMI, BSFT, TSFT, SISFT, SSSFT, WC, HC, TC, WHtR and PBF at the 12th week of intervention. Comparison of the effects of GEBP and AVGHBEP on anthropometric variables and measures of adiposity showed that there was no significant difference between the two groups in weight, BMI, BSFT, TSFT, SISFT, SSSFT, WC, HC, TC, WHR, WHtR, WTR and PBF between the two groups at week 12 of the study. Furthermore, findings on the effects of exercise on programmes on cardiovascular variables revealed that significant reductions occurred in SBP in GBEP group and AVGHBEP group respectively. Comparison of the effects of GBEP and AVGHBEP on cardiovascular variables showed that there was no significant difference in SBP, DBP and HR between the two groups at week 12 of the study. It was concluded that the Audio-Visual Guided Home-based Exercise Programme was as effective as the Gym-Based Exercise Programme in causing a significant reduction in anthropometric variables and body fat among college students who are overweight and obese over a period of twelve weeks. Both Gymnasium-Based Exercise Programme and Audio-Visual Guided Home-Based Exercise Programme led to significant reduction in Systolic Blood Pressure over a period of weeks. Audio-Visual Guided Home-Based Exercise Programme can, therefore, be used as an alternative therapy in the non-pharmacological management of people who are overweight and obese.

Keywords: gym-based exercises, audio-visual guided home-based exercises, anthropometric parameters, cardiovascular parameters, overweight students, obese students

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Authors: Fouzia Shersad, Sabeena Salam

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Introduction: Quality assurance cycles like RADAR, PDCA, ADRI are cycles of planning, implementation, assessment and improvement. These cycles are required when institutions apply for reaccreditation to accreditation bodies and for adoption of holistic models of institutional quality. Method of Study: The adoption of these cycles at the higher education institutions under the Dubai Medical University is studied to explore the feasibility and the benefits in institutions outcomes. After adequate faculty training, these steps were incorporated in all new activities and embedded in every new initiative and approach undertaken at unit and institutional levels. Conclusions: Improvement in student satisfaction rates and performance levels has been achieved. Wherever weaknesses or deficits have been identified, improvement strategies are implemented in a timely manner. The feedback has become an incentive for faculty members to implement new ideas. Implementation of these cycles for core processes at micro and macro levels have ensured that a systematic mechanism for corrective actions existed. This has led to increasing adoption of innovative initiatives. Another outcome was the recognition through national level awards for the overall institutions which have been certified by external reviewers.

Keywords: higher education, quality, accreditation, institutional improvement

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Authors: Hasna Soli

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The main objective of this work is the extraction of sulfur from gypsum here is industrial waste. Indeed the sulfuric acid production, passing through the following process; melting sulfur, filtration of the liquid sulfur, sulfur combustion to produce SO₂, conversion of SO₂ to SO₃ and SO₃ absorption in water to produce H₂SO₄ product as waste CaSO₄ the anhydrous calcium sulfate. The main objectives of this work are improving the industrial practices and to find other ways to manage these solid wastes. It should also assess the consequences of treatment in terms of training and become byproducts. Firstly there will be a characterization of this type of waste by an X-ray diffraction; to obtain phase solid compositions and chemical analysis; gravimetrically and atomic absorption spectrometry or by ICP. The samples are mineralized in suitable acidic or basic solutions. The elements analyzed are CaO, Sulfide (SO₃), Al₂O₃, Fe₂O₃, MgO, SiO₂. Then an analysis by EDS energy dispersive spectrometry using an Oxford EDX probe and differential thermal and gravimetric analyzes. Gypsum’s valuation will be performed. Indeed, the CaSO₄ will be reused to produce sulfuric acid, which will be reintroduced into the production line. The second approach explored in this work is the thermal utilization of solid waste to remove sulfur as a dilute sulfuric acid solution.

Keywords: environment, gypsum, sulfur, waste

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Authors: Minwuyelet Maru, Solomon Habtemariam, Endalamaw Gadissa, Abraham Aseffa

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Background: Tuberculosis is a major public health problem in Ethiopia. The burden of TB is aggravated by emergence and expansion of drug resistant tuberculosis and different lineages of Mycobacterium tuberculosis (M. tuberculosis) have been reported in many parts of the country. Describing strains of Mycobacterial isolates and drug susceptibility pattern is necessary. Method: Sputum samples were collected from smear positive pulmonary TB patients age >= 7 years between October 1, 2012 to September 30, 2013 and Mycobacterial strains isolated on Loweensten Jensen (LJ) media. Each strain was characterized by deletion typing and Spoligotyping. Drug sensitivity testing was determined with the indirect proportion method using Middle brook 7H10 media and association to determine possible risk factors to drug resistance was done. Result: A total of 144 smear positive pulmonary tuberculosis patients were enrolled. The age of participants ranged from 7 to 78 with mean age of 29.22 (±10.77) years. In this study 82.2% (n=97) of the isolates were sensitive to the four first line anti-tuberculosis drugs and resistance to any of the four drugs tested was 17.8% (n=21). A high frequency of any resistance was observed in isoniazid, 13.6%, (n=16) followed by streptomycin, 11.8% (n=14). No significant association of isoniazid resistance with HIV, sex and history of previous TB treatment was observed but there was significant association with age, high between 31-35 years of age (p=0.01). Majority, 89.9% (n=128) of participants were new cases and only 11.1% (n=16) had history of previous TB treatment. No MDR-TB from new cases and 2 MDRTB (13.3%) was isolated from re-treatment cases which was significantly associated with previous TB treatment (p<0.01). Thirty two different types of spoligotype patterns were identified and 74.1% were grouped in to 13 clusters. The dominant strains were SIT 25, 18.1% (n=21), SIT 53, 17.2% (n=20) and SIT 149, 8.6% (n=10). Lineage 4 is the predominant lineage followed by lineage 3 and lineage 7 comprising 65.5% (n=76), 28.4% (n=33) and 6% (n=7) respectively. Majority of strains from lineage 3 and 4 were SIT 25 (63.6%) and SIT 53 (26.3%) whereas SIT 343 was the dominant strain from lineage 7 (71.4%). Conclusion: Wide spread of lineage 3 and lineage 4 of the modern lineage and high number of strain cluster indicates high ongoing transmission. The high proportion resistance to any of the first line anti-tuberculosis drugs may be a potential source in the emergence of MDR-TB. Wide spread of SIT 25 and SIT 53 having a tendency of ease transmission and presence of higher resistance of isoniazid in working and mobile age group, 31-35 years of age may increase risk of drug resistant strains transmission.

Keywords: tuberculosis, drug susceptibility, strain diversity, lineage, Ethiopia, spoligotyping

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Authors: Kartini Ilias, Nabilah Md Ahir, Nor Zafirah Ab Rahman, Safiah Md Yusof, Nuri Naqieyah Radzuan, Siti Sabariah Buhari

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This study aim to develop and assess the effectiveness of an intervention in improving eating habits and physical activity level of university students of UiTM Puncak Alam. The intervention consists of weekly dietary counselling by registered dietitian and high-intensity interval training (HIIT) for three times per week for the duration of 8 weeks. A total of 25 students from the intervention group and 25 students from control group who had BMI equal to or greater than 25kg/m² participated in the study. The results showed a significant reduction in body weight (3.0 kg), body fat percentage (7.9 %), waist circumference (7.3 cm) and BMI (2.9 kg/m²) between pre and post intervention. Besides, there was a significant increase in the level of physical activity among subjects in intervention group. In conclusion, the intervention made an impact on eating habit, physical activity level and improves weight status of the students. It is expected that the intervention could be adopted and implemented by the government and private sector as well as policy-makers in formulating obesity intervention.

Keywords: obesity, diet, obesity intervention, physical activity

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Authors: Tien-Hui Chiang, Q. Zhou

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For structural-functionalism, one set of the common traits of traditional professionals, such as medical practitioners and engineers, can be viewed as the criteria for evaluating whether a given occupation has the right of claiming its professional status or not. Under the influence of this professionalism, teaching practitioners have devoted themselves to acquiring this right as evidenced by the fact that initial training has been extended to even the level of postgraduate. However, for interactionalists, professionalism adopts a predetermined assumption so that it ignores the dynamic nature of social development, which is able to regulate the professional status of a given occupation. Such an interactive approach highlights the concept of professionalization. Furthermore, Marxists argue that structural-functionalists have ignored the impact of proletarianization on the white collar. While professionals gradually lose their control over their practices, the title of profession functions as a self-regulated icon that prevents them from collaborating with the working class and, in turn, creates the ideology of de-politicization sustaining the interests of the ruling class. This article adopts a theoretical analysis on these contradictory arguments. It argues that these criticisms neglect the influence of the institutionalized value system on social operation, which is the core element in sustaining the notion of the profession.

Keywords: teaching profession, professionalism, professionalization, proletarianialization, institutionalized value system

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Authors: Rahul Mohite, Lahcen Ouarbya

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This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

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Authors: G. Anjan Babu, G. Sumana, M. Rajasekhar

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Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multi-layered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinanalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Furthermore, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.

Keywords: dialysis, hereditary, transplantation, polycystic, pathogenesis

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Authors: Ritesh Kumar, Gitanjali Chhetri, Mandira Bhatia, Mohit Mishra, Abhijith Bailur, Abhinav

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The prevalence of the internet and technology in our day to day lives is creating more security issues than ever. The need for protecting and providing a secure access to private and business data has led to the development of many security systems. One of the potential solutions is to employ the bio-metric authentication technique. In this paper we present an innovative biometric authentication method that utilizes a person’s EEG signal, which is acquired in response to an auditory stimulus,and transferred wirelessly to a computer that has the necessary ANN algorithm-Multi layer perceptrol neural network because of is its ability to differentiate between information which is not linearly separable.In order to determine the weights of the hidden layer we use Gaussian random weight initialization. MLP utilizes a supervised learning technique called Back propagation for training the network. The complex algorithm used for EEG classification reduces the chances of intrusion into the protected public or private data.

Keywords: EEG signal, auditory evoked potential, biometrics, multilayer perceptron neural network, back propagation rule, Gaussian random weight initialization

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Authors: Abdolrasoul Daneshjoo

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The aim of this study was studying the effect of acute creatine supplementation on physiological variables of continuous and intermittent soccer activities of men soccer players. 32 soccer players from Tarbiat Moalem University aged (22/3+-1/6) volunteered for this research and were divided into two groups randomly. Both experimental and control groups after 6 days taking supplementation were tested. For measuring height and weight meter and balance were used. Questionnaire for health background, lactate electro, heart beat measuring polar electro, continuous and intermittent training program and time recorder were used for data collection. For data analysis descriptive statistical techniques, two-way ANOVA and F test were used. The result of this study showed increased significantly in heart rate in control group. For control group heart beat was (71/6 +- 3/5) and for experimental group it was (75/3 +- 4/9). No significant differences were observed in players weight after taking creatine.

Keywords: heartbeat, lactate Blood, creatine, soccer players of Tarbiat Moalem University

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Authors: Ioannis Andrianakis, Manolis Falelakis, Maria Pavlidou, Konstantinos Papakonstantinou, Ermioni Avramidou, Dimitrios Kalogiannis, Nikolaos Milios, Katerina Bountakidou, Kiriakos Chatzidimitriou, Panagiotis Panagiotopoulos

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Artistic events, such as concerts and performances, are challenging to organize because they involve many people with different skill sets. Small and medium venues often struggle to afford the costs and overheads of booking and hosting remote artists, especially if they lack sponsors or subsidies. This limits the opportunities for both venues and artists, especially those outside of big cities. However, more and more research shows that audiences prefer smaller-scale events and concerts, which benefit local economies and communities. To address this challenge, our project “PerformAnts: Digital Event Manager-Organizer” aims to develop a smart digital tool that automates and optimizes the processes and costs of live shows and tours. By using machine learning, applying best practices and training users through workshops, our platform offers a comprehensive solution for a growing market, enhances the mobility of artists and the accessibility of venues and allows professionals to focus on the creative aspects of concert production.

Keywords: event organization, creative industries, event promotion, machine learning

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Authors: Priestly Malambo, Sonja Van Putten, Hanlie Botha, Gerrit Stols

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The relevance of what is content is taught in tertiary teacher training has long been in question. This study attempts to understand how advanced mathematics courses equip student teachers to teach functions at secondary school level. This paper reports on an investigation that was conducted in an African university, where preservice teachers were purposefully selected for participation in individual semi-structured interviews after completing a test on functions as taught at secondary school. They were asked to justify their reasoning in the test and to explain functions in a way that might bring about understanding of the topic in someone who did not know how functions work. These were final year preservice mathematics teachers who had studied advanced mathematics courses for three years. More than 50% of the students were not able to explain concepts or to justify their reasoning about secondary school functions in a coherent way. The results of this study suggest that the study of advanced mathematics does not automatically enable students to teach secondary school functions, and that, although these students were able to do advanced mathematics, they were unable to explain the working of functions in a way that would allow them to teach this topic successfully.

Keywords: secondary school, mathematical reasoning, student-teachers, functions

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Authors: Fatima Ibnchahid

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Competence approach is considered, since the early 80's as one of the major development of HR policies. Many approaches to manage the professional skills were declined. Some processes are mature whereas the others have been abandoned. Competence can be defined as the set of knowledge (theoretical and practical), know-how (experience) and life skills (personality traits) mobilized by a person in the company. The skills must master a logistics manager are divided into two main categories: depending on whether technical skills, or managerial skills and human. The firsts are broken down into skills on logistical techniques and on general skills in business, seconds in social skills (self with others) and personal (with oneself). Logisticians are faced with new challenges and new constraints that are revolutionizing the way to treat the physical movement of goods and operations related to information flows that trigger, they control and guide the physical movements of these major changes, we can mention the development of information technology and communication, the emergence of strong environmental and security constraints. These changes have important effects on the skills needs of the members of the logistical function and sensitive development for training requested by logistical managers to perform better in their job changes. In this article, we will address two main points, first, a brief overview of the management skills and secondly answer the question asked in the title of the article to know what are the skills that will master a logistical manager.

Keywords: skills, competence, management, logistical function

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Authors: Fawaz S. Al-Anzi, Dia AbuZeina

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Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit.

Keywords: speech recognition, acoustic features, mel frequency, cepstral coefficients

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Authors: Aran Hakki, Corina Cirstea, Julian Rathke

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Formal methods are yet to be utilized in mainstream software development due to issues in scaling and implementation costs. This work is about developing a scalable, simplified, pragmatic, formal software development method with strong correctness properties and guarantees that are easy prove. The method aims to be easy to learn, use and apply without extensive training and experience in formal methods. Petra is proposed as an object-oriented, process calculus with composable data types and sequential/parallel processes. Petra has a simple denotational semantics, which includes a definition of Correct by Construction. The aim is for Petra is to be standard which can be implemented to execute on various mainstream programming platforms such as Java. Work towards an implementation of Petra as a Java EDSL (Embedded Domain Specific Language) is also discussed.

Keywords: compositionality, formal method, software verification, Java, denotational semantics, rewriting systems, rewriting semantics, parallel processing, object-oriented programming, OOP, programming language, correct by construction

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Authors: Tae An Kim, Hyo Lim Kang, Hye Won Han, Seung Ho Han

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The drive shaft of passenger vehicle has its own function such as transmitting the engine torque from the gearbox and differential gears to the wheels. It must also compensate for all variations in angle or length resulting from manoeuvring and deflection for perfect synchronization between joints. Torsional fatigue failures occur frequently at the connection parts of the spline joints in the end of the drive shaft. In this study, the fatigue life of a drive shaft of passenger vehicle was estimated by using the finite element analysis. A commercial software of n-Code was applied under twisting load conditions, i.e. 0~134kgf•m and 0~188kgf•m, in which the shear strain range-fatigue life relationship considering Signed Shear method, Smith-Watson-Topper equation, Neuber-Hoffman Seeger method, size sensitivity factor and surface roughness effect was taken into account. The estimated fatigue life was verified by a twisting load test of the real drive shaft in a test rig. (Human Resource Training Project for Industry Matched R & D, KIAT, N036200004).

Keywords: drive shaft, fatigue life estimation, passenger vehicle, shear strain range-fatigue life relationship, torsional fatigue failure

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Authors: Yasaswi Palagummi, Sareh Rowlands

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Generalised Zero-Shot Learning, often known as GZSL, is an advanced variant of zero-shot learning in which the samples in the unseen category may be either seen or unseen. GZSL methods typically have a bias towards the seen classes because they learn a model to perform recognition for both the seen and unseen classes using data samples from the seen classes. This frequently leads to the misclassification of data from the unseen classes into the seen classes, making the task of GZSL more challenging. In this work of ours, to solve the GZSL problem, we propose an approach leveraging the Shifted Window based Self-Attention in the Swin Transformer (Swin-GZSL) to work in the inductive GSZL problem setting. We run experiments on three popular benchmark datasets: CUB, SUN, and AWA2, which are specifically used for ZSL and its other variants. The results show that our model based on Swin Transformer has achieved state-of-the-art harmonic mean for two datasets -AWA2 and SUN and near-state-of-the-art for the other dataset - CUB. More importantly, this technique has a linear computational complexity, which reduces training time significantly. We have also observed less bias than most of the existing GZSL models.

Keywords: generalised, zero-shot learning, inductive learning, shifted-window attention, Swin transformer, vision transformer

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Authors: Alireza Monzavi Chaleshtari, Mahnaz Aliakbari Dehkordi, Mina Gholampour, Majid Saffarinia, Tayebeh Mohtashami, Amin Asadi Hieh

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The study aimed to assess the impact of positive psychology education on the social well-being of high school girls experiencing premenstrual syndrome (PMS). The statistical population comprised high school girls with PMS, with 30 randomly selected participants divided into two groups: 15 in the experimental group and 15 in the control group. The research employed a pre-test and post-test design using a standard questionnaire to evaluate premenstrual syndrome symptoms over a 7-day period before menstruation to a maximum of 2 days after menstruation, along with the Social Keys welfare questionnaire. During the study, the experimental group underwent an 8-session positive psychology group program. Data analysis was conducted using analysis of covariance. The results indicated a significant positive effect of positive psychology training on enhancing the social well-being of girls (p < 0.05). In conclusion, the findings suggest that positive psychology interventions can effectively increase social well-being among high school girls experiencing premenstrual syndrome.

Keywords: positive psychology, premenstrual syndrome, social welfare, girls

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Ana Cristina A. P. Carvalho, Solange Teles Da Silva

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In April, 174 countries signed the Paris Agreement, a multilateral agreement on climate change which deals with greenhouse gas emissions, mitigation, adaptation, finance, access to information, transparency, among other subjects related to the environment. Since then, Parties shall cooperate in taking measures, as appropriate, to enhance climate change education, training, public awareness, public participation and public access to information, recognizing the importance of these steps with respect to enhancing actions under this Agreement. This paper aims to analyze the consequences of this new rule in terms of the implementation of the Agreement, collecting data from Brazilian and Canadian legislations in order to identify if these countries have rules complying with the Treaty, the steps that have been already taken and if they could be used as examples for other countries. The analysis will take into consideration the different kinds of climate change information, means of transparency, reliability of the data and how to spread the information. The methodology comprehends a comparative legal research based on both the Paris Agreement and domestic laws of Brazil and Canada, as well as on doctrine and Court decisions. The findings can contribute to the implementation of the Paris Agreement through compliance with this Treaty at countries’ domestic and policy level.

Keywords: climate change information, domestic legislation, Paris Agreement, public policy

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Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

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In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

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Authors: Yi Meng, Yun Gao

Abstract:

Having spent significant periods studying and working in North America and Europe, we, as two Chinese art educators, have been profoundly shaped by both Eastern and Western cultures. Consequently, our ambition is to enrich students' learning experiences by delving into and merging both cultural perspectives for innovative, creative expressions. This exposition draws on our action research study on students' visual literacy practices in a visual literacy course at a prominent Chinese university. The central premise was to explore innovative art forms by cross-utilizing various aspects of diverse cultures. By examining distinct cultural elements, we encouraged students to break away from familiar approaches and forge new paths in their creative endeavors. In implementing our curriculum, we utilized a multimodal pedagogy that deviated from the predominant print-based presentations typically employed in our classroom settings. This pedagogical approach effectively encouraged students to critically analyze the artifact, imbue it with their understanding and perspectives, and then produce an original piece. This approach also motivated students to leverage the semiotic potential of various communicative modes to address diverse cultural issues through their multimodal designs. To demonstrate the potential for cultural amalgamation, we utilized the artwork of Hong Kong-based artist Tik Ka. His works epitomize the fusion of Chinese traditions with Western pop culture, which served as a visual and conceptual reference point for students. Seeing how these distinct cultural elements could coexist and enrich each other in Tik Ka's work was inspiring and motivating for the students. Taken together, these pedagogical strategies helped create a dialogical space where students could actively experience, analyze, and negotiate complex modes of expression. This environment fostered active learning, encouraging students to apply their knowledge, question their assumptions, and reconsider their perspectives. Overall, such a unique approach to visual literacy education has the potential to reshape students' understanding of both cultures. By encouraging them to critically engage with their multimodal designs, we promoted an in-depth, nuanced appreciation of these diverse cultural heritages. The students no longer just interpreted and replicated images—they actively contributed to a dynamic and ongoing conversation between cultures.

Keywords: multimodal pedagogy, creative expressions, visual literacy education, multimodal designs

Procedia PDF Downloads 73